[Andreas Rauber]

 
Collaborative session notes: https://docs.google.com/document/d/1CcgvKowF8krkgCDCWZy-mvqHlHx9Lj8p9bW9…
 
1. Short description of the WG recommendations (for newcomers)
2. Brief reports on new / on-going use cases
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

Additional links to informative material
The WGDC Recommendations in the short form of a 2-page flyer are available at:
https://rd-alliance.org/system/files/documents/RDA-DC-Recommendations_151020.pdf
(http://dx.doi.org/10.15497/RDA00016)
An extended Description of Recommendations is available at:
Bulletin of the IEEE Technical Committee on Digital Libraries, 12:1, 2016.
https://www.rd-alliance.org/group/data-citation-wg/webconference/webconference-data-citation-wg.html
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
https://www.rd-alliance.org/group/data-citation-wg/webconference/webconference-data-citation-wg.html
A comprehensive review of the recommendations and adoptions is available 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
 

Avoid conflict with the following group (1)
Complex Citations Working Group

Brief introduction describing the activities and scope of the group
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:
https://rd-alliance.org/system/files/documents/RDA-DC-Recommendations_151020.pdf
(http://dx.doi.org/10.15497/RDA00016)
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
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
 

Group chair serving as contact person
Andreas Rauber

Meeting objectives
    Re-visiting the recommendations
    Discussion of the HDSR Review Paper on adoptions
    Presenting new adopters’ approaches, solutions and implementations
    Discussing feedback from adopters
    Discussion of novel application domains such as versioning on-line learning/AI systems, cross-links to reproducibility in science, EU-AI regulation, etc.
    Identifying the next steps forward, adaptations required, etc.
 

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