Dynamic simulation periods are specified in Time's definition. This is usually a list of numbers or labels, typically in some unit of time (days, weeks, months, etc.). Use the “Dynamic()” function in your variables to perform dynamic simulation.100[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100]11200Logmtad17. Janta 2003 15:22ktluser15. Mayta 2009 15:07 48,241,4,8,529,466,171,646,6Arial, 130,Model Who_mortality,2,2,0,1,C:\temp\WHO_mortality_data_g.ANAMultivariate DistributionsA library of multivariate distributions.
In a multivariate distribution, each sample is a vector. This vector is identified by an index, identified by the I parameter of the functions in this library. A Mid value from a distribution function will therefore be indexed by I, whlie a Sample from a distribution function is indexed by both I and Run. These distribution functions can also be used from within the Random function to generate a single monte-carlo sample, which will be indexed by I.
This library also contains functions for generating correlated distributions. Correlate_with, for example, allows you to generate a univarite distribution with an arbitrary marginal distribution that has a specified rank correlation with an arbitrary reference distribution. Several functions may be used for generating serial correlations, where each distribution along an index is correlated with the previous point along that index.Lonnie Chrisman, Ph.D.
Lumina Decision Systems
With contributions by:
John Bowers, US FDA.
Max Henrion, Lumina Decision SystemsFri, Aug 01, 2003 7:12 PMLonnieTue, Nov 20, 2007 10:36 AM48,24300,488,052,241,1,1,1,1,1,0,0,0,01,42,10,649,1009,172,401,199,483,316Arial, 15( cv : Number[I,J,Run] ; n :positive ; I,J : Index ;
singleSampleMethod : optional hidden scalar)Wishart(cv,n,I,J)Suppose you sample N samples from a Gaussian(0,cv,I,J) distribution, X[I,R]. (R is the index that indexes each sample, R:=1..N). The Wishart distribution describes the distribution of sum( X * X[I=J], R ). This matrix is dimensioned by I and J and is called the scatter matrix.
A sample drawn from the Wishart is therefore a sample scatter matrix. If you divide that sample by (N-1), you have a sampled covariance matrix.
If you compute a sample covariance matrix from data, and then want to use this in your model, if you just use it directly, you'll be ignoring sampling error. That may be insignificant of N is large. Otherwise, you may want to use:
Wishart( SampleCV, N, I, J) / (N-1)
instead of just SampleCV in your model. The extended variance will account for the uncertainty from the finite sample size that was used to obtain your sample CV.
If you can express a prior probability on covariances in the form of an InvertedWishart distribution, then the posterior distribution, after having computed the sample covariance matrix (assumed to be drawn, by nature, from a Wishart), is also an InvertedWishart.var T := if i<j then 0 else if i=j
then sqrt(ChiSquared(n-i+1, singleSampleMethod:singleSampleMethod))
else (normal(0,1, singleSampleMethod:singleSampleMethod));
var A := MatrixMultiply(T,I,J,Transpose(T,I,J),I,J);
var L := Decompose(A,I,J);
MatrixMultiply(MatrixMultiply(L,I,J,A,I,J),I,J,Transpose(L,I,J),I,J)128,224,1100,162,183,11,538,656cv,n,I,J(psi:Number[I,J,Run] ; n :positive ; I,J : Index ;
singleSampleMethod : optional hidden scalar)Inverted Wishart(Psi,n,I,J)The inverted Wishart distribution represents a distribution of covariance matrices. Each sample from the InvertedWishart is a covariance matrix. It is conjugate to a Wishart distribution, and therefore is sometimes used as a Bayesian prior distribution for covariance. The parameter, Psi, must be a positive definite matrix.
Suppose you represent the prior distribution of covariance using an inverted Wishart distribution: InvertedWishart(Psi,m). You observe some data, X[I,R], where R:=1..N indexes each datapoint and I is the vector dimension, and compute A = Sum( X*X[I=J], R), where A is called the scatter matrix. A is an observation that gives you information about the true covariance matrix, so can use this to obtain a Bayesian posterior distribution on the true covariance given by:
InverseWishart( A+Psi, n+m ).
Invert( Wishart( Invert(Psi,I,J), n, I,J, singleSampleMethod:singleSampleMethod), I, J )352,224,1108,162,102,90,553,530psi,n,I,J( Y : Numeric[I,Run] ; B : Numeric[I,K,Run] ; I,K : Index; C : optional Numeric[K,Run] ;
S : optional Numeric[I,Run] ;
singleSampleMethod : optional hidden scalar )RegressionDist(Y,B,I,K)RegressionDist returns linear regression coefficients as a distribution.
Suppose you have data where Y was produced as:
Y = Sum( C*B, K ) + Normal(0,S)
S is the measurement noise. You have the data (B[I,K] and Y[I]). You might or might not know the measurement noise S. So you perform a linear regression to obtain an estimate of C. Because your estimate is obtained from a finite amount of data, your estimate of C is itself uncertain. This function returns the coefficients C as a distribution (i.e., in Sample mode, it returns a sampling of coefficients indexed by Run and K), reflecting the uncertainty in the estimation of these parameters.
If you know the noise level S in advance, then you can use historical data as a starting point for building a predictive model of Y, as follows:
{ Your model of the dependent variables: }
Variable Y := your historical dependent data, indexed by I
Variable B := your historical independent data, indexed by I,K
Variable X := { indexed by K. Maybe others. Possibly uncertain }
Variable S := { the known noise level }
Chance C := RegressionDist(Y,B,I,K)
Variable Predicted_Y := Sum(C*X,K) + Normal(0,S)
If you don't know the noise level, then you need to estimate it. You'll need it for the normal term of Predicted_Y anyway, and you'll need to do a regression to find it. So you can pass these optional parameters into RegressionDist. The last three lines above become:
Variable E_C := Regression(Y,B,I,K)
Variable S := RegressionNoise( Y,B,I,K,E_C )
Chance C := RegressionDist(Y,B,I,K,E_C)
Variable Predicted_Y := Sum(C*X,K) + Normal(0,S)
If you use RegressionNoise to compute S, you should use Mid(RegressionNoise(...)) for the S parameter. However, when computing S for your prediction, don't RegressionNoise in context. Better is if you don't know the measurement noise in advance, don't supply it as a parameter.if IsNotSpecified(C) Then C := Regression(Y,B,I,K);
If IsNotSpecified(S) Then S := Mid(RegressionNoise(Y,B,I,K,C))
Else S:=Mean(S);
Index K2 := K;
var cv := Invert( Sum( B*B[K=K2]/S^2,I),K,K2);
cv := Average( [cv,Transpose(cv,K,K2)]); {for round off errs}
Gaussian(C,cv,K,K2,singleSampleMethod:singleSampleMethod)152,736,1112,202,115,1,561,882Y,B,I,K,C,S(meanVec : numeric[I,Run],covar : numeric[I,J,Run]; I,J:IndexType ;
Over : ... optional atomic ;
singleSampleMethod : optional hidden scalar )Gaussian( m, cv, i, j )A multi-variate Gaussian distribution based on a mean vector and covariance matrix. The covariance matrix must symmetric and positive-definite. The meanVec is indexed by I. The covariance matrix is 2-D, indexed by I & J. Indexes I & J should be the same length.var S := Decompose(covar,I,J);
var Z := Normal(0,1,over:J,singleSampleMethod:singleSampleMethod);
sum( S*Z,J ) + meanVec112,72,180,162,96,299,486,314meanVec,covar,I,J,Over(alpha : Numeric[I,Run]; I:IndexType ;
Over : ... optional atomic ;
singleSampleMethod : optional hidden scalar)Dirichlet ( a, i )A Dirichlet distribution with parameters alpha_i>0
Each sample of a Dirichlet distribution produces a random vector whose elements sum to 1. It is commonly used to represent second order probability information.
The Dirichlet distribution has a density given by
k * Product( X^(alpha-1), I)
where k is a normalization factor equal to
GammaFn( sum(alpha,I )) / Sum(GammaFn(alpha),I)
The parameters, alpha, can be interpreted as observation counts. The mean is given by the relative values of alpha (normalized to 1), but the variance narrows as the alphas get larger, just as your confidence in a distribution would narrow as you get more samples.
The Dirichlet lends itself to easy Bayesian updating. If you have a prior of alpha0, and you observe Nvar a:=Gamma(alpha,singleSampleMethod:singleSampleMethod);
a/sum(a,I)272,120,158,162,26,18,624,485alpha,I,Over(MeanVec :numeric[I,Run]; Sdeviations : positive[I,Run]; I:IndexType; correlationCoef : numeric[Run];
Over : ... optional atomic ;
singleSampleMethod : optional hidden scalar)BiNormal (m, s, i, c )A 2-D Normal (or Bi-variate Gaussian) distribution with the indicated individual standard deviations (>0) and the indicated correlation coefficient. The index, I, must have exactly 2 elements, Sdeviations must be indexed by I.if size(I)<>2 then
Error("Index to BiNormal must have 2 elements")
else begin
var s := product(Sdeviations,I) * correlationCoef;
Index J:=CopyIndex(I);
Gaussian( meanVec, If I<>J Then s else Sdeviations^2, I,J,
singleSampleMethod: singleSampleMethod )
end288,72,178,162,2,24,525,540MeanVec,Sdeviations,I,correlationCoef,Over(N:Positive ; theta:NonNegative ; I : IndexType;
Over : ... optional atomic ;
singleSampleMethod : hidden optional scalar )Multinomial (n, theta, i )Returns the Multinomial Distribution.
The multinomial distribution is a generalization of the Binomial distribution to N possible outcomes. For example, if you were to roll a fair die N times, an outcome would be the number of times each of the six numbers appears. Theta would be the probability of each outcome, where sum(theta,I)=1, and index I is the list of possible outcome. If theta doesn't sum to 1, it is normalized.
Each sample is a vector indexed by I indicating the number of times the corresponding outcome (die number) occurred during that sample point. Each sample will have the property that sum( result, I ) = N.var z := n;
var k := size(I);
var j:=cumulate(1,I) in I do begin
Index I2 := j..k;
var theta2 := Slice(theta,I,I2); /* unnormalized sub-process */
var p := theta2/sum(theta2, I2);
p := if IsNan(p) then 0 else p;
var xj := Binomial(z,p[I2=j],
singleSampleMethod:singleSampleMethod);
z := z - xj;
xj
end
117,120,185,162,75,167,476,522N,theta,I,Over(dists : Context[I,RunIndex] ; rankcorrs : numeric array[I,J] ;
I,J : IndexType;
RunIndex : optional Index = Run )Correlate Dists (d, rc, i, j )Reorders the samples in dists so as to match the desired rank correlations between distributions as closely as possible. RankCorrs must be positive definite, and the diagonal should contain all ones.
The result will be distributions having the same margins as the original input, but with rank correlations close to those of the rankcorrs matrix.if not IsSampleEvalMode and Handle(RunIndex)=Handle(Run) Then
dists {Mid mode}
Else begin
var u := if Handle(RunIndex)=Handle(run)
Then Sample(Gaussian(0,rankcorrs,I,J))
Else Random(Gaussian(0,rankcorrs,I,J),Over:RunIndex);
var dsort := sortIndex(dists,RunIndex);
var urank := Rank(u,RunIndex);
dists[RunIndex=dsort[RunIndex=urank]]
end136,392,1100,162,301,71,494,521dists,rankcorrs,I,J,RunIndex( S, ref : Context[RunIndex] ; rc : scalar ;
RunIndex : optional Index = Run )Correlate With (s, ref, rc )Reorders the samples of S so that the result is correlated with the reference sample with a rank correlation close to rankcorr.
Example: To generate a logNormal distribution that is highly correlated with Ch1, use, e.g.,: Correlate_With( LogNormal(2,3), Ch1, 0.8 )
Note: This achieves a given unweighted rank correlation. If you have a non-default SampleWeighting of points, the weighted rank correlaton may differ.if IsSampleEvalMode or Handle(runIndex)<>Handle(Run) Then begin
Index q := 1..2;
var u := If Handle(RunIndex)=Handle(Run)
Then binormal( 0, 1, q, rc )
Else Random(binormal(0,1,q,rc),Over:RunIndex);
var rrank := Rank(ref,RunIndex);
var u1sort := sortIndex(u[q=1],RunIndex);
var u2rank := Rank(u[q=2],RunIndex);
var ssort := sortIndex(S,RunIndex);
S[RunIndex=ssort[RunIndex=u2rank[RunIndex=
u1sort[RunIndex=rrank]]]]
end
else {mid mode}
S128,312,196,162,205,170,545,485S,ref,rc,RunIndex(I : IndexType ; R : optional Numeric[I,Run] ;
Over : ... optional atomic ;
singleSampleMethod : optional hidden scalar )Uniform Spherical (i, r )Generates points uniformly on a sphere (or circle or hypersphere).
Each sample generated is indexed by I -- so if I has 3 elements, the points will lie on a sphere.
The mid value is a bit strange here since there isn't really a median that lies on the sphere. Obviously the center of the sphere is the middle value, but that isn't in the allowable range. So, an arbitrary point on the sphere is used.if IsNotSpecified(R) then R:=1;
var u := Normal(0,1,over:I,
singleSampleMethod:singleSampleMethod);
var d := sqrt( sum(u^2,I) );
ifall d=0 and @I then R/sqrt(size(I)) else r*u/d328,168,186,162,151,227,476,424I,R,Over(corr : Numeric[I,J,Run] ; I,J : IndexType ; lb,ub : optional Numeric[I,J,Run] ;
Over : ... optional atomic ;
singleSampleMethod : hidden optional scalar )MultiUniform ( c, i, j, lb, ub )The multi-variate uniform distribution.
Generates vector samples (indexed by I) such that each component has a uniform marginal distribution, and such that each component have the pair-wise correlations given by corr. Indexes I and J must have the same number of elements, corr needs to be symmetric and must obey a certain semidefinite condition (namely that the transformed matrix [ 2*sin(30*cov) ] is positive semidefinite. In most cases, this roughly the same as corr being, or not being, positive semidefinite). Lb and ub can be used to specify upper and lower bounds, either for all components, or individually if these bounds are indexed by I. If lb & ub are omitted, each component will have marginal Uniform(0,1).
The correlation specified in corr is true sample correlation - not rank correlation.
The transformation here is based on:
* Falk, M. (1999), "A simple approach to the generation of uniformly distributed random variables with prescribed correlations," Comm. in Stats - Simulation and Computation 28: 785-791.if IsNotSpecified(lb) then lb:=0;
if IsNotSpecified(ub) then ub := 1;
var R := if I=J then 1 else 2*sin(30*corr);
var g := Gaussian(0,R,I,J,
singleSampleMethod:singleSampleMethod);
Cumnormal( g ) * (ub-lb) + lb132,168,1100,162,67,106,608,611corr,I,J,lb,ub,OverDepricated multi-variate stuffFunctions found in this module are here for legacy reasons. They existed in older versions of the Multivariate library, but have been become obsolete for whatever reason.LonnieMon, Apr 30, 2007 3:49 PM48,2480,944,156,32(X ; I : Index ; J : optional Index ; R : Index)Sample CovarianceThis function is obsolete. In Analytica 4.0, the builtin function Variance can be used to compute a covariance matrix. The equivalent of this function would be: Variance( X, R, CoVarDim:I, CoVarDim2:J ).
Returns a covariance matrix based on the sampled data, X, indexed by I and R. (I is the dimensionality of X, R corresponds to the samples). The result will be indexed by I and J -- supply J to be the same length as I.
Note that the mean is simply Average(X,R), and doen't warrant a separate function.var I2 := if IsNotSpecified(J)
Then (Index K/((identifier of I)&"2") := I do VarTerm(K))
Else VarTerm(J);
var Z:=X-Average(X,R);
var Zt := Z[@I=@I2];
Sum(Z*Zt,R)/(size(R)-1)80,48,148,242,222,299,476,297X,I,J,R(X : array[I,R] ; I,J,R : IndexType)sample correlationThis function is obsolete. A covariance matrix can be computed in Analytica 4.0+ using the built-in function Correlation. The equivalent of this function is Correlation(X,X[@I=@J],R).
Returns a correlation matrix based on data in X, where each data point is a vector indexed by I, and the entries in the correlation matrix are the pair-wise correlations of the columns of data. A second index, J, of size identical to I, is required in order to index the 2-dimensional result.var z:=x-average(x,R);
var zt := slice(z,I,cumulate(1,J));
sum(z*zt,R) / sqrt(sum(z^2,R) * sum(zt^2,R))
208,48,148,242,70,24,523,377X,I,J,RParametric Multivariate Distributions160,40,-1136,12Creating an array of mutually correlated distributions:232,368,-1200,16Creating a single univariate distribution correlated with another existing dist:296,280,-1268,12(m, s, r, y: Numeric ;
over : optional atomic ;
singleSampleMethod : optional hidden scalar )Normal_correl(m, s, r, y)Generates a normal distribution with mean m, standard deviation s, and correlation r with normally distributed value y. In a deterministic context, it will return m.
If y is not normally distributed, the result will also not be normal, and the correlation will be approximate. It generalizes appropriately if any of the parameters are arrays:The result array will have the union of the indexes of the parameters.IF r<-1 OR r>1 THEN Error('Correlation parameter r in function Normal_correl(m, s, r, y) is outside the expected range [-1, 1].');
IFOnly IsSampleEvalMode
THEN m + s * (Sqrt(1-r^2)
* Normal(Sameindexes( 0, m ), Sameindexes( 1, s ),
singleSampleMethod:singleSampleMethod )
+ r * (y - Mean(y))/Sdeviation(y))
ELSE m352,312,1108,162,102,90,503,416m,s,r,y,overMultivariate Internal FunctionsLonnieTue, May 01, 2007 9:29 PM48,24200,944,152,32(x, y)SameIndexes(x,y)Returns an array with the same indexes as y, and value x in each cell.IF y=y THEN x ELSE x120,64,180,20x,y(m, s: Numeric; cm: ArrayType[i, j,Run]; i , j: IndexType ;
Over : ... optional atomic ;
singleSampleMethod : optional hidden scalar )Multinormal(m,s,c,i,j)A multi-variate normal (or Gaussian) distribution with mean m, standard deviation s, and correlation matrix cm. m and s may be scalar or indexed by i. cm must be symmetric, positive-definite, and indexed by i & j, which must be the same length.
Multinormal uses a correlation matrix. Compare with Gaussian, which also defines a multi-variate normal but which uses a covariance matrix.Gaussian(m,cm*s*s[@i=@j],i,j,over,singleSampleMethod)472,72,184,162,391,248,512,343m,s,cm,i,j,OverReshaped distributions:136,448,-1100,16(x : Numeric[R] ; newdist : all Numeric[R] ;
R : optional Index = Run )Dist_reshape(x, newdist)Reshapes the probability distribution of uncertain quantity x so that it has the same marginal probability distribution (i.e, same set of sample values) as newdist, but retains the same ranks as x. Thus:
Rank(Sample(x), Run)
= Rank(Sample(Reshape_dist(x, y)), Run)
In a Mid context, it simply returns the mid value of newdist, with any indexes of x.
The result retains any rank correlations that x may have with other predecessor variables. So, the rank-order correlation between a third variable z and x will be the same as the rank-order correlation between z and a reshaped version of x, i.e.
RankCorrel(x, z) = RankCorrel(Reshape_Dist(x, y), z)
The operation may optionally be applied along an index other than Run.IFOnly IsSampleEvalMode or Handle(R)<>Handle(Run) THEN BEGIN
VAR dsort := SortIndex(newdist, Run);
VAR xranks := Rank(x, Run);
newdist[Run = dsort[Run=xranks]]
END
ELSE newdist * (x=x)152,472,1116,162,102,90,646,469x,newdist,RArrays with serial correlation208,532,-1168,12(m, s, r: Numeric; i: IndexType ;
over : ... optional atomic;
singleSampleMethod : optional hidden scalar )Normal_serial_correl(m,s,r,i)Generates an array over index i of normal distributions with mean m, standard deviation s, and correlation r between successive values over index i. You can give each distribution a different mean and/or standard deviation if m and/or s are arrays indexed by i. If r is indexed by i, r[i=k] specifies the correlation between result[i=k] and result[i=k-1]. (Then the first correlation, slice(r, i, 1) is ignored.)Var x := Normal(0, 1,singleSampleMethod:singleSampleMethod);
(FOR j := i DO
x := Normal_correl( 0, 1, r[i = j],x,
singleSampleMethod:singleSampleMethod ) )
* s + m160,560,1120,162,353,325,540,383m,s,r,i,over(x, m, s, r: Numeric; i: IndexType ;
over : ... optional atomic ;
singleSampleMethod : optional hidden scalar )Normal_additive_gro(x,m,s,r,i)Adds a normally distributed percent growth g with mean m and standard deviation s to x for each value of index i. The growth g for each i has serial correlation r with g for i-1.x *( 1 + Cumulate(Normal_serial_correl(m, s, r, i,
singleSampleMethod:singleSampleMethod), i))159,600,1119,162,102,90,519,306x,m,s,r,i,over(x, m, s, r: Numeric; t: IndexType ;
over : ... optional atomic;
singleSampleMethod : optional hidden scalar )Normal_compound_gro(x,m,s,r,t)An array of values over time index t, starting from with value x, and with compound growth applied for each time interval, with normal uncertainty with mean m and standard deviation s The growth g for each i has correlation r with g for i-1.x * Cumproduct(IF t = Slice(t, 1) THEN 1 ELSE Normal_serial_correl(m, s, r, t, singleSampleMethod:singleSampleMethod ) + 1, t)159,640,1119,162,102,90,529,366x,m,s,r,t,over(x; r; i: IndexType ;
over : ... optional atomic;
singleSampleMethod : optional hidden scalar )Dist_serial_correl(x,r,i)Generates an array y over index i where each y[i] has a marginal distribution identical to x, and serial rank correlation of r with y[i-1]. If x is indexed by i, each y[i] has the same marginal distribution as x[i], but with samples reordered to have the specified rank correlation r between successive values. If r is indexed by i, r[i=k] specifies the rank correlation between y[i=k] and y[i=k-1]. Then the first correlation, r[i=1], is ignored.
In Mid context, it returns Mid(x).
Note: The result retains no probabilistic dependence on x.Dist_reshape(Normal_serial_correl( 0, 1, r, i, singleSampleMethod:singleSampleMethod ), x)408,560,1120,162,302,78,477,447x,r,i,over(x, g, r: Numeric; i: IndexType;
over : ... optional atomic;
singleSampleMethod : optional hidden scalar )Dist_additive_growth(x,g,r,i)Generates an array of values over index i, with the first equal to x, and successive values adding an uncertain growth with probability distribution g, and serial correlation r between growth[i = k] and growth[i=k-1]. x, g, and r each may be indexed by i if you want them to vary over i.x + Cumulate(Dist_serial_correl( g, r, i, singleSampleMethod : singleSampleMethod), i)407,600,1119,162,102,90,506,300x,g,r,i,over(x, g, r; i: IndexType ;
over : ... optional atomic ;
singleSampleMethod : optional hidden scalar )Dist_compound_growth(x,g,r,i)Starts with x and applies a compound growth g for each value of index i. The growth g for each i has correlation r with g for i-1.x * Cumproduct(
IF i = Slice(i, 1) THEN 1
ELSE (Dist_serial_correl( g, r, i,
singleSampleMethod:singleSampleMethod ) + 1)
, i)407,640,1119,162,102,90,489,307x,g,r,i,overDistributions on Linear Regression coefficients296,688,-1256,12( Y : Numeric[I,Run] ; B : Numeric[I,K,Run] ; I,K : Index; C : optional Numeric[K,Run] )RegressionNoise(Y,B,I,K,C)When you have data, Y[I] and B[I,K], generated from an underlying model with unknown coefficients C[k] and S of the form:
Y = Sum( C*B, I) + Normal(0,S)
This function computes an estimate for S.
When using in conjunction with RegressionDist, it is most efficient to provide the optional parameter C to both routines, where C is the expected value of the regression coefficients, obtained from calling Regression(Y,B,I,K). Doing so avoids an unnecessary call to the builtin Regression function.if IsNotSpecified(C) Then C := Regression(Y,B,I,K);
Var resid := Y - Sum(C*B,K);
sqrt( Sum(resid^2,I) / (size(I)-size(K)) );
384,736,1104,202,332,211,498,542Y,B,I,K,C( Y : Numeric[I,Run] ; B : Numeric[I,K,Run] ; I,K : Index; C : optional Numeric[K,Run] ;
S : optional Numeric[I,Run] )RegressionFitProb(Y,B,I,K,C)Once you've obtained regression coefficients C (indexed by K) by calling the Regression function, this function returns the probability that a fit this poor would occur by chance, given the assumption that the data was generated by a process of the form:
Y = Sum( C*B,K) + Normal(0,S)
If this result is very close to zero, it probably indicates that the assumption of linearity is bad. If it is very close to one, then it validates the assumption of linearity.
This is not a distribution function - it does not return a sample when evaluated in Sample mode. However, it does complement the multivariate RegressionDist function also included in this library.
To use, first call the Regression function, then you must either know the measurement knows a priori, or obtain it using the RegressionNoise function.
Var E_C := Regression(Y,B,I,K);
Var S := RegressionNoise(Y,B,I,K,C);
Var PrThisPoor := RegressionFitProb(Y,B,I,K,E_C,S)var resid := Y - sum(C*B,K);
var n := size(I);
var chi2 := sum( resid^2 / Mean(S)^2, I);
GammaI( n/2 - 1, chi2/2 )152,800,1112,202,287,69,586,548Y,B,I,K,C,SMortality with determinantsvar a:= Mortality_part;
var b:= a[.j='List'];
index j:= a.j[@.j=concat([1,4],6..39)];
a:= a[.j=j];
a:= if j='Country' then Country_codes[Country=a[j='Country']&''] else a;
a:= if j='Sex' then sexes[Sex=a[j='Sex']&''] else a;
a:= if j='Diagnosis' and b = '101' then diagnoses[Diagnosis=a[j='Diagnosis']&''] else a;
224,264,148,242,660,40,557,495,0,MIDM[Sys_localindex('I'),Sys_localindex('J')][1,1,1,0]Country['1010','1020','1025','1030','1035','1040','1045','1060','1070','1080','1090','1100','1115','1120','1125','1130','1135','1140','1160','1170','1180','1190','1192','1220','1230','1240','1250','1260','1270','1280','1290','1300','1310','1320','1325','1330','1340','1360','1365','1370','1385','1390','1400','1410','1420','1430','1470','1480','1510','1520','1530','1546','1555','1560','1570','2005','2010','2020','2025','2030','2040','2045','2050','2060','2070','2085','2090','2110','2120','2130','2140','2150','2160','2170','2180','2190','2200','2210','2230','2240','2250','2260','2270','2280','2290','2300','2310','2320','2330','2340','2350','2360','2370','2380','2385','2400','2410','2420','2430','2440','2445','2450','2455','2460','2470','3010','3020','3025','3027','3030','3050','3068','3070','3080','3083','3090','3100','3110','3130','3140','3150','3160','3170','3190','3200','3210','3220','3236','3255','3260','3270','3280','3285','3290','3300','3320','3325','3330','3340','3350','3365','3370','3380','3400','3405','3408','3420','4005','4007','4008','4010','4012','4018','4020','4025','4030','4038','4040','4045','4050','4055','4070','4080','4084','4085','4090','4100','4120','4140','4150','4160','4170','4180','4182','4184','4186','4188','4190','4195','4200','4205','4207','4210','4220','4230','4240','4260','4270','4272','4273','4274','4275','4276','4280','4290','4300','4301','4302','4303','4304','4308','4310','4320','4330','4335','4340','4350','5020','5060','5070','5105','5107','5108','5110','5150','5170','5180','5195','5197','5198','5200','5205','5207']336,216,148,12['1010','1020','1025','1030','1035','1040','1045','1060','1070','1080','1090','1100','1115','1120','1125','1130','1135','1140','1160','1170','1180','1190','1192','1220','1230','1240','1250','1260','1270','1280','1290','1300','1310','1320','1325','1330','1340','1360','1365','1370','1385','1390','1400','1410','1420','1430','1470','1480','1510','1520','1530','1546','1555','1560','1570','2005','2010','2020','2025','2030','2040','2045','2050','2060','2070','2085','2090','2110','2120','2130','2140','2150','2160','2170','2180','2190','2200','2210','2230','2240','2250','2260','2270','2280','2290','2300','2310','2320','2330','2340','2350','2360','2370','2380','2385','2400','2410','2420','2430','2440','2445','2450','2455','2460','2470','3010','3020','3025','3027','3030','3050','3068','3070','3080','3083','3090','3100','3110','3130','3140','3150','3160','3170','3190','3200','3210','3220','3236','3255','3260','3270','3280','3285','3290','3300','3320','3325','3330','3340','3350','3365','3370','3380','3400','3405','3408','3420','4005','4007','4008','4010','4012','4018','4020','4025','4030','4038','4040','4045','4050','4055','4070','4080','4084','4085','4090','4100','4120','4140','4150','4160','4170','4180','4182','4184','4186','4188','4190','4195','4200','4205','4207','4210','4220','4230','4240','4260','4270','4272','4273','4274','4275','4276','4280','4290','4300','4301','4302','4303','4304','4308','4310','4320','4330','4335','4340','4350','5020','5060','5070','5105','5107','5108','5110','5150','5170','5180','5195','5197','5198','5200','5205','5207']Country codesTable(Country)(
'Algeria','Angola','Benin','Botswana','Burkina Faso','Burundi','Cameroon','Cape Verde','Central African Republic','Chad','Comoros','Congo','Cote d’Ivoire','Djibouti','Egypt','Equatorial Guinea','Eritrea','Ethiopia','Gabon','Gambia','Ghana','Guinea','Guinea-Bissau','Kenya','Lesotho','Liberia','Libyan Arab Jamahiriya','Madagascar','Malawi','Mali','Mauritania','Mauritius','Morocco','Mozambique','Namibia','Niger','Nigeria','Reunion','Rodrigues','Rwanda','Sao Tome and Principe','Senegal','Seychelles','Sierra Leone','Somalia','South Africa','Sudan','Swaziland','Togo','Tunisia','Uganda','United Republic of Tanzania','Democratic Republic of the Congo','Zambia','Zimbabwe','Anguilla','Antigua and Barbuda','Argentina','Aruba','Bahamas','Barbados','Belize','Bermuda','Bolivia','Brazil','British Virgin Islands','Canada','Cayman Islands','Chile','Colombia','Costa Rica','Cuba','Dominica','Dominican Republic','Ecuador','El Salvador','Falkland Islands (Malvinas)','French Guiana','Grenada','Guadeloupe','Guatemala','Guyana','Haiti','Honduras','Jamaica','Martinique','Mexico','Montserrat','Netherlands Antilles','Nicaragua','Panama','Paraguay','Peru','Puerto Rico','Saint Kitts and Nevis','Saint Lucia','Saint Pierre and Miquelon','Saint Vincent and Grenadines','Suriname','Trinidad and Tobago','Turks and Caicos Islands','United States of America','Virgin Islands (USA)','Uruguay','Venezuela','Afghanistan','Bahrain','Bangladesh','Bhutan','Brunei Darussalam','Cambodia','China','China: Province of Taiwan only','Cyprus','Democratic Peoples’s Republic of Korea','Hong Kong SAR','India','Indonesia','Iran (Islamic Republic of)','Iraq','Israel','Japan','Jordan','Kuwait','Lao People’s Democratic Republic','Lebanon','Macau','Malaysia','Maldives','Mongolia','Myanmar','Nepal','Oman','Pakistan','Philippines','Qatar','Republic of Korea','Ryu Kyu Islands','Saudi Arabia','Singapore','Sri Lanka','Syrian Arab Republic','Thailand','Turkey','United Arab Emirates','Viet Nam','Yemen','Albania','Armenia','Andorra','Austria','Azerbaijan','Belarus','Belgium','Bosnia and Herzegovina','Bulgaria','Croatia','Czechoslovakia, Former','Czech Republic','Denmark','Estonia','Finland','France','Georgia','Germany','Germany, Former Democratic Republic','Germany, Former Federal Republic','Germany, West Berlin','Greece','Hungary','Iceland','Ireland','Italy','Kazakhstan','Kyrgyzstan','Latvia','Lithuania','Luxembourg','TFYR Macedonia','Malta','Monaco','Montenegro','Netherlands','Norway','Poland','Portugal','Republic of Moldova','Romania','Russian Federation','Serbia','Slovakia','San Marino','Slovenia','Spain','Sweden','Switzerland','Tajikistan','Turkmenistan','Ukraine','USSR, Former','United Kingdom','United Kingdom, England and Wales','United Kingdom, Northern Ireland','United Kingdom, Scotland','Uzbekistan','Yugoslavia, Former','Serbia and Montenegro, Former','Australia','Cook Islands','Fiji','Kiribati','Marshall Islands','Micronesia (Federated States of)','Nauru','New Zealand','Niue','Palau','Papua New Guinea','Samoa','Solomon Islands','Tonga','Tuvalu','Vanuatu')336,184,148,2452425,39321,65535Agegroup a['Deaths1','Deaths2','Deaths3','Deaths4','Deaths5','Deaths6','Deaths7','Deaths8','Deaths9','Deaths10','Deaths11','Deaths12','Deaths13','Deaths14','Deaths15','Deaths16','Deaths17','Deaths18','Deaths19','Deaths20','Deaths21','Deaths22','Deaths23','Deaths24','Deaths25','Deaths26']56,376,148,12['Deaths1','Deaths2','Deaths3','Deaths4','Deaths5','Deaths6','Deaths7','Deaths8','Deaths9','Deaths10','Deaths11','Deaths12','Deaths13','Deaths14','Deaths15','Deaths16','Deaths17','Deaths18','Deaths19','Deaths20','Deaths21','Deaths22','Deaths23','Deaths24','Deaths25','Deaths26']Format a0&(0..9)56,400,148,12['00','01','02','03','04','05','06','07','08','09']Adult formattingTable(Agegroup_a,Format_a)(
'All ages','All ages','All ages','All ages','All ages','All ages','All ages','All ages','All ages','All ages',
'0','0','0','0','0','0','0','0','0','',
'1','1','1-4','1','1-4','1-4','1-4','1-4','1-4','',
'2','2','','2','','','','','','',
'3','3','','3','','','','','','',
'4','4','','4','','','','','','',
