Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10...

Data sharing holds promise for advancing and accelerating science by facilitating and fostering collaboration, reproducibility and optimal use of sparse resources. We argue that despite the existence of general data sharing guidelines (e.g, FAIR-principles), their translation and implementation requires field-specific considerations. Here, we addressed this timely question for the field of experimental research on fear and anxiety and showcase the enormous prospects by illustrating the wealth and richness of a curated data collection of publicly available datasets using the fear conditioning paradigm based on 103 studies and 8839 participants. We highlight challenges encountered when aiming to reuse the available data corpus and derive 10 simple steps for making data sharing in the field more efficient and sustainable and hence facilitating collaboration, cumulative knowledge generation and large scale mega-, meta- and psychometric analyses. We share our vision and first steps towards transforming such curated data collections into a homogenized and dynamically growing database allowing for easy contributions and for living analysis tools for the collective benefit of the research community.