Making sense of Interest Group/Working Group Activity by

RDA Technical Advisory Board

Beth PlaleProfessor of Data Science

Indiana University USA

With special thanks to RDA/US Fellow Nic Weber

• 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 MembersTAB is an elected body

• Jamie Shiers (Switzerland)

• Peter Fox (US)

• Peter Wittenburg (Germany)

• Rainer Stotzka (Germany)

• Simon Cox (Australia)

• Susanna-Assunta Sansone (UK)

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?

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?

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)

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

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

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

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)

Social/organizational + data consumer

Technical + Data Consumer

Technical + Data Provider

Social/organizational + Data Provider

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

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

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

Larger version of full list of term assignment to date.

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