'5-9','5-9','5-9','5-9','5-9','5-9','5-9','5-14','5-14','',
'10-14','10-14','10-14','10-14','10-14','10-14','10-14','','','',
'15-19','15-19','15-19','15-19','15-19','15-19','15-19','15-24','15-24','',
'20-24','20-24','20-24','20-24','20-24','20-24','20-24','','','',
'25-29','25-29','25-29','25-29','25-29','25-29','25-29','25-34','25-34','',
'30-34','30-34','30-34','30-34','30-34','30-34','30-34','','','',
'35-39','35-39','35-39','35-39','35-39','35-39','35-39','35-44','35-44','',
'40-44','40-44','40-44','40-44','40-44','40-44','40-44','','','',
'45-49','45-49','45-49','45-49','45-49','45-49','45-49','45-54','45-54','',
'50-54','50-54','50-54','50-54','50-54','50-54','50-54','','','',
'55-59','55-59','55-59','55-59','55-59','55-59','55-59','55-64','55-64','',
'60-64','60-64','60-64','60-64','60-64','60-64','60-64','','','',
'65-69','65-69','65-69','65-69','65-69','65-69','65 &+','65-74','65 &+','',
'70-74','70-74','70-74','70-74','70-74','70 &+','','','','',
'75-79','75-79','75-79','75 &+','75 &+','','','75 &+','','',
'80-84','80-84','80-84','','','','','','','',
'85-89','85 &+','85 &+','','','','','','','',
'90-94','','','','','','','','','',
'95 &+','','','','','','','','','',
'Unknown','Unknown','Unknown','Unknown','Unknown','Unknown','Unknown','Unknown','Unknown',''
)56,344,148,242,152,162,906,333,0,MIDM2,545,80,463,555,0,MIDM52425,39321,65535[Format_a,Agegroup_a][Format_a,Agegroup_a]Sex['1','2','9']112,296,148,12['1','2','9']SexesTable(Sex)(
'Male','Female','Unspecified')112,264,148,2452425,39321,65535Diagnosis['1000','1001','1002','1003','1004','1005','1006','1007','1008','1009','1010','1011','1012','1013','1014','1015','1016','1017','1018','1019','1020','1021','1022','1023','1024','1025','1026','1027','1028','1029','1030','1031','1032','1033','1034','1035','1036','1037','1038','1039','1040','1041','1042','1043','1044','1045','1046','1047','1048','1049','1050','1051','1052','1053','1054','1055','1056','1057','1058','1059','1060','1061','1062','1063','1064','1065','1066','1067','1068','1069','1070','1071','1072','1073','1074','1075','1076','1077','1078','1079','1080','1081','1082','1083','1084','1085','1086','1087','1088','1089','1090','1091','1092','1093','1094','1095','1096','1097','1098','1099','1100','1101','1102','1103']336,296,148,12['1000','1001','1002','1003','1004','1005','1006','1007','1008','1009','1010','1011','1012','1013','1014','1015','1016','1017','1018','1019','1020','1021','1022','1023','1024','1025','1026','1027','1028','1029','1030','1031','1032','1033','1034','1035','1036','1037','1038','1039','1040','1041','1042','1043','1044','1045','1046','1047','1048','1049','1050','1051','1052','1053','1054','1055','1056','1057','1058','1059','1060','1061','1062','1063','1064','1065','1066','1067','1068','1069','1070','1071','1072','1073','1074','1075','1076','1077','1078','1079','1080','1081','1082','1083','1084','1085','1086','1087','1088','1089','1090','1091','1092','1093','1094','1095','1096','1097','1098','1099','1100','1101','1102','1103']DiagnosesTable(Diagnosis)(
'AAA','A00-B99 ','A00 ','A09 ','A01-A08 ','A15-A16 ','A17-A19 ','A20 ','A33-A35 ','A36 ','A37 ','A39 ','A40-A41 ','A50-A64 ','A80 ','A82 ','A95 ','A90-A94, A96-A99 ','B05 ','B15-B19 ','B20-B24 ','B50-B54 ','B55 ','B56-B57 ','B65 ','A21-A32, A38, A42-A49, A65-A79, A81, A83-A89, B00-B04, B06-B09, B25-B49, B58-B64, B66-B94, B99 ','C00-D48 ','C00-C14 ','C15 ','C16 ','C18-C21 ','C22 ','C25 ','C32 ','C33-C34 ','C43 ','C50 ','C53 ','C54-C55 ','C56 ','C61 ','C67 ','C70-C72 ','C82-C85 ','C90 ','C91-C95 ','C17, C23-C24, C26-C31, C37-C41, C44-C49, C51-C52, C57-C60, C62-C66,C68-C69,C73-C81,C88,C96-C97 ','D00-D48 ','D50-D89 ','D50-D64 ','D65-D89 ','E00-E88 ','E10-E14 ','E40-E46 ','E00-E07, E15-E34, E50-E88 ','F01-F99 ','F10-F19 ','F20-F99 ','G00-G98 ','G00, G03 ','G30 ','G04-G25, G31-G98 ','H00-H57 ','H60-H93 ','I00-I99 ','I00-I09 ','I10-I13 ','I20-I25 ','I26-I51 ','I60-I69 ','I70 ','I71-I99 ','J00-J98 ','J10-J11 ','J12-J18 ','J20-J22 ','J40-J47 ','J00-J06, J30-J39, J60-J98 ','K00-K92 ','K25-K27 ','K70-K76 ','K00-K22, K28-K66, K80-K92 ','L00-L98 ','M00-M99 ','N00-N98 ','N00-N15 ','N17-N98 ','O00-O99 ','O00-O07 ','O10-O92 ','O98-O99 ','O95-O97 ','P00-P96 ','Q00-Q99 ','R00-R99 ','V01-Y89 ','V01-V99 ','W00-W19 ','W65-W74 ','X00-X09 ','X40-X49 ','X60-X84 ','X85-Y09 ','W20-W64, W75-W99, X10-X39, X50-X59, Y10-Y89 ')336,264,148,242,459,297,516,303,0,MIDM52425,39321,65535Adult mortalityindex j:= ['Country','Year','Diagnosis','Sex','Age','Deaths'];
var a:= array(j,Agegroup_a,[1,2,3,4,5,(7..32)]);
a:= Mortality_with_deter[@.j=a];
a:= if j='Age' then Adult_formatting[Format_a=a[j='Age']] else a;
index temp:= 1..size(Agegroup_a)*size(a.i);
a:= concatrows(a,a.i,Agegroup_a,temp);
index i:= subset(a[.j='Deaths']<>'');
a[@temp=i]176,344,148,242,614,34,638,372,0,MIDM[Sys_localindex('I'),Sys_localindex('J')][0,1,1,0][4,5,6,8,10,12,14,16,18,20,22,23,24,25,30,31,32,34,36,38,40,42,44,46,48,49,50,51,56,57,58,60,62,64,66,68,70,72,74,75,76,77,82,83,84,86,88,90,92,94,96,98,100,101,102,103,108,109,110,112,114,116,118,120,122,124,126,127,128,129,134,135,136,138,140,142,144,146,148,150,152,153,154,155,160,161,162,164,166,168,170,172,174,176,178,179,180,181,186,187,188,190,192,194,196,198,200,202,204,205,206,207,212,213,214,216,218,220,222,224,226,228,230,231,232,233,238,239,240,242,244,246,248,250,252,254,256,257,258,259,264,265,266,268,270,272,274,276,278,280,282,283,284,285,290,291,292,294,296,298,300,302,304,306,308,309,310,311,316,317,318,320,322,324,326,328,330,332,334,335,336,337,342,343,344,346,348,350,352,354,356,358,360,361,362,363,368,369,370,372,374,376,378,380,382,384,386,387,388,389,394,395,396,398,400,402,404,406,408,410,412,413,414,415,420,421,422,424,426,428,430,432,434,436,438,439,440,441,446,447,448,450,452,454,456,458,460,462,464,465,466,467,472,473,474,476,478,480,482,484,486,488,490,491,492,493,498,499,500,502,504,506,508,510,512,514,516,517,518,519,524,525,526,528,530,532,534,536,538,540,542,543,544,545,550,551,552,554,556,558,560,562,564,566,568,569,570,571,576,577,578,580,582,584,586,588,590,592,594,595,596,597,602,603,604,606,608,610,612,614,616,618,620,621,622,623,628,629,630,632,634,636,638,640,642,644,646,647,648,649,654,655,656,658,660,662,664,666,668,670,672,673,674,675,680,681,682,684,686,688,690,692,694,696,698,699,700,701,706,707,708,710,712,714,716,718,720,722,724,725,726,727,732,733,734,736,738,740,742,744,746,748,750,751,752,753][Sys_localindex('I'),1,Agegroup_a,1,Sys_localindex('TEMP'),1]Infant mortalityindex j:= ['Country','Year','Diagnosis','Sex','Age','Deaths'];
var a:= array(j,Agegroup_i,[1,2,3,4,6,(33..36)]);
a:= Mortality_with_deter[@.j=a];
a:= if j='Age' then Infant_formatting[Format_i=a[j='Age']] else a;
index temp:= 1..size(Agegroup_i)*size(a.i);
a:= concatrows(a,a.i,Agegroup_i,temp);
index i:= subset(a[.j='Deaths']<>'');
a[@temp=i]280,344,148,242,614,34,638,372,0,MIDM[Sys_localindex('I'),Sys_localindex('J')][4,5,6,8,10,12,14,16,18,20,22,23,24,25,30,31,32,34,36,38,40,42,44,46,48,49,50,51,56,57,58,60,62,64,66,68,70,72,74,75,76,77,82,83,84,86,88,90,92,94,96,98,100,101,102,103,108,109,110,112,114,116,118,120,122,124,126,127,128,129,134,135,136,138,140,142,144,146,148,150,152,153,154,155,160,161,162,164,166,168,170,172,174,176,178,179,180,181,186,187,188,190,192,194,196,198,200,202,204,205,206,207,212,213,214,216,218,220,222,224,226,228,230,231,232,233,238,239,240,242,244,246,248,250,252,254,256,257,258,259,264,265,266,268,270,272,274,276,278,280,282,283,284,285,290,291,292,294,296,298,300,302,304,306,308,309,310,311,316,317,318,320,322,324,326,328,330,332,334,335,336,337,342,343,344,346,348,350,352,354,356,358,360,361,362,363,368,369,370,372,374,376,378,380,382,384,386,387,388,389,394,395,396,398,400,402,404,406,408,410,412,413,414,415,420,421,422,424,426,428,430,432,434,436,438,439,440,441,446,447,448,450,452,454,456,458,460,462,464,465,466,467,472,473,474,476,478,480,482,484,486,488,490,491,492,493,498,499,500,502,504,506,508,510,512,514,516,517,518,519,524,525,526,528,530,532,534,536,538,540,542,543,544,545,550,551,552,554,556,558,560,562,564,566,568,569,570,571,576,577,578,580,582,584,586,588,590,592,594,595,596,597,602,603,604,606,608,610,612,614,616,618,620,621,622,623,628,629,630,632,634,636,638,640,642,644,646,647,648,649,654,655,656,658,660,662,664,666,668,670,672,673,674,675,680,681,682,684,686,688,690,692,694,696,698,699,700,701,706,707,708,710,712,714,716,718,720,722,724,725,726,727,732,733,734,736,738,740,742,744,746,748,750,751,752,753][Agegroup_i,1,Sys_localindex('J'),1,Sys_localindex('I'),1]Agegroup i['IM_Deaths1','IM_Deaths2','IM_Deaths3','IM_Deaths4']400,376,148,12['IM_Deaths1','IM_Deaths2','IM_Deaths3','IM_Deaths4']Format i['01','02','08']400,400,148,12['01','02','08']Infant formattingTable(Agegroup_i,Format_i)(
'0 day','0-6 days','0-365 days',
'1-6 days','','',
'7-27 days','7-27days','',
'28-365 days','28-365 days',''
)400,344,148,242,152,162,906,333,0,MIDM2,654,462,416,186,0,MIDM52425,39321,65535[Format_i,Agegroup_i][Format_i,Agegroup_i]Other partsktluser13. Aprta 2009 8:51 48,24176,488,148,241,0,1,1,1,1,0,,0,1,184,14,1076,621,17ICD10 code[1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010]704,144,148,12[1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010]ICD10 DLN['AAA','A00','A000','A001','A009','A01','A010','A011','A012','A013','A014','A02','A020','A021']704,280,148,12Table(Icd10_code)(
'A-J, K-Y','A00-B99','A00','A09','A01-A08','A15-A16','A17-A19','A20','A33-A35','A36','A37')704,112,148,24var a:= Va3;
a:= for x:= Icd10_code do (
var b:= a[Icd10_code=x];
b:= splittext(b,','));
index temp1:= 1..size(a)/size(Icd10_code);
a:= for x:= icd10_code do ( slice(a[icd10_code=x],temp1));
a:= if a='' then a[@temp1=1] else a;
a:= asciicd(a);
704,192,148,242,749,55,416,303,0,MIDM[Sys_localindex('RANGE'),Icd10_code]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[''][Sys_localindex('TEMP1'),2,Icd10_code,1,Sys_localindex('RANGE'),1](a)Asciicdindex range:= ['Begin','End'];
a:= if a='AAA' then 'A-Y' else a;
a:= for y=a do ( slice(splittext(y&'','-'),@range));
a:= if a=null then '' else Texttrim(a);
a:= if range='End' and a[range='End']='' then a[range='Begin'] else a;
a:= selecttext( a & array(range,['000','999']), 1,4);
a:= asc(selecttext(a,1,1))*1000+evaluate(selecttext(a,2,4));704,40,148,122,428,431,476,224aasciicd(Icd10_dln)704,248,148,242,168,-6246,416,303,0,MIDM[Sys_localindex('RANGE'),Icd10_dln]2,I,4,2,0,0,4,0,$,0,"ABBREV",0(item, set)Belongvar a:= item[.range='Begin']>=set[.range='Begin'] and item[.range='Begin']<=set[.range='End'];
var b:= item[.range='End']>=set[.range='Begin'] and item[.range='End']<=set[.range='End'];
704,64,148,122,262,178,476,224item,setvar a:= splittext(readtextfile('c:\temp\morticd10.csv'),chr(10));
index itemp:= 1..size(a)-2;
a:= slice(a,itemp+1);
index jtemp:= 1..9;
a:= for x:= itemp do (
if mod(x,1000)=0 then ShowProgressBar("Reading","x="&x,x/size(itemp));
slice(splittext(a[itemp=x],','),@jtemp))
{a:= for x:= itemp do ( array(jtemp, splittext(a[itemp=x],',')));
index j:= a[@itemp=1];
index i:= 1..(size(itemp)-1);
a[@jtemp=@j, @itemp=i+1]}88,408,148,242,530,89,476,3892,594,13,512,292,0,MIDM[Sys_localindex('ITEMP'),Sys_localindex('JTEMP')]Countryvar a:= selecttext(Va9,1,4);
a[.itemp=unique(a,a.itemp)]88,456,148,122,274,240,416,303,0,MIDM[1,2,997,1992,2390,3186,3584,4181,5773,7962,8161,10.029K,32.206K,34.144K,35.082K,35.298K,49.793K,50.632K,57.191K,59.935K,69.55K,71.653K,80.73K,84.154K,96.865K,105.413K,108.07K,108.746K,113.212K,113.302K,113.585K,114.492K,155.293K,155.494K,156.46K,157.282K,158.869K,160.126K,230.025K,230.468K,249.276K,249.62K,277.024K,313.564K,328.575K,342.608K,343.436K,354.884K,376.311K,383.433K,385.196K,385.694K,389.831K,393.239K,397.117K,401.193K,449.356K,449.796K,462.079K,472.801K,485.808K,490.889K,500.723K,502.553K,504.372K,504.417K,505.399K,508.31K,512.547K,512.944K,564.976K,566.522K,575.862K,614.847K,624.637K,637.989K,702.015K,727.774K,739.559K,761.515K,796.589K,815.364K,840.535K,869.956K,923.448K,959.532K,964.313K,969.331K,999.799K,1.005434M,1.01125M,1.031072M,1.052635M,1.088111M,1.093342M,1.123324M,1.155531M,1.173456M,1.202857M,1.230554M,1.237732M,1.253389M,1.268452M,1.289162M,1.289631M,1.320788M]1..39328,368,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]1..30328,392,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]MortIcdsliceTable(In3,In4)(
'Country',1400,1410,1420,1430,2130,2140,2110,2120,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,1400,
'Admin1',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'SubDiv',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Year',2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,2001,
'List',101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,101,
'Cause',1000,1000,1001,1001,1002,1002,1003,1003,1004,1004,1005,1005,1006,1006,1007,1007,1008,1008,1009,1009,1010,1010,1011,1011,1012,1012,1013,1013,1014,
'Sex',1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,
'Frmat',7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,
'IM_Frmat',8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,
'Deaths1',332,222,24,14,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,11,7,0,0,0,
'Deaths2',8,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'Deaths3',2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'Deaths4',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths5',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths6',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths7',1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'Deaths8',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths9',9,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'Deaths10',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths11',21,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'Deaths12',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths13',36,12,7,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,
'Deaths14',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths15',39,13,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'Deaths16',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths17',53,21,3,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,
'Deaths18',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths19',68,48,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,
'Deaths20',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths21',95,112,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,6,0,0,0,
'Deaths22',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths23',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths24',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths25',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'Deaths26',0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'IM_Deaths1',8,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
'IM_Deaths2',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'IM_Deaths3',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
'IM_Deaths4',,,,,,,,,,,,,,,,,,,,,,,,,,,,,
)328,336,148,242,200,38,946,475,0,MIDM2,136,146,1110,325,0,MIDM65535,52427,65534[In3,In4][In4,In3]Sex-Sex: male or female.['Male','Female']208,272,148,122,576,190,476,2241,104,114,416,303,0,MIDMOp_en2780Var['Variable','ICD-10 codes','Cause groupings','Sex','All ages','< 1','1-4','5-14','15-24','25-34','35-44','45-54','55-64','65-74','75+','Age not specified']208,104,148,121,232,242,416,303,0,MIDM['Variable','ICD-10 codes','Cause groupings','Sex','All ages','< 1','1-4','5-14','15-24','25-34','35-44','45-54','55-64','65-74','75+','Age not specified']Itemsequence(1,392)208,80,148,121,72,82,416,303,0,MIDM[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392]WHO dataWHO data kopioitu WHO_Cause of Death.xls tiedostosta, joka on samassa hakemistossa tämän mallin kanssa.
Kuolleisuusdata on kopioitu WHO_Cause of Death.xls tiedostoon WHO:n sivuilta 23.1.2003 osoitteesta:
http://www3.who.int/whosis/ (Sivulta linkki: Cause of death statistics -> Table 1 -> Finland - 1996)Table(Var1,Item2)(
'Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py',
'AAA',0,0,0,'A00-B99',0,0,0,'A01',0,0,0,'A00, A02-A09',0,0,0,'A15-A16',0,0,0,'A17-A19',0,0,0,'A37',0,0,0,'A39',0,0,0,'A35',0,0,0,'A40-A41',0,0,0,'A20-A32, A36, A38, A42-49',0,0,0,'B05',0,0,0,'B20-B24',0,0,0,'A70-A74, A80-B34, B05, B20-B24',0,0,0,'B50-B54',0,0,0,'A75-A79, B55-B57, B60, B64',0,0,0,'A50-A64',0,0,0,'A65-A69, B35-B49, B58, B59, B65-B99',0,0,0,'C00-C97',0,0,0,'C00-C14',0,0,0,'C15',0,0,0,'C16',0,0,0,'C18',0,0,0,'C19-C21',0,0,0,'C22',0,0,0,'C32',0,0,0,'C33-C34',0,0,0,'C50',0,0,0,'C53',0,'C54-C55',0,'C61',0,'C67',0,0,0,'C17, C23-C31, C37-C49, C51, C52, C56-C60, C62-C66, C68-C80, C97',0,0,0,'C91-C95',0,0,0,'C81-C90, C96',0,0,0,'D00-D48',0,0,0,'E10-E14',0,0,0,'E00-E07, E15-E34, E65-E68, E70-E88',0,0,0,'E41-E46',0,0,0,'E40, E50-E64',0,0,0,'D50-D64',0,0,0,'D65-D89',0,0,0,'F01-F99',0,0,0,'G00, G03',0,0,0,'G35',0,0,0,'G40-G41',0,0,0,'G04-G31, G36-G37, G43-H95',0,0,0,'I00-I99',0,0,0,'I00-I02',0,0,0,'I05-I09',0,0,0,'I10-I13',0,0,0,'I21, I22',0,0,0,'I20, I24, I25',0,0,0,'I26-I51',0,0,0,'I60-I69',0,0,0,'I70',0,0,0,'I71-I78',0,0,0,'I80-I82',0,0,0,'I83-I99',0,0,0,'J00-J06',0,0,0,'J20-J21',0,0,0,'J12-J18',0,0,0,'J10-J11',0,0,0,'J40-J46',0,0,0,'J22, J30-J39, J47-J98',0,0,0,'K25-K27',0,0,0,'K35-K38',0,0,0,'K40-K46,K56',0,0,0,'K70,K73-K74,K76',0,0,0,'K00-K22, K28-K31, K50-K55, K57-K66, K71, K72, K75, K80-K92',0,0,0,'N00-N07, N13-N19',0,0,0,'N10-N12',0,0,0,'N40',0,'N20-N39, N41-N98',0,0,0,'O00-O07',0,'O20, O46, O67, O72',0,'O13-O16, O21',0,'O85-O92, A34',0,'O10-O12, O22-O75, O95-O97',0,'O98-O99',0,'L00-L98',0,0,0,'M00-M99',0,0,0,'Q03,Q05',0,0,0,'Q20-Q28',0,0,0,'Q00-Q02, Q04, Q06-Q18, Q30-Q99',0,0,0,'P10-P15',0,0,0,'P00-P08, P20-P96, A33',0,0,0,'R54',0,0,0,'R00-R53, R55-R99',0,0,0,'V01-X59, Y40-Y86, Y88',0,0,0,'V02-V04, V09, V12-V14, V19-V79, V86-V89',0,0,0,'V01, V05-V06, V10, V11, V15-V18, V80-V85, V90-V99',0,0,0,'X40-X49',0,0,0,'W00-W19',0,0,0,'X00-X09',0,0,0,'W65-W74',0,0,0,'W24-W31',0,0,0,'W32-W34',0,0,0,'W20-W23, W35-W64, W75-W99, X10-X39, X50-X59, Y85, Y86',0,0,0,'Y40-Y84, Y88',0,0,0,'X60-X84',0,0,0,'X85-Y09',0,0,0,'Y10-Y36, Y87, Y89',0,0,0,
'All causes',0,0,0,'Infectious and parasitic diseases',0,0,0,'Typhoid and paratyphoid fever',0,0,0,'Other intestinal infectious diseases',0,0,0,'Tuberculosis of respiratory system',0,0,0,'Tuberculosis, other forms',0,0,0,'Whooping cough',0,0,0,'Meningococcal infection',0,0,0,'Tetanus',0,0,0,'Septicaemia',0,0,0,'Other bacterial diseases',0,0,0,'Measles',0,0,0,'HIV disease',0,0,0,'Other viral diseases',0,0,0,'Malaria',0,0,0,'Other arthropod-borne diseases',0,0,0,'Sexually transmitted diseases',0,0,0,'Other infectious and parasitic diseases',0,0,0,'Malignant neoplasms',0,0,0,'Malignant neoplasm of lip, oral cavity and pharynx',0,0,0,'Malignant neoplasm of oesophagus',0,0,0,'Malignant neoplasm of stomach',0,0,0,'Malignant neoplasm of colon',0,0,0,'Malignant neoplasm of rectum, rectosigmoid junction and anus',0,0,0,'Malignant neoplasm of liver',0,0,0,'Malignant neoplasm of larynx',0,0,0,'Malignant neoplasm of trachea, bronchus and lung',0,0,0,'Malignant neoplasm of breast',0,0,0,'Malignant neoplasm of cervix uteri',0,'Malignant neoplasm of uterus, other and unspecified',0,'Malignant neoplasm of prostate',0,'Malignant neoplasm of bladder',0,0,0,'Malignant neoplasm of other sites',0,0,0,'Leukaemia',0,0,0,'Other malignant neoplasms of lymphoid and haematopoietic and related tissue',0,0,0,'Benign neoplasm, other and unspecified neoplasm',0,0,0,'Diabetes mellitus',0,0,0,'Other endocrine and metabolic diseases',0,0,0,'Malnutrition',0,0,0,'Other nutritional deficiencies',0,0,0,'Anaemias',0,0,0,'Other diseases of blood and blood-forming organs',0,0,0,'Mental disorders',0,0,0,'Meningitis',0,0,0,'Multiple sclerosis',0,0,0,'Epilepsy',0,0,0,'Other diseases of the nervous system and sense organs',0,0,0,'Diseases of the circulatory system',0,0,0,'Acute rheumatic fever',0,0,0,'Chronic rheumatic heart disease',0,0,0,'Hypertensive disease',0,0,0,'Acute myocardial infarction',0,0,0,'Other ischaemic heart diseases',0,0,0,'Diseases of pulmonary circulation and other forms of heart disease',0,0,0,'Cerebrovascular disease',0,0,0,'Atherosclerosis',0,0,0,'Embolism, thrombosis and other diseases of arteries, arterioles and capillaries',0,0,0,'Phlebitis, thrombophlebitis, venous embolism and thrombosis',0,0,0,'Other diseases of the circulatory system',0,0,0,'Acute upper respiratory infection',0,0,0,'Acute bronchitis and bronchiolitis',0,0,0,'Pneumonia',0,0,0,'Influenza',0,0,0,'Bronchitis, chronic and unspecified, emphysema and asthma',0,0,0,'Other diseases of the respiratory system',0,0,0,'Ulcer of stomach and duodenum',0,0,0,'Appendicitis',0,0,0,'Hernia of abdominal cavity and intestinal obstruction',0,0,0,'Chronic liver disease and cirrhosis',0,0,0,'Other diseases of the digestive system',0,0,0,'Nephritis, nephrotic syndrome and nephrosis',0,0,0,'Infections of kidney',0,0,0,'Hyperplasia of prostate',0,'Other diseases of the genitourinary system',0,0,0,'Abortion',0,'Haemorrhage of pregnancy and childbirth',0,'Toxaemia of pregnancy',0,'Complications of the puerperium',0,'Other direct obstetric causes',0,'Indirect obstetric causes',0,'Diseases of skin and subcutaneous tissue',0,0,0,'Diseases of the musculoskeletal system and connective tissue',0,0,0,'Spina bifida and hydrocephalus',0,0,0,'Congenital anomalies of the circulatory system',0,0,0,'Other congenital anomalies',0,0,0,'Birth trauma',0,0,0,'Other conditions originating in the perinatal period',0,0,0,'Senility',0,0,0,'Signs, symptoms and other ill-defined conditions',0,0,0,'Accidents and adverse effects',0,0,0,'Motor vehicle traffic accidents',0,0,0,'Other transport accidents',0,0,0,'Accidental poisoning',0,0,0,'Accidental falls',0,0,0,'Accidents caused by fire and flames',0,0,0,'Accidental drowning and submersion',0,0,0,'Accidents caused by machinery and by cutting and piercing instruments',0,0,0,'Accidents caused by firearm missile',0,0,0,'All other accidents, including late effects',0,0,0,'Drugs, medicaments causing adverse effects in therapeutic use',0,0,0,'Suicide and self- inflicted injury',0,0,0,'Homicide and injury purposely inflicted by other persons',0,0,0,'Other external causes',0,0,0,
'M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','F','F','F','F','M','M','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','M','M','F','M','F','F','F','F','F','F','F','F','F','F','F','F','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F',
24.266K,24.895K,972.2,947.2,218,220,8.7,8.4,'-','-','-','-',20,49,0.8,1.9,50,31,2,1.2,10,18,0.4,0.7,'-','-','-','-',1,5,0,0.2,1,'-',0,'-',47,54,1.9,2.1,19,15,0.8,0.6,'-','-','-','-',26,3,1,0.1,21,21,0.8,0.8,'-','-','-','-','-','-','-','-','-','-','-','-',23,24,0.9,0.9,5372,4689,215.2,178.4,90,60,3.6,2.3,103,84,4.1,3.2,358,308,14.3,11.7,263,324,10.5,12.3,201,176,8.1,6.7,170,144,6.8,5.5,37,1,1.5,0,1482,397,59.4,15.1,4,774,0.2,29.4,67,2.5,138,5.3,784,31.4,157,56,6.3,2.1,1241,1680,49.7,63.9,162,140,6.5,5.3,320,340,12.8,12.9,88,160,3.5,6.1,226,367,9.1,14,31,39,1.2,1.5,'-',1,'-',0,5,1,0.2,0,13,15,0.5,0.6,13,19,0.5,0.7,838,1840,33.6,70,4,4,0.2,0.2,23,35,0.9,1.3,46,25,1.8,1,451,685,18.1,26.1,10.315K,11.728K,413.2,446.2,'-','-','-','-',30,73,1.2,2.8,118,322,4.7,12.3,4194,3793,168,144.3,2541,2441,101.8,92.9,891,1214,35.7,46.2,1959,3337,78.5,127,121,266,4.8,10.1,415,203,16.6,7.7,41,71,1.6,2.7,5,8,0.2,0.3,2,3,0.1,0.1,8,18,0.3,0.7,1192,1511,47.8,57.5,63,124,2.5,4.7,800,288,32,11,115,90,4.6,3.4,138,162,5.5,6.2,4,9,0.2,0.3,82,101,3.3,3.8,450,184,18,7,350,512,14,19.5,59,53,2.4,2,69,219,2.8,8.3,38,1.5,34,93,1.4,3.5,'-','-','-','-','-','-',2,0.1,'-','-','-','-',8,13,0.3,0.5,58,221,2.3,8.4,1,3,0,0.1,44,40,1.8,1.5,47,52,1.9,2,2,'-',0.1,'-',66,49,2.6,1.9,10,37,0.4,1.4,93,46,3.7,1.8,1706,877,68.3,33.4,286,109,11.5,4.1,81,27,3.2,1,493,131,19.8,5,481,472,19.3,18,55,25,2.2,1,95,23,3.8,0.9,17,2,0.7,0.1,3,'-',0.1,'-',192,85,7.7,3.2,3,3,0.1,0.1,965,282,38.7,10.7,118,51,4.7,1.9,101,27,4,1,
136,102,436.7,344.8,4,1,12.8,3.4,'-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',1,1,3.2,3.4,'-','-','-','-',1,'-',3.2,'-','-','-','-','-','-','-','-','-','-','-','-','-',2,'-',6.4,'-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',1,'-',3.2,'-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',1,'-',3.2,'-','-','-','-','-','-','-','-','-','-','-','-','-',1,3,3.2,10.1,'-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',1,'-',3.4,'-','-','-','-','-','-','-','-',3,1,9.6,3.4,2,2,6.4,6.8,'-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',2,2,6.4,6.8,'-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',1,'-',3.4,'-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',1,'-',3.4,'-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',1,1,3.2,3.4,22,16,70.6,54.1,22,17,70.6,57.5,1,'-',3.2,'-',64,47,205.5,158.9,'-','-','-','-',10,7,32.1,23.7,2,1,6.4,3.4,'-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',1,'-',3.2,'-','-','-','-','-','-','-','-','-',1,1,3.2,3.4,'-','-','-','-','-','-','-','-',2,3,6.4,10.1,1,'-',3.2,'-',
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10.063K,17.747K,10.9619K,8371.2,83,152,90.4,71.7,'-','-','-','-',17,46,18.5,21.7,28,26,30.5,12.3,4,13,4.4,6.1,'-','-','-','-','-',1,'-',0.5,1,'-',1.1,'-',13,34,14.2,16,7,10,7.6,4.7,'-','-','-','-','-','-','-','-',1,11,1.1,5.2,'-','-','-','-','-','-','-','-','-','-','-','-',12,11,13.1,5.2,2056,2216,2239.7,1045.3,19,33,20.7,15.6,30,61,32.7,28.8,139,149,151.4,70.3,93,187,101.3,88.2,75,94,81.7,44.3,67,85,73,40.1,7,'-',7.6,'-',479,173,521.8,81.6,1,245,1.1,115.6,31,14.6,70,33,490,533.8,85,41,92.6,19.3,402,802,437.9,378.3,65,62,70.8,29.2,104,183,113.3,86.3,49,113,53.4,53.3,65,228,70.8,107.5,4,16,4.4,7.5,'-',1,'-',0.5,'-','-','-','-',10,11,10.9,5.2,2,11,2.2,5.2,562,1737,612.2,819.3,'-',1,'-',0.5,3,5,3.3,2.4,1,2,1.1,0.9,231,465,251.6,219.3,4767,9353,5192.8,4411.8,'-','-','-','-',14,44,15.3,20.8,75,273,81.7,128.8,1877,2963,2044.7,1397.6,1134,1991,1235.3,939.2,344,969,374.7,457.1,1035,2663,1127.5,1256.1,95,257,103.5,121.2,174,141,189.5,66.5,16,45,17.4,21.2,3,7,3.3,3.3,'-',2,'-',0.9,7,13,7.6,6.1,871,1361,948.8,642,46,117,50.1,55.2,429,174,467.3,82.1,56,66,61,31.1,67,124,73,58.5,2,7,2.2,3.3,47,85,51.2,40.1,30,52,32.7,24.5,164,424,178.6,200,33,39,35.9,18.4,54,189,58.8,89.2,27,29.4,20,80,21.8,37.7,'-','-','-','-','-','-','-','-','-','-','-','-',3,9,3.3,4.2,22,135,24,63.7,'-','-','-','-','-',2,'-',0.9,7,5,7.6,2.4,'-','-','-','-','-','-','-','-',9,37,9.8,17.5,5,9,5.4,4.2,279,485,303.9,228.8,21,23,22.9,10.8,8,6,8.7,2.8,8,5,8.7,2.4,210,394,228.8,185.8,10,9,10.9,4.2,2,3,2.2,1.4,1,'-',1.1,'-','-','-','-','-',18,44,19.6,20.8,1,1,1.1,0.5,44,15,47.9,7.1,'-',1,'-',0.5,8,5,8.7,2.4,
'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-',0,'-',0,'-',0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-',0,'-','-',0,0,'-',0,'-',0,'-',0,'-',0,'-',0,'-',0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0,'-','-',0,0
)208,48,148,242,239,1,476,5972,47,25,1209,720,0,MIDM2,96,21,1156,479,0,MIDM65535,52427,65534[Var1,Item2][Var1,Item2]Diagnosis1-ICD-10-based diagnostic classification. 103 different diagnoses, including all causes. This index is NOT exclusive but is probably mutually exhaustive in respect to causes of death.var a:= who_data[var1='Cause groupings'];
var b:= a[item2=unique(a,Item2)];
subset(b)336,80,148,122,419,124,476,4151,104,114,512,303,0,MIDM['All causes',0,'Infectious and parasitic diseases','Typhoid and paratyphoid fever','Other intestinal infectious diseases','Tuberculosis of respiratory system','Tuberculosis, other forms','Whooping cough','Meningococcal infection','Tetanus','Septicaemia','Other bacterial diseases','Measles','HIV disease','Other viral diseases','Malaria','Other arthropod-borne diseases','Sexually transmitted diseases','Other infectious and parasitic diseases','Malignant neoplasms','Malignant neoplasm of lip, oral cavity and pharynx','Malignant neoplasm of oesophagus','Malignant neoplasm of stomach','Malignant neoplasm of colon','Malignant neoplasm of rectum, rectosigmoid junction and anus','Malignant neoplasm of liver','Malignant neoplasm of larynx','Malignant neoplasm of trachea, bronchus and lung','Malignant neoplasm of breast','Malignant neoplasm of cervix uteri','Malignant neoplasm of uterus, other and unspecified','Malignant neoplasm of prostate','Malignant neoplasm of bladder','Malignant neoplasm of other sites','Leukaemia','Other malignant neoplasms of lymphoid and haematopoietic and related tissue','Benign neoplasm, other and unspecified neoplasm','Diabetes mellitus','Other endocrine and metabolic diseases','Malnutrition','Other nutritional deficiencies','Anaemias','Other diseases of blood and blood-forming organs','Mental disorders','Meningitis','Multiple sclerosis','Epilepsy','Other diseases of the nervous system and sense organs','Diseases of the circulatory system','Acute rheumatic fever','Chronic rheumatic heart disease','Hypertensive disease','Acute myocardial infarction','Other ischaemic heart diseases','Diseases of pulmonary circulation and other forms of heart disease','Cerebrovascular disease','Atherosclerosis','Embolism, thrombosis and other diseases of arteries, arterioles and capillaries','Phlebitis, thrombophlebitis, venous embolism and thrombosis','Other diseases of the circulatory system','Acute upper respiratory infection','Acute bronchitis and bronchiolitis','Pneumonia','Influenza','Bronchitis, chronic and unspecified, emphysema and asthma','Other diseases of the respiratory system','Ulcer of stomach and duodenum','Appendicitis','Hernia of abdominal cavity and intestinal obstruction','Chronic liver disease and cirrhosis','Other diseases of the digestive system','Nephritis, nephrotic syndrome and nephrosis','Infections of kidney','Hyperplasia of prostate','Other diseases of the genitourinary system','Abortion','Haemorrhage of pregnancy and childbirth','Toxaemia of pregnancy','Complications of the puerperium','Other direct obstetric causes','Indirect obstetric causes','Diseases of skin and subcutaneous tissue','Diseases of the musculoskeletal system and connective tissue','Spina bifida and hydrocephalus','Congenital anomalies of the circulatory system','Other congenital anomalies','Birth trauma','Other conditions originating in the perinatal period','Senility','Signs, symptoms and other ill-defined conditions','Accidents and adverse effects','Motor vehicle traffic accidents','Other transport accidents','Accidental poisoning','Accidental falls','Accidents caused by fire and flames','Accidental drowning and submersion','Accidents caused by machinery and by cutting and piercing instruments','Accidents caused by firearm missile','All other accidents, including late effects','Drugs, medicaments causing adverse effects in therapeutic use','Suicide and self- inflicted injury','Homicide and injury purposely inflicted by other persons','Other external causes']Op_en2779ICD-10index i:= who_data[var1='Cause groupings'];
var a:= who_data[@item2=@i];
a[i=Diagnosis1, var1='ICD-10 codes']336,48,148,242,269,234,476,2242,184,110,867,359,0,MIDM[Var1,Sys_localindex('I')]Units1-Numbers of cases or incidence, i.e. numbers per 100000 person-years.['Number','Number/100000 person-years']208,248,148,121,312,322,416,303,0,MIDMOp_en2784Age group1aFive-year age groups with 1, 1-4, 75+, and not-specified groups.['All ages','< 1','1-4','5-14','15-24','25-34','35-44','45-54','55-64','65-74','75+','Age not specified']96,208,148,122,102,90,476,3461,264,274,416,303,0,MIDM['All ages','< 1','1-4','5-14','15-24','25-34','35-44','45-54','55-64','65-74','75+','Age not specified']Op_en27812D Age groupsusing a:= slice(who_data,var1,Age_groups)
do a208,152,148,241,110,1702,38,30,721,505,0,MIDM[Vars,Age_group1][Vars,1,Age_group1,1,Item2,1]Age groupsTable(Age_group1,vars)(
1,3,4,5,
1,3,4,6,
1,3,4,7,
1,3,4,8,
1,3,4,9,
1,3,4,10,
1,3,4,11,
1,3,4,12,
1,3,4,13,
1,3,4,14,
1,3,4,15,
1,3,4,16
)['Variable','Cause groupings','Sex','Value']96,152,148,241,110,2781,575,134,478,303,0,MIDM1,339,222,718,318,0,MIDM[Vars,Age_group1][Vars,Age_group1]['Variable','Cause groupings','Sex','Value']Mortality in Finland# or 1/100000 pyWHO mortality data for different countriesvar a:= d_age[vars='Cause groupings'];
var b:= 0;
var d:= 0;
var x:= 1;
while x<=size(Item2) do (
var c:= a[Item2=x];
b:= if c=0 then b else c;
d:= if Item2=x then b else d;
x:= x+1);
