File:Online collaborative models.pptx

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This is a lecture about supporting decisions with online collaborative models. It was first presented in the Denvi seminar series in Viikki, 27th May 2015. The lecture was recorded and can be found from N:\YMAL\Presentations\2015\Denvi Online collaborative models 2015-05-27.m4a.

How to support decisions with online collaborative models?

Summary of discussions, interpretations, and conclusions based on data about chemical X and disease Y
Data Hypotheses about chemical X causing disease Y
1) No effect 2) Oxidative stress 3) Receptor
Anecdote - + +
Clinical experience - + +
Structure-activity relationship + - -
Observational study ++ - -
Randomised clinical trial - + +
Multi-center clinical study - ++ ++
Chronic toxicity study in rats - + +
Review + + +
Meta-analysis + + +
Physiologically based pharmacokinetic and dynamic model - ++ +
Proteomics - ++ -
Overall plausibility of hypothesis - ++ +

This is an abstract for the EFSA conference Shaping the future of food safety, together in Milan, 14-16 October 2015 [2].

Transparent decision-making process and openness is a mega-trend. A lot of good quality scientific data is becoming openly available in national and international databases (,,, Also there is a clear societal strive to provide and use open source models and evidence-based decision making and support their open use and development.

However, models alone are not enough. Collaborative, multidisciplinary practices are needed to solve relevant societal problems or questions. The processes must be reliable, reproducible, and transparent to support these studies effectively and efficiently. Shared practices, tools, data, working environments and concerted actions are the way forward to improve science and decision support.

Practices are needed for mutual communication: experts answering policy questions in a defendable and useful way rather than just pushing out data; decision makers more clearly explaining their views using evidence; the focus being on end-users. ICT tools support this transformation.

Open data and open models change the way we think about evidence-based decision making: it is collaborative, dynamic information collection work that produces a quantitative description of the topic in the form of an online collaborative model. An outdated practice is to rely only a long chain of static information products such as scientific articles, reviews, expert reports, policy papers, and finally decision recommendations.

Such a model is more complex than just an online calculator or a piece of text written with online tools. Rather, it is a discussion forum for relevant topics aiming to resolve disputes. It is a scientific platform to present hypotheses and attempt to reject them based on data. It is a database for up-to-date quantitative estimates about important variables. It is a modelling environment optimising decision options based on the variables. It is a resource centre for re-using existing information in similar new cases. And finally, it is a forum for discussions about values and objectives that should be used to choose or reject decision options.

Platforms, tools, and practices for doing all that exist already, but they are not systematically used. I will present how it can be done. We can update our own practices as decision makers, experts and stakeholders to better utilise their potential for added value in societal decision making related to food and other topics.

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current19:55, 11 February 2016 (2.78 MB)Jouni (talk | contribs)New slides about ovariables and OpasnetUtils functionalities.
04:27, 16 October 2015 (2.5 MB)Jouni (talk | contribs)New slides about BRA of Baltic Herring for EFSA meeting 14.-16.10.2015 in Milano. Knowledge crystal concept introduced.
06:01, 27 May 2015 (1.83 MB)Jouni (talk | contribs)A few slides of the example of Climate change policies and health in Kuopio
20:27, 26 May 2015 (1.3 MB)Jouni (talk | contribs)Presentation for Denvi seminar in Helsinki, 27th May, 2015.
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