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
Technical Advisory Board Members
TAB is an elected body
• Beth Plale, co-chair (US)
• Andrew Treloar, co-chair (Australia)
• Bridget Almas (US)
• Carole Palmer (US)
• Chuang Liu (China)
• Francoise Genova (France)
• 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
Social/organizational
solution aimed at data
provider
Social/organizational
solution aimed at data
consumer
Technical solution aimed
at data provider
Technical solution aimed
at data consumer
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
WG IG Name
Quadrant Beneficiary
Solution Keywords
Community Capability Model IG
Q1
65
95 Data Management, Data Literacy, Education
Data for Development
Q1
58
63 Discovery, Knowledge Organization / Representation, Education, Data Literacy
Development of cloud computing capacity and education in developing world research
Q1
60
60 Education, Research Practices
Long tail of research data IG
Q1
66
66 Interoperability, Data Fabric, Knowledge Organization / Representation
Quality of Urban Life Interest Group
Q1
86
60 Governance, Education, Values / Ethics
RDA/CODATA Summer Schools in Data Science and Cloud Computing in the Developing
Q1
World
95
95 Education, Data Literacy, Research Practices
Agriculture Data Interest Group (IGAD)
Q2
80
30 Interoperability, Data Fabric, Knowledge Organization / Representation
Big Data IG
Q2
65
15 Big Data, Interoperability, Discovery, Values / Ethics
Biodiversity Data Integration IG
Q2
51
5 Interoperability, Data Brokering, Knowledge Organization / Representation
Brokering IG
Q2
70
25 Interoperability, Data Brokering, Knowledge Organization / Representation
Data Description Registry Interoperability (DDRI)
Q2
55
15 Interoperability, Brokering, Registry
ELIXIR Bridging Force IG
Q2
60
1 Interoperability, Data Brokering, Infrastructure
Marine Data Harmonization IG
Q2
52
10 Interoperability, Data Brokering, Infrastructure
Metadata IG
Q2
50
50 Knowledge Organization / Representation, Discovery, Metadata
Structural Biology IG
Q2
85
15 Data Brokering, Knowledge Organization / Representation, Metadata
Toxicogenomics Interoperability IG
Q2
55
Wheat Data Interoperability WG
Q2
80
Data in Context IG
Q3
40
45 Values / Ethics, Research Practices, Big Data
Data Type Registries WG
Q3
45
45 Registry, Interoperability, Infrastructure
Domain Repositories Interest Group
Q3
10
40 Infrastructure, Data Management, Data Dissemination / Publication
PID IG
Q3
11
25 Knowledge Organization / Representation, Discovery, Metadata
PID Information Types WG
Q3
15
25 Knowledge Organization / Representation, Discovery, Metadata
Practical Policy WG
Q3
40
45 Governance, Research Practices
Preservation e-Infrastructure IG
Q3
10
20 Infrastructure, Data Management, Data Dissemination / Publication
RDA/WDS Publishing Data Workflows WG
Q3
8
Repository Platforms for Research Data
Q3
12
10 Infrastructure, Data Management, Data Dissemination / Publication, Research Practices
Archiving multimedia interactive /dynamic data and projects
Q4
42
80 Data brokering, Governance, Data Management
Data Foundation and Terminology WG
Q4
30
75 Interoperability, Data Literacy, Data Fabric
RDA/CODATA Legal Interoperability IG
Q4
38
90 Interoperability, Governance
RDA/WDS Publishing Data Cost Recovery for Data Centres
Q4
10
90 Data Dissemination / Publication, Knowledge Organization / Representation
RDA/WDS Publishing Data Services WG
Q4
10
80 Data Dissemination / Publication, Research Practices, Data Management
Repository Audit and Certification DSA–WDS Partnership WG
Q4
25
55 Governance, Infrastructure, Registry
Service Management IG
Q4
10
90 Data management, Governance
Standardisation of Data Categories and Codes WG
Q4
1
5 Interoperability, Data Brokering, Data Management, Metadata
5 Interoperability, Data Brokering, Data Management, Metadata
