The Data Citation WG maintains the RDA Recommendation for Precise, Actionable Reference to Dynamic Data:

Rauber, A., Parsons, M., & RDA Data Citation WG. (2025). Precise, Actionable Reference to Dynamic Data: Recommendations of the Working Group on Data Citation (WGDC) Version: 2025-04-04T08:30Z (Version 2). Research Data Alliance. https://doi.org/10.15497/RDA00132

We recommend an approach 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. This approach appears to apply to all data types and contexts.

We assist institutions in implementing the Recommendation and share lessons learned. We conduct an adoption session at most RDA Plenaries.

We bring 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. ..

For comprehensive review of the recommendations, the wide range of reference implementations and a survey of many adoptions see:

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 concerning their interpretation, please let us know.

Please join the group to follow and participate in our activities.

The Data Citation WG maintains the RDA Recommendation for Precise, Actionable Reference to Dynamic Data:

Rauber, A., Parsons, M., & RDA Data Citation WG. (2025). Precise, Actionable Reference to Dynamic Data: Recommendations of the Working Group on Data Citation (WGDC) Version: 2025-04-04T08:30Z (Version 2). Research Data Alliance. https://doi.org/10.15497/RDA00132

We recommend an approach 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. This approach appears to apply to all data types and contexts.

We assist institutions in implementing the Recommendation and share lessons learned. We conduct an adoption session at most RDA Plenaries.

We bring 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. ..

For comprehensive review of the recommendations, the wide range of reference implementations and a survey of many adoptions see:

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 concerning their interpretation, please let us know.

Please join the group to follow and participate in our activities.