1. Making sense of Interest Group/Working Group Activity by RDA Technical Advisory Board Beth Plale Professor of Data Science Indiana University USA With special thanks to RDA/US Fellow Nic Weber

2. Beth Plale, co-chair (US) Andrew Treloar, co-chair (Australia) Bridget Almas (US) Carole Palmer (US) Chuang Liu (China) Francoise Genova (France) Technical Advisory Board Members TAB is an elected body Jamie Shiers (Switzerland) Peter Fox (US) Peter Wittenburg (Germany) Rainer Stotzka (Germany) Simon Cox (Australia) Susanna-Assunta Sansone (UK)

3. TAB: what it does Case statement review: Reviews and guides case statement creation Liaison: Engages and supports IG/WG activity. Host plenary IG/WG Chairs meetings. Each IG/WG has liaison. Cross group coordination. Plenary planning : with eye towards minimizing overlap and quality proposals Socio-technical vision and strategy: technical scope of RDA, issues of productivity: – e.g., 30% are Working Groups and 70% are Interest Groups. Is that right/good balance?

4. RDA P6: 60 working groups and interest groups 60 WGs and IGs is a lot of activity. How can newcomer possibly make sense of RDA?

5. Conceptualizing RDA Activity through Clustering: A Brief History RDA TAB undertook effort begun in 2014 under lead of TAB co-Chair Dr. B. Plale to better illuminate collective activity of RDA Sources of information influencing – Analysis of WG/IG stated objectives and other information – Numerous discussions with WG/IG chairs and community – Multiple earlier versions of clustering, none of which quite worked (comprehensive, illuminating)

6. Clustering Purpose Guide newcomers find products in progress of interest, and groups to which they can contribute Help externals see scope of solution space of RDA Guide RDA members in gaps and overlaps Help TAB in guidance and evaluation of existing and new groups

7. Clustering along two dimensions Beneficiary dimension: spectrum from data provider to data consumer – Primary beneficiary is data provider (or act of data provisioning) at one end of spectrum or data consumer at other end of spectrum Solution dimension: spectrum from technical to social/organizational – Solution manifests itself most strongly as software or infrastructure (technical) on one hand; or as policy, organizational, governance, educational, or community building (social) on other

8. Technical solution aimed at data provider Technical solution aimed at data consumer Social/organizational solution aimed at data consumer Social/organizational solution aimed at data provider

9. Placing activity on grid Self identification/positioning by WG/IG chairs Activity is represented as single point in grid space labeled by (0, 100) in each dimension Following graphs are for those WG/IGs that have responded to inquiries so far (about 50% have responded)

10. Social/organizational + data consumer

11. Technical + Data Consumer

12. Technical + Data Provider

13. Social/organizational + Data Provider

14. Terms to further describe Use of terms to further describe activity of WG/IG Terms drawn from Data Practices and Curation Vocabulary (DPCVocab) but not limited to

15. For 34 groups who have replied with their info. Location: Q1: UR, Q2: LR, Q3: LL, Q4: UR. Color coded by quadrant and WGs in dark

16. Term Assignment. Orange: social/consumer; Blue: technical/consumer. Terms chosen by group to describe activity more precisely than name alone.

17. Larger version of full list of term assignment to date.

18. Summary Clustering has exposed relatively equal representation of WG/IG activity in each category WG activity more heavily concentrated in technical dimension. TAB discussing solutions to stimulate WG activity on social/organizational dimension RDA/US Fellow: Building clustering into new web- enabled tool to explore RDA activity for RDA site RDA/US Fellow: gather additional information to study RDA (WG/IG engagement: e.g., profiles of those engaged based on organizational affiliation) Whitepaper in preparation on clustering