a:= if vars='Cause groupings' then d else d_age;
a:= mdtable(a,Item2,vars,[Units1,diagnosis1,sex1]);
a:= a[@sex1=@Sex2];
if country1='Finland' then a else a208,216,148,242,650,273,476,4152,83,123,992,591,0,MIDM65535,52427,52427[Age_group1,Diagnosis1][Units1,1,Country1,1,Sex,1,Diagnosis1,1,Age_group1,1]Op_en2778Vars['Variable','Cause groupings','Sex','Value']96,184,148,12['Variable','Cause groupings','Sex','Value']Country1-['Finland']208,296,148,12Op_en2785 Sex['M','F']208,320,148,121,104,114,416,303,0,MIDM['Male','Female']1..26456,160,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]1..206456,184,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206]Mortality Finlandhttp://www.who.int/whosis/database/mort/table1.cfm
Data: Mortality Finland 2006Table(In1,In2)(
'Table 1:','Country:','Year:',' ','ICD Codes','-’','-’','-’','A00-B99’','A00-B99’','A01’','A01’','A00; A02-A09’','A00; A02-A09’','A15-A16’','A15-A16’','A17-A19’','A17-A19’','A37’','A37’','A39’','A39’','A35’','A35’','A40-A41’','A40-A41’','A20-A32; A36; A38; A42-49’','A20-A32; A36; A38; A42-49’','B05’','B05’','B20-B24’','B20-B24’','A70-A74; A80-B34; ¬B05; ¬B20-B24’','A70-A74; A80-B34; ¬B05; ¬B20-B24’','B50-B54’','B50-B54’','A75-A79; B55-B57; B60; B64’','A75-A79; B55-B57; B60; B64’','A50-A64’','A50-A64’','A65-A69; B35-B49; B58; B59; B65-B99’','A65-A69; B35-B49; B58; B59; B65-B99’','C00-C97’','C00-C97’','C00-C14’','C00-C14’','C15’','C15’','C16’','C16’','C18’','C18’','C19-C21’','C19-C21’','C22’','C22’','C32’','C32’','C33-C34’','C33-C34’','C50’','C50’','C53’','C54-C55’','C61’','C67’','C67’','C17; C23-C31; C37-C49; C51; C52; C56-C60; C62-C66; C68-C80; C97’','C17; C23-C31; C37-C49; C51; C52; C56-C60; C62-C66; C68-C80; C97’','C91-C95’','C91-C95’','C81-C90; C96’','C81-C90; C96’','D00-D48’','D00-D48’','E10-E14’','E10-E14’','E00-E07; E15-E34; E65-E68; E70-E88’','E00-E07; E15-E34; E65-E68; E70-E88’','E41-E46’','E41-E46’','E40; E50-E64’','E40; E50-E64’','D50-D64’','D50-D64’','D65-D89’','D65-D89’','F01-F99’','F01-F99’','G00; G03’','G00; G03’','G35’','G35’','G40-G41’','G40-G41’','G04-G31; G36-G37; G43-H95’','G04-G31; G36-G37; G43-H95’','I00-I99’','I00-I99’','I00-I02’','I00-I02’','I05-I09’','I05-I09’','I10-I13’','I10-I13’','I21; I22’','I21; I22’','I20; I24; I25’','I20; I24; I25’','I26-I51’','I26-I51’','I60-I69’','I60-I69’','I70’','I70’','I71-I78’','I71-I78’','I80-I82’','I80-I82’','I83-I99’','I83-I99’','J00-J06’','J00-J06’','J20-J21’','J20-J21’','J12-J18’','J12-J18’','J10-J11’','J10-J11’','J40-J46’','J40-J46’','J22; J30-J39; J47-J98’','J22; J30-J39; J47-J98’','K25-K27’','K25-K27’','K35-K38’','K35-K38’','K40-K46;K56’','K40-K46;K56’','K70;K73-K74;K76’','K70;K73-K74;K76’','K00-K22; K28-K31; K50-K55; K57-K66; K71; K72; K75; K80-K92’','K00-K22; K28-K31; K50-K55; K57-K66; K71; K72; K75; K80-K92’','N00-N07; N13-N19’','N00-N07; N13-N19’','N10-N12’','N10-N12’','N40’','N20-N39; N41-N98’','N20-N39; N41-N98’','O00-O07’','O20; O46; O67; O72’','O13-O16; O21’','O85-O92; A34’','O10-O12; O22-O75; O95-O97; minus (O46; O67;O72)’','O98-O99’','L00-L98’','L00-L98’','M00-M99’','M00-M99’','Q03;Q05’','Q03;Q05’','Q20-Q28’','Q20-Q28’','Q00-Q02; Q04; Q06-Q18; Q30-Q99’','Q00-Q02; Q04; Q06-Q18; Q30-Q99’','P10-P15’','P10-P15’','P00-P08; P20-P96; A33’','P00-P08; P20-P96; A33’','R54’','R54’','R00-R53; R55-R99’','R00-R53; R55-R99’','V01-X59; Y40-Y86; Y88’','V01-X59; Y40-Y86; Y88’','V02-V04; V09; V12-V14; V19-V79; V86-V89’','V02-V04; V09; V12-V14; V19-V79; V86-V89’','V01; V05-V06; V10; V11; V15-V18; V80-V85; V90-V99’','V01; V05-V06; V10; V11; V15-V18; V80-V85; V90-V99’','X40-X49’','X40-X49’','W00-W19’','W00-W19’','X00-X09’','X00-X09’','W65-W74’','W65-W74’','W24-W31’','W24-W31’','W32-W34’','W32-W34’','W20-W23; W35-W64; W75-W99; X10-X39; X50-X59; Y85; Y86’','W20-W23; W35-W64; W75-W99; X10-X39; X50-X59; Y85; Y86’','Y40-Y84; Y88’','Y40-Y84; Y88’','X60-X84’','X60-X84’','X85-Y09’','X85-Y09’','Y10-Y36; Y87; Y89’','Y10-Y36; Y87; Y89’',' ','Demographic Data',,,
' Numbers of registered deaths','Finland',2006,,'Cause Groupings','All causes','All causes','All causes','Infectious and parasitic diseases','Infectious and parasitic diseases','Typhoid and paratyphoid fever','Typhoid and paratyphoid fever','Other intestinal infectious diseases','Other intestinal infectious diseases','Tuberculosis of respiratory system','Tuberculosis of respiratory system','Tuberculosis other forms','Tuberculosis other forms','Whooping cough','Whooping cough','Meningococcal infection','Meningococcal infection','Tetanus','Tetanus','Septicaemia','Septicaemia','Other bacterial diseases','Other bacterial diseases','Measles','Measles','HIV disease','HIV disease','Other viral diseases','Other viral diseases','Malaria','Malaria','Other arthropod-borne diseases','Other arthropod-borne diseases','Sexually transmitted diseases','Sexually transmitted diseases','Other infectious and parasitic diseases','Other infectious and parasitic diseases','Malignant neoplasms','Malignant neoplasms','Malignant neoplasm of lip oral cavity and pharynx','Malignant neoplasm of lip oral cavity and pharynx','Malignant neoplasm of oesophagus','Malignant neoplasm of oesophagus','Malignant neoplasm of stomach','Malignant neoplasm of stomach','Malignant neoplasm of colon','Malignant neoplasm of colon','Malignant neoplasm of rectum rectosigmoid junction and anus','Malignant neoplasm of rectum rectosigmoid junction and anus','Malignant neoplasm of liver','Malignant neoplasm of liver','Malignant neoplasm of larynx','Malignant neoplasm of larynx','Malignant neoplasm of trachea bronchus and lung','Malignant neoplasm of trachea bronchus and lung','Malignant neoplasm of breast','Malignant neoplasm of breast','Malignant neoplasm of cervix uteri','Malignant neoplasm of uterus other and unspecified','Malignant neoplasm of prostate','Malignant neoplasm of bladder','Malignant neoplasm of bladder','Malignant neoplasm of other sites','Malignant neoplasm of other sites','Leukaemia','Leukaemia','Other malignant neoplasms of lymphoid and haematopoietic and related tissue','Other malignant neoplasms of lymphoid and haematopoietic and related tissue','Benign neoplasm other and unspecified neoplasm','Benign neoplasm other and unspecified neoplasm','Diabetes mellitus','Diabetes mellitus','Other endocrine and metabolic diseases','Other endocrine and metabolic diseases','Malnutrition','Malnutrition','Other nutritional deficiencies','Other nutritional deficiencies','Anaemias','Anaemias','Other diseases of blood and blood-forming organs','Other diseases of blood and blood-forming organs','Mental disorders','Mental disorders','Meningitis','Meningitis','Multiple sclerosis','Multiple sclerosis','Epilepsy','Epilepsy','Other diseases of the nervous system and sense organs','Other diseases of the nervous system and sense organs','Diseases of the circulatory system','Diseases of the circulatory system','Acute rheumatic fever','Acute rheumatic fever','Chronic rheumatic heart disease','Chronic rheumatic heart disease','Hypertensive disease','Hypertensive disease','Acute myocardial infarction','Acute myocardial infarction','Other ischaemic heart diseases','Other ischaemic heart diseases','Diseases of pulmonary circulation and other forms of heart disease','Diseases of pulmonary circulation and other forms of heart disease','Cerebrovascular disease','Cerebrovascular disease','Atherosclerosis','Atherosclerosis','Embolism thrombosis and other diseases of arteries arterioles and capillaries','Embolism thrombosis and other diseases of arteries arterioles and capillaries','Phlebitis thrombophlebitis venous embolism and thrombosis','Phlebitis thrombophlebitis venous embolism and thrombosis','Other diseases of the circulatory system','Other diseases of the circulatory system','Acute upper respiratory infection','Acute upper respiratory infection','Acute bronchitis and bronchiolitis','Acute bronchitis and bronchiolitis','Pneumonia','Pneumonia','Influenza','Influenza','Bronchitis chronic and unspecified emphysema and asthma','Bronchitis chronic and unspecified emphysema and asthma','Other diseases of the respiratory system','Other diseases of the respiratory system','Ulcer of stomach and duodenum','Ulcer of stomach and duodenum','Appendicitis','Appendicitis','Hernia of abdominal cavity and intestinal obstruction','Hernia of abdominal cavity and intestinal obstruction','Chronic liver disease and cirrhosis','Chronic liver disease and cirrhosis','Other diseases of the digestive system','Other diseases of the digestive system','Nephritis nephrotic syndrome and nephrosis','Nephritis nephrotic syndrome and nephrosis','Infections of kidney','Infections of kidney','Hyperplasia of prostate','Other diseases of the genitourinary system','Other diseases of the genitourinary system','Abortion','Haemorrhage of pregnancy and childbirth','Toxaemia of pregnancy','Complications of the puerperium','Other direct obstetric causes','Indirect obstetric causes','Diseases of skin and subcutaneous tissue','Diseases of skin and subcutaneous tissue','Diseases of the musculoskeletal system and connective tissue','Diseases of the musculoskeletal system and connective tissue','Spina bifida and hydrocephalus','Spina bifida and hydrocephalus','Congenital anomalies of the circulatory system','Congenital anomalies of the circulatory system','Other congenital anomalies','Other congenital anomalies','Birth trauma','Birth trauma','Other conditions originating in the perinatal period','Other conditions originating in the perinatal period','Senility','Senility','Signs symptoms and other ill-defined conditions','Signs symptoms and other ill-defined conditions','Accidents and adverse effects','Accidents and adverse effects','Motor vehicle traffic accidents','Motor vehicle traffic accidents','Other transport accidents','Other transport accidents','Accidental poisoning','Accidental poisoning','Accidental falls','Accidental falls','Accidents caused by fire and flames','Accidents caused by fire and flames','Accidental drowning and submersion','Accidental drowning and submersion','Accidents caused by machinery and by cutting and piercing instruments','Accidents caused by machinery and by cutting and piercing instruments','Accidents caused by firearm missile','Accidents caused by firearm missile','All other accidents including late effects','All other accidents including late effects','Drugs medicaments causing adverse effects in therapeutic use','Drugs medicaments causing adverse effects in therapeutic use','Suicide and self- inflicted injury','Suicide and self- inflicted injury','Homicide and injury purposely inflicted by other persons','Homicide and injury purposely inflicted by other persons','Other external causes','Other external causes',,'Live births',30.005K,28.835K,
,,,,'Sex','M','F','U','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','F','F','M','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','M','F','F','F','F','F','F','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F',,'Sex','M','F',
,,,,'All Ages',24.335K,23.771K,0,154,181,0,0,18,26,9,5,4,3,0,0,3,2,0,0,60,62,19,27,0,0,6,3,11,12,0,0,0,0,0,2,24,39,5738,5039,87,58,110,81,308,218,306,324,239,192,244,177,34,7,1464,541,4,856,57,140,814,162,65,1434,1809,178,147,354,367,115,154,241,232,48,63,2,0,4,2,5,19,19,14,831,1854,9,6,28,35,57,34,1185,1847,9576,10.404K,0,0,14,36,188,351,2876,2451,3189,3217,1011,1140,1785,2753,56,125,387,239,65,87,5,5,1,0,2,5,314,304,3,11,848,352,172,114,103,111,6,4,101,145,783,293,392,471,69,91,46,146,26,24,68,0,1,0,1,1,1,21,13,84,203,3,6,27,32,56,59,0,0,43,30,2,8,184,93,2069,1014,207,86,134,16,644,217,642,536,83,26,105,30,15,0,4,0,234,103,1,0,802,259,75,30,67,21,,'All Ages',2.578046M,2.688222M,
,,,,' ',943.9,884.3,,6,6.7,0,0,0.7,1,0.3,0.2,0.2,0.1,0,0,0.1,0.1,0,0,2.3,2.3,0.7,1,0,0,0.2,0.1,0.4,0.4,0,0,0,0,0,0.1,0.9,1.5,222.6,187.4,3.4,2.2,4.3,3,11.9,8.1,11.9,12.1,9.300000000000001,7.1,9.5,6.6,1.3,0.3,56.8,20.1,0.2,31.8,2.1,5.2,31.6,6.3,2.4,55.6,67.3,6.9,5.5,13.7,13.7,4.5,5.7,9.300000000000001,8.6,1.9,2.3,0.1,0,0.2,0.1,0.2,0.7,0.7,0.5,32.2,69,0.3,0.2,1.1,1.3,2.2,1.3,46,68.7,371.4,387,0,0,0.5,1.3,7.3,13.1,111.6,91.2,123.7,119.7,39.2,42.4,69.2,102.4,2.2,4.6,15,8.9,2.5,3.2,0.2,0.2,0,0,0.1,0.2,12.2,11.3,0.1,0.4,32.9,13.1,6.7,4.2,4,4.1,0.2,0.1,3.9,5.4,30.4,10.9,15.2,17.5,2.7,3.4,1.8,5.4,1,0.9,2.5,0,0,0,0,0,0,0.8,0.5,3.3,7.6,0.1,0.2,1,1.2,2.2,2.2,0,0,1.7,1.1,0.1,0.3,7.1,3.5,80.3,37.7,8,3.2,5.2,0.6,25,8.1,24.9,19.9,3.2,1,4.1,1.1,0.6,0,0.2,0,9.1,3.8,0,0,31.1,9.6,2.9,1.1,2.6,0.8,,,,,
,,,,'Under 1',97,71,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,2,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,12,5,19,20,0,0,43,29,0,0,11,4,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,,'Under 1',29.679K,28.585K,
,,,,' ',323.3,246.2,,6.7,3.5,0,0,3.3,0,0,0,0,0,0,0,0,0,0,0,3.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,0,6.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,0,3.5,0,0,0,3.5,0,0,10,6.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,3.5,3.3,6.9,3.3,3.5,0,0,0,0,0,0,0,0,0,0,3.3,3.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,40,17.3,63.3,69.40000000000001,0,0,143.3,100.6,0,0,36.7,13.9,6.7,3.5,0,0,0,0,0,0,0,0,3.3,0,0,0,0,0,0,0,3.3,3.5,0,0,0,0,3.3,0,0,3.5,,,,,
,,,,'1 to 4',12,15,0,2,3,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,3,0,0,0,0,0,0,1,0,3,3,0,0,1,1,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,,'1 to 4',116.692K,111.585K,
,,,,' ',10.3,13.4,,1.7,2.7,0,0,0.9,0,0,0,0,0,0,0,0,0.9,0,0,0,0.9,0,0,0,0,0,0,0.9,0.9,0,0,0,0,0,0,0,0,2.6,1.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.7,0.9,0.9,0.9,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,0.9,2.7,0,0,0,0,0,0,0.9,0,2.6,2.7,0,0,0.9,0.9,0,0,0,0,0.9,0,0.9,1.8,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,,,,,
,,,,'5 to 14',43,31,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,4,4,0,0,1,0,0,0,5,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,2,3,4,1,0,0,0,0,0,0,0,0,0,0,3,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,4,1,0,0,0,0,0,0,2,0,8,6,2,3,2,1,0,0,1,0,0,0,0,1,0,0,0,0,3,1,0,0,3,1,0,1,1,0,,'5 to 14',315.017K,302.489K,
,,,,' ',13.7,10.2,,0.3,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0.3,2.5,2.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.3,1.3,1.3,1.3,0,0,0.3,0,0,0,1.6,1.3,0,0,0,0,0,0.3,0,0,0.3,0,0,0,0,0,0,0.3,0.6,1,1.3,0.3,0,0,0,0,0,0,0,0,0,0,1,0,0.3,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0.7,1.3,0.3,0,0,0,0,0,0,0.6,0,2.5,2,0.6,1,0.6,0.3,0,0,0.3,0,0,0,0,0.3,0,0,0,0,1,0.3,0,0,1,0.3,0,0.3,0.3,0,,,,,
,,,,'15 to 24',317,81,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,13,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,5,4,4,5,2,1,1,0,1,0,2,5,0,0,0,0,0,0,0,0,2,1,0,0,0,0,2,3,7,1,10,7,0,0,0,0,0,0,0,0,0,0,7,1,2,3,0,0,1,1,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,2,0,0,0,0,0,0,5,2,143,21,61,13,11,1,44,5,6,1,5,0,6,0,2,0,1,0,7,1,0,0,108,19,10,1,8,3,,'15 to 24',335.416K,320.699K,
,,,,' ',94.5,25.3,,0.6,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,4.1,0,0,0,0,0,0,0,0,0,0,0.3,0.9,0,0,0,0,0,0,0,0,0,0,0,1.5,1.2,1.2,1.6,0.6,0.3,0.3,0,0.3,0,0.6,1.6,0,0,0,0,0,0,0,0,0.6,0.3,0,0,0,0,0.6,0.9,2.1,0.3,3,2.2,0,0,0,0,0,0,0,0,0,0,2.1,0.3,0.6,0.9,0,0,0.3,0.3,0,0.6,0,0,0,0,0,0,0.3,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0.3,0.3,0,0.6,0.6,0,0,0,0,0,0,1.5,0.6,42.6,6.5,18.2,4.1,3.3,0.3,13.1,1.6,1.8,0.3,1.5,0,1.8,0,0.6,0,0.3,0,2.1,0.3,0,0,32.2,5.9,3,0.3,2.4,0.9,,,,,
,,,,'25 to 34',354,112,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,24,19,0,0,1,0,0,3,0,0,2,0,0,0,0,0,0,1,0,5,2,0,0,0,0,12,8,4,0,5,0,0,0,3,4,1,0,0,0,0,0,0,0,1,0,3,5,0,0,0,0,4,1,5,3,26,13,0,0,0,0,1,0,7,0,1,1,9,3,5,8,0,0,3,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,9,1,6,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,1,1,0,5,3,0,0,0,0,0,0,7,6,118,11,30,7,8,0,58,4,5,0,2,0,8,0,2,0,1,0,4,0,0,0,113,33,10,6,10,2,,'25 to 34',329.601K,313.759K,
,,,,' ',107.4,35.7,,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,7.3,6.1,0,0,0.3,0,0,1,0,0,0.6,0,0,0,0,0,0,0.3,0,1.6,0.6,0,0,0,0,3.6,2.5,1.2,0,1.5,0,0,0,0.9,1.3,0.3,0,0,0,0,0,0,0,0.3,0,0.9,1.6,0,0,0,0,1.2,0.3,1.5,1,7.9,4.1,0,0,0,0,0.3,0,2.1,0,0.3,0.3,2.7,1,1.5,2.5,0,0,0.9,0.3,0,0,0,0,0,0,0,0,0.6,0.3,0,0,0,0,0,0,0,0,0,0,0.3,0,2.7,0.3,1.8,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0.6,0.3,0,0.3,0.3,0,1.5,1,0,0,0,0,0,0,2.1,1.9,35.8,3.5,9.1,2.2,2.4,0,17.6,1.3,1.5,0,0.6,0,2.4,0,0.6,0,0.3,0,1.2,0,0,0,34.3,10.5,3,1.9,3,0.6,,,,,
,,,,'35 to 44',746,315,0,6,4,0,0,1,0,0,0,1,1,0,0,0,0,0,0,3,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,76,101,3,1,4,1,7,6,7,3,2,2,2,1,0,0,8,3,0,41,5,1,0,3,0,33,32,5,3,2,2,3,1,17,4,4,2,0,0,0,0,0,1,1,0,21,4,0,1,2,3,9,3,6,4,124,38,0,0,0,0,1,1,27,3,14,4,44,7,29,22,0,0,5,0,3,1,1,0,0,0,0,1,6,1,0,0,0,3,3,0,1,2,0,1,1,1,56,13,15,2,1,0,0,0,0,0,0,0,1,0,1,1,0,1,0,3,2,0,0,2,5,1,1,0,0,0,1,0,0,14,6,219,61,21,9,15,2,104,31,34,4,9,6,17,3,1,0,0,0,18,6,0,0,130,39,13,6,11,1,,'35 to 44',365.461K,353.529K,
,,,,' ',204.1,89.09999999999999,,1.6,1.1,0,0,0.3,0,0,0,0.3,0.3,0,0,0,0,0,0,0.8,0.6,0,0,0,0,0.3,0.3,0,0,0,0,0,0,0,0,0,0,20.8,28.6,0.8,0.3,1.1,0.3,1.9,1.7,1.9,0.8,0.5,0.6,0.5,0.3,0,0,2.2,0.8,0,11.6,1.4,0.3,0,0.8,0,9,9.1,1.4,0.8,0.5,0.6,0.8,0.3,4.7,1.1,1.1,0.6,0,0,0,0,0,0.3,0.3,0,5.7,1.1,0,0.3,0.5,0.8,2.5,0.8,1.6,1.1,33.9,10.7,0,0,0,0,0.3,0.3,7.4,0.8,3.8,1.1,12,2,7.9,6.2,0,0,1.4,0,0.8,0.3,0.3,0,0,0,0,0.3,1.6,0.3,0,0,0,0.8,0.8,0,0.3,0.6,0,0.3,0.3,0.3,15.3,3.7,4.1,0.6,0.3,0,0,0,0,0,0,0,0.3,0,0.3,0.3,0,0.3,0,0.8,0.6,0,0,0.5,1.4,0.3,0.3,0,0,0,0.3,0,0,3.8,1.7,59.9,17.3,5.7,2.5,4.1,0.6,28.5,8.800000000000001,9.300000000000001,1.1,2.5,1.7,4.7,0.8,0.3,0,0,0,4.9,1.7,0,0,35.6,11,3.6,1.7,3,0.3,,,,,
,,,,'45 to 54',2049,915,0,12,7,0,0,0,1,1,0,0,0,0,0,1,1,0,0,6,2,1,0,0,0,2,1,0,1,0,0,0,0,0,0,1,1,312,346,10,6,8,2,13,19,13,21,21,7,15,7,3,0,63,34,1,122,6,5,8,6,1,119,96,11,8,21,12,3,5,37,15,4,3,0,0,0,0,1,0,3,2,49,16,2,1,7,10,12,7,29,13,546,158,0,0,0,1,19,5,131,13,149,20,139,39,80,66,2,0,18,3,8,10,0,1,0,0,0,0,17,4,0,0,19,12,3,1,13,2,1,0,7,2,242,72,56,16,2,2,2,1,0,2,0,0,0,0,0,0,0,3,0,3,2,2,0,4,4,5,8,0,0,0,0,0,0,38,11,403,117,26,9,24,3,194,68,73,18,23,3,19,8,5,0,0,0,39,8,0,0,179,65,20,7,11,6,,'45 to 54',386.717K,383.119K,
,,,,' ',529.8,238.8,,3.1,1.8,0,0,0,0.3,0.3,0,0,0,0,0,0.3,0.3,0,0,1.6,0.5,0.3,0,0,0,0.5,0.3,0,0.3,0,0,0,0,0,0,0.3,0.3,80.7,90.3,2.6,1.6,2.1,0.5,3.4,5,3.4,5.5,5.4,1.8,3.9,1.8,0.8,0,16.3,8.9,0.3,31.8,1.6,1.3,2.1,1.6,0.3,30.8,25.1,2.8,2.1,5.4,3.1,0.8,1.3,9.6,3.9,1,0.8,0,0,0,0,0.3,0,0.8,0.5,12.7,4.2,0.5,0.3,1.8,2.6,3.1,1.8,7.5,3.4,141.2,41.2,0,0,0,0.3,4.9,1.3,33.9,3.4,38.5,5.2,35.9,10.2,20.7,17.2,0.5,0,4.7,0.8,2.1,2.6,0,0.3,0,0,0,0,4.4,1,0,0,4.9,3.1,0.8,0.3,3.4,0.5,0.3,0,1.8,0.5,62.6,18.8,14.5,4.2,0.5,0.5,0.5,0.3,0,0.5,0,0,0,0,0,0,0,0.8,0,0.8,0.5,0.5,0,1,1,1.3,2.1,0,0,0,0,0,0,9.800000000000001,2.9,104.2,30.5,6.7,2.3,6.2,0.8,50.2,17.7,18.9,4.7,5.9,0.8,4.9,2.1,1.3,0,0,0,10.1,2.1,0,0,46.3,17,5.2,1.8,2.8,1.6,,,,,
,,,,'55 to 64',4009,1732,0,16,8,0,0,0,0,0,0,2,1,0,0,0,0,0,0,9,4,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,2,1096,790,33,8,35,11,81,25,50,34,50,22,45,25,7,2,322,110,2,185,11,25,55,17,5,317,270,20,14,62,43,10,9,39,19,7,10,1,0,1,2,1,1,3,1,50,18,6,2,9,9,14,7,62,73,1366,339,0,0,2,1,33,9,429,64,486,79,166,55,189,103,0,0,49,13,10,15,2,0,1,0,0,0,30,12,0,0,87,26,18,8,31,7,1,1,13,10,320,109,80,26,7,1,3,2,0,2,1,0,0,0,0,0,0,7,1,14,15,1,3,5,2,10,7,0,0,0,0,0,0,43,17,494,138,24,16,33,2,171,70,142,19,18,8,28,7,4,0,2,0,72,16,0,0,132,50,16,3,13,5,,'55 to 64',355.493K,363.495K,
,,,,' ',1127.7,476.5,,4.5,2.2,0,0,0,0,0,0,0.6,0.3,0,0,0,0,0,0,2.5,1.1,0,0,0,0,0,0.3,1.1,0,0,0,0,0,0,0,0.3,0.6,308.3,217.3,9.300000000000001,2.2,9.800000000000001,3,22.8,6.9,14.1,9.4,14.1,6.1,12.7,6.9,2,0.6,90.59999999999999,30.3,0.6,50.9,3,6.9,15.5,4.8,1.4,89.2,74.3,5.6,3.9,17.4,11.8,2.8,2.5,11,5.2,2,2.8,0.3,0,0.3,0.6,0.3,0.3,0.8,0.3,14.1,5,1.7,0.6,2.5,2.5,3.9,1.9,17.4,20.1,384.3,93.3,0,0,0.6,0.3,9.300000000000001,2.5,120.7,17.6,136.7,21.7,46.7,15.1,53.2,28.3,0,0,13.8,3.6,2.8,4.1,0.6,0,0.3,0,0,0,8.4,3.3,0,0,24.5,7.2,5.1,2.2,8.699999999999999,1.9,0.3,0.3,3.7,2.8,90,30,22.5,7.2,2,0.3,0.8,0.6,0,0.6,0.3,0,0,0,0,0,0,2,0.3,3.9,4.1,0.3,0.8,1.4,0.6,2.8,1.9,0,0,0,0,0,0,12.1,4.7,139,38,6.8,4.4,9.300000000000001,0.6,48.1,19.3,39.9,5.2,5.1,2.2,7.9,1.9,1.1,0,0.6,0,20.3,4.4,0,0,37.1,13.8,4.5,0.8,3.7,1.4,,,,,
,,,,'65 to 74',5166,2804,0,33,28,0,0,4,4,2,2,0,0,0,0,1,0,0,0,12,10,9,5,0,0,1,0,1,2,0,0,0,0,0,0,3,5,1615,1142,30,9,32,15,79,52,76,70,61,47,77,37,11,2,453,147,1,187,8,30,201,41,13,408,418,47,25,98,82,29,24,63,42,8,11,0,0,2,0,1,2,2,2,94,43,1,1,6,8,7,5,212,173,2100,875,0,0,3,5,32,25,638,232,753,224,172,109,358,231,11,5,118,33,14,10,1,1,0,0,0,0,42,17,0,0,187,66,51,12,22,11,3,1,19,13,122,54,62,51,10,10,5,5,6,2,5,0,0,0,0,0,0,2,2,20,28,0,0,1,5,6,7,0,0,0,0,0,0,40,16,288,113,18,15,21,2,64,24,112,40,17,5,13,4,1,0,0,0,42,23,0,0,93,28,3,3,9,1,,'65 to 74',208.581K,248.032K,
,,,,' ',2476.7,1130.5,,15.8,11.3,0,0,1.9,1.6,1,0.8,0,0,0,0,0.5,0,0,0,5.8,4,4.3,2,0,0,0.5,0,0.5,0.8,0,0,0,0,0,0,1.4,2,774.3,460.4,14.4,3.6,15.3,6,37.9,21,36.4,28.2,29.2,18.9,36.9,14.9,5.3,0.8,217.2,59.3,0.5,75.40000000000001,3.2,12.1,96.40000000000001,19.7,5.2,195.6,168.5,22.5,10.1,47,33.1,13.9,9.699999999999999,30.2,16.9,3.8,4.4,0,0,1,0,0.5,0.8,1,0.8,45.1,17.3,0.5,0.4,2.9,3.2,3.4,2,101.6,69.7,1006.8,352.8,0,0,1.4,2,15.3,10.1,305.9,93.5,361,90.3,82.5,43.9,171.6,93.09999999999999,5.3,2,56.6,13.3,6.7,4,0.5,0.4,0,0,0,0,20.1,6.9,0,0,89.7,26.6,24.5,4.8,10.5,4.4,1.4,0.4,9.1,5.2,58.5,21.8,29.7,20.6,4.8,4,2.4,2,2.9,1,2,0,0,0,0,0,0,1,0.8,9.6,11.3,0,0,0.5,2,2.9,2.8,0,0,0,0,0,0,19.2,6.5,138.1,45.6,8.6,6,10.1,0.8,30.7,9.699999999999999,53.7,16.1,8.199999999999999,2,6.2,1.6,0.5,0,0,0,20.1,9.300000000000001,0,0,44.6,11.3,1.4,1.2,4.3,0.4,,,,,
,,,,'Over 75',11.542K,17.695K,0,77,129,0,0,11,21,6,3,1,1,0,0,0,0,0,0,27,43,9,22,0,0,0,0,4,8,0,0,0,0,0,2,19,29,2592,2616,11,34,30,52,128,113,160,196,103,114,104,104,13,3,618,246,0,316,25,79,550,95,46,534,975,82,86,164,227,68,114,81,148,13,26,1,0,1,0,2,14,9,9,611,1767,0,1,4,5,8,6,861,1574,5399,8972,0,0,9,29,102,311,1644,2139,1786,2889,470,925,1121,2319,43,120,193,188,30,49,1,3,0,0,2,4,216,268,3,11,554,245,97,93,36,89,1,1,59,119,34,44,172,374,49,78,36,138,20,18,60,0,0,0,0,0,0,8,10,42,155,0,0,0,7,3,7,0,0,0,0,2,8,23,31,391,543,25,14,19,4,9,15,269,454,7,4,13,5,0,0,0,0,48,47,1,0,44,24,1,3,4,2,,'Over 75',135.396K,262.936K,
,,,,' ',8524.6,6729.8,,56.9,49.1,0,0,8.1,8,4.4,1.1,0.7,0.4,0,0,0,0,0,0,19.9,16.4,6.6,8.4,0,0,0,0,3,3,0,0,0,0,0,0.8,14,11,1914.4,994.9,8.1,12.9,22.2,19.8,94.5,43,118.2,74.5,76.09999999999999,43.4,76.8,39.6,9.6,1.1,456.4,93.59999999999999,0,120.2,9.5,30,406.2,70.2,17.5,394.4,370.8,60.6,32.7,121.1,86.3,50.2,43.4,59.8,56.3,9.6,9.9,0.7,0,0.7,0,1.5,5.3,6.6,3.4,451.3,672,0,0.4,3,1.9,5.9,2.3,635.9,598.6,3987.6,3412.2,0,0,6.6,11,75.3,118.3,1214.2,813.5,1319.1,1098.7,347.1,351.8,827.9,882,31.8,45.6,142.5,71.5,22.2,18.6,0.7,1.1,0,0,1.5,1.5,159.5,101.9,2.2,4.2,409.2,93.2,71.59999999999999,35.4,26.6,33.8,0.7,0.4,43.6,45.3,25.1,16.7,127,142.2,36.2,29.7,26.6,52.5,14.8,13.3,22.8,0,0,0,0,0,0,5.9,3.8,31,58.9,0,0,0,2.7,2.2,2.7,0,0,0,0,1.5,3,17,11.8,288.8,206.5,18.5,5.3,14,1.5,6.6,5.7,198.7,172.7,5.2,1.5,9.6,1.9,0,0,0,0,35.5,17.9,0.7,0,32.5,9.1,0.7,1.1,3,0.8,,,,,
,,,,'Age not specified',0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,'Age not specified',0,0
)456,128,148,242,20,20,1145,580,0,MIDM2,22,41,1237,402,0,MIDM65535,52427,65534[In1,In2][In1,In2]WHO mortality datavar a:= mortality_finland;
index temp:= a[@in1=2]&'+'&a[@in1=3];
a:= a[@in2=@temp];
a:= a[temp=Diagnosis3&'+'&Sex1];
a:= a[@sex1=@Sex2];
var b:= array(Age,sequence(4,24,2));
var c:= array(Age,sequence(5,25,2));
a:= array(Parameter1,[a[@in1=b], a[@in1=c]]);
if Country1=0 and Year=0 then a else a576,128,148,242,23,27,745,457,0,MIDM[Sex,Diagnosis][Index Diagnosis][7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202][Parameter1,1,Country1,1,Year,1,Age,1,Diagnosis,1,Sex,2]Op_en2778Diagnosisvar a:= Mortality_finland[@in1=2, in2=6..202];
unique(a,a)576,160,148,122,120,130,416,303,0,MIDM[Self,Sex]['All causes','Infectious and parasitic diseases','Typhoid and paratyphoid fever','Other intestinal infectious diseases','Tuberculosis of respiratory system','Tuberculosis other forms','Whooping cough','Meningococcal infection','Tetanus','Septicaemia','Other bacterial diseases','Measles','HIV disease','Other viral diseases','Malaria','Other arthropod-borne diseases','Sexually transmitted diseases','Other infectious and parasitic diseases','Malignant neoplasms','Malignant neoplasm of lip oral cavity and pharynx','Malignant neoplasm of oesophagus','Malignant neoplasm of stomach','Malignant neoplasm of colon','Malignant neoplasm of rectum rectosigmoid junction and anus','Malignant neoplasm of liver','Malignant neoplasm of larynx','Malignant neoplasm of trachea bronchus and lung','Malignant neoplasm of breast','Malignant neoplasm of cervix uteri','Malignant neoplasm of uterus other and unspecified','Malignant neoplasm of prostate','Malignant neoplasm of bladder','Malignant neoplasm of other sites','Leukaemia','Other malignant neoplasms of lymphoid and haematopoietic and related tissue','Benign neoplasm other and unspecified neoplasm','Diabetes mellitus','Other endocrine and metabolic diseases','Malnutrition','Other nutritional deficiencies','Anaemias','Other diseases of blood and blood-forming organs','Mental disorders','Meningitis','Multiple sclerosis','Epilepsy','Other diseases of the nervous system and sense organs','Diseases of the circulatory system','Acute rheumatic fever','Chronic rheumatic heart disease','Hypertensive disease','Acute myocardial infarction','Other ischaemic heart diseases','Diseases of pulmonary circulation and other forms of heart disease','Cerebrovascular disease','Atherosclerosis','Embolism thrombosis and other diseases of arteries arterioles and capillaries','Phlebitis thrombophlebitis venous embolism and thrombosis','Other diseases of the circulatory system','Acute upper respiratory infection','Acute bronchitis and bronchiolitis','Pneumonia','Influenza','Bronchitis chronic and unspecified emphysema and asthma','Other diseases of the respiratory system','Ulcer of stomach and duodenum','Appendicitis','Hernia of abdominal cavity and intestinal obstruction','Chronic liver disease and cirrhosis','Other diseases of the digestive system','Nephritis nephrotic syndrome and nephrosis','Infections of kidney','Hyperplasia of prostate','Other diseases of the genitourinary system','Abortion','Haemorrhage of pregnancy and childbirth','Toxaemia of pregnancy','Complications of the puerperium','Other direct obstetric causes','Indirect obstetric causes','Diseases of skin and subcutaneous tissue','Diseases of the musculoskeletal system and connective tissue','Spina bifida and hydrocephalus','Congenital anomalies of the circulatory system','Other congenital anomalies','Birth trauma','Other conditions originating in the perinatal period','Senility','Signs symptoms and other ill-defined conditions','Accidents and adverse effects','Motor vehicle traffic accidents','Other transport accidents','Accidental poisoning','Accidental falls','Accidents caused by fire and flames','Accidental drowning and submersion','Accidents caused by machinery and by cutting and piercing instruments','Accidents caused by firearm missile','All other accidents including late effects','Drugs medicaments causing adverse effects in therapeutic use','Suicide and self- inflicted injury','Homicide and injury purposely inflicted by other persons','Other external causes']AgeaFive-year age groups with 1, 1-4, 75+, and not-specified groups.['All Ages','Under 1','1 to 4','5 to 14','15 to 24','25 to 34','35 to 44','45 to 54','55 to 64','65 to 74','Over 75']576,184,148,122,102,90,476,3461,264,274,416,303,0,MIDMOp_en2781Year[2006]576,208,148,12Parameter['# deaths','Mortality']576,240,148,20var a:= Morticdslice;
index i:= 1..(size(in4)-1);
index jtemp:= a[@in4=1];
a:= a[@in3=@jtemp, @in4=@i+1];
var b:= a[jtemp='List'];
index j:= jtemp[@jtemp=concat([1,4],6..39)];
a:= a[jtemp=j];
a:= if j='Country' then Country_codes[Country=a[j='Country']&''] else a;
a:= if j='Sex' then sexes[Sex=a[j='Sex']&''] else a;
a:= if j='Cause' and b = 101 then diagnoses[Diagnosis=a[j='Cause']&''] else a;
448,336,148,242,660,40,557,377,0,MIDM[Sys_localindex('I'),Sys_localindex('J')]var a:= splittext(readtextfile('c:\temp\morticd10.csv'),chr(10));
index itemp:= 1..size(a)-1;
a:= slice(a,itemp);
index jtemp:= 1..1;
a:= for x:= itemp do (
if mod(x,1000)=0 then ShowProgressBar("Reading","x="&x,x/size(itemp));
slice(splittext(a[itemp=x],','),1));
unique(a,itemp)96,48,148,242,530,89,476,3892,594,13,512,491,0,MIDM[Self,['JTEMP']]2,I,4,2,0,0,4,0,$,0,"ABBREV",0['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']var a:= Morticdslice[in3=1];
a:= unique(a,in4)328,448,148,242,630,189,416,303,0,MIDMTable(Countries)(
1,2,997,1992,2390,3186,3584,4181,5773,7962,8161,10.029K,32.206K,34.144K,35.082K,35.298K,49.793K,50.632K,57.191K,59.935K,69.55K,71.653K,80.73K,84.154K,96.865K,105.413K,108.07K,108.746K,113.212K,113.302K,113.585K,114.492K,155.293K,155.494K,156.46K,157.282K,158.869K,160.126K,230.025K,230.468K,249.276K,249.62K,277.024K,313.564K,328.575K,342.608K,343.436K,354.884K,376.311K,383.433K,385.196K,385.694K,389.831K,393.239K,397.117K,401.193K,449.356K,449.796K,462.079K,472.801K,485.808K,490.889K,500.723K,502.553K,504.372K,504.417K,505.399K,508.31K,512.547K,512.944K,564.976K,566.522K,575.862K,614.847K,624.637K,637.989K,702.015K,727.774K,739.559K,761.515K,796.589K,815.364K,840.535K,869.956K,923.448K,959.532K,964.313K,969.331K,999.799K,1.005434M,1.01125M,1.031072M,1.052635M,1.088111M,1.093342M,1.123324M,1.155531M,1.173456M,1.202857M,1.230554M,1.237732M,1.253389M,1.268452M,1.289162M,1.289631M,1.320787M)704,336,148,242,845,22,374,498,0,MIDM65535,52427,655342,I,4,2,0,0,4,0,$,0,"ABBREV",0Countries1..106704,368,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106]Choose groupChoice(Self,1,False)568,424,148,24[Formnode Va15]52425,39321,65535['Adult mortality','Infant mortality']Va130528,464,1104,121,0,0,1,0,0,0,72,0,152425,39321,65535Choose_groupMortality partindex j:= splittext(Morticd10[@.i=1],',');
var part:= slice(Parts,Choose_part)..slice(Parts,Choose_part+1)-1;
var a:= for x:= part do (
if mod(x,1000)=0 then ShowProgressBar("Reading","x="&x,x/size(part));
slice(splittext(Morticd10[.i=x],','),@j));
index i:= 1..(size(part));
for y:= j do (slice(a[j=y],i))224,184,148,242,530,89,476,3892,130,44,512,491,0,MIDM[Sys_localindex('I'),Sys_localindex('J')][1,1,1,0]['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']Morticd10var a:= splittext(readtextfile('c:\temp\morticd10.csv'),chr(10));
index i:= 1..size(a)-1;
a:= slice(a,i)224,120,148,242,530,89,476,3892,594,13,512,491,0,MIDM39325,65535,39321[Self,['JTEMP']]['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']Choose partChoice(Uploadpart,1,False)112,184,148,24[Formnode Choose_part1]52425,39321,65535['item 1']Uploadpart1..size(Parts)-1112,216,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13]Parts[2,1992,50.632K,105.413K,160.126K,230.025K,277.024K,313.564K,354.884K,401.193K,449.796K,500.723K,564.976K,614.847K,637.989K,702.015K,761.515K,815.364K,869.956K,923.448K,969.331K,1.005434M,1.052635M,1.123324M,1.173456M,1.202857M,1.253389M,1.320788M]112,120,148,2465535,52427,655342,I,4,2,0,0,4,0,$,0,"ABBREV",0Total mortalityindex i:= 1..(size(Adult_mortality.i)+size(Infant_mortality.i));
concat(Infant_mortality[.j=Adult_mortality.j],Adult_mortality,Infant_mortality.i,Adult_mortality.i,i)
{var a:= if Choose_group= 'Adult mortality' then Adult_mortality else Infant_mortality;
a}
{array(a.j,[a,a,a,a,a,evaluate(a)])}224,416,148,242,254,104,416,303,0,MIDM[Sys_localindex('I'),Sys_localindex('J')][0,1,1,0]Choose part0136,20,1120,121,0,0,1,0,0,0,72,0,152425,39321,65535Choose_partOpasnet base connectionInterface for uploading data to and downloading from the Opasnet Base.
<a href="http://en.opasnet.org/w/Image:Opasnet_base_connection.ANA">Wiki description</a>Jouni Tuomisto9. maata 2008 10:42ktluser7. Mayta 2009 6:40 48,2464,488,048,291,0,0,1,1,1,0,0,0,01,118,75,586,554,172,102,90,476,224Arial, 15100,1,1,1,1,9,2970,2100,15,0This module saves model results into the Opasnet Base. You need your Opasnet username and password for that. You must fill in all tables before the process is completed. Fill in the data below from top to bottom.
If an object with the same Ident already exists in the Opasnet Base, the information will be added to that object.
