Metrics News

  • To highlight uncertain norms in authorship, John P. A. Ioannidis, Richard Klavans and Kevin W. Boyack identified the most prolific scientists of recent years.
  • Industry-funded RCTs are the minority in intensive care. We found no evidence that industry-funded trials in intensive care yield more favorable results or are less likely to reach unfavorable conclusions.
  • Some nutrition scientists and much of the public often consider epidemiologic associations of nutritional factors to represent causal effects that can inform public health policy and guidelines. However, the emerging picture of nutritional epidemiology is difficult to reconcile with good scientific principles. The field needs radical reform.
  • 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.
  • The vast majority of scientific articles published to-date have not been accompanied by concomitant publication of the underlying research data upon which they are based. This state of affairs precludes the routine re-use and re-analysis of research data, undermining the efficiency of the scientific enterprise, and compromising the credibility of claims that cannot be independently verified. It may be especially important to make data available for the most influential studies that have provided a foundation for subsequent research and theory development. Therefore, we launched an initiative—the Data Ark—to examine whether we could retrospectively enhance the preservation and accessibility of important scientific data. Here we report the outcome of our efforts to retrieve, preserve, and liberate data from 111 of the most highly-cited articles published in psychology and psychiatry between 2006–2011 (n = 48) and 2014–2016 (n = 63). Most data sets were not made available (76/111, 68%, 95% CI [60, 77]), some were only made available with restrictions (20/111, 18%, 95% CI [10, 27]), and few were made available in a completely unrestricted form (15/111, 14%, 95% CI [5, 22]). Where extant data sharing systems were in place, they usually (17/22, 77%, 95% CI [54, 91]) did not allow unrestricted access. Authors reported several barriers to data sharing, including issues related to data ownership and ethical concerns. The Data Ark initiative could help preserve and liberate important scientific data, surface barriers to data sharing, and advance community discussions on data stewardship.

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