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No research is done in a void: science is constantly expanding previous hypotheses, building upon past knowledge. We live in a digital age where information is ubiquitous, yet we struggle to preserve accurate machine readable and quantitative descriptions of our research compromising our capacity to use them in our inferences. In the following talk I will show how and why we incorporate assumptions in our studies based on three experiments we have conducted: (i) dissociating metacognitive subdomains in medial and lateral anterior prefrontal cortex, (ii) relating reading comprehension to individual differences in the default mode network, and (iii) exploring neural correlates of the content and form of self-generated thoughts. This will be followed by introducing a new inference method - probabilistic Regions of Interest (pROI) - which allows the use of prior knowledge in the form of a probabilistic map. This approach provides the middle ground between ROI and full brain analysis, by giving researchers more flexibility in formalizing priors. The quality of prior probability maps based on the literature can be improved by using unthresholded statistical maps instead of peak coordinates. To facilitate this we have created NeuroVault.org - a community - wide effort to collect unthresholded statistical maps. Taking the initiative a step further I will describe the concept of data papers - publications purely dedicated to datasets. Together those three mechanisms (pROI, NeuroVault.org and data papers) are a small but significant steps towards better, more reusable and reproducible science.