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Will standardised research data policies make publishing more FAIR?

Investigate and analyze.

Over recent years, reproducibility and transparency have been hot topics in scientific publishing. While many journals, funding agencies and institutions have introduced research data policies to combat the reproducibility crisis, there is a call to better implement FAIR principles, which aim to make data findable, accessible, interoperable and reusable, in order to maximise their use.

Advances in data transparency have been made in recent years, but there is still room for improvement. For example, the editor of Molecular Brain, Tsuyoshi Miyakawa, recently reported that since 2017, he has requested raw data for 41 articles ahead of acceptance, of which 97% withdrew or were rejected due to insufficient raw data. As a result, the journal’s research data policy, which stated that relevant raw data for studies must be available upon request, was updated to require deposition of the data on which the conclusions of the manuscript rely.

Although uptake of data policies is on the rise, variation between policies can make compliance challenging. In response, the Research Data Alliance (RDA) recently published a Research Data Policy Framework to help promote data sharing. After reviewing the policies of multiple publishers and building consensus with stakeholders via the RDA Data Policy Standardisation and Implementation Interest Group, they defined 14 features of journal research data policies arranged into six standard policy types/tiers. Topics include:

  • Data citation
  • Data repositories
  • Data availability statements
  • Data standards and formats
  • Peer review of research data

Both Miyakawa and the authors of the framework emphasise the need for collaboration across the wider research community to support and drive implementation and adoption of research data policies. Barend Mons, co-leader of the GO FAIR initiative, shares this view in a recent Nature World View article, and goes as far to say that 5% of research funds should go towards ensuring all research data are reusable by humans and machines alike, with funding withheld unless research data policies are in place. Although this suggestion may not be popular amongst funders and academics, he maintains that there is excellent return on investment due to the time and money currently lost to ‘data wrangling’.

As Mons notes, the key is to build capacity, enable collaboration, and share good practices so that good data stewardship becomes the rule, not the exception.

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Summary by Robyn Foster PhD from Aspire Scientific

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With thanks to our sponsors, Aspire Scientific Ltd and NetworkPharma Ltd


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