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The revolution in experimental techniques has opened the door to very large datasets with a limited number of observations. An essential challenge is to support this change by developing a culture of data expertise.
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Scientific disciplines generate a large amount of data through experiments. A large part of scientists' work consists in exploiting this data: storing it, representing it, extracting useful or important information from it, then modelling, statistical models, predictions, testing it, etc.

As these data become more and more complex, not all scientists are necessarily equipped to respond to the challenges posed by these data, which become more and more technical over time.


The DATAIA Institute offers tools to help scientists to analyze data.


To create new collaborations and boost data analysis, a new approach includes supporting the creation of challenges to identify the best strategies for a given problem and combine them to create a more effective overall model.

As part of the Center for Data Science, researchers and engineers from the Parietal team at the Inria-Saclay center launched a challenge on autism in 2018 based on images of the brains of children with or without autism.  For example, learning specialists are developing predictive models for diagnosing the disease. This challenge helped to improve the prediction score and boost the quality of the diagnosis of autism by 10%. 

In e-science, another type of approach is to establish the causal structure of phenomena rather than prediction: it is a very active branch of learning.

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