Getting Started

For more information or help on any of the topics raised here, please contact Joris van Rossum, STM Research Data Director, at rossum@stm-assoc.org

SHARE – choosing a data policy

The Research Data Alliance Data Policy Standardisation and Implementation Interest Group was set up in recognition of the fact that policy makers and funders were increasingly announcing data policies for researchers.

The RDA group developed a framework containing 6 policy tiers and consisting of 14 features. As well as explanatory information on the policies themselves, the document contains implementation notes and template policy texts. 

They advise publishers to work with journal communities (academic editors, boards, learned societies) to ensure each title selects a policy that is realistic for authors and reviewers to manage. Over time, implementation, compliance and workloads can be monitored. When appropriate, the policy itself can be reviewed to see if the journal is ready to move to the next tier.

Figure reproduced from Hrynaszkiewicz, I, Simons, N, Hussain, A, Grant, R and Goudie, S. 2020. Developing a
Research Data Policy Framework for All Journals and Publishers. Data Science Journal, DOI: https://doi.org/10.5334/dsj-2020-006

Once they have become sufficiently familiar with the data policy and its implications, STM advises publishers to start using existing knowledge and workflows to expose the data and enable researchers to start meaningfully sharing it.

Crossref has worked hard to facilitate the inclusion of dataset links within journal articles, and STM supports its core message to publishers to collect data citation information – either in the references or as a relationship in the article’s metadata record – and send the data to Crossref. If this is done correctly, then various APIs will be able to extract information and provide further aggregation services.

Figure reproduced from presentation given by Rachael Lammey of Crossref at the STM December 2019 workshop. Full slide deck available here.

Full instructions for marking up data and software references can be found on the Crossref site here.

The next step is to ensure that the data themselves are cited correctly, and that everyone in the workflow chain – editors, typesetters, and publishers – is aware of what needs to be done.

CITE – implementing a data policy

In 2017, a group of people working in publishing convened through a FORCE11/NIH BioCADDIE initiative, to develop a data citation roadmap for publishers. Recognising the challenges involved in changing workflows, they provided the following:

  • the rationale for, and examples of, data citation (including a link to the JATS recommendations for tagging data availability statements)
  • a checklist of points to consider and potentially take action upon, throughout the lifecycle of a research article: Pre-submission, Submission, Production, and Publication

These latter points include revising editor training and advocacy material, through Author FAQs, and updating customer services information. And there is advice on avoiding pitfalls such as inadvertently contravening a double-blind peer review system by not removing author information from an embargoed dataset.

Figure reproduced from Cousijn, H., Kenall, A., Ganley, E. et al. A data citation roadmap for scientific publishers. Sci Data 5, 180259 (2018) doi:10.1038/sdata.2018.259