Before you start, make sure that you have created an object page in the Opasnet wiki for each object (study or variable) you want to upload.276,76,-1268,68Username0272,156,1160,121,0,0,1,0,0,0,142,0,152425,39321,65535Opasnet_usernamePassword0272,180,1160,121,0,0,1,0,0,0,142,0,152425,39321,65535Opasnet_passwordNumber of indices0272,380,1152,121,0,0,1,0,0,0,72,0,152425,39321,65535N_indicesStudy or variable info0276,556,1156,121,0,0,1,0,0,0,90,0,152425,39321,65535Object_info1Upload a data table. Indices are determinants of your study objects, such as sex or observation year. Parameters are those that are measured, such as body weight or pollutant concentration.272,372,-1160,1321,0,0,1,0,1,0,,0,2,693,146,476,224(Advanced users of Analytica Enterprise may upload whole models. For this option, see below.)276,220,-1268,20Finally, fill in the name (a description that may be longer than an identifier) and the unit of measurement. Then press the button Upload data.276,524,-1268,20Study Ident0272,357,1152,131,0,0,1,0,0,0,158,0,152425,39321,65535Study_identUpload a modelktluser1. Aprta 2009 9:38 48,24152,792,148,241,1,0,1,1,1,0,,0,1,883,39,373,406,17Additional partsThis module contains nodes that have been developed for a particular detail, like managing the Sett and Item tables. However, these tasks are not very important for the basic functionalitites, so we leave the development of them later. You must come back to these when there is more time.ktluser22. Marta 2009 22:43 48,24296,424,148,241,1,0,1,1,1,0,,0,1,679,19,567,227,17IndicesThis makes a list of all indices (including decision nodes) that are used by the variables in Object1.0{index a:= indexnames(evaluate(Objects_excl_indices));
a:= if a='Object1' or a='Objects_excl_indices' then 0 else 1;
subset(a)}232,168,148,132,102,90,476,4642,32,349,416,303,0,MIDM[Objects_excl_indices]['Age','Country','Year','Sex']W SettMakes a list of sets for the Sett table. There are three major kinds of sets: Indices belonging to an assessment, variables belonging to an assessment, and variables belonging to a run. Indices belonging to a dimension are NOT created with this node.index i:= ['Assessment','Assessment','Run'];
index j:= ['id','Obj_id','Sty_id'];
array(j,[
(Cardinals[table1='Sett']+@i)&'',
findid(Objects1[Object_all=i, .j='Ident'], Obj, 'Ident'),
array(i,[3,4,9])])200,24,148,162,740,132,495,4442,661,16,416,340,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[Index Table1, Variable Cardinals, Function Findid, Variable Obj]100,1,1,1,1,9,2970,2100,15,0[]W ItemMakes a list of items of sets into the Item table. This node does NOT handle indices of a dimensions, but they must be described elsewhere. For types of sets, see Write_sett.index j:= ['id','Sett_id','Obj_id','Fail'];
index k:= types(1);
index L:= types(6);
var c:= if sett.j='Obj_id' then sett&'+'&sett[.j='Sty_id'] else sett;
c:= findid(W_sett[.j='Obj_id']&'+'&W_sett[.j='Sty_id'], c, 'Obj_id');
var a:= array(j,k, [0, slice(c,1), k, 0]);
var b:= array(j,L,[0, slice(c,2), L, 0]);
index m:= 1..(size(k)+size(L));
a:= concat(a,b,k,l,m);
b:= array(j,k, [0, slice(c,3), k, 0]);
index i:= 1..(size(m)+size(k));
a:= concat(a,b,m,k,i);
if j='id' then cardinals[table1='Item']+@i else a;
200,72,148,162,80,84,476,4732,921,13,345,638,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0100,1,1,1,1,9,2970,2100,15,0[][Self,1,Sys_localindex('J'),1,Sys_localindex('K'),1]Is the data probabilistic0152,120,1140,131,0,0,1,0,0,0,72,0,152425,39321,65535Is_the_data_probabilObjects excl indices['Morbidity__diseases_']368,136,148,242,958,152,321,4812,328,338,416,361,0,MIDM52425,39321,65535['Morbidity__diseases_']Is the data probabilisticChoice(Self,1,False)96,48,148,24[Formnode Is_the_data_probabi1]52425,39321,65535['No','Yes']Object allList of variables, indices, assessment, and run to be stored into the Opasnet Base. Assessment is not included in the simpler version.concat(['Object'],Indices1)&''312,80,148,131,1,1,1,1,1,0,0,0,02,49,109,558,5272,200,210,688,358,0,MIDM[Self]['Object','Age','Country','Year','Sex','Morbidity']Indicesobservations[@observation=1, @field1=1..size(field1)]304,48,148,12[0,1,1,0]['Age','Country','Year','Sex','Morbidity']Te11Upload an Analytica model. First, switch the choice "Data table or model?" to Analytica model. Note! You can insert several variables at the same time. Each variable or study MUST have at least one index. Analytica identifiers are used to find the right nodes. Ident is the identifier of the description page from the Opasnet wiki. If Probabilistic? is 1, a sample of the distribution is stored; if it is 0, the mean or the point estimate is stored.168,230,-5152,1371,0,0,1,0,1,0,,0,Dependency graphktluser29. Decta 2008 21:51 48,2464,496,148,241,13,30,902,527,1792,1,1,0,2,9,2970,2100,15,0Cardinals:
all tables0192,56,148,2439325,65535,39321Objects:
identifier
id
ident
Name
Unit
Typ_id
etcCardinals__all_table320,120,148,76Obj:
id
Ident
Name
Unit
Typ_id
etcObjects__192,184,148,6765535,45873,39321Sett:
id
Obj_id
Typ_idObj__id_ident_name_u192,328,148,4065535,45873,39321Item:
id
Sett_id
Obj_id
FailSett__id_obj_id_typ_56,329,148,4965535,45873,39321Inf:
id
Begin
End
Who
UrlObj__id_ident_name_u56,186,148,5865535,45873,39321Loc:
id
Obj_id_d
Location
DescriptionObj__id_ident_name_u320,336,148,5265535,45873,39321Inp_locres:
Locres_id
Location
Res_id
Roww_id
Vident
Obj_id_v
Obj_id_r
Mean
NLoc__id_obj_id_d_loc456,336,148,92Locres:
id
Res_id
Roww_idInp_locres__locres_i592,424,148,4065535,45873,39321Res:
id
Obj_id_v
Obj_id_r
Mean
NInp_locres__locres_i592,312,148,5865535,45873,39321Sam:
id
Res_id
Sample
ResultSample__id_res_id_sa592,120,148,5265535,45873,39321Descr:
id
DescrSample__id_res_id_sa592,216,148,3165535,45873,39321The arrows only show sequential dependencies. This means that e.g. Cardinals is a parent to many other nodes as well, but the critical values in Cardinals only change before Objects is defined, and there is no need to update Cardinals during the writing process.
Orange nodes are actual Tables in Opasnet Base. Green nodes are SQL queries from Opasnet Base. Blue nodes are computed in Analytica.752,168,-1112,152Sample:
id
Res_id
Sample
Result
DescrObjects__456,121,148,58R Objects192,176,-156,801,0,0,1,0,1,0,,0,R Structure256,332,-1120,681,0,0,1,0,1,0,,0,R Cardinals192,48,-156,401,0,0,1,0,1,0,,0,Writerjtue24. maata 2009 9:36 48,24184,424,148,241,644,21,555,414,17W LocMakes a table to be written to the Loc table.index j:= ['id','Obj_id_i','Location','Roww','Description'];
var a:= Locations[.j=j];
var b:= a[j='Obj_id_i'];
array(j,[textify(cardinals[table1='Loc']+a), findid(b,Obj,'Ident'), a, textify(a), a])464,296,148,122,156,83,476,2452,642,68,515,278,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0100,1,1,1,1,9,2970,2100,15,0[][Sys_localindex('I'),16,Sys_localindex('I'),1,Sys_localindex('J'),1]W LoccellSlices fields that are needed in the Locres table from Inp_locres.index j:= ['id','Cell_id','Loc_id'];
var a:= Loccells;
var b:= textify(findid(a[.j='Loc_id'], Obj, 'Ident'));
var c:= textify(a[.j='Location']);
b:= findid(b&'+'&c, (if Loc.j='Obj_id_i' then Loc&'+'&Loc[.j='Location'] else Loc), 'Obj_id_i');
a:= a[.j=j];
a:= array(j,[(a+cardinals[table1='Loccell']),(a+cardinals[table1='Cell']), b]);
textify(a)464,192,148,162,791,179,476,3872,632,155,619,303,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[]W CellSlices the fields that are needed in the Res table. Removes duplicate rows.index j:= ['id','Obj_id_v','Obj_id_r','Mean','SD','N'];
var a:= Loccells;
var b:= a[.j='Cell_id']+cardinals[table1='Cell'];
a:= a[.j=j];
var c:= findid(a[@j=2], Obj, 'Ident');
var d:= w_obj[.j='Ident'];
d:= findid(d, Obj, 'Ident')[@.i=size(w_obj.i)];
a:= array(j, [textify(b),c, d, a, a, textify(a)]);
index i:= unique(a,a.i);
a:= a[.i=i];
if a=null then '' else a464,160,148,162,782,213,476,3792,85,231,505,368,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[]WikisNames of different wikis used.Table(Self)(
'Op_en','Op_fi','Heande','En','Fi','Erac','Beneris','Intarese','Piltti','Kantiva','Bioher','Heimtsa')[1,2,3,4,5,8,9,10,11,13,14,15]344,24,148,1665535,52427,65534[Self]Object typesTypes of different objects that may exist in Analytica or Opasnet Base. Types that have the same number are treated equally in these systems.Table(Self)(
'Variable','Dimension','Method','Model','Class','Index','Nugget','Encyclopedia article','Run','Chance','Decision','Objective','Constant','Determ','Module','Library','Form')[1,2,3,4,5,6,7,8,9,1,10,1,1,1,4,4,4]64,328,148,202,56,132,476,2242,674,34,416,606,0,MIDM2,636,151,416,390,0,MIDM65535,52427,65534W ObjSelects relevant information for the Obj table from Objects1 node.index j:= ['id','Ident','Name','Unit','Objtype_id','Page','Wiki_id'];
var a:= Objects;
var b:= if a[.j='Ident'] = 0 then -1 else a[.j='Ident'];
b:= findid(b, Obj, 'Ident');
b:= if b='0' then cardinals[table1='Obj']+a[.j='id'] else b;
a:= if a.j='id' then b else a;
a:= a[.j=j];
a:= if j='Ident' and a[j='Ident']='' then a[j='id'] else a;
a:= if j='Ident' and a.i='Run' and isnumber(append_to_run) then append_to_run else a;
textify(a)464,104,148,162,510,359,476,2872,98,141,1023,259,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[][]W ResinfoIf the result is not a number, then the actual result text can be written into the Description field of the Descr table. Makes a list of text values to be written into the Descr table.index j:= ['id','Restext'];
var a:= Results;
index i:= subset(a[.j='Restext']);
a:= a[.j=j, .i=i];
a:= array(j, [textify(a+Cardinals[table1='Res']), a])464,232,148,162,674,46,476,2592,670,328,416,303,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[]W ObjinfoMakes a list of objects that contains some additional information to be written into the Inf table.index j:= ['Obj_id','Acttype_id','Who','Comments'];
var a:= Objects;
var b:= if a[.j='Ident'] = 0 then -1 else a[.j='Ident'];
b:= findid(b, Obj, 'Ident');
b:= if b='0' then cardinals[table1='Obj']+a[.j='id'] else b;
a:= if j='Obj_id' then b else a[.j=j];
a:= if a = null or a='' then 0 else a;
index i:= subset(if sum(a, j) = 0 then 0 else 1);
a:= a[.i=i];
a:= if a=null or a=0 then '' else a&''464,72,148,162,773,40,476,3402,34,427,690,274,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[][Sys_localindex('J'),1,Sys_localindex('I'),1,Sys_localindex('J'),1]W Resindex j:= ['id','Cell_id','Obs','Result'];
var a:= Results;
index i:= subset(if a[.j='Result']=null and a[.j='Description']=0 then 0 else 1);
a:= a[.j=j, .i=i];
a:= array(j, [textify(a+Cardinals[table1='Res']), textify(a+ Cardinals[table1='Cell']), textify(a),a]);
if a=null then '' else a
464,264,148,132,539,477,582,2972,62,129,609,303,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[][]Number of variablesAdditional information for each index and decision node. Description node is the name of a node containing information about the locations of the index. It must be indexed by the index.1192,328,148,222,140,217,476,2242,605,351,664,303,0,MIDM2,506,220,684,303,0,MIDM[Formnode Number_of_variables1]52425,39321,65535[Indices][Indices][1,1,0,1]Variablevar a:= if data_table_or_model_ = 'Data table' then 1 else N_variables;
'Var'&1..a192,264,148,131,1,1,1,1,1,0,,0,2,107,331,416,303,0,MIDM[1,1,1,0]['Var1']VariablesAdditional information for each index and decision node. Description node is the name of a node containing information about the locations of the index. It must be indexed by the index.Table(Variable1,Varinfo)(
'Total_mortality','Op_en2693',0
)192,232,148,222,140,217,476,2242,11,16,553,630,0,MIDM2,506,220,684,303,0,MIDM[Formnode Variables1]52425,39321,65535[Varinfo,Variable1][Varinfo,Variable1][0,1,1,0]Number of indices564,64,148,24[Formnode Number_of_indices1]52425,39321,65535[1,1,0,1]Number of observations2464,112,152,24[Formnode Number_of_observati1]52425,39321,65535Object infoTable(Object_info,Object1)(
'Testvariable','Country','Year','Diagnosis','Sex','Age',
'# deaths/a','-','a','-','-','a'
)192,72,148,132,102,90,476,3492,571,27,659,478,0,MIDM2,184,194,660,316,0,MIDM[Formnode Study_or_variable_i1]52425,39321,65535[Object_info,Object1][Object_info,Object1]Object info['Name','Unit']192,96,148,12['Name','Unit']LoccellsMakes a list of all locations in all results in all variables. The list is as long as is needed for the Loccell table. A subset is taken then for the Cell table.
1) Initialises local variables, and slices variables from Object1.
2)-4) Does the process for each variable one at a time. This happens in function Loccell.
5) Makes i the row index.
, @observation=@cell_id+1var output:= 0;
output:= if data_table_or_model_ = 'Data table' then
Doloccell(Data_table)
else (
var e:= 0;
var f:= 0;
var x:= 1;
while x<= size(variable1) do (
var a:= sample(evaluate(variables[@variable1=x, varinfo='Analytica identifier']));
var b:= sdeviation(a);
a:= mean(a);
index temp:= concat(indexnames(a),['Result']);
index i:= 1..size(a);
a:= mdarraytotable(a, i, temp);
b:= mdarraytotable(b, i, temp);
index j:= concat(temp,['SD']);
index temp2:= ['Result'];
a:= concat(a, b[temp=temp2], temp, temp2, j);
a:= Doloccell(a, x, e, f);
e:= e+size(a.i);
f:= f+size(i);
output:= if x=1 then a else for y:= output.j do ( concat(output[.j=y], a[.j=y]) );
x:= x+1) ;
output);
index i:= 1..size(output)/size(output.j);
for y:= output.j do (slice(output[.j=y],i))344,160,148,162,729,93,526,7182,31,123,656,488,0,MIDM[Sys_localindex('I'),Sys_localindex('J'),Undefined,Undefined,Undefined,1]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[][Undefined][Sys_localindex('CONV2'),1,Parameter,1,Sys_localindex('L'),1]ResultsThe usage of local variables: a: the temporary variable that is being edited. e: cardinal of the Cell table. f: cardinal of the Res table. j: output column headings. i: output row numbers. NOTE! ONLY THE DETERMINISTIC VERSION WORKS AT THE MOMENT.
1) Only one piece of information (Observations) is included.
2)-5) The process is done for each variable one at a time (this is indexed by x).
3) Several within-loop local variables are initiated.
4) The variable is given index runn which is equal to run if probabilistic and [0] if not. The array is flattened first to 2-D, the value only is kept.
5) Variables are concatenated to each other.
6) Index i is made the index of the implicit index.
NOTE! This node MUST be formatted to Integer, otherwise Res_id will be stored in a wrong format.var output:= 0;
output:= if data_table_or_model_ = 'Data table' then
Doresult(Data_table, 0)
else (
var e:= 0;
var f:= 0;
var x:= 1;
while x<= size(variable1) do (
var a:= sample(evaluate(variables[@variable1=x, varinfo='Analytica identifier']));
index j:= concat(indexnames(max(a,run)),['Result']);
index i:= 1..size(max(a,run));
a:= mdarraytotable(a, i, j);
a:= Doresult(a, variables[@variable1=x, varinfo='Probabilistic?'], e, f);
e:= max(a[.j='Cell_id'],a.i);
f:= max(a[.j='id'],a.i);
output:= if x=1 then a else for y:= output.j do ( concat(output[.j=y], a[.j=y]) );
x:= x+1) ;
output);
index i:= 1..size(output)/size(output.j);
output:= for y:= output.j do (slice(output[.j=y],i))344,232,148,162,50,25,585,6152,583,137,469,411,0,MIDM[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[Run,2,Sys_localindex('J'),1,Sys_localindex('I'),1]LocationsThe format of this node MUST be integer, so that the id and Roww values are stored correctly.var output:= 0;
output:= if data_table_or_model_ = 'Data table' then
Dolocation(Data_table)
else (
var x:= 1;
while x<= size(variable1) do (
var a:= mean(evaluate(variables[@variable1=x, varinfo='Analytica identifier']));
index j:= concat(indexnames(a),['Result']);
index i:= 1..size(a);
a:= mdarraytotable(a, i, j);
a:= Dolocation(a);
output:= if x=1 then a else for y:= output.j do ( concat(output[.j=y], a[.j=y]) );
x:= x+1) ;
output);
index i:= 1..size(output)/size(output.j);
output:= for y:= output.j do (slice(output[.j=y],i));
if output.j='id' then i else output344,296,148,162,650,38,476,5812,440,15,788,326,0,MIDM[Sys_localindex('J'),Sys_localindex('I')]2,I,4,2,0,0,4,0,$,0,"ABBREV",0[0][Sys_localindex('D'),1,Object_all3,1,Age,1]# of observed parameters164,264,148,31[Formnode A__of_observed_para1]52425,39321,65535ParameterObservations[@Observation=1, @Field1=N_indices+(1..N_parameters)]192,184,148,122,746,363,416,303,0,MIDM['Fish','Samplesize','Minsize','Maxsize','137Cs_Bq/kgtpVammala','137Cs_Bq/kgtpSTUK']Data tableindex j:= ['Country','Year','Diagnosis','Sex','Age','Parameter','Result'];
var a:= Total_mortality[.j=j];
a:= array(j,[a,a,a,a,a,'Deaths',Total_mortality[.j='Deaths']]);
{index h:= data_table2[@.i=1, @.j=1..N_indices];
index j:= concat(h,['Parameter','Result']);
index parameter:= data_table2[@.i=1, @.j=N_indices+(1..size(data_table2.j)-N_indices)];
index i:= 1..(size(data_table2.i)-1)*(size(parameter));
index loccell_id:= 1..(size(i)*size(h));
var conv:= if j='Result' then @parameter+N_indices else @j;
index conv2:= 1..size(data_table2.i)-1;
var a:= data_table2[@.j=conv, @.i=conv2+1];
a:= if j='Parameter' then parameter else a;
a:= concatrows(a, parameter, conv2, i);
if j='Result' then evaluate(a) else a}192,160,148,132,473,241,482,3262,65,349,611,318,0,MIDM[Sys_localindex('J'),Sys_localindex('I')][1,1,1,0][Sys_localindex('PARAMETER'),1,Sys_localindex('J'),1,Sys_localindex('CONV2'),1]Varinfo['Analytica identifier','Ident','Probabilistic?']192,288,148,122,90,166,416,303,0,MIDM['Analytica identifier','Ident','Probabilistic?']ObjectsIndex j:= ['id','Ident','Name','Unit','Objtype_id','Acttype_id','Page','Wiki_id', 'Who','Comments','Probabilistic?','Description node'];
index i:= concat(Object1,['Run']);
var a:= if j='Ident' and i = 'Var1' then study_ident else null;
a:= if data_table_or_model_ = 'Data table' then a else Variables[Variable1=i, Varinfo=j];
a:= if a=null then Object_info1[Object1=i, Object_info=j] else a;
var b:= findintext(wikis,a[j='Ident']);
var c:= sum(if b=0 then 0 else @wikis,wikis);
b:= sum(if b=0 then 0 else b+textlength(Wikis),wikis);
b:= if b = 0 then 2664 else selecttext(a[j='Ident'],b);
a:= array(j,[
@i,
if @i>size(variable1) and @i<size(i) then i else a,
if i ='Run' then 'Analytica '&Analyticaedition&', ('&Analyticaplatform&'), Version: '&Analyticaversion&', Samplesize: '&samplesize else a,
a,
if @i<=size(variable1) then 1 else if @i=size(i) then 9 else 6,
if @i<=size(variable1) then 11 else 1,
if i='Run' then '2817' else b&'',
if c=0 then 1&'' else c&'',
opasnet_username, '', 0, '']);
a:= if a = null then '' else a
344,72,148,162,669,75,479,5512,106,165,976,300,0,MIDM[Sys_localindex('I'),Sys_localindex('J')]Objectreset;
['Var1','Country','Year','Diagnosis','Sex','Age'];
{reset;
var a:= indexnames(evaluate(Variables[Varinfo='Analytica identifier']));
a:= if a='Variable1' or a='Run' then 0 else 1;
a:= subset(a);
a:= if data_table_or_model_ = 'Data table' then data_table2[@.i=1, @.j=1..n_indices] else a;
var b:= if data_table_or_model_ = 'Data table' then "['Var1']" else "variable1";
concat(evaluate(b),a)}192,120,148,132,59,17,502,3382,718,27,416,303,0,MIDM['Var1','Country','Year','Diagnosis','Sex','Age']Study Ident'Op_en2778'192,32,148,16[Formnode Study_ident1]52425,39321,65535[1,1,0,1]Observations'A1 B1 C1 4 5 6
2 3 4 5 6 7
3 4 5 6 7 8
4 5 6 7 8 9
5 6 7 8 9 10
6 7 8 9 10 11
7 8 9 10 11 12
8 9 10 11 12 13
9 10 11 12 13 14
10 11 12 13 14 15
11 12 13 14 15 16
12 13 14 15 16 17
13 14 15 16 17 18'64,160,152,162,637,65,476,362[Formnode Observations3]52425,39321,65535Data tablevar a:= splittext(textreplace(Observations2, chr(10),'',true),chr(13));
index i:= 1..size(a);
a:= slice(a,i);
a:= splittext(a, chr(9));
index j:= 1..size(a)/size(i);
for y:= i do (slice(a[i=y],j))64,208,148,162,621,69,476,3622,764,339,416,303,0,MIDM[Sys_localindex('J'),Sys_localindex('I')][1,1,1,0]TestvariableTable(testindex)(
uniform(0,1))['item 1']72,384,148,24[1,0,0,0]['item 1']Testindex['item 1']72,416,148,12['item 1']W CellSlices the fields that are needed in the Res table. Removes duplicate rows.(if w_cell.j = 'Mean' then '' else W_cell)568,128,148,162,782,213,476,3792,85,231,505,368,0,MIDM65535,45873,39321[Sys_localindex('J'),Sys_localindex('I')]2,D,4,2,0,0,4,0,$,0,"ABBREV",0[]Append to run''344,120,048,201,1,1,1,1,1,0,0,0,0[Formnode Append_to_run1]52425,39321,65535Data table or model?Choice(Self,1,False)64,424,148,24[Formnode Data_table_or_model1]['Data table','Analytica model'][1,1,0,0]Data table or model?0176,21,1160,131,0,0,1,0,0,0,142,0,1Data_table_or_model_Number of variables0160,320,1140,121,0,0,1,0,0,0,72,0,152425,39321,65535N_variablesVariables0164,345,1140,121,0,0,1,0,0,0,72,0,152425,39321,65535VariablesReaderktluser3. Augta 2008 18:31jtue9. lokta 2008 14:01 48,24296,496,148,241,1,1,1,1,1,0,0,0,01,785,211,477,360,17Arial, 15(vident:text, runident:optional)Read meanReads the mean data about the vident variable from the Opasnet Base. Uses the runident run if specified; otherwise uses the newest run of that variable.
PARAMETERS:
* Vident: the Ident of the variable in the Opasnet Base.
* Runident: the Ident of the run from which the results will be brought. If omitted, the newest result will be brought.if isnotspecified(runident) then runident:= identfind(newestrun(vident));
var a:= '
SELECT Var.Ident as Vident, Var.Name as Vname, Var.Unit as Vunit, Cell.id, Ind.Ident as Iident, Location, Mean, N, Run.Name as Rname, Run.Ident AS Runident
FROM Obj as Var, Cell, Loccell, Loc, Obj as Ind, Obj as Run
WHERE Cell.Obj_id_r = Run.id
AND Cell.Obj_id_v = Var.id
AND Loccell.Cell_id = Cell.id
AND Loccell.Loc_id = Loc.id
AND Loc.Obj_id_i = Ind.id
AND Var.Ident = '&chr(39)&vident&chr(39)&'
AND Run.ident = '&chr(39)&runident&chr(39)
;
index i:= DBquery(Odbc,a);
index j:= dblabels(i);
dbtable(i,j)56,88,148,122,585,25,516,58939325,65535,39321vident,runident(vident:text)NewestrunThis function checks for the newest result (according to run_id) of the variable. The function is used if the user does not define the run_id as an optional parameter in functions Read_mean and Read_sample.
PARAMETERS:
* Vident: the Ident of the variable in the Opasnet Base.index i:= DBquery(Odbc,'
SELECT Obj_id_r
FROM Cell, Obj as Var
WHERE Var.id = Cell.Obj_id_v
AND Var.Ident = "'&vident&'"
GROUP BY Var.id, Obj_id_r
');
index j:= dblabels(i);
max(max(dbtable(i,j),i),j)56,16,148,122,678,59,476,56639325,65535,39321vident(vident:text, runident:optional)Read sampleReads the sample data about the vident variable from the Opasnet Base. Uses the runident run if specified; otherwise uses the newest run of that variable.
PARAMETERS:
* Vident: the name of the variable in the Opasnet Base.
* Runident: the Ident of the run from which the results will be brought. If omitted, the newest result will be brought.if isnotspecified(runident) then runident:= identfind(newestrun(vident));
var a:= '
SELECT Temp.id, Obs, Result, Restext
FROM
(SELECT Cell.id, Res.id AS Res_id, Obs, Result, Obj_id_r
FROM Cell, Res, Obj AS Run, Obj AS Var
WHERE Var.Ident = '&chr(39)&vident&chr(39)&'
AND Cell.Obj_id_v = Var.id
AND Cell.Obj_id_r = Run.id
AND Run.Ident = '&chr(39)&Runident&chr(39)&'
AND Res.Cell_id = Cell.id) AS Temp
LEFT JOIN Resinfo ON
Temp.Res_id = Resinfo.id
';
index i:= DBquery(Odbc,a);
index j:= dblabels(i);
dbtable(i,j)56,120,148,222,700,47,516,61239325,65535,39321vident,runidentEnter variable Ident'Op_en1912'168,83,148,27[Formnode Enter_variable1]52425,39321,65535Enter variable0288,24,1176,131,0,0,1,0,0,0,170,0,152425,39321,65535Enter_variableNewest runnewestrun(Enter_variable)288,60,148,12Var inforead_mean(Enter_variable)288,108,148,122,56,66,1205,308,0,MIDM[Sys_localindex('J'),Sys_localindex('I')](a,inde)MakeindThe input table a must have a structure that is also used as input for MDTable function. The function removes one column with location information and makes a dimension (index) with the locations in the column. Inde is the (local) index that will be added. Note that unlike MDTable function, this can use local indices in the output.if size(a.m)= 1 then a else (
a:= if inde = a[@.m=1] then a else 0;
index m:= slice(a.m,(2..size(a.m)));
a:= a[.m=m])56,176,148,122,283,62,476,224a,inde(a)Get res.idMakes a multi-dimensional array with the same structure as the original variable that was stored into the Opasnet Base. However, the indices do not have original names. They are named In1, In2,... The contents of the array are the res.ids of the variable. The input parameter must be a 2D table with the structure that comes from the Read_mean function.
1) Slices the necessary columns from the input table and converts that to a 2D table that has the same structure as is used for input to the function MDTable.
2) Defines the local indices (up to 10), and changes a location column to a dimension one at a time until all columns have been changed.index k:= ['Iident','Location','id'];
a:= a[.j=k];
index L:= a[@k=1]&'+'&textify(a[@k=3]);
index m:= concat(a[.i=unique(a[@k=1],a.i), @k=1],['Result']);
index n:= a[.i=unique(a[@k=3],a.i), @k=3];
a:= a[@.i=@L];
a:= a[L=(m)&'+'&textify(n), @k=2];
a:= if m='Result' then n else a;
index in1:= a[n=unique(a[@m=1],n),@m=1];
index in2:= a[n=unique(a[@m=2],n),@m=2];
index in3:= a[n=unique(a[@m=3],n),@m=3];
index In4:= a[n=unique(a[@m=4],n),@m=4];
index In5:= a[n=unique(a[@m=5],n),@m=5];
index in6:= a[n=unique(a[@m=6],n),@m=6];
index in7:= a[n=unique(a[@m=7],n),@m=7];
index in8:= a[n=unique(a[@m=8],n),@m=8];
index in9:= a[n=unique(a[@m=9],n),@m=9];
index in10:= a[n=unique(a[@m=10],n),@m=10];
a:= makeind(a, in1);
a:= makeind(a, in2);
a:= makeind(a, in3);
a:= makeind(a, in4);
a:= makeind(a, in5);
a:= makeind(a, in6);
a:= makeind(a, in7);
a:= makeind(a, in8);
a:= makeind(a, in9);
a:= makeind(a, in10);
sum(sum(a,a.m),a.n)56,152,148,122,669,44,476,545aVar meanget_mean(Enter_variable)288,132,148,122,547,35,416,622,0,MIDM[Sys_localindex('IN2'),Sys_localindex('IN3')][Sys_localindex('IN1'),1,Sys_localindex('IN4'),1,Sys_localindex('IN5'),1,Sys_localindex('IN3'),1,Sys_localindex('IN2'),1](vident:text, runident:optional)Get meanGives the mean result of a (multidimensional) variable stored in the Opasnet Base. The procedure is simple because it utilises the variable structure (with res_ids) derived by the get_res_id function.var a:= read_mean(vident, runident);
index o:= a[.j='id'];
var output:= a[@.i=@o, .j='Mean'];
a:= get_res_id(a);
output[o=a]56,200,148,122,665,82,476,428vident,runident(vident:text, runident:optional)Get sampleGives the sample result of a (multidimensional) variable stored in the Opasnet Base. The procedure is simple because it utilises the variable structure (with res_ids) derived by the get_res_id function.
Note that if the Analytica samplesize is smaller than the samplesize stored in the Opasnet Base, the extra samples will be discarded. If the samplesize is larger, the remaining rows will be null.
1) Brings the data into the right structure.
2) Chooses whether the actual result is numerical (in the Result column) or text (in the Description column).var a:= read_sample(vident, runident);
var b:= textify(get_res_id(read_mean(vident,runident)));
index k:= textify(a[.j='id'])&'+'&textify(a[.j='Obs']);
index runn:= textify(min(a[.j='Obs'])..max(a[.j='Obs']));
a:= a[@.i=@k];
a:= a[k=b&'+'&runn];
a:= if max(runn)=0 then a[@runn=1] else a[@runn=@run];
var c:= if a[.j='Restext']='' then 0 else 1;
c:= sum(sum(sum(sum(sum(sum(sum(sum(sum(sum(c))))))))));
if c=0 then a[.j='Result'] else a[.j='Restext']56,224,148,122,641,28,476,556vident,runidentVar sampleget_sample(Enter_variable)288,156,148,122,226,324,416,303,0,MEAN[Sys_localindex('IN5'),Sys_localindex('IN3')][Sys_localindex('IN1'),1,Sys_localindex('IN2'),1,Sys_localindex('IN4'),1,Sys_localindex('IN3'),1,Sys_localindex('J'),1,Sys_localindex('IN5'),1](runid)IdentfindFinds the Ident for the run (or another object) that has the id runid.index i:= DBquery(Odbc,'
SELECT Ident
FROM Obj
WHERE Obj.id = "'&runid&'"
');
index j:= dblabels(i);
var a:= dbtable(i,j);
a[@i=1, @j=1]56,64,148,122,732,65,516,58939325,65535,39321runidVar run infoDescribes the runs of the defined variable. This should be made a function.var_run_info(Enter_variable)288,84,148,122,136,146,1111,285,0,MIDM[Sys_localindex('J'),Sys_localindex('I')](vident:text)Var run infoThis function checks for the newest result (according to run_id) of the variable. The function is used if the user does not define the run_id as an optional parameter in functions Read_mean and Read_sample.
PARAMETERS:
* Vident: the Ident of the variable in the Opasnet Base.var a:= '
SELECT Var.Ident, Var.Name, Var.Unit, Run.Ident AS Runident, Act.When, Act.Who, Run.Name as Method
FROM Obj as Var, Obj as Run, Cell, Objinfo AS Act
WHERE Var.Ident = '&chr(39)&vident&chr(39)&'
AND Var.id = Cell.Obj_id_v
AND Run.id = Cell.Obj_id_r
AND Run.id = Act.id
GROUP BY Var.id, Run.id
';
index i:= DBquery(Odbc,a);
index j:= dblabels(i);
dbtable(i,j)56,40,148,132,678,59,476,56639325,65535,39321videntUse these functions to retireve data from the Opasnet base:
* Newest_run: finds the newest run of the object.
* Var_run_info: Finds the run information of the object.
* Read_mean: Reads the means of each cell.
* Get_mean
* Get_sample: Reads the whole sample.
Note! These should be updated when we get experience about what we actually want out.280,285,-1168,101(a)TextifyChanges an integer of any length to a text value. This bypasses the number formatting problem that tends to convert e.g. 93341 to '93.34K'. If the number is not integer, up to three digits after the decimal point will be taken as well.for y[]:= a do (
if istext(y) then y else (
var x:= 1;
var b:= '';
var c:= if y<1 then 1 else floor(logten(y))+1;
while x<= c do (
b:= (y-floor(y/10)*10)&b;
y:= floor(y/10);
x:= x+1);
b) )56,248,148,122,102,90,476,371aDetailsktluser8. Decta 2008 3:01 48,24184,496,148,241,30,288,495,456,17(a; x:optional = 1; e, f:optional=0)Doloccell2) Only the deterministic information about variables are considered (therefore mean). Makes a 2D table of the locres info.
3) Makes a table with fields required by the Loccell and Cell tables.
4) Reduces one dimension by expanding the length from the length of Cell to that of Loccell.index j:= ['id', 'Location', 'Cell_id', 'Loc_id', 'Obj_id_v', 'Obj_id_r', 'Mean', 'SD', 'N'];
index h:= a.j[@.j=1..size(a.j)-2];
index i:= 1..size(a.i)*size(h);
var c:= Objects[@.i=x];
a:= array(j,[
i[@i=@a.i+size(a.i)*(@h-1)]+e,
a[.j=h]&'',
a.i+f,
h,
c[.j='Ident'],
'',
a[.j='Result'],
a[.j='SD'],
if c[.j='Probabilistic?']=0 then 0 else samplesize]);
concatrows(a,h,a.i,i)400,288,148,132,627,78,556,561a,x,e,f(a: prob; probabilistic; e, f: optional=0)Doresultindex runn:= if Probabilistic=1 then copyindex(run) else [0];
index i:= (1..size(max(a.i,run))*size(runn))+f;
a:= if Probabilistic=1 then a[run=runn] else (if runn=0 then a else a);
a:= a[.j='Result'];
index j:= ['id','Cell_id','Obs','Result','Restext'];
a:= array(j,[0, a.i+e, runn, a , 0]);
a:= concatrows(a,a.i,runn, i);
a:= if j='id' then i else a400,264,148,132,687,174,476,526a,probabilistic,e,f(a)Dolocationvar b:= [0];
var c:= [0];
var e:= [0];
var f:= [0];
var x:= 1;
while x<= size(a.j)-1 do (
var h:= a[@.j=x];
var d:= h[.i=unique(h,h.i)];
b:= concat(b,d);
c:= concat(c,(if d=0 then slice(a.j,x) else slice(a.j,x)));
e:= concat(e,1..size(d));
x:= x+1);
index i:= 1..size(b)-1;
index j:= ['id','Obj_id_i', 'Location', 'Roww', 'Description'];
array(j,[i, slice(c,i+1), slice(b,i+1)&'', slice(e,i+1), '']);400,240,148,122,671,164,503,486aConcatenation UDFsThis library contains functions to make various instances of concatenation more convenient. Concat3 thru Concat10 are generalizations of the built-in Concat function which concatenate from 3 to 10 arrays in a single call (while the built-in Concat concatenates two arrays). ConcatRows concatenates all the rows of a single array.David Kendall & Lonnie ChrismanMon, Jan 26, 2004 8:49 AMLonnieWed, Sep 05, 2007 3:23 PM48,24184,328,168,201,0,0,1,1,1,0,0,0,01,50,200,488,454,23(A1, A2, A3: ArrayType; I1, I2, I3, J: IndexType )Concat3Concatenates three arrays, A1, A2, and A3. I1, I2, and I3 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, and I3Index I12 := Concat(I1,I2);
Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, J )88,64,148,262,56,56,986,596A1,A2,A3,I1,I2,I3,J(A1, A2, A3, A4: ArrayType; I1, I2, I3, I4, J: IndexType )Concat4Concatenates four arrays, A1, A2, A3, and A4. I1, I2, I3, and I4 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, I3, and I4.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, J);
192,64,148,242,30,30,986,596A1,A2,A3,A4,I1,I2,I3,I4,J0(A1, A2, A3, A4, A5, A6, A7, A8, A9: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, J: IndexType)Concat9Concatenates nine arrays, A1, ..., A9. I1, ..., I9 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I9.Index I12 := Concat(I1,I2);
Index I123 := Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Index I123456 := Concat(I12345, I6);
Index I1234567 := Concat(I123456, I7);
Index I12345678 := Concat(I1234567, I8);
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, I123456),
A7, I123456, I7, I1234567),
A8, I1234567, I8, I12345678),
A9, I12345678, I9, J);88,232,148,242,27,120,469,638A1,A2,A3,A4,A5,A6,A7,A8,A9,I1,I2,I3,I4,I5,I6,I7,I8,I9,J0(A1, A2, A3, A4, A5: ArrayType; I1, I2, I3, I4, I5, J: IndexType )Concat5Concatenates five arrays, A1, ..., A5. I1, ..., I5 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I5.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, J);88,120,148,242,160,160,986,596A1,A2,A3,A4,A5,I1,I2,I3,I4,I5,J(A1, A2, A3, A4, A5, A6: ArrayType; I1, I2, I3, I4, I5, I6, J: IndexType )Concat6Concatenates six arrays, A1, ..., A6. I1, ..., I6 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I6.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, J);192,120,148,242,644,94,602,712A1,A2,A3,A4,A5,A6,I1,I2,I3,I4,I5,I6,J0(A1, A2, A3, A4, A5, A6, A7: ArrayType; I1, I2, I3, I4, I5, I6, I7, J: IndexType )Concat7Concatenates seven arrays, A1, ..., A7. I1, ..., I7 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I7.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Index I123456 := Concat(I12345, I6);
Concat(
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, I123456),
A7, I123456, I7, J);88,176,148,242,580,98,551,565A1,A2,A3,A4,A5,A6,A7,I1,I2,I3,I4,I5,I6,I7,J(A1, A2, A3, A4, A5, A6, A7, A8: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, J: IndexType )Concat8Concatenates eight arrays, A1, ..., A8. I1, ..., I8 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I8.Index I12 := Concat(I1,I2);
Index I123:= Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Index I123456 := Concat(I12345, I6);
Index I1234567 := Concat(I123456, I7);
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, I123456),
A7, I123456, I7, I1234567),
A8, I1234567, I8, J);192,176,148,242,12,98,561,737A1,A2,A3,A4,A5,A6,A7,A8,I1,I2,I3,I4,I5,I6,I7,I8,J0(A1, A2, A3, A4, A5, A6, A7, A8, A9, A10: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, I10, J: IndexType)Concat10Concatenates ten arrays, A1, ..., A10. I1, ..., I10 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I10.Index I12 := Concat(I1,I2);
Index I123 := Concat(I12, I3);
Index I1234 := Concat(I123, I4);
Index I12345 := Concat(I1234, I5);
Index I123456 := Concat(I12345, I6);
Index I1234567 := Concat(I123456, I7);
Index I12345678 := Concat(I1234567, I8);
Index I123456789 := Concat(I12345678, I9);
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat(
Concat( A1,A2,I1,I2,I12 ),
A3, I12, I3, I123),
A4, I123, I4, I1234),
A5, I1234, I5, I12345),
A6, I12345, I6, I123456),
A7, I123456, I7, I1234567),
A8, I1234567, I8, I12345678),
A9, I12345678, I9, I123456789),
A10, I123456789, I10, J);192,232,148,242,542,93,632,744A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,I1,I2,I3,I4,I5,I6,I7,I8,I9,I10,J0(A : ArrayType ; RowIndex,ColIndex,ResultIndex : IndexType)ConcatRows (A,I,J,K)Takes an array, A indexed by RowIndex & ColIndex, and concatenates each row, henceforth flattening the array by one dimension. The result is indexed by ResultIndex, which must be an index with size(RowIndex) * size(ColIndex) elements.index L := [ identifier of RowIndex, identifier of ColIndex, "val"];
slice(Mdarraytotable(A,ResultIndex,L),L,3)320,64,164,242,499,85,478,348A,RowIndex,ColIndex,ResultIndexODBC LibraryLonnieThu, Sep 11, 1997 2:15 PMLonnieTue, Feb 05, 2008 10:03 AM48,2456,328,152,201,1,1,1,1,1,0,0,0,01,20,272,499,462,17Arial, 13(A:ArrayType;I:IndexType;L:IndexType;row:IndexType;dbTableName)InsertRecSqlGenerates the SQL "INSERT INTO" statement for one line of table A. A is a 2-D table indexed by rows I and columns L. L's domain serves as the column names in the database table. dbTableName is the name of the table in the database. The result begins with two semi-colons, since it will be used with an SQL statement preceeding it.
