e-Science, Research Data and Libaries

LIBER: e-Science WorkshopRob Grime-Science Coordinator, Tilburg UniversityExecutive manager Open Data Foundation (ODaF)December 5th, Bristol 2011

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e-Science, Research Data and Libraries

Overview of this presentation:

1. Open Data Foundation (ODaF)

2. e-Science

3. Research Data Life Cycle: Data Documentation Initiative (DDI 3)

4. Technology for Statistical Data and Metadata Exchange (SDMX)

5. Role of Libraries

Main issue of my talk:

• What kind of problems can be solved with metadata management?

• How and where can metadata management help libraries to support research?

• What sort of data services could libraries develop?

LIBER e-Science Workshop 10-04-2023

What is ODaF?

The Open Data Foundation (ODaF) is a non-profit organization

promoting the adoption of global metadata standards and the development of open-source solutions for the management and use of statistical data.

We focus on improving data and metadata accessibility and overall quality in support of research, policy making, and transparency, in the fields of Social, Behavioral and Economic sciences.

ODaF is heavily involved in developing and promoting SDMX and DDI 3

Why ODaF? The Open Data Foundation (ODaF) was established to fill a gap in the area of statistical data and metadata management in Social, Behavioral and Economic sciences (SBE).

The adoption of metadata specifications (DC, DDI, SDMX, ISO/IEC 11179, ISO19115) has been impaired by the LACK OF TOOLS and agreed guidelines for their use.

Building such tools requires the coordination of strong information technology and cross-domain expertise that is NOT typically a function of these agencies. This is not by lack of interest: it is simply not their mandate, mission or responsibility.

What does ODaF do?1. Support and coordinate the development of open-source

tools for management of statistical data and metadata

2. Provide technical assistance to agencies for the adoption of metadata specifications, best practices in data management, and capacity building

3. Provide access to public metadata collections and registries

4. Promote international cooperation and address global issues

5. Develop training resources and reference materials

6. Provide web-based facilities to foster the dialog betweenvarious communities

Adopters/Interest in SDMX1. European Central Bank (ECB)2. International Monetary Fund (IMF)3. United Nations (MDG, WHO, UNESCO) 4. World Bank (WB)5. UNESCO (Education)6. > 100 National Statistical Offices (NSO’s)

Adopters/Interest in DDI37. Australian Bureau of Statistics8. CESSDA partners9. OECD 10.Research Data Centers (CentERdata)

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e-Science and Research Data

1. e-Science is about

Digital Curation

Automated Capture

Tools Development

2. Three characteristics of the “Digital Revolution”:

More Data

Data Sharing

Data Life Cycle

3. Metadata management is a critical issue to all of these!


Machine actionable!

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DDI 3 Lifecycle Model

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Structure of the General Statistical Business Process Model (GSBPM)

Process

Phases

Sub-processes

(Descriptions)

Source: Steven Vale, UNECE, 2010

DDI 3 Use Cases

• Study design/survey instrumentation• Questionnaire generation/data collection and procesing• Data recoding, aggregation and other processing• Data dissemination/discovery• Archival ingestion/metadata value-add• Question/concept/variable banks• DDI for use within a research project• Capture of metadata regarding data use• Metadata mining for comparison, etc.• Generating instruction packages/presentations

LIBER e-Science Workshop

DDI 3 Perspective

Producers

Archivists

Users

General Public

Policy Makers

Sponsors

Media/Press

Academic

Business

Government

Source: Pascal Heus, ODaF

DDI 3 Technical Overview

• DDI 3 is composed of several schemas• Use only what you need!• Schemas represent modules, sub-modules (substitutions), reusable,

external schemas

• archive• comparative• conceptualcomponent• datacollection• dataset• dcelements• DDIprofile• ddi-xhtml11• ddi-xhtml11-model-1• ddi-xhtml11-modules-1• group• inline_ncube_recordlayout

• instance• logicalproduct• ncube_recordlayout• physicaldataproduct• physicalinstance• proprietary_record_layout (beta)• reusable• simpledc20021212• studyunit• tabular_ncube_recordlayout• xml• set of xml schemas to support xhtml

Source: Arofan Gregory/Wendy Thomas

Computers need structure of data

• Concepts

• Code lists

• Data values

• How these fit together

Unit Multiplier

Unit

Topic

Time/Frequency

CountryStock/Flow

Data Set Structure:Concepts

16457

Q,ZA,B,1,1999-06-30=16547

Data Makes Sense

Quarterly, South Africa, Bank Loans, Stocks, for 30 June 1999

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Libraries and Research Data Involvement

Four key areas of activity:

1. Data Availability

2. Data Discovery Services

3. Access and Accessibility

4. Delivery Services


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Data Availability Data Discovery Access and Accessibility

Delivery

Registries Research data portals

Metadata management tools(distributed access, secured access to data structures)

Enhanced Publications

Data Archiving (Repositories)

Subject repositories Research Data Warehousing

Data Publications and Data Journals

Collection building(application of ontologies) +

Resource Aggregation (Disciplinary)

Data Curation Supplementary materials

“Dark Archive Materials”

Locally produced or reused research data

Metadata Mining(“mash ups”)

Data Security and Data PrivacyDigital Rights Management (DRM)

Data Dissemination

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Library and IT Services,Tilburg University

1. Research data services: registering, archiving, accessibility

2. Link publications, research data and supplementary materials

3. Data discovery services: subject portals European Values Study

4. Lobby to value research data as scientific output

5. Lobby for a generally adopted research data policy


Disclaimer

“No one, including NSF is quite sure what is meant by DATA MANAGEMENT Or PLAN.”

Christine Borgman (DCC, Chicago, 2010)

Thanks for your attention!