10 Data Dissemination / Publication, Knowledge Organization / Representation
90 Interoperability, Data Fabric, Knowledge Organization / Representation
WG IG NameQuadrant Beneficiary
Solution Keywords
Community Capability Model IG Q1
Q1
Data for Development
65
58
Quadrant Beneficiary
Solution Keywords
95 Data Management,
Education 95 Data Management, Data L
Q1 Data Literacy, 65
63 Discovery, Knowledge
Organization
Education,
Data Org
Lite
Q1
58/ Representation,
63 Discovery,
Knowledge
d education
in developing
world research
Q1
60 in developing
60 Education,
Development
of cloud computing
capacity and education
world Research
research
Q1 Practices
Long tail of research data IG
Q1
66
60
60 Education, Research Pract
66 Interoperability, Q1
Data Fabric, Knowledge
Organization
/ Representation
66
66 Interoperability,
Data Fabri
60 Governance, Education,
Values / Ethics
Q1
86
60 Governance, Education, Va
Q1
86
Quality of Urban Life Interest Group
nce RDA/CODATA
and Cloud Computing
the Developing
Q1
World and
95Cloud Computing
95 Education,
Literacy,World
Research
SummerinSchools
in Data Science
in theData
Developing
Q1
95Practices95 Education, Data Literacy, R
Q2
Agriculture Data Interest Group (IGAD)
80
30 Interoperability, Q2
Data Fabric, Knowledge
Organization
/ Representation
80
30 Interoperability,
Data Fabri
Q2
65
15 Big Data, Interoperability,
Discovery,
Ethics
Q2
65 Values /15
Big Data, Interoperability, D
Biodiversity Data Integration IG Q2
51
5 Interoperability, Q2
Data Brokering, Knowledge
Organization
/ Representation
51
5 Interoperability,
Data Broke
Q2
70
25 Interoperability, Q2
Data Brokering, Knowledge
Organization
/ Representation
70
25 Interoperability,
Data Broke
55
15 Interoperability, Q2
Brokering, Registry55
Big Data IG
Brokering IG
RI)Data Description Registry Interoperability
Q2
(DDRI)
15 Interoperability, Brokering,
Q2
60
1 Interoperability, Q2
Data Brokering, Infrastructure
60
1 Interoperability, Data Broke
Marine Data Harmonization IG Q2
52
52
10 Interoperability, Data Broke
10 Interoperability, Q2
Data Brokering, Infrastructure
Metadata IG
Q2
50
Q2
50
50 Knowledge
Organization /
50 Knowledge Organization
/ Representation,
Discovery,
Metadata
Structural Biology IG
Q2
85
85
15 Data Brokering,
Knowledge
15 Data Brokering,Q2
Knowledge Organization
/ Representation,
Metadata
Toxicogenomics Interoperability IG
Q2
55
55 Management,
5 Interoperability,
5 Interoperability, Q2
Data Brokering, Data
Metadata Data Broke
Wheat Data Interoperability WG Q2
80
Data in Context IG
40
80 Management,
5 Interoperability,
5 Interoperability, Q2
Data Brokering, Data
Metadata Data Broke
40 Big Data45 Values / Ethics, Research P
45 Values / Ethics, Q3
Research Practices,
ELIXIR Bridging Force IG
Data Type Registries WG
Q3
Q3
45
45 Registry, Interoperability, In
Q3
45
45 Registry, Interoperability,
Infrastructure
Term
Assignment.
Orange:
social/consumer;
Domain Repositories Interest Group
Q3 Management, 10
40 Infrastructure,
Data Manag
Q3
10
40 Infrastructure, Data
Data Dissemination
/ Publication
PID IG
Q3
11
25 Knowledge
Organization /
Q3
11
25 Knowledge Organization
/ Representation,
Discovery,
Metadata
Blue:
technical/consumer.
Terms
chosen
by
group
PID Information Types WG
Q3
15
25 Knowledge
Organization /
Q3
15
25 Knowledge Organization
/ Representation,
Discovery,
Metadata
Practical Policy WG
Q3
45 Governance, Research Pra
Q3
40
45 Governance, Research
Practices 40
to
describe
activity
more
precisely
than
name
Preservation e-Infrastructure IG Q3
Q3 Management, 10
20 Infrastructure,
Data Manag
10
20 Infrastructure, Data
Data Dissemination
/ Publication
RDA/WDS Publishing Data Workflows
Q3 / Publication, Knowledge
8
10
Data Dissemination
/ Publi
Q3 WG
8
10 Data Dissemination
Organization
/ Representatio
alone.