29.8.2008 Jouni Tuomisto
I added the parameter IGNORE because it ignores rows that would cause duplicate-key violations. This way, there is no need to check for e.g. existing locations of new indices.
6.1.2009 Jouni Tuomisto
I changed the A[I=row] to A[@I=@row] because the original function does not work correctly, if there are non-unique rows in the index.(';;INSERT IGNORE INTO ' & dbTableName & '(' & JoinText(L,L,',') & ') VALUES (' & Vallist(A[@I=@row],L)) & ') '184,32,152,242,591,203,487,469A,I,L,row,dbTableName(V:ArrayType;I:IndexType)ValListTakes a list of values, and returns a string which the concatenation of each value, separated by commas, and with each value quoted.JoinText( '''' & V & '''', I, ',')72,32,052,242,642,360,476,224V,I1,F,4,14,0,0(Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName)WriteTableSql(Table,Rows,Labels,dbTableName)Returns the SQL that will write the table to the database table.
This can be used as the second argument to DBWrite.
This SQL statement replaces the entire contents of an existing table with the new data.'DELETE FROM '& Dbtablename & JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex)328,32,188,242,728,341,510,476Tabl,RowIndex,LabelIndex,dbTableName(Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName)AppendTableSql(Table,Rows,Labels,dbTableName)Returns the SQL that will write the table to the database table.
This can be used as the second argument to DBWrite.
This SQL statement replaces the entire contents of an existing table with the new data.JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex)328,88,188,242,559,127,510,476Tabl,RowIndex,LabelIndex,dbTableName(table:texttype)CardBrings the largest id number from the table defined in the parameter.index i:= DBquery(odbc,'
SELECT MAX(id) AS id
FROM '&table&'
');
index j:= dblabels(i);
max(max(DBTable(i, j ),i),j)56,272,148,122,102,90,476,33139325,65535,39321tableTablesList of such tables in Opasnet Base that are being written to by this module.['Obj','Cell','Loc','Loccell','Sett','Item','Res']280,256,148,132,15,594,158,227,0,MIDM[Variable W_sett]['Obj','Cell','Loc','Loccell','Sett','Item','Res']CardinalsThe largest id values for the selected Opasnet Base tables. The table is updated by pressing the R_cardinals button.Table(Table1)(
713,95.816K,2163,523.832K,47,146,1.209976M
)280,232,148,122,634,394,476,3322,193,270,416,303,0,MIDM2,87,329,416,303,0,MIDM39325,65535,393212,I,4,2,0,0,4,0,$,0,"ABBREV",0[Variable W_sett](in, table; cond:texttype)FindidThis function gets an id from a table.
in: the property for which the id is needed. In MUST be unique in cond.
table: the table from where the id is brought. The table MUST have .j as the column index, .i as the row index, and a column named 'id'.
cond: the name of the field that is compared with in. Cond must be text.index L:= in[.i=unique(in, in.i)];
var a:= if (L&' ') = (table[.j=cond]&' ') then table[.j='id'] else 0;
a:= sum(a, table.i)&'';
a[.L=in]56,248,148,122,636,101,494,398in,table,cond[Variable W_sett](type)TypesFinds the objects that are of the object type "type" (the only parameter of this function). Based on the information in Objects1.var a:= if Objects1[.j='Typ_id']=type then 1 else 0;
Objects1[Object_all=subset(a),.j='id']56,224,148,122,551,191,476,344type(var, table)WriteFor Lumina AWP use the following should be used:
'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97;Database=opasnet_base;User=resultwriter; Password=;Option=3'
For internal THL use the following should be used:
'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=opasnet_base;User=resultwriter; Password='&writerpsswd&';Option=3'if size(var)>0 then dbwrite((if platform = 'Lumina AWP' then 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97' else 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102')&';Database=opasnet_base;User=resultwriter; Password='&writerpsswd&';Option=3'
, appendtablesql(var,var.i, var.j, table&' '))56,296,148,122,776,65,476,457var,tableODBC writeFor Lumina AWP use the following should be used:
'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97;Database=opasnet_base;User=resultwriter; Password=;Option=3'
For internal THL use the following should be used:
'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=opasnet_base;User=resultwriter; Password='&writerpsswd&';Option=3'var a:= if platform='Lumina AWP' then 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97' else 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102';
a&';Database=opasnet_base;User=resultwriter; Password='&writerpsswd&';Option=3'168,232,148,121,1,0,1,1,1,0,,0,2,102,90,495,3462,168,178,833,303,0,MIDM[]Opasnet usernameThe username for Opasnet wiki'Add username'168,161,148,221,1,1,1,1,1,0,0,0,0[Formnode Username1]52425,39321,65535Opasnet passwordThe user's password for Opasnet wiki.'Add password'168,200,148,221,1,1,1,1,1,0,0,0,0[Formnode Password1]52425,39321,65535ODBCContains the parameters for the open database connectivity (ODBC).
For Lumina AWP use the following should be used:
'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97;Database=opasnet_base;User=result_reader; Password=ora4ever;Option=3'
For THL internal use the following should be used:
'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=opasnet_base;User=result_reader; Password=ora4ever;Option=3'var a:= if platform='Lumina AWP' then 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97' else 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102';
a&';Database=opasnet_base;User=result_reader; Password=ora4ever;Option=3'168,128,148,121,1,0,1,1,1,0,,0,2,102,90,508,4202,56,66,918,303,0,MIDMDimindex i:= copyindex(D_i);
index j:= copyindex(D_j);
Dim1[d_i=i, d_j=j]400,160,148,131,1,0,1,1,1,0,0,0,02,89,98,476,2242,635,328,556,489,0,MIDM19661,54073,65535[D_i,D_j][Sys_localindex('J'),Sys_localindex('I')]Indindex i:= copyindex(I_i);
index j:= copyindex(I_j);
Ind1[I_i=i, I_j=j]400,184,148,131,1,0,1,1,1,0,0,0,02,380,47,476,2962,490,110,649,655,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')]Locindex i:= copyindex(L_i);
index j:= copyindex(L_j);
Loc1[L_i=i, L_j=j]400,96,148,131,1,0,1,1,1,0,0,0,02,370,45,476,4452,43,42,1147,516,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')]ObjThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.index i:= copyindex(O_i);
index j:= copyindex(O_j);
Obj2[O_i=i, O_j=j]400,48,148,131,1,0,1,1,1,0,0,0,02,378,21,493,5012,21,103,977,421,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')][Variable W_sett]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]Standard versions400,112,-172,1001,0,0,1,0,1,0,,0,D_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]168,24,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]D_j['id','Ident','Name']168,48,148,12['id','Ident','Name']I_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]168,72,148,12[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]I_j['id','Iident','Iname','Did','Dident','Dname']168,96,148,12['id','Iident','Iname','Did','Dident','Dname']L_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,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53,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320]56,24,148,13[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320]O_j['id','Ident','Name','Unit','Objtype_id','Page','Wiki_id']56,48,148,13['id','Ident','Name','Unit','Objtype_id','Page','Wiki_id']SettThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.index i:= copyindex(S_i);
index j:= copyindex(S_j);
Sett1[S_i=i, S_j=j]400,72,148,131,1,0,1,1,1,0,0,0,02,378,21,493,5012,227,134,319,515,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]ItemThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.index i:= copyindex(It_i);
index j:= copyindex(It_j);
Item1[it_i=i, it_j=j]400,120,148,131,1,0,1,1,1,0,0,0,02,378,21,493,5012,298,216,382,519,0,MIDM19661,54073,65535[Sys_localindex('J'),Sys_localindex('I')]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]It_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]56,168,148,13[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]It_j['id','Sett_id','Obj_id','Fail']56,192,148,13['id','Sett_id','Obj_id','Fail']S_i[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]56,72,148,13[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]S_j['id','Obj_id','Settype_id']56,96,148,13['id','Obj_id','Settype_id']DimTable(D_i,D_j)(
43,'Vehicle_type','Vehicle type',
45,'Transport_mode','Transport mode',
46,'Cost_type','Cost type',
47,'Composite_fraction','Composite fraction',
51,'Food_source','The method for food production',
52,'Feed_pollutant','Decision about fish feed',
53,'Salmon_recomm','Decision about samon consumption recommendation',
32,'0','No dimension has been identified',
54,'Parameter','Statistical and other parameters of a variable',
42,'Environ_compartment','Environmental compartment',
41,'Emission_source','Emission source',
36,'Pollutant','Pollutant',
34,'Health_impact','Health impact',
33,'Decision','Possible range of decisions for a single decision-maker',
35,'Time','Time',
40,'Period','Period',
48,'Age','Age',
37,'Spatial_location','Spatial location',
38,'Length','Length',
49,'Municipality_fin','Municipalities in Finland',
44,'Person_or_group','Person or group',
39,'Non_health_impact','Non-health impact'
)280,160,148,131,1,1,1,1,1,0,0,0,02,89,98,476,2242,604,56,556,489,0,MIDM39325,65535,39321[D_i,D_j][D_j,D_i]IndTable(I_i,I_j)(
55,'Salmon_decision','',33,'Decision','Possible range of decisions for a single decision-maker',
80,'Reg_poll','',33,'Decision','Possible range of decisions for a single decision-maker',
81,'Recommendation1','',33,'Decision','Possible range of decisions for a single decision-maker',
83,'H1899','',33,'Decision','Possible range of decisions for a single decision-maker',
84,'H1898','',33,'Decision','Possible range of decisions for a single decision-maker',
56,'Hma_area','',37,'Spatial_location','Spatial location',
57,'Hma_region','',37,'Spatial_location','Spatial location',
58,'Hma_zone','',37,'Spatial_location','Spatial location',
88,'Condb_location1','',37,'Spatial_location','Spatial location',
93,'Op_en2672','',37,'Spatial_location','Spatial location',
59,'Year_1','',35,'Time','Time',
61,'Year_2','',35,'Time','Time',
82,'Year3','',35,'Time','Time',
60,'Op_en2665','Cause of death 1',34,'Health_impact','Health impact',
62,'Cause_of_death_2','',34,'Health_impact','Health impact',
85,'Cause_of_death3','',34,'Health_impact','Health impact',
63,'Length_1','',38,'Length','Length',
70,'Output_1','',39,'Non_health_impact','Non-health impact',
65,'Period_1','',40,'Period','Period',
86,'Run','',32,'0','No dimension has been identified',
71,'Vehicle_noch','',43,'Vehicle_type','Vehicle type',
92,'Vehicle_1','',43,'Vehicle_type','Vehicle type',
72,'Stakeholder_1','',44,'Person_or_group','Person or group',
73,'Mode1','',45,'Transport_mode','Transport mode',
74,'Cost_structure_1','',46,'Cost_type','Cost type',
75,'Comp_fr_1','',47,'Composite_fraction','Composite fraction',
76,'Age1','',48,'Age','Age',
77,'Municipality_fin1','',49,'Municipality_fin','Municipalities in Finland',
79,'Salmon1','',51,'Food_source','The method for food production',
78,'Pollutant1','',36,'Pollutant','Pollutant',
89,'Condb_agent1','',36,'Pollutant','Pollutant',
91,'Condb_agent2','',36,'Pollutant','Pollutant',
87,'Condb_compartment1','',42,'Environ_compartment','Environmental compartment',
90,'Condb_param1','',54,'Parameter','Statistical and other parameters of a variable'
)280,184,148,131,1,1,1,1,1,0,0,0,02,380,47,476,2962,232,242,874,303,0,MIDM2,12,22,876,493,0,MIDM39325,65535,39321[I_j,I_i][I_j,I_i]LocTable(L_i,L_j)(
1,1,'Business as usual',0,'',1,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
2,1,'Recommend restrictions to salmon consumption',0,'',2,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
3,1,'Stricter limits for fish feed pollutants',0,'',3,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
4,1,'Restrictions to salmon consumption AND stricter fish feed limits',0,'',4,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
26,2,'All causes',0,'',26,'Op_en2693','Testvariable','kg',1,2693,1,
197,6,'>= 5 km',0,'',197,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0,
196,6,'< 5 km',0,'',196,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0,
8,3,'2020',0,'',8,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
7,3,'1997',0,'',7,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
10,2,'Cardiopulmonary',0,'',10,'Op_en2693','Testvariable','kg',1,2693,1,
11,2,'Lung cancer',0,'',11,'Op_en2693','Testvariable','kg',1,2693,1,
12,2,'All others',0,'',12,'Op_en2693','Testvariable','kg',1,2693,1,
27,5,'Downtown',0,'',27,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
28,5,'Centre',0,'',28,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
29,5,'Suburb',0,'',29,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
30,5,'Länsi-Espoo',0,'',30,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
31,5,'Pohjois-Espoo',0,'',31,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
32,5,'Etelä-Espoo',0,'',32,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
33,5,'Keski-Espoo',0,'',33,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
34,5,'Länsi-Vantaa',0,'',34,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
35,5,'Keski-Vantaa',0,'',35,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
36,5,'Pohjois-Vantaa',0,'',36,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
37,5,'Itä-Vantaa',0,'',37,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
38,5,'Kanta-Helsinki',0,'',38,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
39,5,'Länsi-Helsinki',0,'',39,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
40,5,'Vanha-Helsinki',0,'',40,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
41,5,'Konalanseutu',0,'',41,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
42,5,'Pakilanseutu',0,'',42,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
43,5,'Malminseutu',0,'',43,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
44,5,'Itä-Helsinki',0,'',44,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
45,5,'1001',0,'',45,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
46,5,'1002',0,'',46,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
47,5,'1003',0,'',47,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
48,5,'1004',0,'',48,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
49,5,'1005',0,'',49,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
50,5,'1006',0,'',50,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
51,5,'1007',0,'',51,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
52,5,'1008',0,'',52,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
53,5,'1009',0,'',53,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
54,5,'1010',0,'',54,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
55,5,'1011',0,'',55,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
56,5,'1012',0,'',56,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
57,5,'1013',0,'',57,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
58,5,'1014',0,'',58,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
59,5,'1015',0,'',59,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
60,5,'1016',0,'',60,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
61,5,'1017',0,'',61,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
62,5,'1018',0,'',62,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
63,5,'1019',0,'',63,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
64,5,'1020',0,'',64,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
65,5,'1021',0,'',65,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
66,5,'1022',0,'',66,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
67,5,'1023',0,'',67,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
68,5,'1024',0,'',68,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
69,5,'1025',0,'',69,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
70,5,'1026',0,'',70,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
71,5,'1027',0,'',71,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
72,5,'1028',0,'',72,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
73,5,'1029',0,'',73,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
74,5,'1030',0,'',74,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
75,5,'1031',0,'',75,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
76,5,'1032',0,'',76,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
77,5,'1033',0,'',77,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
78,5,'1034',0,'',78,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
79,5,'1035',0,'',79,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
80,5,'1036',0,'',80,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
81,5,'1037',0,'',81,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
82,5,'1038',0,'',82,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
83,5,'1039',0,'',83,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
84,5,'1040',0,'',84,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
85,5,'1041',0,'',85,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
86,5,'1042',0,'',86,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
87,5,'1043',0,'',87,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
88,5,'1044',0,'',88,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
89,5,'1045',0,'',89,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
90,5,'1046',0,'',90,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
91,5,'1047',0,'',91,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
92,5,'1048',0,'',92,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
93,5,'1049',0,'',93,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
94,5,'1050',0,'',94,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
95,5,'1051',0,'',95,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
96,5,'1052',0,'',96,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
97,5,'1053',0,'',97,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
98,5,'1054',0,'',98,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
99,5,'1055',0,'',99,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
100,5,'1056',0,'',100,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
101,5,'1057',0,'',101,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
102,5,'1058',0,'',102,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
103,5,'1059',0,'',103,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
104,5,'1060',0,'',104,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
105,5,'1061',0,'',105,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
106,5,'1062',0,'',106,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
107,5,'1063',0,'',107,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
108,5,'1064',0,'',108,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
109,5,'1065',0,'',109,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
110,5,'1066',0,'',110,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
111,5,'1067',0,'',111,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
112,5,'1068',0,'',112,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
113,5,'1069',0,'',113,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
114,5,'1070',0,'',114,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
115,5,'1071',0,'',115,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
116,5,'1072',0,'',116,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
117,5,'1073',0,'',117,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
118,5,'1074',0,'',118,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
119,5,'1075',0,'',119,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
120,5,'1076',0,'',120,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
121,5,'1077',0,'',121,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
122,5,'1078',0,'',122,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
123,5,'1079',0,'',123,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
124,5,'1080',0,'',124,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
125,5,'1081',0,'',125,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
126,5,'1082',0,'',126,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
127,5,'1083',0,'',127,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
128,5,'1084',0,'',128,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
129,5,'1085',0,'',129,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
130,5,'1086',0,'',130,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
131,5,'1087',0,'',131,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
132,5,'1088',0,'',132,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
133,5,'1089',0,'',133,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
134,5,'1090',0,'',134,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
135,5,'1091',0,'',135,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
136,5,'1092',0,'',136,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
137,5,'1093',0,'',137,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
138,5,'1094',0,'',138,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
139,5,'1095',0,'',139,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
140,5,'1096',0,'',140,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
141,5,'1097',0,'',141,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
142,5,'1098',0,'',142,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
143,5,'1099',0,'',143,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
144,5,'1100',0,'',144,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
145,5,'1101',0,'',145,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
146,5,'1102',0,'',146,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
147,5,'1103',0,'',147,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
148,5,'1104',0,'',148,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
149,5,'1105',0,'',149,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
150,5,'1106',0,'',150,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
151,5,'1107',0,'',151,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
152,5,'1108',0,'',152,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
153,5,'1109',0,'',153,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
154,5,'1110',0,'',154,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
155,5,'1111',0,'',155,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
156,5,'1112',0,'',156,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
157,5,'1113',0,'',157,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
158,5,'1114',0,'',158,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
159,5,'1115',0,'',159,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
160,5,'1116',0,'',160,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
161,5,'1117',0,'',161,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
162,5,'1118',0,'',162,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
163,5,'1119',0,'',163,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
164,5,'1120',0,'',164,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
165,5,'1121',0,'',165,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
166,5,'1122',0,'',166,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
167,5,'1123',0,'',167,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
168,5,'1124',0,'',168,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
169,5,'1125',0,'',169,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
170,5,'1126',0,'',170,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
171,5,'1127',0,'',171,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
172,5,'1128',0,'',172,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
173,5,'1129',0,'',173,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
174,5,'1130',0,'',174,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
175,35,'2000',0,'',175,'Time','Time','s or date',2,2497,1,
176,3,'2001',0,'',176,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
177,3,'2002',0,'',177,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
178,3,'2003',0,'',178,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
179,3,'2004',0,'',179,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
180,3,'2005',0,'',180,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
181,3,'2006',0,'',181,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
182,3,'2007',0,'',182,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
183,3,'2008',0,'',183,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
184,3,'2009',0,'',184,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
185,3,'2010',0,'',185,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
186,3,'2011',0,'',186,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
187,3,'2012',0,'',187,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
188,3,'2013',0,'',188,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
189,3,'2014',0,'',189,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
190,3,'2015',0,'',190,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
191,3,'2016',0,'',191,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
192,3,'2017',0,'',192,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
193,3,'2018',0,'',193,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
194,3,'2019',0,'',194,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
418,1,'BAU3',0,'',418,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
198,8,' 6.00-20.00',0,'',198,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0,
199,8,'20.00-24.00',0,'',199,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0,
200,8,' 0.00- 6.00',0,'',200,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0,
364,7,'Trips',0,'',364,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
365,7,'Trips by vehicle',0,'',365,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
366,7,'Vehicle km',0,'',366,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
367,7,'Parking lot',0,'',367,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
368,7,'Link intensity',0,'',368,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
369,7,'Vehicles',0,'',369,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
370,7,'Waiting',0,'',370,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
371,11,'Bus no change',0,'',371,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
372,11,'Bus one change',0,'',372,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
373,11,'Cab no change',0,'',373,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
374,11,'Cab one change',0,'',374,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
375,11,'Cab non-full',0,'',375,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
376,11,'Car',0,'',376,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
377,11,'No-change',0,'',377,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
378,12,'Passenger',0,'',378,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0,
379,12,'Society',0,'',379,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0,
380,13,'Car',0,'',380,'Bw1','Human body weight in Harjavalta','kg',1,2475,1,
381,13,'Composite',0,'',381,'Bw1','Human body weight in Harjavalta','kg',1,2475,1,
382,14,'Vehicle',0,'',382,'Testvariable2','Another variable for testing','kg',1,0,0,
383,14,'Driver',0,'',383,'Testvariable2','Another variable for testing','kg',1,0,0,
384,14,'Driving',0,'',384,'Testvariable2','Another variable for testing','kg',1,0,0,
385,14,'Parking',0,'',385,'Testvariable2','Another variable for testing','kg',1,0,0,
386,14,'Parking land',0,'',386,'Testvariable2','Another variable for testing','kg',1,0,0,
387,14,'Emissions',0,'',387,'Testvariable2','Another variable for testing','kg',1,0,0,
388,14,'Time',0,'',388,'Testvariable2','Another variable for testing','kg',1,0,0,
389,14,'Accidents',0,'',389,'Testvariable2','Another variable for testing','kg',1,0,0,
390,14,'Ticket',0,'',390,'Testvariable2','Another variable for testing','kg',1,0,0,
391,15,'0',0,'',391,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
392,15,'0.02',0,'',392,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
393,15,'0.05',0,'',393,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
394,15,'0.1',0,'',394,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
395,15,'0.25',0,'',395,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
396,15,'0.4',0,'',396,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
397,15,'0.45',0,'',397,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
398,15,'0.5',0,'',398,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
399,15,'0.55',0,'',399,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
400,15,'0.65',0,'',400,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
401,15,'0.75',0,'',401,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
402,15,'0.9',0,'',402,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
403,15,'1',0,'',403,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
404,16,'18-65',0,'',404,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1,
405,16,'3',0,'',405,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1,
406,17,'Harjavalta',0,'',406,'Op_en1903','Persistent pollutant concentrations in salmon','µg/kg',1,1903,1,
407,36,'Dieldrin',0,'',407,'Pollutant','Pollutant','-',2,2493,1,
408,36,'Toxaphene',0,'',408,'Pollutant','Pollutant','-',2,2493,1,
409,36,'Dioxin',0,'',409,'Pollutant','Pollutant','-',2,2493,1,
410,36,'PCB',0,'',410,'Pollutant','Pollutant','-',2,2493,1,
411,42,'Farmed salmon',0,'',411,'Environ_compartment','Environmental compartment','-',2,2490,1,
412,42,'Wild salmon',0,'',412,'Environ_compartment','Environmental compartment','-',2,2490,1,
413,42,'Market salmon',0,'',413,'Environ_compartment','Environmental compartment','-',2,2490,1,
414,33,'BAU',0,'',414,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
415,33,'More actions',0,'',415,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
416,33,'BAU2',0,'',416,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
417,33,'Restrict farmed salmon use',0,'',417,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
419,1,'More actions',0,'',419,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
421,1,'Restrict farmed salmon use2',0,'',421,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
422,34,'Cardiovascular',0,'',422,'Health_impact','Health impact','',2,2495,1,
423,10,'Home indoor',0,'Abbreviation in the Concentration database: I',423,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
424,10,'(Home) outdoor',0,'Abbreviation in the Concentration database: O',424,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
425,10,'(Personal) Work',0,'Abbreviation in the Concentration database: W',425,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
426,10,'Personal',0,'Abbreviation in the Concentration database: P',426,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
427,10,'Drinking water',0,'Abbreviation in the Concentration database: DW',427,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
428,10,'Indoor dust',0,'Abbreviation in the Concentration database: ID',428,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
429,10,'Human',0,'Abbreviation in the Concentration database: H',429,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
430,10,'Soil',0,'Abbreviation in the Concentration database: S',430,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
431,10,'Beverage',0,'Abbreviation in the Concentration database: B',431,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
432,10,'Food',0,'Abbreviation in the Concentration database: F',432,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
433,10,'In-Vehicle',0,'Abbreviation in the Concentration database: IV',433,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
434,10,'School',0,'Abbreviation in the Concentration database: SC',434,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
435,5,'Athens',0,'Country: Greece. Abbreviation in the Concentration Database: A',435,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
436,5,'Antwerp',0,'Country: Belgium. Abbreviation in the Concentration Database: ANT',436,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
437,5,'Antioch-Pittsburg',0,'Country: USA. Abbreviation in the Concentration Database: AP',437,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
438,5,'Antioch-Pittsburg A-P',0,'Country: USA. Abbreviation in the Concentration Database: A-P',438,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
439,5,'Arizona',0,'Country: USA. Abbreviation in the Concentration Database: AZ',439,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
440,5,'Basel',0,'Country: Germany. Abbreviation in the Concentration Database: B',440,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
441,5,'Baltimore',0,'Country: USA. Abbreviation in the Concentration Database: BAL',441,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
442,5,'Bayonne',0,'Country: USA. Abbreviation in the Concentration Database: BAY',442,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
443,5,'Bayonne-Ellizabeth',0,'Country: USA. Abbreviation in the Concentration Database: BE',443,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
444,5,'Copenhagen',0,'Country: Denmark. Abbreviation in the Concentration Database: C',444,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
445,5,'California',0,'Country: USA. Abbreviation in the Concentration Database: CA',445,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
446,5,'Columbus',0,'Country: USA. Abbreviation in the Concentration Database: CO',446,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
447,5,'Daegu',0,'Country: South Korea. Abbreviation in the Concentration Database: D',447,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
448,5,'Devils Lake',0,'Country: USA. Abbreviation in the Concentration Database: DLA',448,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
449,5,'Dublin',0,'Country: Ireland. Abbreviation in the Concentration Database: DU',449,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
450,5,'Elizabeth',0,'Country: USA. Abbreviation in the Concentration Database: ELI',450,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
451,5,'EPA Region 5.',0,'Country: USA. Abbreviation in the Concentration Database: EPA5',451,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
452,5,'Flanders',0,'Country: Belgium. Abbreviation in the Concentration Database: FLA',452,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
453,5,'Florence',0,'Country: Italy. Abbreviation in the Concentration Database: FL',453,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
454,5,'Grenoble',0,'Country: France. Abbreviation in the Concentration Database: G',454,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
455,5,'Germany',0,'Country: Germany. Abbreviation in the Concentration Database: GE',455,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
456,5,'Genoa',0,'Country: Italy. Abbreviation in the Concentration Database: GEN',456,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
457,5,'Greensboro GNC',0,'Country: USA. Abbreviation in the Concentration Database: GNC',457,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
458,5,'Greensboro',0,'Country: USA. Abbreviation in the Concentration Database: GRB',458,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
460,5,'Helsinki',0,'Country: Finland. Abbreviation in the Concentration Database: H',460,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
461,5,'Hannover',0,'Country: Germany. Abbreviation in the Concentration Database: HA',461,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
462,5,'Ile de France',0,'Country: France. Abbreviation in the Concentration Database: IDF',462,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
463,5,'Los Angeles',0,'Country: USA. Abbreviation in the Concentration Database: LA',463,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
464,5,'Milan',0,'Country: Italy. Abbreviation in the Concentration Database: M',464,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
465,5,'Minneapolis',0,'Country: USA. Abbreviation in the Concentration Database: MP',465,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
466,5,'Minnesota',0,'Country: USA. Abbreviation in the Concentration Database: MS',466,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
467,5,'Murcia',0,'Country: Spain. Abbreviation in the Concentration Database: MU',467,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
468,5,'Mexico City',0,'Country: Mexico. Abbreviation in the Concentration Database: MXC',468,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
469,5,'Oxford',0,'Country: England. Abbreviation in the Concentration Database: O',469,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
470,5,'Prague',0,'Country: Czech. Abbreviation in the Concentration Database: P',470,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
471,5,'Padua',0,'Country: Italy. Abbreviation in the Concentration Database: PA',471,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
472,5,'Puebla',0,'Country: Mexico. Abbreviation in the Concentration Database: PB',472,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
473,5,'Rouen',0,'Country: France. Abbreviation in the Concentration Database: R',473,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
475,5,'Strasbourg',0,'Country: France. Abbreviation in the Concentration Database: STR',475,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
476,5,'Umbria region',0,'Country: Italy. Abbreviation in the Concentration Database: UMB',476,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
477,5,'United States',0,'Country: USA. Abbreviation in the Concentration Database: USA',477,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
478,5,'Valdez',0,'Country: USA. Abbreviation in the Concentration Database: VAL',478,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
479,5,'Woodland',0,'Country: USA. Abbreviation in the Concentration Database: WDL',479,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
480,4,'66-25-1',0,'hexanal',480,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
481,4,'71-36-3',0,'1-butanol',481,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
482,4,'71-43-2',0,'benzene',482,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
483,4,'78-83-1',0,'2-methyl-1-propanol',483,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
484,4,'79-00-5',0,'1,1,2-trichloroethane',484,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
485,4,'79-01-6',0,'trichloroethene',485,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
486,4,'80-56-8',0,'alfa-pinene',486,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
487,4,'91-20-3',0,'naphtalene',487,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
488,4,'95-47-6',0,'o-xylene',488,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
489,4,'95-63-6',0,'trimethylbenzenes',489,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
490,4,'100-41-4',0,'ethylbenzene',490,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
491,4,'100-42-5',0,'styrene',491,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
492,4,'100-52-7',0,'benzaldehyde',492,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
493,4,'103-65-1',0,'propylbenzene',493,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
494,4,'104-76-7',0,'2-ethylhexanol',494,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
495,4,'108-38-3',0,'m(&p)-xylene',495,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
496,4,'108-88-3',0,'toluene',496,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
497,4,'108-95-2',0,'phenol',497,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
498,4,'110-54-3',0,'hexane',498,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
499,4,'110-82-7',0,'cyclohexane',499,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
500,4,'111-76-2',0,'ethanol, 2-butoxy-',500,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
501,4,'111-84-2',0,'nonane',501,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
502,4,'111-87-5',0,'1-octanol',502,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
503,4,'124-13-0',0,'octanal',503,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
504,4,'124-18-5',0,'decane',504,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
505,4,'127-18-4',0,'tetrachloroethene',505,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
506,4,'138-86-3',0,'d-limonene',506,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
507,4,'872-50-4',0,'2-pyrrolidinone, 1-methyl-',507,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
508,4,'1120-21-4',0,'undecane',508,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
509,4,'13466-78-9',0,'3-caren',509,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
510,4,'TVOC',0,'Toluene based total VOC',510,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
511,4,'67-66-3',0,'chloroform',511,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
512,4,'106-46-7',0,'1,4-dichlorobenzene',512,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
514,4,'56-23-5',0,'carbon tetrachloride',514,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
515,4,'75-09-2',0,'methylene chloride',515,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
517,4,'127-91-3',0,'b-pinene',517,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
520,4,'142-82-5',0,'n-heptane',520,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
521,4,'111-65-9',0,'n-octane',521,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
525,4,'112-40-3',0,'n-dodecane',525,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
526,4,'629-50-5',0,'n-tridecane',526,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
527,4,'629-59-4',0,'n-tetradecane',527,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
528,4,'629-62-9',0,'n-pentadecane',528,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
529,4,'107-83-5',0,'2-methylpentane',529,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
530,4,'96-14-0',0,'3-methylpentane',530,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
531,4,'565-59-3',0,'2,3-dimethylpentane',531,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
532,4,'591-76-4',0,'2-methylhexane',532,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
533,4,'589-34-4',0,'3-methylhexane',533,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
534,4,'592-27-8',0,'2-methylheptane',534,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
535,4,'589-81-1',0,'3-methylheptane',535,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
536,4,'96-37-7',0,'methylcyclopentane',536,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
537,4,'108-87-2',0,'methylcyclohexane',537,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
538,4,'526-73-8',0,'1,2,3-trimethylbenzene',538,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
540,4,'108-67-8',0,'1,3,5 trimethylbenzene',540,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
541,4,'4994-16-5',0,'4-phenylcyclohexene',541,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
542,4,'1,1,1-trichloroethane',0,'1,1,1-trichloroethane',542,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
