Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal Cognition. Issue 8 (15th August 2018)

• Record Type: Journal Article
• Authors: Hardwicke, Tom E.; Mathur, Maya B.; MacDonald, Kyle; Nilsonne, Gustav; Banks, George C.; Kidwell, Mallory C.; Hofelich Mohr, Alicia; Clayton, Elizabeth; Yoon, Erica J.; Henry Tessler, Michael; Lenne, Richie L.; Altman, Sara; Long, Bria; Frank, Michael C.
• Is Part Of: Royal Society open science. Volume 5:Issue 8(2018)
• Publication Date: 2018-08-15
• Subjects: open data -- reproducibility -- open science -- meta-science -- interrupted time series -- journal policy; Science -- Periodicals; 500
• DOI: 10.1098/rsos.180448
• Languages: English
• ISSNs: 2054-5703

Abstract

Abstract : Access to data is a critical feature of an efficient, progressive and ultimately self-correcting scientific ecosystem. But the extent to which in-principle benefits of data sharing are realized in practice is unclear. Crucially, it is largely unknown whether published findings can be reproduced by repeating reported analyses upon shared data ('analytic reproducibility'). To investigate this, we conducted an observational evaluation of a mandatory open data policy introduced at the journal Cognition . Interrupted time-series analyses indicated a substantial post-policy increase in data available statements (104/417, 25% pre-policy to 136/174, 78% post-policy), although not all data appeared reusable (23/104, 22% pre-policy to 85/136, 62%, post-policy). For 35 of the articles determined to have reusable data, we attempted to reproduce 1324 target values. Ultimately, 64 values could not be reproduced within a 10% margin of error. For 22 articles all target values were reproduced, but 11 of these required author assistance. For 13 articles at least one value could not be reproduced despite author assistance. Importantly, there were no clear indications that original conclusions were seriously impacted. Mandatory open data policies can increase the frequency and quality of data sharing. However, suboptimal data curation, unclear analysis specification and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings.