A pragmatic approach to reusable research outputs

Science is an incremental process that produces and builds on more than journal articles¹. Code, data, results, or tools of previous finished or unfinished projects (research outputs) fuel new undertakings.Reusing research objects allows for reproduction, verification, and extending existing work, evidence synthesis, and minimizing duplicate efforts². The more reusable outputs are, at any stage of a project, the better. The FAIR principles³ center around richly curated metadata to reach maximal reusability. In practice, creating fully FAIR resources is difficult in systems that yet lack or fail to incentivize the necessary standards and procedures. But reusability should be improved nevertheless.We highlight four widely accessible strategies that can elevate reusability as a byproduct of pragmatic research data management, even when compliance to FAIR is not yet possible. ¹ Mons, B. (2018). Data Stewardship for Open Science: Implementing FAIR Principles. CRC Press. ISBN 9780815348184² Thanos, C. (2017). Research Data Reusability: Conceptual Foundations, Barriers and Enabling Technologies. doi.org/10.3390/publications5010002³ Wilkinson, M. D. et al., (2016). The FAIR Guiding Principles for scientific data management and stewardship. doi.org/10.1038/sdata.2016.1