545,4,'141-78-6',0,'ethylacetate',545,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
546,4,'123-86-4',0,'n-butylacetate',546,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
547,4,'78-93-3',0,'methyl ethyl ketone',547,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
548,4,'106-35-4',0,'3-heptatone',548,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
549,4,'93-58-3',0,'methyl benzoate',549,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
552,4,'123-51-3',0,'iso-amyl alcohol<sup>a</sup>',552,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
554,4,'67-63-0',0,'2-propanol<sup>a</sup>',554,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
555,4,'1634-04-4',0,'t-butyl methylether',555,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
556,4,'7439-92-1',0,'lead',556,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
557,4,'7440-38-2',0,'arsenic',557,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
558,4,'7440-43-9',0,'cadmium',558,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
559,4,'7440-39-3',0,'barium',559,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
560,4,'7440-47-3',0,'chrome',560,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
561,4,'7440-50-8',0,'copper',561,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
562,4,'7439-96-5',0,'manganese',562,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
563,4,'7440-02-0',0,'nickel',563,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
564,4,'7782-49-2',0,'selenium',564,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
565,4,'7440-62-2',0,'vanadium',565,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
566,4,'7440-66-6',0,'zinc',566,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
567,4,'71-55-6',0,'1,1,1-trichloroethane',567,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
568,4,'7439-97-6',0,'mercury',568,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
570,4,'60-27-5',0,'creatinine',570,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
571,4,'7429-90-5',0,'aluminium',571,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
572,4,'7440-70-2',0,'calcium',572,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
573,4,'7439-95-4',0,'magnesium',573,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
574,4,'7723-14-0',0,'phosphorus',574,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
575,4,'7440-24-6',0,'strontium',575,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
576,4,'7439-89-6',0,'iron',576,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
577,4,'7440-09-7',0,'potassium',577,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
578,4,'7440-23-5',0,'sodium',578,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
579,4,'58-89-9',0,'lindane',579,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
580,4,'52645-53-1',0,'permenthrine',580,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
581,4,'107-13-1',0,'acrylonitrile',581,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
582,4,'79-06-1',0,'acrylamide',582,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
589,4,'611-14-3',0,'1-ethyl 2methyl benzene',589,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
592,4,'109-66-0',0,'n-pentane',592,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
593,4,'7785-26-4',0,'alpha-pinene',593,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
594,4,'5989-27-5',0,'d-limonene',594,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
596,4,'106-99-0',0,'butadiene',596,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
597,4,'74-84-0',0,'ethane',597,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
598,4,'74-85-1',0,'ethylene',598,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
599,4,'74-86-2',0,'acetylene',599,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
600,4,'107-06-2',0,'1,2-dichloroethane',600,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
601,4,'106-42-3',0,'p-xylene',601,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
603,4,'98-82-8',0,'isopropylbenzene',603,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
604,4,'110-86-1',0,'pyridine',604,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
606,4,'109-06-8',0,'2-picoline',606,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
608,4,'108-99-6',0,'3-picoline',608,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
609,4,'108-89-4',0,'4-picoline',609,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
610,4,'104-51-8',0,'n-butylbenzene',610,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
611,4,'536-78-7',0,'3-ethylpyridine',611,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
613,4,'25551-13-7',0,'trimethylbenzene',613,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
618,4,'1336-36-3',0,'PCBs',618,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
619,4,'3547-04-4',0,'DDE',619,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
620,4,'118-74-1',0,'HCB',620,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
621,4,'5315-79-7',0,'1-hydroxypyrene',621,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
623,4,'1330-20-7',0,'xylenes',623,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
624,4,'37210-16-5',0,'CO2',624,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
625,4,'630-08-0',0,'CO',625,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
626,4,'54-11-5',0,'nicotine',626,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
628,4,'3588-17-8',0,'trans,trans-Muconic acid',628,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
629,4,'50-32-8',0,'benzo(a)pyrene',629,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
631,4,'590-86-3',0,'isovaleraldehyde',631,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
632,4,'123-38-6',0,'propionaldehyde',632,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
633,4,'123-72-8',0,'n-butyraldehyde',633,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
634,4,'75-07-0',0,'acetaldehyde',634,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
636,4,'50-00-0',0,'formaldehyde',636,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
637,4,'110-62-3',0,'valeraldehyde',637,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
638,4,'4170-30-3',0,'crotonaldehyde',638,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
639,22,'n',0,'Number of observations',639,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
640,22,'n_lt_LOQ',0,'Number of observations below level of quantitation',640,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
641,22,'F0.10',0,'Fractile 0.1',641,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
642,22,'F0.50',0,'Fractile 0.5',642,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
643,22,'F0.90',0,'Fractile 0.9',643,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
644,22,'F0.95',0,'Fractile 0.95',644,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
645,22,'Mean',0,'Arithmetic mean',645,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
646,22,'GeoMean',0,'Geometric mean',646,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
647,5,'ang',0,'Anglian Water ',647,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
648,5,'bou',0,'Bristol Water ',648,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
649,5,'brw',0,'Bournemouth & West hants ',649,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
650,5,'caw',0,'Cambridge Water ',650,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
651,5,'cho',0,'Cholderton Water ',651,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
652,5,'dcc',0,'Dee Valley Water ',652,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
653,5,'eas',0,'Welsh Water ',653,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
654,5,'ess',0,'Essex and Suffolk Water ',654,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
655,5,'fol',0,'Folkestone & Dover Water ',655,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
656,5,'har',0,'Hartlepool Water ',656,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
657,5,'mik',0,'Mid Kent Water ',657,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
658,5,'nor',0,'Northumbrian Water ',658,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
659,5,'nww',0,'Portsmouth Water ',659,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
660,5,'por',0,'Sutton & East Surrey Water ',660,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
661,5,'sea',0,'South East Water ',661,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
662,5,'sev',0,'Southern Water ',662,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
663,5,'sos',0,'South Staffordshire Water ',663,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
664,5,'sou',0,'Severn Trent Water ',664,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
665,5,'sww',0,'South West Water ',665,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
666,5,'teh',0,'Tendring Hundred Water ',666,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
667,5,'tha',0,'Thames Water ',667,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
668,5,'thr',0,'Three Valleys Water ',668,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
669,5,'wes',0,'United Utilties (North West Water) ',669,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
670,5,'wrx',0,'Wessex Water ',670,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
671,5,'yor',0,'Yorkshire Water',671,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
672,25,'BAU',0,'0.00000000000000',672,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1,
673,25,'Restrict farmed salmon use',0,'0.00000000000000',673,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1,
674,26,'BAU',0,'0.00000000000000',674,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1,
675,26,'More actions',0,'0.00000000000000',675,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1,
676,130,'Dieldrin',0,'0.00000000000000',676,'Op_en2705','Pollutant','-',6,2705,1,
677,130,'Toxaphene',0,'0.00000000000000',677,'Op_en2705','Pollutant','-',6,2705,1,
678,130,'Dioxin',0,'0.00000000000000',678,'Op_en2705','Pollutant','-',6,2705,1,
679,130,'PCB',0,'0.00000000000000',679,'Op_en2705','Pollutant','-',6,2705,1,
680,131,'Farmed salmon',0,'0.00000000000000',680,'Op_en2706','Salmon type','-',6,2706,1,
681,131,'Wild salmon',0,'0.00000000000000',681,'Op_en2706','Salmon type','-',6,2706,1,
682,131,'Market salmon',0,'0.00000000000000',682,'Op_en2706','Salmon type','-',6,2706,1,
685,133,'Cardiovascular',0,'0.00000000000000',685,'Op_en2707','Cause of death3','ICD-10',6,2707,1,
688,135,'2000',0,'0.00000000000000',688,'Op_en2708','Year3','year',6,2708,1,
689,185,'Male',0,'0.00000000000000',689,'Op_en2780','Sex','-',6,2780,1,
690,185,'Female',0,'0.00000000000000',690,'Op_en2780','Sex','-',6,2780,1,
691,186,'All causes',0,'AAA',691,'Op_en2779','Diagnosis1','-',6,2779,1,
692,186,'Infectious and parasitic diseases',0,'A00-B99',692,'Op_en2779','Diagnosis1','-',6,2779,1,
693,186,'Typhoid and paratyphoid fever',0,'A01',693,'Op_en2779','Diagnosis1','-',6,2779,1,
694,186,'Other intestinal infectious diseases',0,'A00, A02-A09',694,'Op_en2779','Diagnosis1','-',6,2779,1,
695,186,'Tuberculosis of respiratory system',0,'A15-A16',695,'Op_en2779','Diagnosis1','-',6,2779,1,
696,186,'Tuberculosis, other forms',0,'A17-A19',696,'Op_en2779','Diagnosis1','-',6,2779,1,
697,186,'Whooping cough',0,'A37',697,'Op_en2779','Diagnosis1','-',6,2779,1,
698,186,'Meningococcal infection',0,'A39',698,'Op_en2779','Diagnosis1','-',6,2779,1,
699,186,'Tetanus',0,'A35',699,'Op_en2779','Diagnosis1','-',6,2779,1,
700,186,'Septicaemia',0,'A40-A41',700,'Op_en2779','Diagnosis1','-',6,2779,1,
701,186,'Other bacterial diseases',0,'A20-A32, A36, A38, A42-49',701,'Op_en2779','Diagnosis1','-',6,2779,1,
702,186,'Measles',0,'B05',702,'Op_en2779','Diagnosis1','-',6,2779,1,
703,186,'HIV disease',0,'B20-B24',703,'Op_en2779','Diagnosis1','-',6,2779,1,
704,186,'Other viral diseases',0,'A70-A74, A80-B34, B05, B20-B24',704,'Op_en2779','Diagnosis1','-',6,2779,1,
705,186,'Malaria',0,'B50-B54',705,'Op_en2779','Diagnosis1','-',6,2779,1,
706,186,'Other arthropod-borne diseases',0,'A75-A79, B55-B57, B60, B64',706,'Op_en2779','Diagnosis1','-',6,2779,1,
707,186,'Sexually transmitted diseases',0,'A50-A64',707,'Op_en2779','Diagnosis1','-',6,2779,1,
708,186,'Other infectious and parasitic diseases',0,'A65-A69, B35-B49, B58, B59, B65-B99',708,'Op_en2779','Diagnosis1','-',6,2779,1,
709,186,'Malignant neoplasms',0,'C00-C97',709,'Op_en2779','Diagnosis1','-',6,2779,1,
710,186,'Malignant neoplasm of lip, oral cavity and pharynx',0,'C00-C14',710,'Op_en2779','Diagnosis1','-',6,2779,1,
711,186,'Malignant neoplasm of oesophagus',0,'C15',711,'Op_en2779','Diagnosis1','-',6,2779,1,
712,186,'Malignant neoplasm of stomach',0,'C16',712,'Op_en2779','Diagnosis1','-',6,2779,1,
713,186,'Malignant neoplasm of colon',0,'C18',713,'Op_en2779','Diagnosis1','-',6,2779,1,
714,186,'Malignant neoplasm of rectum, rectosigmoid junction and anus',0,'C19-C21',714,'Op_en2779','Diagnosis1','-',6,2779,1,
715,186,'Malignant neoplasm of liver',0,'C22',715,'Op_en2779','Diagnosis1','-',6,2779,1,
716,186,'Malignant neoplasm of larynx',0,'C32',716,'Op_en2779','Diagnosis1','-',6,2779,1,
717,186,'Malignant neoplasm of trachea, bronchus and lung',0,'C33-C34',717,'Op_en2779','Diagnosis1','-',6,2779,1,
718,186,'Malignant neoplasm of breast',0,'C50',718,'Op_en2779','Diagnosis1','-',6,2779,1,
719,186,'Malignant neoplasm of cervix uteri',0,'C53',719,'Op_en2779','Diagnosis1','-',6,2779,1,
720,186,'Malignant neoplasm of uterus, other and unspecified',0,'C54-C55',720,'Op_en2779','Diagnosis1','-',6,2779,1,
721,186,'Malignant neoplasm of prostate',0,'C61',721,'Op_en2779','Diagnosis1','-',6,2779,1,
722,186,'Malignant neoplasm of bladder',0,'C67',722,'Op_en2779','Diagnosis1','-',6,2779,1,
723,186,'Malignant neoplasm of other sites',0,'C17, C23-C31, C37-C49, C51, C52, C56-C60, C62-C66, C68-C80, C97',723,'Op_en2779','Diagnosis1','-',6,2779,1,
724,186,'Leukaemia',0,'C91-C95',724,'Op_en2779','Diagnosis1','-',6,2779,1,
725,186,'Other malignant neoplasms of lymphoid and haematopoietic and related tissue',0,'C81-C90, C96',725,'Op_en2779','Diagnosis1','-',6,2779,1,
726,186,'Benign neoplasm, other and unspecified neoplasm',0,'D00-D48',726,'Op_en2779','Diagnosis1','-',6,2779,1,
727,186,'Diabetes mellitus',0,'E10-E14',727,'Op_en2779','Diagnosis1','-',6,2779,1,
728,186,'Other endocrine and metabolic diseases',0,'E00-E07, E15-E34, E65-E68, E70-E88',728,'Op_en2779','Diagnosis1','-',6,2779,1,
729,186,'Malnutrition',0,'E41-E46',729,'Op_en2779','Diagnosis1','-',6,2779,1,
730,186,'Other nutritional deficiencies',0,'E40, E50-E64',730,'Op_en2779','Diagnosis1','-',6,2779,1,
731,186,'Anaemias',0,'D50-D64',731,'Op_en2779','Diagnosis1','-',6,2779,1,
732,186,'Other diseases of blood and blood-forming organs',0,'D65-D89',732,'Op_en2779','Diagnosis1','-',6,2779,1,
733,186,'Mental disorders',0,'F01-F99',733,'Op_en2779','Diagnosis1','-',6,2779,1,
734,186,'Meningitis',0,'G00, G03',734,'Op_en2779','Diagnosis1','-',6,2779,1,
735,186,'Multiple sclerosis',0,'G35',735,'Op_en2779','Diagnosis1','-',6,2779,1,
736,186,'Epilepsy',0,'G40-G41',736,'Op_en2779','Diagnosis1','-',6,2779,1,
737,186,'Other diseases of the nervous system and sense organs',0,'G04-G31, G36-G37, G43-H95',737,'Op_en2779','Diagnosis1','-',6,2779,1,
738,186,'Diseases of the circulatory system',0,'I00-I99',738,'Op_en2779','Diagnosis1','-',6,2779,1,
739,186,'Acute rheumatic fever',0,'I00-I02',739,'Op_en2779','Diagnosis1','-',6,2779,1,
740,186,'Chronic rheumatic heart disease',0,'I05-I09',740,'Op_en2779','Diagnosis1','-',6,2779,1,
741,186,'Hypertensive disease',0,'I10-I13',741,'Op_en2779','Diagnosis1','-',6,2779,1,
742,186,'Acute myocardial infarction',0,'I21, I22',742,'Op_en2779','Diagnosis1','-',6,2779,1,
743,186,'Other ischaemic heart diseases',0,'I20, I24, I25',743,'Op_en2779','Diagnosis1','-',6,2779,1,
744,186,'Diseases of pulmonary circulation and other forms of heart disease',0,'I26-I51',744,'Op_en2779','Diagnosis1','-',6,2779,1,
745,186,'Cerebrovascular disease',0,'I60-I69',745,'Op_en2779','Diagnosis1','-',6,2779,1,
746,186,'Atherosclerosis',0,'I70',746,'Op_en2779','Diagnosis1','-',6,2779,1,
747,186,'Embolism, thrombosis and other diseases of arteries, arterioles and capillaries',0,'I71-I78',747,'Op_en2779','Diagnosis1','-',6,2779,1,
748,186,'Phlebitis, thrombophlebitis, venous embolism and thrombosis',0,'I80-I82',748,'Op_en2779','Diagnosis1','-',6,2779,1,
749,186,'Other diseases of the circulatory system',0,'I83-I99',749,'Op_en2779','Diagnosis1','-',6,2779,1,
750,186,'Acute upper respiratory infection',0,'J00-J06',750,'Op_en2779','Diagnosis1','-',6,2779,1,
751,186,'Acute bronchitis and bronchiolitis',0,'J20-J21',751,'Op_en2779','Diagnosis1','-',6,2779,1,
752,186,'Pneumonia',0,'J12-J18',752,'Op_en2779','Diagnosis1','-',6,2779,1,
753,186,'Influenza',0,'J10-J11',753,'Op_en2779','Diagnosis1','-',6,2779,1,
754,186,'Bronchitis, chronic and unspecified, emphysema and asthma',0,'J40-J46',754,'Op_en2779','Diagnosis1','-',6,2779,1,
755,186,'Other diseases of the respiratory system',0,'J22, J30-J39, J47-J98',755,'Op_en2779','Diagnosis1','-',6,2779,1,
756,186,'Ulcer of stomach and duodenum',0,'K25-K27',756,'Op_en2779','Diagnosis1','-',6,2779,1,
757,186,'Appendicitis',0,'K35-K38',757,'Op_en2779','Diagnosis1','-',6,2779,1,
758,186,'Hernia of abdominal cavity and intestinal obstruction',0,'K40-K46,K56',758,'Op_en2779','Diagnosis1','-',6,2779,1,
759,186,'Chronic liver disease and cirrhosis',0,'K70,K73-K74,K76',759,'Op_en2779','Diagnosis1','-',6,2779,1,
760,186,'Other diseases of the digestive system',0,'K00-K22, K28-K31, K50-K55, K57-K66, K71, K72, K75, K80-K92',760,'Op_en2779','Diagnosis1','-',6,2779,1,
761,186,'Nephritis, nephrotic syndrome and nephrosis',0,'N00-N07, N13-N19',761,'Op_en2779','Diagnosis1','-',6,2779,1,
762,186,'Infections of kidney',0,'N10-N12',762,'Op_en2779','Diagnosis1','-',6,2779,1,
763,186,'Hyperplasia of prostate',0,'N40',763,'Op_en2779','Diagnosis1','-',6,2779,1,
764,186,'Other diseases of the genitourinary system',0,'N20-N39, N41-N98',764,'Op_en2779','Diagnosis1','-',6,2779,1,
765,186,'Abortion',0,'O00-O07',765,'Op_en2779','Diagnosis1','-',6,2779,1,
766,186,'Haemorrhage of pregnancy and childbirth',0,'O20, O46, O67, O72',766,'Op_en2779','Diagnosis1','-',6,2779,1,
767,186,'Toxaemia of pregnancy',0,'O13-O16, O21',767,'Op_en2779','Diagnosis1','-',6,2779,1,
768,186,'Complications of the puerperium',0,'O85-O92, A34',768,'Op_en2779','Diagnosis1','-',6,2779,1,
769,186,'Other direct obstetric causes',0,'O10-O12, O22-O75, O95-O97',769,'Op_en2779','Diagnosis1','-',6,2779,1,
770,186,'Indirect obstetric causes',0,'O98-O99',770,'Op_en2779','Diagnosis1','-',6,2779,1,
771,186,'Diseases of skin and subcutaneous tissue',0,'L00-L98',771,'Op_en2779','Diagnosis1','-',6,2779,1,
772,186,'Diseases of the musculoskeletal system and connective tissue',0,'M00-M99',772,'Op_en2779','Diagnosis1','-',6,2779,1,
773,186,'Spina bifida and hydrocephalus',0,'Q03,Q05',773,'Op_en2779','Diagnosis1','-',6,2779,1,
774,186,'Congenital anomalies of the circulatory system',0,'Q20-Q28',774,'Op_en2779','Diagnosis1','-',6,2779,1,
775,186,'Other congenital anomalies',0,'Q00-Q02, Q04, Q06-Q18, Q30-Q99',775,'Op_en2779','Diagnosis1','-',6,2779,1,
776,186,'Birth trauma',0,'P10-P15',776,'Op_en2779','Diagnosis1','-',6,2779,1,
777,186,'Other conditions originating in the perinatal period',0,'P00-P08, P20-P96, A33',777,'Op_en2779','Diagnosis1','-',6,2779,1,
778,186,'Senility',0,'R54',778,'Op_en2779','Diagnosis1','-',6,2779,1,
779,186,'Signs, symptoms and other ill-defined conditions',0,'R00-R53, R55-R99',779,'Op_en2779','Diagnosis1','-',6,2779,1,
780,186,'Accidents and adverse effects',0,'V01-X59, Y40-Y86, Y88',780,'Op_en2779','Diagnosis1','-',6,2779,1,
781,186,'Motor vehicle traffic accidents',0,'V02-V04, V09, V12-V14, V19-V79, V86-V89',781,'Op_en2779','Diagnosis1','-',6,2779,1,
782,186,'Other transport accidents',0,'V01, V05-V06, V10, V11, V15-V18, V80-V85, V90-V99',782,'Op_en2779','Diagnosis1','-',6,2779,1,
783,186,'Accidental poisoning',0,'X40-X49',783,'Op_en2779','Diagnosis1','-',6,2779,1,
784,186,'Accidental falls',0,'W00-W19',784,'Op_en2779','Diagnosis1','-',6,2779,1,
785,186,'Accidents caused by fire and flames',0,'X00-X09',785,'Op_en2779','Diagnosis1','-',6,2779,1,
786,186,'Accidental drowning and submersion',0,'W65-W74',786,'Op_en2779','Diagnosis1','-',6,2779,1,
787,186,'Accidents caused by machinery and by cutting and piercing instruments',0,'W24-W31',787,'Op_en2779','Diagnosis1','-',6,2779,1,
788,186,'Accidents caused by firearm missile',0,'W32-W34',788,'Op_en2779','Diagnosis1','-',6,2779,1,
789,186,'All other accidents, including late effects',0,'W20-W23, W35-W64, W75-W99, X10-X39, X50-X59, Y85, Y86',789,'Op_en2779','Diagnosis1','-',6,2779,1,
790,186,'Drugs, medicaments causing adverse effects in therapeutic use',0,'Y40-Y84, Y88',790,'Op_en2779','Diagnosis1','-',6,2779,1,
791,186,'Suicide and self- inflicted injury',0,'X60-X84',791,'Op_en2779','Diagnosis1','-',6,2779,1,
792,186,'Homicide and injury purposely inflicted by other persons',0,'X85-Y09',792,'Op_en2779','Diagnosis1','-',6,2779,1,
793,186,'Other external causes',0,'Y10-Y36, Y87, Y89',793,'Op_en2779','Diagnosis1','-',6,2779,1,
794,187,'Number',0,'0.00000000000000',794,'Op_en2784','Units1','-',6,2784,1,
795,187,'Number/100000 person-years',0,'0.00000000000000',795,'Op_en2784','Units1','-',6,2784,1,
796,188,'All ages',0,'0.00000000000000',796,'Op_en2781','Age group1','a',6,2781,1,
797,188,'< 1',0,'0.00000000000000',797,'Op_en2781','Age group1','a',6,2781,1,
798,188,'1-4',0,'0.00000000000000',798,'Op_en2781','Age group1','a',6,2781,1,
799,188,'5-14',0,'0.00000000000000',799,'Op_en2781','Age group1','a',6,2781,1,
800,188,'15-24',0,'0.00000000000000',800,'Op_en2781','Age group1','a',6,2781,1,
801,188,'25-34',0,'0.00000000000000',801,'Op_en2781','Age group1','a',6,2781,1,
802,188,'35-44',0,'0.00000000000000',802,'Op_en2781','Age group1','a',6,2781,1,
803,188,'45-54',0,'0.00000000000000',803,'Op_en2781','Age group1','a',6,2781,1,
804,188,'55-64',0,'0.00000000000000',804,'Op_en2781','Age group1','a',6,2781,1,
805,188,'65-74',0,'0.00000000000000',805,'Op_en2781','Age group1','a',6,2781,1,
806,188,'75+',0,'0.00000000000000',806,'Op_en2781','Age group1','a',6,2781,1,
807,188,'Age not specified',0,'0.00000000000000',807,'Op_en2781','Age group1','a',6,2781,1,
808,189,'Finland',0,'0.00000000000000',808,'Country1','Country1','-',6,2785,1,
809,193,'All',0,'0.00000000000000',809,'Age2','Age2','a',6,2812,1,
810,193,'0-64',0,'0.00000000000000',810,'Age2','Age2','a',6,2812,1,
811,193,'64+',0,'0.00000000000000',811,'Age2','Age2','a',6,2812,1,
812,194,'Austria',0,'0.00000000000000',812,'Country2','Country2','-',6,2813,1,
813,194,'Belgium',0,'0.00000000000000',813,'Country2','Country2','-',6,2813,1,
814,194,'Bulgaria',0,'0.00000000000000',814,'Country2','Country2','-',6,2813,1,
815,194,'Cyprus',0,'0.00000000000000',815,'Country2','Country2','-',6,2813,1,
816,194,'Czech Republic',0,'0.00000000000000',816,'Country2','Country2','-',6,2813,1,
817,194,'Denmark',0,'0.00000000000000',817,'Country2','Country2','-',6,2813,1,
818,194,'Estonia',0,'0.00000000000000',818,'Country2','Country2','-',6,2813,1,
819,194,'Finland',0,'0.00000000000000',819,'Country2','Country2','-',6,2813,1,
820,194,'France',0,'0.00000000000000',820,'Country2','Country2','-',6,2813,1,
821,194,'Germany',0,'0.00000000000000',821,'Country2','Country2','-',6,2813,1,
822,194,'Greece',0,'0.00000000000000',822,'Country2','Country2','-',6,2813,1,
823,194,'Hungary',0,'0.00000000000000',823,'Country2','Country2','-',6,2813,1,
824,194,'Ireland',0,'0.00000000000000',824,'Country2','Country2','-',6,2813,1,
825,194,'Italy',0,'0.00000000000000',825,'Country2','Country2','-',6,2813,1,
826,194,'Latvia',0,'0.00000000000000',826,'Country2','Country2','-',6,2813,1,
827,194,'Luxembourg',0,'0.00000000000000',827,'Country2','Country2','-',6,2813,1,
828,194,'Malta',0,'0.00000000000000',828,'Country2','Country2','-',6,2813,1,
829,194,'Netherlands',0,'0.00000000000000',829,'Country2','Country2','-',6,2813,1,
830,194,'Poland',0,'0.00000000000000',830,'Country2','Country2','-',6,2813,1,
831,194,'Portugal',0,'0.00000000000000',831,'Country2','Country2','-',6,2813,1,
832,194,'Romania',0,'0.00000000000000',832,'Country2','Country2','-',6,2813,1,
833,194,'Slovakia',0,'0.00000000000000',833,'Country2','Country2','-',6,2813,1,
834,194,'Slovenia',0,'0.00000000000000',834,'Country2','Country2','-',6,2813,1,
835,194,'Spain',0,'0.00000000000000',835,'Country2','Country2','-',6,2813,1,
836,194,'Sweden',0,'0.00000000000000',836,'Country2','Country2','-',6,2813,1,
837,194,'United Kingdom',0,'0.00000000000000',837,'Country2','Country2','-',6,2813,1,
838,194,'EU ',0,'0.00000000000000',838,'Country2','Country2','-',6,2813,1,
839,195,'1970',0,'0.00000000000000',839,'Year2','Year2','a',6,2814,1,
840,195,'1971',0,'0.00000000000000',840,'Year2','Year2','a',6,2814,1,
841,195,'1972',0,'0.00000000000000',841,'Year2','Year2','a',6,2814,1,
842,195,'1973',0,'0.00000000000000',842,'Year2','Year2','a',6,2814,1,
843,195,'1974',0,'0.00000000000000',843,'Year2','Year2','a',6,2814,1,
844,195,'1975',0,'0.00000000000000',844,'Year2','Year2','a',6,2814,1,
845,195,'1976',0,'0.00000000000000',845,'Year2','Year2','a',6,2814,1,
846,195,'1977',0,'0.00000000000000',846,'Year2','Year2','a',6,2814,1,
847,195,'1978',0,'0.00000000000000',847,'Year2','Year2','a',6,2814,1,
848,195,'1979',0,'0.00000000000000',848,'Year2','Year2','a',6,2814,1,
849,195,'1980',0,'0.00000000000000',849,'Year2','Year2','a',6,2814,1,
850,195,'1981',0,'0.00000000000000',850,'Year2','Year2','a',6,2814,1,
851,195,'1982',0,'0.00000000000000',851,'Year2','Year2','a',6,2814,1,
852,195,'1983',0,'0.00000000000000',852,'Year2','Year2','a',6,2814,1,
853,195,'1984',0,'0.00000000000000',853,'Year2','Year2','a',6,2814,1,
854,195,'1985',0,'0.00000000000000',854,'Year2','Year2','a',6,2814,1,
855,195,'1986',0,'0.00000000000000',855,'Year2','Year2','a',6,2814,1,
856,195,'1987',0,'0.00000000000000',856,'Year2','Year2','a',6,2814,1,
857,195,'1988',0,'0.00000000000000',857,'Year2','Year2','a',6,2814,1,
858,195,'1989',0,'0.00000000000000',858,'Year2','Year2','a',6,2814,1,
859,195,'1990',0,'0.00000000000000',859,'Year2','Year2','a',6,2814,1,
860,195,'1991',0,'0.00000000000000',860,'Year2','Year2','a',6,2814,1,
861,195,'1992',0,'0.00000000000000',861,'Year2','Year2','a',6,2814,1,
862,195,'1993',0,'0.00000000000000',862,'Year2','Year2','a',6,2814,1,
863,195,'1994',0,'0.00000000000000',863,'Year2','Year2','a',6,2814,1,
864,195,'1995',0,'0.00000000000000',864,'Year2','Year2','a',6,2814,1,
865,195,'1996',0,'0.00000000000000',865,'Year2','Year2','a',6,2814,1,
866,195,'1997',0,'0.00000000000000',866,'Year2','Year2','a',6,2814,1,
867,195,'1998',0,'0.00000000000000',867,'Year2','Year2','a',6,2814,1,
868,195,'1999',0,'0.00000000000000',868,'Year2','Year2','a',6,2814,1,
869,195,'2000',0,'0.00000000000000',869,'Year2','Year2','a',6,2814,1,
870,195,'2001',0,'0.00000000000000',870,'Year2','Year2','a',6,2814,1,
871,195,'2002',0,'0.00000000000000',871,'Year2','Year2','a',6,2814,1,
872,195,'2003',0,'0.00000000000000',872,'Year2','Year2','a',6,2814,1,
873,195,'2004',0,'0.00000000000000',873,'Year2','Year2','a',6,2814,1,
874,195,'2005',0,'0.00000000000000',874,'Year2','Year2','a',6,2814,1,
875,195,'2006',0,'0.00000000000000',875,'Year2','Year2','a',6,2814,1,
876,195,'2007',0,'0.00000000000000',876,'Year2','Year2','a',6,2814,1,
877,196,'Male',0,'0.00000000000000',877,'Sex2','Sex2','-',6,2815,1,
878,196,'Female',0,'0.00000000000000',878,'Sex2','Sex2','-',6,2815,1,
879,196,'All',0,'0.00000000000000',879,'Sex2','Sex2','-',6,2815,1,
883,207,'All causes',0,'-’',883,'Diagnosis2','Diagnosis2','ICD-10',6,2835,1,
884,207,'Infectious and parasitic diseases',0,'A00-B99’',884,'Diagnosis2','Diagnosis2','ICD-10',6,2835,1,
885,207,'Typhoid and paratyphoid fever',0,'A01’',885,'Diagnosis2','Diagnosis2','ICD-10',6,2835,1,
919,36,'ncd',1,'0.00000000000000',919,'Pollutant','Pollutant','-',2,2493,1,
920,36,'o31',2,'0.00000000000000',920,'Pollutant','Pollutant','-',2,2493,1,
921,36,'p10',3,'0.00000000000000',921,'Pollutant','Pollutant','-',2,2493,1,
922,36,'p25',4,'0.00000000000000',922,'Pollutant','Pollutant','-',2,2493,1,
923,36,'s10',5,'0.00000000000000',923,'Pollutant','Pollutant','-',2,2493,1,
924,36,'s25',6,'0.00000000000000',924,'Pollutant','Pollutant','-',2,2493,1,
925,36,'som',7,'0.00000000000000',925,'Pollutant','Pollutant','-',2,2493,1,
933,277,'AD',1,'0.00000000000000',933,'CountryID','Country identifier','-',6,2664,1,
934,278,'crops',1,'0.00000000000000',934,'Receptor','Receptor of the impact','-',6,2664,1,
935,278,'human',2,'0.00000000000000',935,'Receptor','Receptor of the impact','-',6,2664,1,
936,279,'total',1,'0.00000000000000',936,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
937,279,'potato',2,'0.00000000000000',937,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
938,279,'rice',3,'0.00000000000000',938,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
939,279,'sugar beet',4,'0.00000000000000',939,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
940,279,'sunflower seed',5,'0.00000000000000',940,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
941,279,'tobacco',6,'0.00000000000000',941,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
942,279,'wheat',7,'0.00000000000000',942,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
943,279,'adults_20',8,'0.00000000000000',943,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
944,279,'adults_27',9,'0.00000000000000',944,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
945,279,'adults_ab15',10,'0.00000000000000',945,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
946,279,'children_5_14',11,'0.00000000000000',946,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
947,279,'adults_15_64',12,'0.00000000000000',947,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
948,279,'adults_30',13,'0.00000000000000',948,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
949,279,'infants',14,'0.00000000000000',949,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
950,279,'adults_18_64',15,'0.00000000000000',950,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
951,279,'adults_65',16,'0.00000000000000',951,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
952,280,'add. fertil. needed [kg]',1,'0.00000000000000',952,'Impact','Impact in the receptor','-',6,2664,1,
953,280,'yield loss [dt]',2,'0.00000000000000',953,'Impact','Impact in the receptor','-',6,2664,1,
954,280,'Bronchodilator usage',3,'0.00000000000000',954,'Impact','Impact in the receptor','-',6,2664,1,
955,280,'chronic bronchitis',4,'0.00000000000000',955,'Impact','Impact in the receptor','-',6,2664,1,
956,280,'Lower resp. symptoms',5,'0.00000000000000',956,'Impact','Impact in the receptor','-',6,2664,1,
957,280,'Cardiac hosp.admissions',6,'0.00000000000000',957,'Impact','Impact in the receptor','-',6,2664,1,
958,280,'resp. hosp. admission',7,'0.00000000000000',958,'Impact','Impact in the receptor','-',6,2664,1,
959,280,'Restr. activity days',8,'0.00000000000000',959,'Impact','Impact in the receptor','-',6,2664,1,
960,280,'Work loss days',9,'0.00000000000000',960,'Impact','Impact in the receptor','-',6,2664,1,
961,280,'chronic’ YOLL',10,'0.00000000000000',961,'Impact','Impact in the receptor','-',6,2664,1,
962,280,'IncreasedInfantMort',11,'0.00000000000000',962,'Impact','Impact in the receptor','-',6,2664,1,
963,280,'Minor RAD',12,'0.00000000000000',963,'Impact','Impact in the receptor','-',6,2664,1,
964,280,'LRSwo cough',13,'0.00000000000000',964,'Impact','Impact in the receptor','-',6,2664,1,
965,280,'acute’ YOLL',14,'0.00000000000000',965,'Impact','Impact in the receptor','-',6,2664,1,
973,282,'Hornung, 1997',1,'0.00000000000000',973,'FunctionReference','Reference for the impact function','-',6,2664,1,
974,282,'Mills et al. 2003',2,'0.00000000000000',974,'FunctionReference','Reference for the impact function','-',6,2664,1,
975,282,'NEEDS_PPM10',3,'0.00000000000000',975,'FunctionReference','Reference for the impact function','-',6,2664,1,
976,282,'NEEDS_PPM25',4,'0.00000000000000',976,'FunctionReference','Reference for the impact function','-',6,2664,1,
977,282,'NEEDS_SIA10',5,'0.00000000000000',977,'FunctionReference','Reference for the impact function','-',6,2664,1,
978,282,'NEEDS_SIA25',6,'0.00000000000000',978,'FunctionReference','Reference for the impact function','-',6,2664,1,
979,282,'NEEDS_SOMO35',7,'0.00000000000000',979,'FunctionReference','Reference for the impact function','-',6,2664,1,
980,301,'Kiikoinen',1,'',980,'Municipality','Municipality in Finland','-',6,2664,1,
981,301,'Lavia',2,'',981,'Municipality','Municipality in Finland','-',6,2664,1,
982,301,'Mouhijärvi',3,'',982,'Municipality','Municipality in Finland','-',6,2664,1,
983,301,'Suodenniemi',4,'',983,'Municipality','Municipality in Finland','-',6,2664,1,
984,301,'Vammala',5,'',984,'Municipality','Municipality in Finland','-',6,2664,1,
985,301,'Äetsä',6,'',985,'Municipality','Municipality in Finland','-',6,2664,1,
986,302,'Kuorsumaanjärvi',1,'',986,'Lake','Lake in Finland','-',6,2664,1,
987,302,'Lavijärvi',2,'',987,'Lake','Lake in Finland','-',6,2664,1,
988,302,'Karhijärvi',3,'',988,'Lake','Lake in Finland','-',6,2664,1,
989,302,'Saarijärvi',4,'',989,'Lake','Lake in Finland','-',6,2664,1,
990,302,'Iso-Poikkelus',5,'',990,'Lake','Lake in Finland','-',6,2664,1,
991,302,'Pääjärvi',6,'',991,'Lake','Lake in Finland','-',6,2664,1,
992,302,'Kirkkojärvi',7,'',992,'Lake','Lake in Finland','-',6,2664,1,
993,302,'Kankaanjärvi',8,'',993,'Lake','Lake in Finland','-',6,2664,1,
994,302,'Valkeajärvi',9,'',994,'Lake','Lake in Finland','-',6,2664,1,
995,302,'Hurttionjärvi',10,'',995,'Lake','Lake in Finland','-',6,2664,1,
996,302,'Kulovesi',11,'',996,'Lake','Lake in Finland','-',6,2664,1,
997,302,'Rekujärvi',12,'',997,'Lake','Lake in Finland','-',6,2664,1,
998,302,'Pieni_Haukijärvi',13,'',998,'Lake','Lake in Finland','-',6,2664,1,
999,302,'Latvajärvi',14,'',999,'Lake','Lake in Finland','-',6,2664,1,
1000,302,'Murtojärvi',15,'',1000,'Lake','Lake in Finland','-',6,2664,1,
1001,302,'Miekkajärvi',16,'',1001,'Lake','Lake in Finland','-',6,2664,1,
1002,302,'Potkujärvi',17,'',1002,'Lake','Lake in Finland','-',6,2664,1,
1003,302,'Joutsijärvi',18,'',1003,'Lake','Lake in Finland','-',6,2664,1,
1004,302,'Rautavesi',19,'',1004,'Lake','Lake in Finland','-',6,2664,1,
1005,302,'Vähä-Poikelus',20,'',1005,'Lake','Lake in Finland','-',6,2664,1,
1006,302,'Iso-Lattio',21,'',1006,'Lake','Lake in Finland','-',6,2664,1,
1007,302,'Otajärvi',22,'',1007,'Lake','Lake in Finland','-',6,2664,1,
1008,302,'Ylinen/Ritajärvi',23,'',1008,'Lake','Lake in Finland','-',6,2664,1,
1009,302,'Houhajärvi',24,'',1009,'Lake','Lake in Finland','-',6,2664,1,
1010,302,'Alinen/Ritajärvi',25,'',1010,'Lake','Lake in Finland','-',6,2664,1,
1011,302,'Pitkäjärvi',26,'',1011,'Lake','Lake in Finland','-',6,2664,1,
1012,302,'Ylistenjärvi',27,'',1012,'Lake','Lake in Finland','-',6,2664,1,
1013,302,'Riippilänjärvi',28,'',1013,'Lake','Lake in Finland','-',6,2664,1,
1014,302,'Aurajärvi',29,'',1014,'Lake','Lake in Finland','-',6,2664,1,
1015,302,'Tyrisevä',30,'',1015,'Lake','Lake in Finland','-',6,2664,1,
1016,302,'Kivijärvi',31,'',1016,'Lake','Lake in Finland','-',6,2664,1,
1017,302,'Kiimajärvi',32,'',1017,'Lake','Lake in Finland','-',6,2664,1,
1018,303,'07.09.05',1,'',1018,'Date','Date of observation','date',6,2664,1,
1019,303,'13.09.05',2,'',1019,'Date','Date of observation','date',6,2664,1,
1020,303,'11.10.05',3,'',1020,'Date','Date of observation','date',6,2664,1,
1021,303,'18.07.05',4,'',1021,'Date','Date of observation','date',6,2664,1,
1022,303,'16.08.05',5,'',1022,'Date','Date of observation','date',6,2664,1,
1023,303,'06.09.05',6,'',1023,'Date','Date of observation','date',6,2664,1,
1024,303,'25.09.05',7,'',1024,'Date','Date of observation','date',6,2664,1,
1025,303,'23.08.05',8,'',1025,'Date','Date of observation','date',6,2664,1,
1026,303,'03.09.05',9,'',1026,'Date','Date of observation','date',6,2664,1,
1027,303,'04.07.05',10,'',1027,'Date','Date of observation','date',6,2664,1,
1028,303,'07.07.05',11,'',1028,'Date','Date of observation','date',6,2664,1,
1029,303,'28.07.05',12,'',1029,'Date','Date of observation','date',6,2664,1,
1030,303,'10.07.05',13,'',1030,'Date','Date of observation','date',6,2664,1,
1031,303,'11.07.05',14,'',1031,'Date','Date of observation','date',6,2664,1,
1032,303,'12.07.05',15,'',1032,'Date','Date of observation','date',6,2664,1,
1033,303,'19.07.05',16,'',1033,'Date','Date of observation','date',6,2664,1,
1034,303,'25.07.05',17,'',1034,'Date','Date of observation','date',6,2664,1,
1035,303,'27.07.05',18,'',1035,'Date','Date of observation','date',6,2664,1,
1036,303,'01.08.05',19,'',1036,'Date','Date of observation','date',6,2664,1,
1037,303,'02.08.05',20,'',1037,'Date','Date of observation','date',6,2664,1,
1038,303,'06.08.05',21,'',1038,'Date','Date of observation','date',6,2664,1,
1039,303,'09.08.05',22,'',1039,'Date','Date of observation','date',6,2664,1,
1040,303,'10.08.05',23,'',1040,'Date','Date of observation','date',6,2664,1,
1041,303,'17.08.05',24,'',1041,'Date','Date of observation','date',6,2664,1,
1042,303,'01.09.05',25,'',1042,'Date','Date of observation','date',6,2664,1,
1043,303,'27.08.05',26,'',1043,'Date','Date of observation','date',6,2664,1,
1044,303,'29.08.05',27,'',1044,'Date','Date of observation','date',6,2664,1,
1045,303,'12.09.05',28,'',1045,'Date','Date of observation','date',6,2664,1,
1046,303,'20.09.05',29,'',1046,'Date','Date of observation','date',6,2664,1,
1047,303,'28.09.05',30,'',1047,'Date','Date of observation','date',6,2664,1,
1048,303,'27.09.05',31,'',1048,'Date','Date of observation','date',6,2664,1,
1049,303,'02.10.05',32,'',1049,'Date','Date of observation','date',6,2664,1,
1050,303,'02.11.05',33,'',1050,'Date','Date of observation','date',6,2664,1,
1051,303,'03.10.05',34,'',1051,'Date','Date of observation','date',6,2664,1,
1052,303,'22.08.05',35,'',1052,'Date','Date of observation','date',6,2664,1,
1053,303,'30.08.05',36,'',1053,'Date','Date of observation','date',6,2664,1,
1054,303,'05.09.05',37,'',1054,'Date','Date of observation','date',6,2664,1,
1055,304,'Ahven',1,'',1055,'Fish','Fish species','-',6,0,5,
1056,304,'Hauki',2,'',1056,'Fish','Fish species','-',6,0,5,
1057,305,'12',1,'',1057,'Samplesize','Number of samples taken','#',6,2664,1,
1058,305,'3',2,'',1058,'Samplesize','Number of samples taken','#',6,2664,1,
1059,305,'4',3,'',1059,'Samplesize','Number of samples taken','#',6,2664,1,
1060,305,'6',4,'',1060,'Samplesize','Number of samples taken','#',6,2664,1,
1061,305,'1',5,'',1061,'Samplesize','Number of samples taken','#',6,2664,1,
1062,305,'25',6,'',1062,'Samplesize','Number of samples taken','#',6,2664,1,
1063,305,'26',7,'',1063,'Samplesize','Number of samples taken','#',6,2664,1,
1064,305,'19',8,'',1064,'Samplesize','Number of samples taken','#',6,2664,1,
1065,305,'20',9,'',1065,'Samplesize','Number of samples taken','#',6,2664,1,
1066,305,'24',10,'',1066,'Samplesize','Number of samples taken','#',6,2664,1,
1067,305,'32',11,'',1067,'Samplesize','Number of samples taken','#',6,2664,1,
1068,305,'34',12,'',1068,'Samplesize','Number of samples taken','#',6,2664,1,
1069,305,'2',13,'',1069,'Samplesize','Number of samples taken','#',6,2664,1,
1070,305,'15',14,'',1070,'Samplesize','Number of samples taken','#',6,2664,1,
1071,305,'11',15,'',1071,'Samplesize','Number of samples taken','#',6,2664,1,
1072,305,'18',16,'',1072,'Samplesize','Number of samples taken','#',6,2664,1,
1073,305,'13',17,'',1073,'Samplesize','Number of samples taken','#',6,2664,1,
1074,305,'5',18,'',1074,'Samplesize','Number of samples taken','#',6,2664,1,
1075,305,'31',19,'',1075,'Samplesize','Number of samples taken','#',6,2664,1,
1076,305,'40',20,'',1076,'Samplesize','Number of samples taken','#',6,2664,1,
1077,305,'39',21,'',1077,'Samplesize','Number of samples taken','#',6,2664,1,
1078,305,'28',22,'',1078,'Samplesize','Number of samples taken','#',6,2664,1,
1079,305,'22',23,'',1079,'Samplesize','Number of samples taken','#',6,2664,1,
1080,305,'21',24,'',1080,'Samplesize','Number of samples taken','#',6,2664,1,
1081,305,'23',25,'',1081,'Samplesize','Number of samples taken','#',6,2664,1,
1082,305,'8',26,'',1082,'Samplesize','Number of samples taken','#',6,2664,1,
1083,305,'45',27,'',1083,'Samplesize','Number of samples taken','#',6,2664,1,
1084,305,'17',28,'',1084,'Samplesize','Number of samples taken','#',6,2664,1,
1085,305,'37',29,'',1085,'Samplesize','Number of samples taken','#',6,2664,1,
1086,305,'16',30,'',1086,'Samplesize','Number of samples taken','#',6,2664,1,
1087,305,'27',31,'',1087,'Samplesize','Number of samples taken','#',6,2664,1,
1088,305,'35',32,'',1088,'Samplesize','Number of samples taken','#',6,2664,1,
1089,305,'33',33,'',1089,'Samplesize','Number of samples taken','#',6,2664,1,
1090,305,'48',34,'',1090,'Samplesize','Number of samples taken','#',6,2664,1,
1091,305,'7',35,'',1091,'Samplesize','Number of samples taken','#',6,2664,1,
1092,306,'21',1,'',1092,'Minsize','Minimum size','cm',6,2664,1,
1093,306,'31',2,'',1093,'Minsize','Minimum size','cm',6,2664,1,
1094,306,'50',3,'',1094,'Minsize','Minimum size','cm',6,2664,1,
1095,306,'25',4,'',1095,'Minsize','Minimum size','cm',6,2664,1,
1096,306,'56',5,'',1096,'Minsize','Minimum size','cm',6,2664,1,
1097,306,'10',6,'',1097,'Minsize','Minimum size','cm',6,2664,1,
1098,306,'24',7,'',1098,'Minsize','Minimum size','cm',6,2664,1,
1099,306,'14',8,'',1099,'Minsize','Minimum size','cm',6,2664,1,
1100,306,'32',9,'',1100,'Minsize','Minimum size','cm',6,2664,1,
1101,306,'11',10,'',1101,'Minsize','Minimum size','cm',6,2664,1,
1102,306,'17',11,'',1102,'Minsize','Minimum size','cm',6,2664,1,
1103,306,'12',12,'',1103,'Minsize','Minimum size','cm',6,2664,1,
1104,306,'15',13,'',1104,'Minsize','Minimum size','cm',6,2664,1,
1105,306,'9',14,'',1105,'Minsize','Minimum size','cm',6,2664,1,
1106,306,'8',15,'',1106,'Minsize','Minimum size','cm',6,2664,1,
1107,306,'165',16,'',1107,'Minsize','Minimum size','cm',6,2664,1,
1108,306,'16',17,'',1108,'Minsize','Minimum size','cm',6,2664,1,
1109,306,'37',18,'',1109,'Minsize','Minimum size','cm',6,2664,1,
1110,306,'72',19,'',1110,'Minsize','Minimum size','cm',6,2664,1,
1111,306,'19',20,'',1111,'Minsize','Minimum size','cm',6,2664,1,
1112,306,'27',21,'',1112,'Minsize','Minimum size','cm',6,2664,1,
1113,306,'13',22,'',1113,'Minsize','Minimum size','cm',6,2664,1,
1114,306,'42',23,'',1114,'Minsize','Minimum size','cm',6,2664,1,
1115,306,'7',24,'',1115,'Minsize','Minimum size','cm',6,2664,1,
1116,306,'49',25,'',1116,'Minsize','Minimum size','cm',6,2664,1,
1117,306,'44',26,'',1117,'Minsize','Minimum size','cm',6,2664,1,
1118,306,'20',27,'',1118,'Minsize','Minimum size','cm',6,2664,1,
1119,306,'46',28,'',1119,'Minsize','Minimum size','cm',6,2664,1,
1120,306,'36',29,'',1120,'Minsize','Minimum size','cm',6,2664,1,
1121,306,'39',30,'',1121,'Minsize','Minimum size','cm',6,2664,1,
1122,306,'29',31,'',1122,'Minsize','Minimum size','cm',6,2664,1,
1123,307,'30',1,'',1123,'Maxsize','Maximum size','cm',6,2664,1,
1124,307,'41',2,'',1124,'Maxsize','Maximum size','cm',6,2664,1,
1125,307,'55',3,'',1125,'Maxsize','Maximum size','cm',6,2664,1,
