The goal of the adoption sessions is to facilitate exchange between groups that have been implementing the recommendations and groups that are interested in implementing them within their own infrastructure. the goal is to share the lessons learned, challenges identified, or to discuss concerns with respect to certain parts of the recommendations, clarify questions, establish contact with experts who may have addressed a specific setting for data citation already.
Furthermore, we have initiated the process of revising the recommendations, incoprporating lessons learned from several years of deployment and adoptions across many settings. This session will seve to collect feedback on the proposed revisions,and to raise issues to be addressed.
We will thus, as part of this session:
- Re-visit the recommendations and answer questions concerning specific aspects
- Discuss the current status of the revision of the recommendations following initial discussions at P20
- Present new adopters' approaches, solutions and implementations
- Discuss feedback from adopters
- Discuss of novel application domains such as versioning on-line learning/AI systems, cross-links to reproducibility in science, EU-AI regulation, etc.
- Identify the next steps forward, adaptations required, etc.
1. Short presentation of the WG recommendations (for newcomers)
2. Reports on use cases, adoptions in progress
3. Presentations by institutions considering adoption, specific setting, challenges identified, ...
4. Discussion of novel application domains such as versioning on-line learning/AI systems, cross-links to reproducibility in science, EU-AI regulation
5. Other issues, next steps
- Data Center Operators wishing to provide precise identification and citation services
- Researchers wanting to encourage their data center operators to provide data identification and citation services
The RDA Working Group on Data Citation (WG-DC) brings together experts addressing the issues, requirements, advantages and shortcomings of existing approaches for efficiently 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 WGDC Recommendations in the short form of a 2-page flyer are available at:
An extended Description of Recommendations is available at: Bulletin of the IEEE Technical Committee on Digital Libraries, 12:1, 2016. (https://zenodo.org/record/4048304)
Webinar recordings as well as slide sets and supporting papers by adopters presenting their experience in implementing the recommendations are collected at the RDA WGDC webinar page at
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
Slide decks from the previous plenary meetings are available in the WG file repository at https://www.rd-alliance.org/node/141/file-repository
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.