Repository Platforms for Research
Q3 Management, 12
10 Infrastructure,
Data Manag
Q3Data
12
10 Infrastructure, Data
Data Dissemination
/ Publication,
Resea
WG IG Name
Quadrant Beneficiary
Solution Keywords
Community Capability Model IG
Q1
65
95 Data Management, Data Literacy, Education
Data for Development
Q1
58
63 Discovery, Knowledge Organization / Representation, Education, Data Literacy
Development of cloud computing capacity and education in developing world research
Q1
60
60 Education, Research Practices
Long tail of research data IG
Q1
66
66 Interoperability, Data Fabric, Knowledge Organization / Representation
Quality of Urban Life Interest Group
Q1
86
60 Governance, Education, Values / Ethics
RDA/CODATA Summer Schools in Data Science and Cloud Computing in the Developing
Q1
World
95
95 Education, Data Literacy, Research Practices
Agriculture Data Interest Group (IGAD)
Q2
80
30 Interoperability, Data Fabric, Knowledge Organization / Representation
Big Data IG
Q2
65
15 Big Data, Interoperability, Discovery, Values / Ethics
Biodiversity Data Integration IG
Q2
51
5 Interoperability, Data Brokering, Knowledge Organization / Representation
Brokering IG
Q2
70
25 Interoperability, Data Brokering, Knowledge Organization / Representation
Data Description Registry Interoperability (DDRI)
Q2
55
15 Interoperability, Brokering, Registry
ELIXIR Bridging Force IG
Q2
60
1 Interoperability, Data Brokering, Infrastructure
Marine Data Harmonization IG
Q2
52
10 Interoperability, Data Brokering, Infrastructure
Metadata IG
Q2
50
50 Knowledge Organization / Representation, Discovery, Metadata
Structural Biology IG
Q2
85
15 Data Brokering, Knowledge Organization / Representation, Metadata
Toxicogenomics Interoperability IG
Q2
55
Wheat Data Interoperability WG
Q2
80
Data in Context IG
Q3
40
45 Values / Ethics, Research Practices, Big Data
Data Type Registries WG
Q3
45
45 Registry, Interoperability, Infrastructure
Domain Repositories Interest Group
Q3
10
40 Infrastructure, Data Management, Data Dissemination / Publication
PID IG
Q3
11
25 Knowledge Organization / Representation, Discovery, Metadata
PID Information Types WG
Q3
15
25 Knowledge Organization / Representation, Discovery, Metadata
Practical Policy WG
Q3
40
45 Governance, Research Practices
Preservation e-Infrastructure IG
Q3
10
20 Infrastructure, Data Management, Data Dissemination / Publication
RDA/WDS Publishing Data Workflows WG
Q3
8
Repository Platforms for Research Data
Q3
12
10 Infrastructure, Data Management, Data Dissemination / Publication, Research P
Archiving multimedia interactive /dynamic data and projects
Q4
42
80 Data brokering, Governance, Data Management
Data Foundation and Terminology WG
Q4
30
75 Interoperability, Data Literacy, Data Fabric
RDA/CODATA Legal Interoperability IG
Q4
38
90 Interoperability, Governance
RDA/WDS Publishing Data Cost Recovery for Data Centres
Q4
10
90 Data Dissemination / Publication, Knowledge Organization / Representation
RDA/WDS Publishing Data Services WG
Q4
10
80 Data Dissemination / Publication, Research Practices, Data Management
Repository Audit and Certification DSA–WDS Partnership WG
Q4
25
55 Governance, Infrastructure, Registry
Service Management IG
Q4
10
90 Data management, Governance
Standardisation of Data Categories and Codes WG
Q4
1
5 Interoperability, Data Brokering, Data Management, Metadata
5 Interoperability, Data Brokering, Data Management, Metadata
10 Data Dissemination / Publication, Knowledge Organization / Representation
90 Interoperability, Data Fabric, Knowledge Organization / Representation
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 webenabled 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