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The Data Citation WG has delivered its outputs and is now primarily focusing on supporting adoption by maintaining these outputs, assisting institutions in implementing the recommendations and sharing the lessons learned.
The RDA Working Group on Data Citation (WG-DC) brings together experts addressing the issues, requirements, advantages and shortcomings of existing approaches for efficiently and precisely identifying and citing arbitrary subsets of (potentially highly dynamic) data. It’s recommendations are based upon on (1) timestamping and versioning changes to evolving data and (2) identifying arbitrary subsets by assigning PIDs to the queries selecting the according subsets and are applicable across all types of data, such as e.g. collections of files, relational databases, multidimensional data cubes or regions in images..
The final recommendations of the WGDC are available at https://rd-alliance.org/system/files/documents/RDA-DC-Recommendations_151020.pdf (2-page flyer), with a slightly more extensive report having been published in the Bulletin of the IEEE Technical Committee on Digital Libraries, 12:1, 2016 (DOI: 10.5281/zenodo.4048304)
The WGDC has moved on into an adoption phase, supporting numerous data centers in implementing these recommendations and collecting feedback and lessons learned on the way. Please follow the discussion forum and wiki for updates on the status of these pilots. We are running a series of webinars where adopters present their implementations of the recommendations and their experiences in setting up the according services. Details on these webinars, recordings, slides and supporting materials are available from the WGDC Webinar page at
https://www.rd-alliance.org/group/data-citation-wg/webconference/webconference-data-citation-wg.html
A comprehensive review of the recommendations, the wide range of reference implementations as well as a survey of all adoptions reported over the years has recently been published in the Harvard Data Science Review: Rauber, A., Gößwein, B., Zwölf, C. M., Schubert, C., Wörister, F., Duncan, J., … Parsons, M. A. (2021). Precisely and Persistently Identifying and Citing Arbitrary Subsets of Dynamic Data. Harvard Data Science Review, 3(4). https://doi.org/10.1162/99608f92.be565013
If you are interested in adopting these recommendations, would like to present your implementation of the recommendations, or if you have any questions concenring their interpretation, please let us know.