Computational reproducibility: A simplified framework for data curators

Phrases like the 'data deluge' and the 'reproducibility crisis' may serve to further the impression that data curation is hard and that research data management is "basically fighting against chaos" (Briney, 2019). If trying to manage research data is chaotic, then the management of computationally-derived data presents an even bigger challenge due to the multiplicity of operating systems, coding languages, dependencies, and file types. This is exacerbated by the reality that most researchers and librarians are not formally trained as programmers. As data curators and managers, we may need to reconsider if the complete replication of research, especially computational research, is a realistic goal in all instances. Can curated data still be useful if it is only 'a little bit' reproducible or 'just about' reproducible? This purpose of this presentation is to propose an approach of 'just enough' data curation by arguing that partial reproducibility is better than nothing at all (Broman, n.d.). By focusing on incremental progress rather than prescriptive rules, researchers and curators can build their knowledge and skills as the need arises. A computational reproducibility framework, developed for the Canadian Data Curation Forum, will serve as the model for this approach, which combines learning about reproducibility with improving reproducibility. Computational reproducibility leads to better and more transparent research, but fear of a crisis and focus on perfection shouldn't prevent curation that may be 'good enough.' This presentation will discuss concrete and actionable steps to help researchers, data curators, and data managers improve their understanding and practice of computational reproducibility. Briney, K. (2019). Data management is hard and everyone is bad at it. This includes data managers more often that we care to admit. Data management is basically fighting against chaos.https://twitter.com/mykola/status/1198719315589160960 … [Tweet]. Broman, K. (n.d.). Initial steps toward reproducible research. https://kbroman.org/steps2rr/