CURE-FAIR WG: Progress Update

Even as FAIR data practices become normative in the research community, FAIR data alone is not sufficient to guarantee the computational reproducibility of published research findings. Computational reproducibility (the ability to recreate computational results from the data and code used by the original researcher) is essential to preserve a complete scientific record, to verify scientific claims, to do science and build upon the findings, and to teach. Curating for reproducibility (CURE) includes activities that ensure that statistical and analytic claims about given data can be reproduced with that data. To curate for computational reproducibility requires code review and result verification. The goal of the CURE-FAIR WG is to establish guidelines and identify best practices for curating for reproducible and FAIR data and code. The ultimate objective is to improve FAIR-ness and long-term usability of “reproducible file bundles” across domains. This working group will be a focal point within RDA for guidelines and standards for curating for reproducible and FAIR data and code and engaging the RDA community on the issue. CURE-FAIR will work with aligned RDA IGs and WGs. As stated in the group’s Case Statement, final output will be presented in January 2022.