Please note that this website has been used throughout the project. This page ("About") has been added after the data analysis was conducted and the corresponding manuscript was submitted for publication. The remainder of the website remained unchanged. That is, the information contained on the pages "The Project", "Data & Analysis", and "Prediction Markets" is identical to the information provided to analysis team members and prediction market traders during various phases of the project.
At the project's outset, the signed-up teams were provided with the following "instructions". To complete the team members' registration to the project, all members of the analysis team were required to sign this consent form. Once the agreement has been signed by all team members, they were provided with access to the raw data.
Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed.
We put in substantial efforts to achieve full transparency and reproducibility of our findings. All materials and analysis code of this project are available for download and fully reproducible. A full description of the experimental procedures, validations, and the dataset is available in a Data Descriptor (Botvinik-Nezer et al. (2019), Scientific Data). We strongly believe that such practices are essential for scientific impact and credibility.