1126,307,'56',4,'',1126,'Maxsize','Maximum size','cm',6,2664,1,
1127,307,'15',5,'',1127,'Maxsize','Maximum size','cm',6,2664,1,
1128,307,'31',6,'',1128,'Maxsize','Maximum size','cm',6,2664,1,
1129,307,'12',7,'',1129,'Maxsize','Maximum size','cm',6,2664,1,
1130,307,'20',8,'',1130,'Maxsize','Maximum size','cm',6,2664,1,
1131,307,'54',9,'',1131,'Maxsize','Maximum size','cm',6,2664,1,
1132,307,'16',10,'',1132,'Maxsize','Maximum size','cm',6,2664,1,
1133,307,'21',11,'',1133,'Maxsize','Maximum size','cm',6,2664,1,
1134,307,'25',12,'',1134,'Maxsize','Maximum size','cm',6,2664,1,
1135,307,'19',13,'',1135,'Maxsize','Maximum size','cm',6,2664,1,
1136,307,'70',14,'',1136,'Maxsize','Maximum size','cm',6,2664,1,
1137,307,'23',15,'',1137,'Maxsize','Maximum size','cm',6,2664,1,
1138,307,'135',16,'',1138,'Maxsize','Maximum size','cm',6,2664,1,
1139,307,'22',17,'',1139,'Maxsize','Maximum size','cm',6,2664,1,
1140,307,'49',18,'',1140,'Maxsize','Maximum size','cm',6,2664,1,
1141,307,'85',19,'',1141,'Maxsize','Maximum size','cm',6,2664,1,
1142,307,'17',20,'',1142,'Maxsize','Maximum size','cm',6,2664,1,
1143,307,'48',21,'',1143,'Maxsize','Maximum size','cm',6,2664,1,
1144,307,'45',22,'',1144,'Maxsize','Maximum size','cm',6,2664,1,
1145,307,'13',23,'',1145,'Maxsize','Maximum size','cm',6,2664,1,
1146,307,'14',24,'',1146,'Maxsize','Maximum size','cm',6,2664,1,
1147,307,'44',25,'',1147,'Maxsize','Maximum size','cm',6,2664,1,
1148,307,'57',26,'',1148,'Maxsize','Maximum size','cm',6,2664,1,
1149,307,'24',27,'',1149,'Maxsize','Maximum size','cm',6,2664,1,
1150,307,'29',28,'',1150,'Maxsize','Maximum size','cm',6,2664,1,
1151,307,'46',29,'',1151,'Maxsize','Maximum size','cm',6,2664,1,
1152,307,'39',30,'',1152,'Maxsize','Maximum size','cm',6,2664,1,
1153,307,'50',31,'',1153,'Maxsize','Maximum size','cm',6,2664,1,
1154,307,'38',32,'',1154,'Maxsize','Maximum size','cm',6,2664,1,
1155,307,'58',33,'',1155,'Maxsize','Maximum size','cm',6,2664,1,
1156,307,'64',34,'',1156,'Maxsize','Maximum size','cm',6,2664,1,
1157,307,'11',35,'',1157,'Maxsize','Maximum size','cm',6,2664,1,
1158,307,'32',36,'',1158,'Maxsize','Maximum size','cm',6,2664,1,
1159,307,'67',37,'',1159,'Maxsize','Maximum size','cm',6,2664,1,
1160,308,'28',1,'',1160,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1161,308,'27',2,'',1161,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1162,308,'25',3,'',1162,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1163,308,'32',4,'',1163,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1164,308,'41',5,'',1164,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1165,308,'18',6,'',1165,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1166,308,'34',7,'',1166,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1167,308,'358',8,'',1167,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1168,308,'391',9,'',1168,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1169,308,'996',10,'',1169,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1170,308,'419',11,'',1170,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1171,308,'373',12,'',1171,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1172,308,'560',13,'',1172,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1173,308,'696',14,'',1173,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1174,308,'9',15,'',1174,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1175,308,'4',16,'',1175,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1176,308,'<15',17,'',1176,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1177,308,'290',18,'',1177,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1178,308,'451',19,'',1178,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1179,308,'496',20,'',1179,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1180,308,'1310',21,'',1180,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1181,308,'1800',22,'',1181,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1182,308,'1420',23,'',1182,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1183,308,'949',24,'',1183,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1184,308,'1940',25,'',1184,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1185,308,'124',26,'',1185,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1186,308,'1070',27,'',1186,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1187,308,'1410',28,'',1187,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1188,308,'1560',29,'',1188,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1189,308,'250',30,'',1189,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1190,308,'304',31,'',1190,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1191,308,'723',32,'',1191,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1192,308,'717',33,'',1192,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1193,308,'788',34,'',1193,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1194,308,'273',35,'',1194,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1195,308,'163',36,'',1195,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1196,308,'136',37,'',1196,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1197,308,'172',38,'',1197,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1198,308,'500',39,'',1198,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1199,308,'472',40,'',1199,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1200,308,'425',41,'',1200,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1201,308,'572',42,'',1201,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1202,308,'66',43,'',1202,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1203,308,'90',44,'',1203,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1204,308,'412',45,'',1204,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1205,308,'240',46,'',1205,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1206,308,'261',47,'',1206,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1207,308,'287',48,'',1207,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1208,308,'302',49,'',1208,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1209,308,'345',50,'',1209,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1210,308,'237',51,'',1210,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1211,308,'80',52,'',1211,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1212,308,'53',53,'',1212,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1213,308,'252',54,'',1213,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1214,308,'532',55,'',1214,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1215,308,'1150',56,'',1215,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1216,308,'158',57,'',1216,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1217,308,'211',58,'',1217,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1218,308,'355',59,'',1218,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1219,308,'889',60,'',1219,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1220,308,'37',61,'',1220,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1221,308,'56',62,'',1221,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1222,308,'101',63,'',1222,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1223,308,'146',64,'',1223,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1224,308,'149',65,'',1224,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1225,308,'193',66,'',1225,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1226,308,'15',67,'',1226,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1227,308,'39',68,'',1227,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1228,308,'1350',69,'',1228,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1229,308,'1030',70,'',1229,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1230,308,'48',71,'',1230,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1231,308,'116',72,'',1231,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1232,308,'130',73,'',1232,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1233,308,'31',74,'',1233,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1234,308,'63',75,'',1234,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1235,308,'40',76,'',1235,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
1236,309,'33',1,'',1236,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1237,309,'32',2,'',1237,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1238,309,'36',3,'',1238,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1239,309,'35',4,'',1239,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1240,309,'14',5,'',1240,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1241,309,'31',6,'',1241,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1242,309,'357',7,'',1242,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1243,309,'390',8,'',1243,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1244,309,'1075',9,'',1244,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1245,309,'422',10,'',1245,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1246,309,'367',11,'',1246,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1247,309,'554',12,'',1247,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1248,309,'724',13,'',1248,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1249,309,'3',14,'',1249,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1250,309,'5',15,'',1250,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1251,309,'6',16,'',1251,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1252,309,'286',17,'',1252,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1253,309,'482',18,'',1253,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1254,309,'512',19,'',1254,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1255,309,'1266',20,'',1255,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1256,309,'1709',21,'',1256,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1257,309,'1416',22,'',1257,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1258,309,'945',23,'',1258,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1259,309,'1852',24,'',1259,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1260,309,'123',25,'',1260,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1261,309,'1066',26,'',1261,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1262,309,'1437',27,'',1262,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1263,309,'1587',28,'',1263,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1264,309,'262',29,'',1264,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1265,309,'303',30,'',1265,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1266,309,'721',31,'',1266,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1267,309,'737',32,'',1267,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1268,309,'811',33,'',1268,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1269,309,'282',34,'',1269,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1270,309,'172',35,'',1270,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1271,309,'146',36,'',1271,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1272,309,'178',37,'',1272,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1273,309,'500',38,'',1273,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1274,309,'444',39,'',1274,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1275,309,'575',40,'',1275,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1276,309,'71',41,'',1276,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1277,309,'92',42,'',1277,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1278,309,'435',43,'',1278,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1279,309,'257',44,'',1279,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1280,309,'275',45,'',1280,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1281,309,'271',46,'',1281,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1282,309,'307',47,'',1282,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1283,309,'354',48,'',1283,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
1361,54,'Samplesize',1,' ',1361,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1362,54,'Minsize',2,' ',1362,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1363,54,'Maxsize',3,' ',1363,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1364,54,'137Cs_Bq/kgtpVammala',4,' ',1364,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1365,54,'137Cs_Bq/kgtpSTUK',5,' ',1365,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1366,54,'Hg_mg/kgtp',6,' ',1366,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1367,48,'All',1,' ',1367,'Age','Age','a',2,2497,1,
1368,48,'-64',2,' ',1368,'Age','Age','a',2,2497,1,
1369,48,'65+',3,' ',1369,'Age','Age','a',2,2497,1,
1370,422,'Austria',1,' ',1370,'Country','Country of observation','-',6,2664,1,
1371,422,'Belgium',2,' ',1371,'Country','Country of observation','-',6,2664,1,
1372,422,'Bulgaria',3,' ',1372,'Country','Country of observation','-',6,2664,1,
1373,422,'Cyprus',4,' ',1373,'Country','Country of observation','-',6,2664,1,
1374,422,'Czech Republic',5,' ',1374,'Country','Country of observation','-',6,2664,1,
1375,422,'Denmark',6,' ',1375,'Country','Country of observation','-',6,2664,1,
1376,422,'Estonia',7,' ',1376,'Country','Country of observation','-',6,2664,1,
1377,422,'Finland',8,' ',1377,'Country','Country of observation','-',6,2664,1,
1378,422,'France',9,' ',1378,'Country','Country of observation','-',6,2664,1,
1379,422,'Germany',10,' ',1379,'Country','Country of observation','-',6,2664,1,
1380,422,'Greece',11,' ',1380,'Country','Country of observation','-',6,2664,1,
1381,422,'Hungary',12,' ',1381,'Country','Country of observation','-',6,2664,1,
1382,422,'Ireland',13,' ',1382,'Country','Country of observation','-',6,2664,1,
1383,422,'Italy',14,' ',1383,'Country','Country of observation','-',6,2664,1,
1384,422,'Latvia',15,' ',1384,'Country','Country of observation','-',6,2664,1,
1385,422,'Luxembourg',16,' ',1385,'Country','Country of observation','-',6,2664,1,
1386,422,'Malta',17,' ',1386,'Country','Country of observation','-',6,2664,1,
1387,422,'Netherlands',18,' ',1387,'Country','Country of observation','-',6,2664,1,
1388,422,'Poland',19,' ',1388,'Country','Country of observation','-',6,2664,1,
1389,422,'Portugal',20,' ',1389,'Country','Country of observation','-',6,2664,1,
1390,422,'Romania',21,' ',1390,'Country','Country of observation','-',6,2664,1,
1391,422,'Slovakia',22,' ',1391,'Country','Country of observation','-',6,2664,1,
1392,422,'Slovenia',23,' ',1392,'Country','Country of observation','-',6,2664,1,
1393,422,'Spain',24,' ',1393,'Country','Country of observation','-',6,2664,1,
1394,422,'Sweden',25,' ',1394,'Country','Country of observation','-',6,2664,1,
1395,422,'United Kingdom',26,' ',1395,'Country','Country of observation','-',6,2664,1,
1396,422,'EU ',27,' ',1396,'Country','Country of observation','-',6,2664,1,
1397,423,'1970',1,' ',1397,'Year','Year of observation','a',6,2664,1,
1398,423,'1971',2,' ',1398,'Year','Year of observation','a',6,2664,1,
1399,423,'1972',3,' ',1399,'Year','Year of observation','a',6,2664,1,
1400,423,'1973',4,' ',1400,'Year','Year of observation','a',6,2664,1,
1401,423,'1974',5,' ',1401,'Year','Year of observation','a',6,2664,1,
1402,423,'1975',6,' ',1402,'Year','Year of observation','a',6,2664,1,
1403,423,'1976',7,' ',1403,'Year','Year of observation','a',6,2664,1,
1404,423,'1977',8,' ',1404,'Year','Year of observation','a',6,2664,1,
1405,423,'1978',9,' ',1405,'Year','Year of observation','a',6,2664,1,
1406,423,'1979',10,' ',1406,'Year','Year of observation','a',6,2664,1,
1407,423,'1980',11,' ',1407,'Year','Year of observation','a',6,2664,1,
1408,423,'1981',12,' ',1408,'Year','Year of observation','a',6,2664,1,
1409,423,'1982',13,' ',1409,'Year','Year of observation','a',6,2664,1,
1410,423,'1983',14,' ',1410,'Year','Year of observation','a',6,2664,1,
1411,423,'1984',15,' ',1411,'Year','Year of observation','a',6,2664,1,
1412,423,'1985',16,' ',1412,'Year','Year of observation','a',6,2664,1,
1413,423,'1986',17,' ',1413,'Year','Year of observation','a',6,2664,1,
1414,423,'1987',18,' ',1414,'Year','Year of observation','a',6,2664,1,
1415,423,'1988',19,' ',1415,'Year','Year of observation','a',6,2664,1,
1416,423,'1989',20,' ',1416,'Year','Year of observation','a',6,2664,1,
1417,423,'1990',21,' ',1417,'Year','Year of observation','a',6,2664,1,
1418,423,'1991',22,' ',1418,'Year','Year of observation','a',6,2664,1,
1419,423,'1992',23,' ',1419,'Year','Year of observation','a',6,2664,1,
1420,423,'1993',24,' ',1420,'Year','Year of observation','a',6,2664,1,
1421,423,'1994',25,' ',1421,'Year','Year of observation','a',6,2664,1,
1422,423,'1995',26,' ',1422,'Year','Year of observation','a',6,2664,1,
1423,423,'1996',27,' ',1423,'Year','Year of observation','a',6,2664,1,
1424,423,'1997',28,' ',1424,'Year','Year of observation','a',6,2664,1,
1425,423,'1998',29,' ',1425,'Year','Year of observation','a',6,2664,1,
1426,423,'1999',30,' ',1426,'Year','Year of observation','a',6,2664,1,
1427,423,'2000',31,' ',1427,'Year','Year of observation','a',6,2664,1,
1428,423,'2001',32,' ',1428,'Year','Year of observation','a',6,2664,1,
1429,423,'2002',33,' ',1429,'Year','Year of observation','a',6,2664,1,
1430,423,'2003',34,' ',1430,'Year','Year of observation','a',6,2664,1,
1431,423,'2004',35,' ',1431,'Year','Year of observation','a',6,2664,1,
1432,423,'2005',36,' ',1432,'Year','Year of observation','a',6,2664,1,
1433,423,'2006',37,' ',1433,'Year','Year of observation','a',6,2664,1,
1434,423,'2007',38,' ',1434,'Year','Year of observation','a',6,2664,1,
1435,424,'Male',1,' ',1435,'Sex','Sex of a person','-',6,2664,1,
1436,424,'Female',2,' ',1436,'Sex','Sex of a person','-',6,2664,1,
1437,424,'All',3,' ',1437,'Sex','Sex of a person','-',6,2664,1,
1438,54,'Morbidity',1,' ',1438,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1455,422,'Liechtenstein',17,'',1455,'Country','Country of observation','-',6,2664,1,
1456,422,'Lithuania',18,'',1456,'Country','Country of observation','-',6,2664,1,
1467,480,'1. Combustion installations',1,'',1467,'CITL sector','CITL sector','-',6,2664,1,
1468,480,'2. Mineral oil refineries',2,'',1468,'CITL sector','CITL sector','-',6,2664,1,
1469,480,'3. Coke ovens',3,'',1469,'CITL sector','CITL sector','-',6,2664,1,
1470,480,'4. Metal ore roasting or sintering',4,'',1470,'CITL sector','CITL sector','-',6,2664,1,
1471,480,'5. Pig iron or steel',5,'',1471,'CITL sector','CITL sector','-',6,2664,1,
1472,480,'6. Cement clinker or lime',6,'',1472,'CITL sector','CITL sector','-',6,2664,1,
1473,480,'7. Glass including glass fibre',7,'',1473,'CITL sector','CITL sector','-',6,2664,1,
1474,480,'8. Ceramic products by firing',8,'',1474,'CITL sector','CITL sector','-',6,2664,1,
1475,480,'9. Pulp, paper and board',9,'',1475,'CITL sector','CITL sector','-',6,2664,1,
1476,480,'99. Other activity opted-in',10,'',1476,'CITL sector','CITL sector','-',6,2664,1,
1477,481,'Allocated allowances',1,'',1477,'CITL_information','CITL information','-',6,2664,1,
1478,481,'Surrendered allowances',2,'',1478,'CITL_information','CITL information','-',6,2664,1,
1479,481,'Verified emissions',3,'',1479,'CITL_information','CITL information','-',6,2664,1,
1483,423,'2008',4,'',1483,'Year','Year of observation','a',6,2664,1,
1484,54,'ETS data',1,'',1484,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1488,493,'All causes',1,'',1488,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1489,493,'Infectious and parasitic diseases',2,'',1489,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1490,493,'Typhoid and paratyphoid fever',3,'',1490,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1491,493,'Other intestinal infectious diseases',4,'',1491,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1492,493,'Tuberculosis of respiratory system',5,'',1492,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1493,493,'Tuberculosis other forms',6,'',1493,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1494,493,'Whooping cough',7,'',1494,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1495,493,'Meningococcal infection',8,'',1495,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1496,493,'Tetanus',9,'',1496,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1497,493,'Septicaemia',10,'',1497,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1498,493,'Other bacterial diseases',11,'',1498,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1499,493,'Measles',12,'',1499,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1500,493,'HIV disease',13,'',1500,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1501,493,'Other viral diseases',14,'',1501,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1502,493,'Malaria',15,'',1502,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1503,493,'Other arthropod-borne diseases',16,'',1503,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1504,493,'Sexually transmitted diseases',17,'',1504,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1505,493,'Other infectious and parasitic diseases',18,'',1505,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1506,493,'Malignant neoplasms',19,'',1506,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1507,493,'Malignant neoplasm of lip oral cavity and pharynx',20,'',1507,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1508,493,'Malignant neoplasm of oesophagus',21,'',1508,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1509,493,'Malignant neoplasm of stomach',22,'',1509,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1510,493,'Malignant neoplasm of colon',23,'',1510,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1511,493,'Malignant neoplasm of rectum rectosigmoid junction and anus',24,'',1511,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1512,493,'Malignant neoplasm of liver',25,'',1512,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1513,493,'Malignant neoplasm of larynx',26,'',1513,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1514,493,'Malignant neoplasm of trachea bronchus and lung',27,'',1514,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1515,493,'Malignant neoplasm of breast',28,'',1515,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1516,493,'Malignant neoplasm of cervix uteri',29,'',1516,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1517,493,'Malignant neoplasm of uterus other and unspecified',30,'',1517,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1518,493,'Malignant neoplasm of prostate',31,'',1518,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1519,493,'Malignant neoplasm of bladder',32,'',1519,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1520,493,'Malignant neoplasm of other sites',33,'',1520,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1521,493,'Leukaemia',34,'',1521,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1522,493,'Other malignant neoplasms of lymphoid and haematopoietic and related tissue',35,'',1522,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1523,493,'Benign neoplasm other and unspecified neoplasm',36,'',1523,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1524,493,'Diabetes mellitus',37,'',1524,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1525,493,'Other endocrine and metabolic diseases',38,'',1525,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1526,493,'Malnutrition',39,'',1526,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1527,493,'Other nutritional deficiencies',40,'',1527,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1528,493,'Anaemias',41,'',1528,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1529,493,'Other diseases of blood and blood-forming organs',42,'',1529,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1530,493,'Mental disorders',43,'',1530,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1531,493,'Meningitis',44,'',1531,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1532,493,'Multiple sclerosis',45,'',1532,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1533,493,'Epilepsy',46,'',1533,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1534,493,'Other diseases of the nervous system and sense organs',47,'',1534,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1535,493,'Diseases of the circulatory system',48,'',1535,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1536,493,'Acute rheumatic fever',49,'',1536,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1537,493,'Chronic rheumatic heart disease',50,'',1537,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1538,493,'Hypertensive disease',51,'',1538,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1539,493,'Acute myocardial infarction',52,'',1539,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1540,493,'Other ischaemic heart diseases',53,'',1540,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1541,493,'Diseases of pulmonary circulation and other forms of heart disease',54,'',1541,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1542,493,'Cerebrovascular disease',55,'',1542,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1543,493,'Atherosclerosis',56,'',1543,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1544,493,'Embolism thrombosis and other diseases of arteries arterioles and capillaries',57,'',1544,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1545,493,'Phlebitis thrombophlebitis venous embolism and thrombosis',58,'',1545,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1546,493,'Other diseases of the circulatory system',59,'',1546,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1547,493,'Acute upper respiratory infection',60,'',1547,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1548,493,'Acute bronchitis and bronchiolitis',61,'',1548,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1549,493,'Pneumonia',62,'',1549,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1550,493,'Influenza',63,'',1550,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1551,493,'Bronchitis chronic and unspecified emphysema and asthma',64,'',1551,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1552,493,'Other diseases of the respiratory system',65,'',1552,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1553,493,'Ulcer of stomach and duodenum',66,'',1553,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1554,493,'Appendicitis',67,'',1554,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1555,493,'Hernia of abdominal cavity and intestinal obstruction',68,'',1555,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1556,493,'Chronic liver disease and cirrhosis',69,'',1556,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1557,493,'Other diseases of the digestive system',70,'',1557,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1558,493,'Nephritis nephrotic syndrome and nephrosis',71,'',1558,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1559,493,'Infections of kidney',72,'',1559,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1560,493,'Hyperplasia of prostate',73,'',1560,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1561,493,'Other diseases of the genitourinary system',74,'',1561,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1562,493,'Abortion',75,'',1562,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1563,493,'Haemorrhage of pregnancy and childbirth',76,'',1563,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1564,493,'Toxaemia of pregnancy',77,'',1564,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1565,493,'Complications of the puerperium',78,'',1565,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1566,493,'Other direct obstetric causes',79,'',1566,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1567,493,'Indirect obstetric causes',80,'',1567,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1568,493,'Diseases of skin and subcutaneous tissue',81,'',1568,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1569,493,'Diseases of the musculoskeletal system and connective tissue',82,'',1569,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1570,493,'Spina bifida and hydrocephalus',83,'',1570,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1571,493,'Congenital anomalies of the circulatory system',84,'',1571,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1572,493,'Other congenital anomalies',85,'',1572,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1573,493,'Birth trauma',86,'',1573,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1574,493,'Other conditions originating in the perinatal period',87,'',1574,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1575,493,'Senility',88,'',1575,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1576,493,'Signs symptoms and other ill-defined conditions',89,'',1576,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1577,493,'Accidents and adverse effects',90,'',1577,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1578,493,'Motor vehicle traffic accidents',91,'',1578,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1579,493,'Other transport accidents',92,'',1579,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1580,493,'Accidental poisoning',93,'',1580,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1581,493,'Accidental falls',94,'',1581,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1582,493,'Accidents caused by fire and flames',95,'',1582,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1583,493,'Accidental drowning and submersion',96,'',1583,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1584,493,'Accidents caused by machinery and by cutting and piercing instruments',97,'',1584,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1585,493,'Accidents caused by firearm missile',98,'',1585,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1586,493,'All other accidents including late effects',99,'',1586,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1587,493,'Drugs medicaments causing adverse effects in therapeutic use',100,'',1587,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1588,493,'Suicide and self- inflicted injury',101,'',1588,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1589,493,'Homicide and injury purposely inflicted by other persons',102,'',1589,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1590,493,'Other external causes',103,'',1590,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1591,48,'All Ages',1,'',1591,'Age','Age','a',2,2497,1,
1592,48,'Under 1',2,'',1592,'Age','Age','a',2,2497,1,
1593,48,'1 to 4',3,'',1593,'Age','Age','a',2,2497,1,
1594,48,'5 to 14',4,'',1594,'Age','Age','a',2,2497,1,
1595,48,'15 to 24',5,'',1595,'Age','Age','a',2,2497,1,
1596,48,'25 to 34',6,'',1596,'Age','Age','a',2,2497,1,
1597,48,'35 to 44',7,'',1597,'Age','Age','a',2,2497,1,
1598,48,'45 to 54',8,'',1598,'Age','Age','a',2,2497,1,
1599,48,'55 to 64',9,'',1599,'Age','Age','a',2,2497,1,
1600,48,'65 to 74',10,'',1600,'Age','Age','a',2,2497,1,
1601,48,'Over 75',11,'',1601,'Age','Age','a',2,2497,1,
1603,496,'# deaths',1,'',1603,'Parameter1','’Parameter','# or 1/100000 py',6,2664,1,
1604,496,'Mortality',2,'',1604,'Parameter1','’Parameter','# or 1/100000 py',6,2664,1,
1605,422,'Seychelles',1,'',1605,'Country','Country of observation','-',6,2664,1,
1606,422,'Brunei Darussalam',2,'',1606,'Country','Country of observation','-',6,2664,1,
1621,493,'1000',1,'',1621,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1622,493,'1001',2,'',1622,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1623,493,'1002',3,'',1623,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1624,493,'1003',4,'',1624,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1625,493,'1004',5,'',1625,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1626,493,'1005',6,'',1626,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1627,493,'1006',7,'',1627,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1628,493,'1007',8,'',1628,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1629,493,'1008',9,'',1629,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1630,493,'1009',10,'',1630,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1631,493,'1010',11,'',1631,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1632,493,'1011',12,'',1632,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1633,493,'1012',13,'',1633,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1634,493,'1013',14,'',1634,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1635,493,'1014',15,'',1635,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1636,493,'1015',16,'',1636,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1637,493,'1016',17,'',1637,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1638,493,'1017',18,'',1638,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1639,493,'1018',19,'',1639,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1640,493,'1019',20,'',1640,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1641,493,'1020',21,'',1641,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1642,493,'1021',22,'',1642,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1643,493,'1022',23,'',1643,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1644,493,'1023',24,'',1644,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1645,493,'1024',25,'',1645,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1646,493,'1025',26,'',1646,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1647,493,'1026',27,'',1647,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1648,493,'1027',28,'',1648,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1649,493,'1028',29,'',1649,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1650,493,'1029',30,'',1650,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1651,493,'1030',31,'',1651,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1652,493,'1031',32,'',1652,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1653,493,'1032',33,'',1653,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1654,493,'1033',34,'',1654,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1655,493,'1034',35,'',1655,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1656,493,'1035',36,'',1656,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1657,493,'1036',37,'',1657,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1658,493,'1037',38,'',1658,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1659,493,'1038',39,'',1659,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1660,493,'1039',40,'',1660,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1661,493,'1040',41,'',1661,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1662,493,'1041',42,'',1662,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1663,493,'1042',43,'',1663,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1664,493,'1043',44,'',1664,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1665,493,'1044',45,'',1665,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1666,493,'1045',46,'',1666,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1667,493,'1046',47,'',1667,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1668,493,'1047',48,'',1668,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1669,493,'1048',49,'',1669,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1670,493,'1049',50,'',1670,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1671,493,'1050',51,'',1671,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1672,493,'1051',52,'',1672,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1673,493,'1052',53,'',1673,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1674,493,'1053',54,'',1674,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1675,493,'1054',55,'',1675,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1676,493,'1055',56,'',1676,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1677,493,'1056',57,'',1677,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1678,493,'1057',58,'',1678,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1679,493,'1058',59,'',1679,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1680,493,'1059',60,'',1680,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1681,493,'1060',61,'',1681,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1682,493,'1061',62,'',1682,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1683,493,'1062',63,'',1683,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1684,493,'1063',64,'',1684,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1685,493,'1064',65,'',1685,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1686,493,'1065',66,'',1686,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1687,493,'1066',67,'',1687,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1688,493,'1067',68,'',1688,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1689,493,'1068',69,'',1689,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1690,493,'1069',70,'',1690,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1691,493,'1070',71,'',1691,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1692,493,'1071',72,'',1692,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1693,493,'1072',73,'',1693,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1694,493,'1073',74,'',1694,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1695,493,'1074',75,'',1695,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1696,493,'1075',76,'',1696,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1697,493,'1076',77,'',1697,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1698,493,'1077',78,'',1698,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1699,493,'1078',79,'',1699,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1700,493,'1079',80,'',1700,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1701,493,'1080',81,'',1701,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1702,493,'1081',82,'',1702,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1703,493,'1082',83,'',1703,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1704,493,'1083',84,'',1704,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1705,493,'1084',85,'',1705,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1706,493,'1085',86,'',1706,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1707,493,'1086',87,'',1707,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1708,493,'1087',88,'',1708,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1709,493,'1088',89,'',1709,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1710,493,'1089',90,'',1710,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1711,493,'1090',91,'',1711,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1712,493,'1091',92,'',1712,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1713,493,'1092',93,'',1713,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1714,493,'1093',94,'',1714,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1715,493,'1094',95,'',1715,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1716,493,'1095',96,'',1716,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1717,493,'1096',97,'',1717,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1718,493,'1097',98,'',1718,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1719,493,'1098',99,'',1719,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1720,493,'1099',100,'',1720,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1721,493,'1100',101,'',1721,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1722,493,'1101',102,'',1722,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1723,493,'1102',103,'',1723,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1724,493,'1103',104,'',1724,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
1729,48,'1-4',1,'',1729,'Age','Age','a',2,2497,1,
1730,48,'5-14',2,'',1730,'Age','Age','a',2,2497,1,
1731,48,'15-24',3,'',1731,'Age','Age','a',2,2497,1,
1732,48,'25-34',4,'',1732,'Age','Age','a',2,2497,1,
1733,48,'35-44',5,'',1733,'Age','Age','a',2,2497,1,
1734,48,'45-54',6,'',1734,'Age','Age','a',2,2497,1,
1735,48,'55-64',7,'',1735,'Age','Age','a',2,2497,1,
1736,48,'65-74',8,'',1736,'Age','Age','a',2,2497,1,
1737,48,'75 &+',9,'',1737,'Age','Age','a',2,2497,1,
1738,48,'Unknown',10,'',1738,'Age','Age','a',2,2497,1,
1740,48,'0',12,'',1740,'Age','Age','a',2,2497,1,
1741,48,'1',13,'',1741,'Age','Age','a',2,2497,1,
1742,48,'2',14,'',1742,'Age','Age','a',2,2497,1,
1743,48,'3',15,'',1743,'Age','Age','a',2,2497,1,
1744,48,'4',16,'',1744,'Age','Age','a',2,2497,1,
1745,48,'5-9',17,'',1745,'Age','Age','a',2,2497,1,
1746,48,'10-14',18,'',1746,'Age','Age','a',2,2497,1,
1747,48,'15-19',19,'',1747,'Age','Age','a',2,2497,1,
1748,48,'20-24',20,'',1748,'Age','Age','a',2,2497,1,
1749,48,'25-29',21,'',1749,'Age','Age','a',2,2497,1,
1750,48,'30-34',22,'',1750,'Age','Age','a',2,2497,1,
1751,48,'35-39',23,'',1751,'Age','Age','a',2,2497,1,
1752,48,'40-44',24,'',1752,'Age','Age','a',2,2497,1,
1753,48,'45-49',25,'',1753,'Age','Age','a',2,2497,1,
1754,48,'50-54',26,'',1754,'Age','Age','a',2,2497,1,
1755,48,'55-59',27,'',1755,'Age','Age','a',2,2497,1,
1756,48,'60-64',28,'',1756,'Age','Age','a',2,2497,1,
1757,48,'65-69',29,'',1757,'Age','Age','a',2,2497,1,
1758,48,'70-74',30,'',1758,'Age','Age','a',2,2497,1,
1759,48,'75-79',31,'',1759,'Age','Age','a',2,2497,1,
1760,48,'80-84',32,'',1760,'Age','Age','a',2,2497,1,
1761,48,'85 &+',33,'',1761,'Age','Age','a',2,2497,1,
1763,54,'Deaths',1,'',1763,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1764,36,'PM',1,'',1764,'Pollutant','Pollutant','-',2,2493,1,
1765,36,'CO2',2,'',1765,'Pollutant','Pollutant','-',2,2493,1,
1766,43,'Bus',1,'',1766,'Vehicle_type','Vehicle type','-',2,0,0,
1767,43,'Minibus',2,'',1767,'Vehicle_type','Vehicle type','-',2,0,0,
1768,43,'Car (d)',3,'',1768,'Vehicle_type','Vehicle type','-',2,0,0,
1769,43,'Car (g)',4,'',1769,'Vehicle_type','Vehicle type','-',2,0,0,
1770,539,'Injuries',1,'',1770,'Accidents','Accident type','-',6,2664,1,
1771,539,'Deaths',2,'',1771,'Accidents','Accident type','-',6,2664,1,
1775,567,' 6.00-20.00',1,'',1775,'Period1','Different times of day','h',6,2664,1,
1776,567,'20.00-24.00',2,'',1776,'Period1','Different times of day','h',6,2664,1,
1777,567,' 0.00- 6.00',3,'',1777,'Period1','Different times of day','h',6,2664,1,
1778,570,'0',1,'',1778,'Hour','Hour of day','h',6,2664,1,
1779,570,'1',2,'',1779,'Hour','Hour of day','h',6,2664,1,
1780,570,'2',3,'',1780,'Hour','Hour of day','h',6,2664,1,
1781,570,'3',4,'',1781,'Hour','Hour of day','h',6,2664,1,
1782,570,'4',5,'',1782,'Hour','Hour of day','h',6,2664,1,
1783,570,'5',6,'',1783,'Hour','Hour of day','h',6,2664,1,
1784,570,'6',7,'',1784,'Hour','Hour of day','h',6,2664,1,
1785,570,'7',8,'',1785,'Hour','Hour of day','h',6,2664,1,
1786,570,'8',9,'',1786,'Hour','Hour of day','h',6,2664,1,
1787,570,'9',10,'',1787,'Hour','Hour of day','h',6,2664,1,
1788,570,'10',11,'',1788,'Hour','Hour of day','h',6,2664,1,
1789,570,'11',12,'',1789,'Hour','Hour of day','h',6,2664,1,
1790,570,'12',13,'',1790,'Hour','Hour of day','h',6,2664,1,
1791,570,'13',14,'',1791,'Hour','Hour of day','h',6,2664,1,
1792,570,'14',15,'',1792,'Hour','Hour of day','h',6,2664,1,
1793,570,'15',16,'',1793,'Hour','Hour of day','h',6,2664,1,
1794,570,'16',17,'',1794,'Hour','Hour of day','h',6,2664,1,
1795,570,'17',18,'',1795,'Hour','Hour of day','h',6,2664,1,
1796,570,'18',19,'',1796,'Hour','Hour of day','h',6,2664,1,
1797,570,'19',20,'',1797,'Hour','Hour of day','h',6,2664,1,
1798,570,'20',21,'',1798,'Hour','Hour of day','h',6,2664,1,
1799,570,'21',22,'',1799,'Hour','Hour of day','h',6,2664,1,
1800,570,'22',23,'',1800,'Hour','Hour of day','h',6,2664,1,
1801,570,'23',24,'',1801,'Hour','Hour of day','h',6,2664,1,
1802,54,'Fraction',1,'',1802,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
1803,422,'BE',1,'',1803,'Country','Country of observation','-',6,2664,1,
1804,422,'BG',2,'',1804,'Country','Country of observation','-',6,2664,1,
1805,422,'CZ',3,'',1805,'Country','Country of observation','-',6,2664,1,
1806,422,'DK',4,'',1806,'Country','Country of observation','-',6,2664,1,
1807,422,'DE',5,'',1807,'Country','Country of observation','-',6,2664,1,
1808,422,'EE',6,'',1808,'Country','Country of observation','-',6,2664,1,
1809,422,'IE',7,'',1809,'Country','Country of observation','-',6,2664,1,
1810,422,'EL',8,'',1810,'Country','Country of observation','-',6,2664,1,
1811,422,'ES',9,'',1811,'Country','Country of observation','-',6,2664,1,
1812,422,'FR',10,'',1812,'Country','Country of observation','-',6,2664,1,
1813,422,'IT',11,'',1813,'Country','Country of observation','-',6,2664,1,
1814,422,'CY',12,'',1814,'Country','Country of observation','-',6,2664,1,
1815,422,'LV',13,'',1815,'Country','Country of observation','-',6,2664,1,
1816,422,'LT',14,'',1816,'Country','Country of observation','-',6,2664,1,
1817,422,'LU',15,'',1817,'Country','Country of observation','-',6,2664,1,
1818,422,'HU',16,'',1818,'Country','Country of observation','-',6,2664,1,
1819,422,'MT',17,'',1819,'Country','Country of observation','-',6,2664,1,
1820,422,'NL',18,'',1820,'Country','Country of observation','-',6,2664,1,
1821,422,'AT',19,'',1821,'Country','Country of observation','-',6,2664,1,
1822,422,'PL',20,'',1822,'Country','Country of observation','-',6,2664,1,
1823,422,'PT',21,'',1823,'Country','Country of observation','-',6,2664,1,
1824,422,'RO',22,'',1824,'Country','Country of observation','-',6,2664,1,
1825,422,'SI',23,'',1825,'Country','Country of observation','-',6,2664,1,
1826,422,'SK',24,'',1826,'Country','Country of observation','-',6,2664,1,
1827,422,'FI',25,'',1827,'Country','Country of observation','-',6,2664,1,
1828,422,'SE',26,'',1828,'Country','Country of observation','-',6,2664,1,
1829,422,'UK',27,'',1829,'Country','Country of observation','-',6,2664,1,
1884,609,'BAU',1,'',1884,'Pesticide_regulation','Pesticide regulation in Europe','-',6,2664,1,
1885,609,'Strict regulation',2,'',1885,'Pesticide_regulation','Pesticide regulation in Europe','-',6,2664,1,
1968,611,'BAU',1,'',1968,'Meat_consumption','Meat consumption in Europe','-',6,2664,1,
1969,611,'Low',2,'',1969,'Meat_consumption','Meat consumption in Europe','-',6,2664,1,
1970,612,'Beef',1,'',1970,'Animal','Farm animal species','-',6,2664,1,
1971,612,'Pork',2,'',1971,'Animal','Farm animal species','-',6,2664,1,
1972,612,'Poultry',3,'',1972,'Animal','Farm animal species','-',6,2664,1,
1973,612,'Sheep',4,'',1973,'Animal','Farm animal species','-',6,2664,1,
2093,613,'Urine',1,'',2093,'Waste','Livestock waste type','-',6,2664,1,
2094,613,'Manure',2,'',2094,'Waste','Livestock waste type','-',6,2664,1,
2097,54,'B1',1,'',2097,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
2098,637,'A2',1,'',2098,'A1','Testindex','-',6,2664,1,
2099,637,'A3',2,'',2099,'A1','Testindex','-',6,2664,1,
2156,665,'Dieldrin',1,'',2156,'Pesticide','','',6,2664,1,
2159,610,'BAU',1,'',2159,'Irrigation_practices','Irrigation practices of agricultural land in Europe','-',6,2664,1,
2160,610,'Efficient',2,'',2160,'Irrigation_practices','Irrigation practices of agricultural land in Europe','-',6,2664,1,
2161,671,'BAU',1,'',2161,'Biofuels','Pesticide regulation in Europe','-',6,2664,1,
2162,671,'20 % of traffic fuels',2,'',2162,'Biofuels','Pesticide regulation in Europe','-',6,2664,1,
2163,682,'item 1',1,'',2163,'Testindex','Testindex','kg',6,2664,1,
2176,493,'AAA',1,'',2176,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2177,493,'A00-B99 ',2,'',2177,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2178,493,'A00 ',3,'',2178,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2179,493,'A09 ',4,'',2179,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2180,493,'A01-A08 ',5,'',2180,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2181,493,'A15-A16 ',6,'',2181,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2182,493,'A17-A19 ',7,'',2182,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2183,493,'A20 ',8,'',2183,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2184,493,'A33-A35 ',9,'',2184,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2185,493,'A36 ',10,'',2185,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2186,493,'A37 ',11,'',2186,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2187,493,'A39 ',12,'',2187,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2188,493,'A40-A41 ',13,'',2188,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2189,493,'A50-A64 ',14,'',2189,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2190,493,'A80 ',15,'',2190,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2191,493,'A82 ',16,'',2191,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2192,493,'A95 ',17,'',2192,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2193,493,'A90-A94, A96-A99 ',18,'',2193,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2194,493,'B05 ',19,'',2194,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2195,493,'B15-B19 ',20,'',2195,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2196,493,'B20-B24 ',21,'',2196,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2197,493,'B50-B54 ',22,'',2197,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2198,493,'B55 ',23,'',2198,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2199,493,'B56-B57 ',24,'',2199,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2200,493,'B65 ',25,'',2200,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2201,493,'A21-A32, A38, A42-A49, A65-A79, A81, A83-A89, B00-B04, B06-B09, B25-B49, B58-B64, B66-B94, B99 ',26,'',2201,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2202,493,'C00-D48 ',27,'',2202,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2203,493,'C00-C14 ',28,'',2203,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2204,493,'C15 ',29,'',2204,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2205,493,'C16 ',30,'',2205,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2206,493,'C18-C21 ',31,'',2206,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2207,493,'C22 ',32,'',2207,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2208,493,'C25 ',33,'',2208,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2209,493,'C32 ',34,'',2209,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2210,493,'C33-C34 ',35,'',2210,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2211,493,'C43 ',36,'',2211,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2212,493,'C50 ',37,'',2212,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2213,493,'C53 ',38,'',2213,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2214,493,'C54-C55 ',39,'',2214,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2215,493,'C56 ',40,'',2215,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2216,493,'C61 ',41,'',2216,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2217,493,'C67 ',42,'',2217,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2218,493,'C70-C72 ',43,'',2218,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2219,493,'C82-C85 ',44,'',2219,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2220,493,'C90 ',45,'',2220,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2221,493,'C91-C95 ',46,'',2221,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2222,493,'C17, C23-C24, C26-C31, C37-C41, C44-C49, C51-C52, C57-C60, C62-C66,C68-C69,C73-C81,C88,C96-C97 ',47,'',2222,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2223,493,'D00-D48 ',48,'',2223,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2224,493,'D50-D89 ',49,'',2224,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2225,493,'D50-D64 ',50,'',2225,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2226,493,'D65-D89 ',51,'',2226,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2227,493,'E00-E88 ',52,'',2227,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2228,493,'E10-E14 ',53,'',2228,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2229,493,'E40-E46 ',54,'',2229,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2230,493,'E00-E07, E15-E34, E50-E88 ',55,'',2230,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2231,493,'F01-F99 ',56,'',2231,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2232,493,'F10-F19 ',57,'',2232,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2233,493,'F20-F99 ',58,'',2233,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2234,493,'G00-G98 ',59,'',2234,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2235,493,'G00, G03 ',60,'',2235,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2236,493,'G30 ',61,'',2236,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2237,493,'G04-G25, G31-G98 ',62,'',2237,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2238,493,'H00-H57 ',63,'',2238,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2239,493,'H60-H93 ',64,'',2239,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2240,493,'I00-I99 ',65,'',2240,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2241,493,'I00-I09 ',66,'',2241,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2242,493,'I10-I13 ',67,'',2242,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2243,493,'I20-I25 ',68,'',2243,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2244,493,'I26-I51 ',69,'',2244,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2245,493,'I60-I69 ',70,'',2245,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2246,493,'I70 ',71,'',2246,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2247,493,'I71-I99 ',72,'',2247,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2248,493,'J00-J98 ',73,'',2248,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2249,493,'J10-J11 ',74,'',2249,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2250,493,'J12-J18 ',75,'',2250,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2251,493,'J20-J22 ',76,'',2251,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2252,493,'J40-J47 ',77,'',2252,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2253,493,'J00-J06, J30-J39, J60-J98 ',78,'',2253,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2254,493,'K00-K92 ',79,'',2254,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2255,493,'K25-K27 ',80,'',2255,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2256,493,'K70-K76 ',81,'',2256,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2257,493,'K00-K22, K28-K66, K80-K92 ',82,'',2257,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2258,493,'L00-L98 ',83,'',2258,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2259,493,'M00-M99 ',84,'',2259,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2260,493,'N00-N98 ',85,'',2260,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2261,493,'N00-N15 ',86,'',2261,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2262,493,'N17-N98 ',87,'',2262,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2263,493,'O00-O99 ',88,'',2263,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2264,493,'O00-O07 ',89,'',2264,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2265,493,'O10-O92 ',90,'',2265,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2266,493,'O98-O99 ',91,'',2266,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2267,493,'O95-O97 ',92,'',2267,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2268,493,'P00-P96 ',93,'',2268,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2269,493,'Q00-Q99 ',94,'',2269,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2270,493,'R00-R99 ',95,'',2270,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2271,493,'V01-Y89 ',96,'',2271,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2272,493,'V01-V99 ',97,'',2272,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2273,493,'W00-W19 ',98,'',2273,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2274,493,'W65-W74 ',99,'',2274,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2275,493,'X00-X09 ',100,'',2275,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2276,493,'X40-X49 ',101,'',2276,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2277,493,'X60-X84 ',102,'',2277,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2278,493,'X85-Y09 ',103,'',2278,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2279,493,'W20-W64, W75-W99, X10-X39, X50-X59, Y10-Y89 ',104,'',2279,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
2282,48,'0-365 days',1,'',2282,'Age','Age','a',2,2497,1,
2283,48,'0 day',2,'',2283,'Age','Age','a',2,2497,1,
2284,48,'1-6 days',3,'',2284,'Age','Age','a',2,2497,1,
2285,48,'7-27 days',4,'',2285,'Age','Age','a',2,2497,1,
2286,48,'28-365 days',5,'',2286,'Age','Age','a',2,2497,1
)280,96,148,131,1,1,1,1,1,0,0,0,02,370,45,476,4452,518,523,725,303,0,MIDM2,404,34,750,516,0,MIDM39325,65535,39321[L_j,L_i][L_j,L_i]ObjThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.Table(O_i,O_j)(
1,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1,
2,'Op_en2693','Testvariable','kg',1,2693,1,
3,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1,
4,'Op_en2205','Bus engine technology','see wiki page',1,2205,1,
5,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1,
6,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0,
7,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1,
8,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0,
9,'Fig_3_cost_by_source','Cost by source','e/trip',1,0,0,
10,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0,
11,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0,
12,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0,
13,'Bw1','Human body weight in Harjavalta','kg',1,2475,1,
14,'Testvariable2','Another variable for testing','kg',1,0,0,
15,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0,
16,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1,
17,'Op_en1903','Persistent pollutant concentrations in salmon','µg/kg',1,1903,1,
18,'Op_en1905','Exposure to persistent pollutants due to salmon in the population of the Western Europe','µg/kg/d',1,1905,1,
19,'Op_en1906','Dose-response function of persistent pollutants','(mg/kg/d)-1',1,1906,1,
20,'Op_en1907','Omega-3 content in salmon','g/g',1,1907,1,
21,'Op_en1908','Omega-3 intake due to salmon in the population of the Western Europe','g/d',1,1908,1,
22,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1,
23,'Op_en1911','Cardiovascular mortality in the Western Europe','cases/a',1,1911,1,
24,'Op_en1912','Cardiovascular effects of omega-3 in salmon in teh Western Europe','avoided cases/a',1,1912,1,
25,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1,
26,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1,
27,'Op_en1902','Persistent pollutant concentrations in fish feed','fraction',1,1902,1,
28,'Op_en1904','Salmon intake in the population of the Western Europe','g/d',1,1904,1,
29,'Op_en1909','ERF of omega-3 fatty acids on cardiovascular effects','1/(g/d)',1,1909,1,
30,'Op_en2556','Personal exposures to volatile organic compounds in Germany','ug/m^3',1,2556,1,
31,'Op_en2406','Excess cases of iMetHb in England and Wales','number',1,2406,1,
33,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1,
34,'Health_impact','Health impact','',2,2495,1,
35,'Time','Time','s or date',2,2497,1,
36,'Pollutant','Pollutant','-',2,2493,1,
37,'Spatial_location','Spatial location',' km or °',2,2498,1,
38,'Length','Length','km',2,2498,1,
39,'Non_health_impact','Non-health impact','-',2,2500,1,
40,'Period','Period','s',2,2497,1,
41,'Emission_source','Emission source','-',2,2492,1,
42,'Environ_compartment','Environmental compartment','-',2,2490,1,
43,'Vehicle_type','Vehicle type','-',2,0,0,
44,'Person_or_group','Person or group','-',2,2499,1,
45,'Transport_mode','Transport mode','-',2,0,0,
46,'Cost_type','Cost type','-',2,0,0,
47,'Composite_fraction','Composite fraction','fraction',2,0,0,
48,'Age','Age','a',2,2497,1,
49,'Municipality_fin','Municipalities in Finland','-',2,2498,1,
51,'Food_source','The method for food production','-',2,0,0,
52,'Feed_pollutant','Decision about fish feed','-',2,0,0,
53,'Salmon_recomm','Decision about samon consumption recommendation','-',2,0,0,
32,'0','No dimension has been identified','-',2,0,0,
54,'Parameter','Statistical and other parameters of a variable','-',2,0,0,
55,'Salmon_decision','','',6,0,0,
56,'Hma_area','','',6,0,0,
57,'Hma_region','','',6,0,0,
58,'Hma_zone','','',6,0,0,
59,'Year_1','','',6,0,0,
60,'Op_en2665','Cause of death 1','ICD-10',6,2665,1,
61,'Year_2','','',6,0,0,
62,'Cause_of_death_2','','',6,0,0,
63,'Length_1','','',6,0,0,
70,'Output_1','','',6,0,0,
65,'Period_1','','',6,0,0,
86,'Run','','',6,0,0,
71,'Vehicle_noch','','',6,0,0,
72,'Stakeholder_1','','',6,0,0,
73,'Mode1','','',6,0,0,
74,'Cost_structure_1','','',6,0,0,
75,'Comp_fr_1','','',6,0,0,
76,'Age1','','',6,0,0,
77,'Municipality_fin1','','',6,0,0,
82,'Year3','','',6,0,0,
81,'Recommendation1','','',6,0,0,
80,'Reg_poll','','',6,0,0,
79,'Salmon1','','',6,0,0,
78,'Pollutant1','','',6,0,0,
83,'H1899','','',6,0,0,
84,'H1898','','',6,0,0,
85,'Cause_of_death3','','',6,0,0,
87,'Condb_compartment1','','',6,0,0,
88,'Condb_location1','','',6,0,0,
89,'Condb_agent1','','',6,0,0,
90,'Condb_param1','','',6,0,0,
91,'Condb_agent2','','',6,0,0,
92,'Vehicle_1','','',6,0,0,
93,'Op_en2672','','',6,0,0,
94,'94','Analytica','',9,0,0,
95,'95','Analytica 4.1.0.9','',9,0,0,
97,'97','Analytica 4.1.0.9, CompositeTraffic_1_0_6.ana v. 11:47, 1000 iterations','',9,0,0,
99,'99','Analytica 4.1.0.9, RDB connection.ANA, 100 iterations','',9,0,0,
100,'100','RDB connection.ANA v. 1.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
101,'101','RDB connection.ANA v. 2.9.2008. Test data only., Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
102,'102','RDB connection.ANA v. 3.9.2008 b. Test data only., Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
103,'103','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
104,'104','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
105,'105','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
106,'106','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0,
107,'107','Farmed salmon.ANA 8.9.2008, RDB connection.ANA 8.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0,
108,'108','Farmed salmon.ANA 8.9.2008, RDB connection.ANA 8.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
98,'98','Test','',9,0,0,
109,'109',' CompositeTraffic_1_0_6.ANA 16.9.2008, RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0,
110,'110',' CompositeTraffic_1_0_6.ANA 16.9.2008, RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0,
111,'111','RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
112,'112','RDB connection.ANA 9.10.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0,
113,'113','RDB connection.ANA 9.10.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0,
114,'Op_en1896','Benefit-risk assessment on farmed salmon','',4,1896,1,
130,'Op_en2705','Pollutant','-',6,2705,1,
131,'Op_en2706','Salmon type','-',6,2706,1,
133,'Op_en2707','Cause of death3','ICD-10',6,2707,1,
135,'Op_en2708','Year3','year',6,2708,1,
137,'Op_en2694','Testrun 1: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 10','',9,2694,1,
159,'Op_eni1896','Benefit-risk assessment of farmed salmon','',4,0,1,
160,'Op_eni2694','Testrun 1: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 10','',9,0,1,
183,'Op_eni2695','Testrun 2: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 1000','',9,0,1,
184,'Op_en2778','Mortality in Finland','# or 1/100000 py',1,2778,1,
185,'Op_en2780','Sex','-',6,2780,1,
186,'Op_en2779','Diagnosis1','-',6,2779,1,
187,'Op_en2784','Units1','-',6,2784,1,
188,'Op_en2781','Age group1','a',6,2781,1,
189,'Country1','Country1','-',6,2785,1,
191,'Op_en2695','Testrun 2: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','',9,2695,1,
192,'Op_en2811','Morbidity data for Europe','1/100000 a',1,2811,1,
193,'Age2','Age2','a',6,2812,1,
194,'Country2','Country2','-',6,2813,1,
195,'Year2','Year2','a',6,2814,1,
196,'Sex2','Sex2','-',6,2815,1,
207,'Diagnosis2','Diagnosis2','ICD-10',6,2835,1,
203,'203','Testrun 2: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','',9,2817,1,
210,'Op_en2495','Health outcome1','-',6,2495,1,
211,'Op_en2922','Ecosense test study','Euro',7,2922,1,
212,'212.00000000000000','Country identifier','-',6,2664,1,
213,'213.00000000000000','Receptor of the impact','-',6,2664,1,
214,'214.00000000000000','Receptor subgroup','-',6,2664,1,
215,'215.00000000000000','Impact in the receptor','-',6,2664,1,
216,'216.00000000000000','Pollutant causing the impact','-',6,2664,1,
217,'217.00000000000000','Reference for the impact function','-',6,2664,1,
218,'Op_en2817','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0.00000000000000',9,2817,1,
220,'220.00000000000000','Country identifier','-',6,2664,1,
221,'221.00000000000000','Receptor of the impact','-',6,2664,1,
222,'222.00000000000000','Receptor subgroup','-',6,2664,1,
223,'223.00000000000000','Impact in the receptor','-',6,2664,1,
224,'224.00000000000000','Pollutant causing the impact','-',6,2664,1,
225,'225.00000000000000','Reference for the impact function','-',6,2664,1,
227,'227.00000000000000','Country identifier','-',6,2664,1,
228,'228.00000000000000','Receptor of the impact','-',6,2664,1,
229,'229.00000000000000','Receptor subgroup','-',6,2664,1,
230,'230.00000000000000','Impact in the receptor','-',6,2664,1,
231,'231.00000000000000','Pollutant causing the impact','-',6,2664,1,
232,'232.00000000000000','Reference for the impact function','-',6,2664,1,
234,'234.00000000000000','Country identifier','-',6,2664,1,
235,'235.00000000000000','Receptor of the impact','-',6,2664,1,
236,'236.00000000000000','Receptor subgroup','-',6,2664,1,
237,'237.00000000000000','Impact in the receptor','-',6,2664,1,
238,'238.00000000000000','Pollutant causing the impact','-',6,2664,1,
239,'239.00000000000000','Reference for the impact function','-',6,2664,1,
241,'241.00000000000000','Country identifier','-',6,2664,1,
242,'242.00000000000000','Receptor of the impact','-',6,2664,1,
243,'243.00000000000000','Receptor subgroup','-',6,2664,1,
244,'244.00000000000000','Impact in the receptor','-',6,2664,1,
245,'245.00000000000000','Pollutant causing the impact','-',6,2664,1,
246,'246.00000000000000','Reference for the impact function','-',6,2664,1,
248,'248.00000000000000','Country identifier','-',6,2664,1,
249,'249.00000000000000','Receptor of the impact','-',6,2664,1,
250,'250.00000000000000','Receptor subgroup','-',6,2664,1,
251,'251.00000000000000','Impact in the receptor','-',6,2664,1,
252,'252.00000000000000','Pollutant causing the impact','-',6,2664,1,
253,'253.00000000000000','Reference for the impact function','-',6,2664,1,
255,'255.00000000000000','Country identifier','-',6,2664,1,
256,'256.00000000000000','Receptor of the impact','-',6,2664,1,
257,'257.00000000000000','Receptor subgroup','-',6,2664,1,
258,'258.00000000000000','Impact in the receptor','-',6,2664,1,
259,'259.00000000000000','Pollutant causing the impact','-',6,2664,1,
260,'260.00000000000000','Reference for the impact function','-',6,2664,1,
262,'262.00000000000000','Country identifier','-',6,2664,1,
263,'263.00000000000000','Receptor of the impact','-',6,2664,1,
264,'264.00000000000000','Receptor subgroup','-',6,2664,1,
265,'265.00000000000000','Impact in the receptor','-',6,2664,1,
266,'266.00000000000000','Pollutant causing the impact','-',6,2664,1,
267,'267.00000000000000','Reference for the impact function','-',6,2664,1,
269,'269.00000000000000','Country identifier','-',6,2664,1,
270,'270.00000000000000','Receptor of the impact','-',6,2664,1,
271,'271.00000000000000','Receptor subgroup','-',6,2664,1,
272,'272.00000000000000','Impact in the receptor','-',6,2664,1,
273,'273.00000000000000','Pollutant causing the impact','-',6,2664,1,
274,'274.00000000000000','Reference for the impact function','-',6,2664,1,
275,'275.00000000000000','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0.00000000000000',9,2817,1,
277,'CountryID','Country identifier','-',6,2664,1,
278,'Receptor','Receptor of the impact','-',6,2664,1,
279,'ReceptorSubGroup','Receptor subgroup','-',6,2664,1,
280,'Impact','Impact in the receptor','-',6,2664,1,
282,'FunctionReference','Reference for the impact function','-',6,2664,1,
283,'283.00000000000000','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0.00000000000000',9,2817,1,
291,'291','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
299,'299','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
300,'Op_fi1669','Cesium-137 elintarvikkeissa','Bq or mg/kg f.w.',7,1669,2,
301,'Municipality','Municipality in Finland','-',6,2664,1,
302,'Lake','Lake in Finland','-',6,2664,1,
303,'Date','Date of observation','date',6,2664,1,
304,'Fish','Fish species','-',6,0,5,
305,'Samplesize','Number of samples taken','#',6,2664,1,
306,'Minsize','Minimum size','cm',6,2664,1,
307,'Maxsize','Maximum size','cm',6,2664,1,
308,'137Cs_Bq/kgtpVammala','Concentration of Cs-137 in sample, measured by Vammala','Bq/kg f.w.',6,2664,1,
309,'137Cs_Bq/kgtpSTUK','Concentration of Cs-137 in sample, measured by STUK','Bq/kg f.w.',6,2664,1,
310,'Hg_mg/kgtp','Concentration of methyl mercury','mg/kg f.w.',6,2664,1,
311,'311','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
323,'323','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
335,'335','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
347,'347','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
359,'359','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
371,'371','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
383,'383','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
395,'395','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
407,'407','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
419,'419','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
422,'Country','Country of observation','-',6,2664,1,
423,'Year','Year of observation','a',6,2664,1,
424,'Sex','Sex of a person','-',6,2664,1,
425,'Morbidity','Morbidity of a person','1/100000 py',6,2664,1,
426,'426','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
433,'433','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
434,'Op_fi2811','Morbidity in Europe','Bq or mg/kg f.w.',7,2811,1,
440,'440','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
447,'447','Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','0',9,2817,1,
448,'Op_fi2818','Morbidity in Europe','a',1,2818,2,
453,'453','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
459,'459','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
465,'465','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
471,'471','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
477,'477','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
478,'Op_en2943','EU ETS data from CITL','tonne of CO2-equ',1,2943,1,
480,'CITL sector','CITL sector','-',6,2664,1,
481,'CITL_information','CITL information','-',6,2664,1,
483,'483','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
489,'489','Analytica ADE Optimizer, (Windows), Version: 40.2K, Samplesize: 100','',9,2817,1,
493,'Diagnosis','Diagnosis','ICD-10',6,2664,1,
496,'Parameter1','’Parameter','# or 1/100000 py',6,2664,1,
497,'497','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
504,'504','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
511,'511','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
518,'518','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
519,'Op_en2987','Costs of unit emissions of air pollutants','e/kg',1,2987,1,
521,'521','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
527,'527','Analytica ADE Optimizer, (Windows), Version: 4.02e+004, Samplesize: 100','',9,2817,1,
529,'Op_en2988','Unit cost of driving','e/km',1,2988,1,
530,'Op_en2989','Emission factors of cars on air pollutants','g/km',1,2989,1,
533,'533','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
537,'Op_en2990','Traffic accidents in the Helsinki metropolitan area’','#/a',1,2990,1,
539,'Accidents','Accident type','-',6,2664,1,
541,'541','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
549,'549','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
557,'557','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
558,'Op_en2995','Total amount of car kilometres driven in the Helsinki metropolitan area','km/d',1,2995,1,
559,'559','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
561,'561','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
563,'563','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
565,'565','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
567,'Period1','Different times of day','h',6,2664,1,
568,'568','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
569,'Op_en2999','Population time-activity patterns','fraction',1,2999,1,
570,'Hour','Hour of day','h',6,2664,1,
571,'571','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 2','',9,2817,1,
574,'574','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 2','',9,2817,1,
577,'577','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 2','',9,2817,1,
580,'580','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 2','',9,2817,1,
581,'Op_en3003','Type of a random car','-',1,3003,1,
582,'582','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','-',9,2817,1,
585,'585.00000000000000','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
588,'588','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 1000','',9,2817,1,
589,'Op_en3010','Neurodevelopmental effect of nitrate','# cases/a',1,3010,1,
591,'591','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
593,'Op_en3011','Cancer due to pesticides in Europe','# cases/a',1,3011,1,
594,'Op_en3012','Gastric infections in Europe','# cases/a',1,3012,1,
595,'Op_en3013','Pesticide regulation in Europe','-',1,3013,1,
596,'Op_en3014','Irrigation practices of agricultural land in Europe','-',1,3014,1,
597,'Op_en3015','Biofuel production in Europe','-',1,3015,1,
598,'Op_en3016','Meat consumption in Europe','-',1,3016,1,
599,'Op_en3017','Population in Europe','#',1,3017,1,
600,'Op_en3018','Number of farm animals in Europe','#',1,3018,1,
601,'Op_en3019','Fertilizer use in Europe','ton/a',1,3019,1,
602,'Op_en3020','Pesticide use in Europe','kg/a',1,3020,1,
603,'Op_en3021','Pesticide exposure in Europe','mg/kg/a',1,3021,1,
604,'Op_en3022','ERF of pesticides on cancer','1 per (mg/kg/d)',1,3022,1,
605,'Op_en3023','Livestock wastes in Europe','ton/a',1,3023,1,
606,'Op_en3024','Nitrate leaching and run-off in Europe','ton/a',1,3024,1,
607,'Op_en3025','Livestock waste leaching and run-off in Europe','ton/a',1,3025,1,
609,'Pesticide_regulation','Pesticide regulation in Europe','-',6,2664,1,
610,'Irrigation_practices','Irrigation practices of agricultural land in Europe','-',6,2664,1,
611,'Meat_consumption','Meat consumption in Europe','-',6,2664,1,
612,'Animal','Farm animal species','-',6,2664,1,
613,'Waste','Livestock waste type','-',6,2664,1,
614,'614','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 200','',9,2817,1,
632,'632','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 200','',9,2817,1,
635,'635','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
637,'A1','Testindex','-',6,2664,1,
638,'638','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
641,'641','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
644,'644','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
647,'647','Analytica ADE Optimizer, (Windows), Version: 40.2K, Samplesize: 100','',9,2817,1,
665,'Pesticide','','',6,2664,1,
666,'666','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 200','',9,2817,1,
671,'Biofuels','Pesticide regulation in Europe','-',6,2664,1,
675,'675','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 200','',9,2817,1,
678,'B1','Testindex','-',6,2664,1,
679,'C1','Testindex','-',6,2664,1,
680,'680','Analytica ADE Optimizer, (Windows), Version: 40.2K, Samplesize: 100','',9,2817,1,
682,'Testindex','Testindex','kg',6,2664,1,
683,'683','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
686,'686','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
689,'689','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
692,'692','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
695,'695','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
698,'698','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
701,'701','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
704,'704','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 100','',9,2817,1,
707,'707','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 10','',9,2817,1,
710,'710','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 10','',9,2817,1,
713,'713','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 10','',9,2817,1,
720,'720','Analytica Enterprise, (Windows), Version: 40.1K, Samplesize: 10','',9,2817,1
)280,48,148,131,1,1,1,1,1,0,0,0,02,378,21,493,5012,152,162,1057,343,0,MIDM2,573,21,700,421,0,MIDM39325,65535,39321[O_j,O_i][O_j,O_i]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]SettThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.Table(S_i,S_j)(
1,33,1,
2,37,1,
5,35,1,
6,34,1,
9,38,1,
10,39,1,
11,40,1,
12,32,1,
13,43,1,
14,44,1,
15,45,1,
16,46,1,
17,47,1,
18,48,1,
19,49,1,
23,51,1,
24,36,1,
28,42,1,
31,54,1,
35,114,3,
38,137,9,
37,114,4,
39,159,3,
41,160,9,
44,183,9,
47,191,9,
46,184,4,
45,184,3
)280,72,148,131,1,1,1,1,1,0,0,0,02,378,21,493,5012,529,143,700,421,0,MIDM39325,65535,39321[S_j,S_i][S_i,S_j]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]ItemThis node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information.Table(It_i,It_j)(
1,1,55,0,
2,2,56,0,
3,2,57,0,
4,2,58,0,
5,5,59,0,
6,6,60,0,
7,5,61,0,
8,6,62,0,
9,9,63,0,
10,11,65,0,
11,10,70,0,
12,13,71,0,
13,14,72,0,
14,15,73,0,
15,16,74,0,
16,17,75,0,
17,18,76,0,
18,19,77,0,
19,24,78,0,
20,23,79,0,
21,1,80,0,
22,1,81,0,
23,5,82,0,
24,1,83,0,
25,1,84,0,
26,6,85,0,
27,12,86,0,
28,28,87,0,
29,2,88,0,
30,24,89,0,
31,31,90,0,
32,24,91,0,
33,13,92,0,
34,2,93,0,
35,35,28,0
)280,120,148,131,1,1,1,1,1,0,0,0,02,378,21,493,5012,529,143,700,421,0,MIDM39325,65535,39321[It_j,It_i][It_i,It_j]['H1991'][Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1]AssessmentDO NOT REMOVE THIS NODE. It is needed for computing the Objects node.ktluser29. Decta 2008 21:51 48,24168,256,152,121,11,11,550,300,17(var, table)Write1if size(var)>0 then appendtablesql(var,var.i, var.j, table&' ')56,368,148,132,284,58,476,224var,table''168,280,052,121,1,1,1,1,1,0,0,0,02,163,375,476,224[Formnode Writerpsswd1]52425,39321,65535PlatformChoice(Self,2,False,1)320,328,148,12[Formnode Platform1]52425,39321,65535['Lumina AWP','THL computer']study_ident;
n_indices;
n_parameters;
n_variables56,400,148,121,1,1,1,1,1,0,0,0,0Platform0176,44,1160,121,0,0,1,0,0,0,142,0,152425,39321,65535PlatformWriterpsswd0176,68,1160,121,0,0,1,0,0,0,142,0,152425,39321,65535WriterpsswdTable format for inputjtue30. huhta 2009 12:39 48,24120,568,148,32Number of observations0200,60,1152,121,0,0,1,0,0,0,72,0,152425,39321,65535N_observationsObservations0200,84,1152,121,0,0,1,0,0,0,72,0,152425,39321,65535Observations# of observed parameters0200,36,1152,121,0,0,1,0,0,0,72,0,152425,39321,65535N_parametersFieldvar a:= 'index'&1..N_indices;
{a:= if Is_the_data_probabil='Yes' then concat(a,['Iteration']) else a;}
var b:= 'parameter'&1..N_parameters;
concat(a, b)108,152,148,122,683,44,416,303,0,MIDM[1,1,1,0]['index1','parameter1']Observationconcat(['Identifier'],'Obs'&1..N_observations)108,176,148,122,557,134,416,303,0,MIDM['Identifier','Obs1','Obs2','Obs3','Obs4','Obs5','Obs6','Obs7','Obs8','Obs9','Obs10','Obs11','Obs12','Obs13','Obs14','Obs15','Obs16','Obs17','Obs18','Obs19','Obs20','Obs21','Obs22','Obs23','Obs24']ObservationsTable(Field1,Observation)(
'Hour',0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,
'Fraction',9.056669999999999m,5.851670000000002m,4.245000000000001m,4.80833m,3.24833m,7.26m,0.02535,0.07911667,0.0853,0.0351,0.03153333,0.02771667,0.03276667,0.03183333,0.04346667,0.05795,0.10268333,0.09278333,0.06351667,0.04721667,0.03596667,0.0209,0.01215833,9.61333m
)108,128,152,162,746,25,497,657,0,MIDM2,644,62,513,572,0,MIDM[Formnode Observations1]52425,39321,65535[Field1,Observation][Field1,Observation]Data tableindex h:= observations[@observation=1, @field1=1..N_indices];
index j:= concat(h,['Parameter','Result']);
index i:= 1..(size(observation)-1)*size(parameter);
index loccell_id:= 1..(size(i)*size(h));
var conv:= if j='Result' then @parameter+N_indices else @j;
index conv2:= 1..N_observations;
var a:= observations[@field1=conv, @observation=conv2+1];
a:= if j='Parameter' then parameter else a;
a:= concatrows(a, parameter, conv2, i);112,240,148,132,102,90,482,3262,654,192,611,318,0,MIDM[Sys_localindex('I'),Sys_localindex('J')][Parameter,6,Sys_localindex('CONV2'),1,Sys_localindex('L'),1]Upload an Analytica model. This functionality does not work with the web version. It requires that you have Analytica Enterprise and an editable version of Opasnet Base Connection.152,744,-1144,801,0,0,1,0,1,0,,0,2,693,146,476,224For advanced users: you may upload whole models, or non-public data that can only be seen by people with a special permission.276,636,-1268,20424,744,-1120,801,0,0,1,0,1,0,,0,2,693,146,476,224Observations0272,480,1152,121,0,0,1,0,0,0,158,0,152425,39321,65535Observations2Copy your data table into Observations. NOTE! 1) You must have field names in the first row. 2) Paste between 'exclamation marks'.272,430,-1152,38Performance ProfilerUse this library to see which variables and functions are taking most of the computation time or memory when running your model. You need Analytica Enterprise, Power Player, or ADE - it will not work for lesser editions of Analytica. Here's how to use it:
1. First run your model, i.e. show (and therefore compute) results for the outputs you are interested in timing.
2. Click the "Performance profiles" button to display the Bytes (memory used) and CPU msecs (milliseconds of computation time by the computers "Central Processing Unit") for each object (variable or function).
NOTES:
A. Select option from "Sort objects by" to control how results are sorted.
B. It omits objects with negligible CPU time (less than 1 millisecond) AND less than 25 Bytes memory.
C. "msecs w. ancestors" means the CPU time to compute the object PLUS all its ancestors or predecessors.
D. "Bytes" shows the memory usage after computing the variable. Some objects with complex expressions may use much more memory during the computation.
E. In Performance Profiles, double-click on an Object title (in Object_ID index or its Module title to open its Object window.
If you want to add in the time for further calculations:
3. Make those calculations by showing results for those variables.
4. Click button "Update Profiles"
5. Click the "Performance profiles" button again.
If you want to time some different calculations:
6. Click "Zero out times"
7. Change relevant inputs to cause their dependents to need to be recomputed.
8. Show results for outputs of interest.
9. Click button "Update Profiles"
10. Click the "Performance profiles" button to see the new timings and memory usage.
Copyright 2007 Lumina Decision Systems, IncLonnie Chrisman & Alex GrahamSun, Jul 13, 2003 12:18 PMLonnieFri, Oct 05, 2007 11:08 AM48,24424,488,060,241,1,1,1,1,1,0,0,0,01,578,450,349,151,212,372,189,507,744Arial, 15Sort profiled objects by0168,83,1154,130,0,0,0,0,0,0,170Sort_objects_byOrdered profiles1168,112,1154,141,0,0,1,0,0,0,72,0,1Performance_profiles168,68,-1160,601,0,0,0,1,1,0,,0,Profiler detailsMax HenrionWed 03-Oct-07 8:20 AM48,24168,168,148,241,377,221,454,301,21(m: Object)Profiler_Descendants(m)Returns a list including module m and all its descendants, i.e. objects (variables, functions, and modules) contained in m - and in any modules it contains, recursively -- excluding this Profiling module.VAR res := [Handle(m)];
VAR c := contains OF m ;
IFONLY (c) = NULL OR Identifier m = Id_of_this_library THEN res
ELSE BEGIN
FOR v := c Do BEGIN
VAR d := Profiler_descendants(v);
res := Concat(res, d);
0
END;
res
END184,182,168,242,75,242,580,585m1Sort objects bySelect field to sort profiled objects. "CPU secs" or "KBytes" are most useful. "Class" doesn't work.Choice(Profiler_fields,4,0)336,248,148,242,102,90,523,465[Formnode Sort_profiled_objec1]['item 1']Profiler fields['Class','Module','Bytes','CPU msecs','msecs w ancestors']96,54,148,24['Class','Module','Bytes','CPU msecs','msecs w ancestors']Attrib name by profile fieldTable(Profiler_fields)(
'Class',0,'Memory','EvaluationTime','EvaluationTimeAll')208,55,148,312,352,26,430,218,0,MIDM(o: atomic handle)ProfileObj(o)Returns a profile (with Class, Bytes, Memory, CPU seconds, CPU seconds w ancestors) for the object with identifier o.FOR field := Profiler_fields DO
IFONLY field = 'Module' THEN (Isin OF o)
ELSE IF field = 'Bytes' THEN MemoryInUseBy(Handle(o))
ELSE (VAR attrib := Attrib_name_by_profi[Profiler_fields=field];
IF attrib ='Class' THEN (attrib OF o) ELSE (attrib OF o)*1000)336,54,156,242,432,9,541,583o()ProfileAll()Returns a profile (with Class, Bytes, CPU msecs, msecs w ancestors) for all the variables in this model whose CPU msecs>0 or Bytes > 24, excluding those in the Profiler.var top := topmodel_id;
MetaIndex allobjs := Profiler_descendants(top);
VAR allProfiles := Profileobj(allobjs);
MetaIndex Objs := Subset(allProfiles[Profiler_fields='CPU msecs'] > 0 OR
allProfiles[Profiler_fields='Bytes'] > 24 OR
allProfiles[Profiler_fields='msecs w ancestors']);
allProfiles[allobjs = Objs]336,120,156,242,438,69,525,397TopModel IDReset_profiler; VAR m := Isin OF Self;
WHILE (NOT IsUndef(Isin OF m )) DO m := Isin OF m 208,118,148,24Performance profilesShows the Class, Kbytes (memory used), CPU secs (to compute this object), CPU secs w. ancestors (time to compute this variable and all its predecessors) for all objects (variables and functions) with non-zero computation time, sorted as requested.VAR Profiles := Profileall();
var sortField := Profiles[Profiler_fields=Sort_objects_by];
if Sort_objects_by<>"Class" then sortField := -sortField;
MetaIndex object_id := SortIndex(sortField, Profiles.Objs);
Profiles[.Objs = object_id]336,182,152,242,102,90,497,4942,560,23,700,396,0,MIDM[Formnode Ordered_profiles1]{!40000|Att_graphvaluerange Performance_profiles:1,,,,,,10}
{!40000|Att_graphindexrange Profiler_fields:1,,,,,,10}
{!40000|Flip:8}
{!40000|Att_catlinestyle Graph_primary_valdim:9}[Profiler_fields,Sys_localindex('OBJECT_ID')]1,I,6,3,1,0[Profiler_fields,2,Sys_localindex('OBJECTS_IDS'),1]Reset ProfilerUsed to reset the computations for profiling.196,118,148,24ID of this libraryIdentifier Isin OF Isin OF Self 184,248,148,24Append_to_run0136,44,1120,121,0,0,1,0,0,0,72,0,152425,39321,65535Append_to_runUpload data1192,68,148,12Upload_data