Building a research data infrastructure for educational ... · Building a research data...

Open Science Conference Berlin 21.-22. März 2017

Building a research data infrastructure for educational studies in Germany

- Experiences of the FDB-project

Alexia Meyermann, DIPF

forschungsdaten-bildung.de // Open Science Conference // Berlin, 21.-22. März 2017

Open Educational Data Why not accomplished yet?

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Experiences of the German Network for Educational Data („FDB-Project“)

Researchers of the funding programme „EBF-programme“

Part of the Data Infrastructure

- approx. 300 projects*

- approx. 200 data sets*

- 144 projects documented (09/2016)

- 46 data sets shared (09/2016)

* figures are estimated because of missing responses

sharing

2013-2016:

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Experiences of the German Network for Educational Data („FDB-Project“)

Researchers of the funding programme „EBF-programme“

Part of the Data Infrastructure

- approx. 300 projects*

- approx. 200 data sets*

- 144 projects documented (09/2016)

- 46 data sets shared (09/2016)

* figures are estimated because of missing responses

sharing

2013-2016:

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an educational researchers‘ task: motivation, capabilities Motivation

• obligations to share (e.g. monitoring and evaluation systems, journal requirements, funding requirements)

• incentives to share (e.g. data publications, citation rate)

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Capabilities • resources (time, money, personnel) • skills (research data management)

Some measures have already been taken by various stakeholders funding requirements and opportunities, citation and data journals FDB-project offers data management training, info material, counseling



an educational researchers‘ task: motivation, capabilities a research data infrastucture‘s task

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an educational researchers‘ task: motivation, capabilities a research data infrastucture‘s task

How should a research data infrastructure

for educational studies look like?

How to address the specific needs of educational studies?

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Educational Studies Characteristics and Needs

Sociology, economy, psychology, …

Data types, methods, terminology

High data output

Sensitive data; subject to approval by local authorities

specialized services sufficient capacities

Educational research

Requirements on data infrastructure

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multidisciplinary heterogeneous productive complex legal and regulatory environment

forschungsdaten-bildung.de // Open Science Conference // Berlin, 21.-22. März 2017 9

subject-specific, institutional data centers

focus: Large-Scale-Surveys; In-House-Data

small scale studies, qualitative data underrepresented

diversified services, complexity

Data Infrastructure for social, behavioural and economic sciences in Germany* Characteristics

* accredited by the German Data Form, www.ratswd.de, plus GESIS und Qualiservice

http://www.ratswd.de/


Data Infrastructure Needs addressed?


+ subject-specific, high-level services guaranteed

+ high and increasing number of data centers

- complexity: no joint point of access, no joint overview of services and data

- capacities not sufficient, not adequate: certain data types are underrepresented



Requirements

Infrastructure

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Data Infrastructure Needs addressed?

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Requirements

Infrastructure + subject-specific, high-level

services guaranteed + high and increasing number of data centers

- complexity: no joint point of access, no joint overview of services and data

- capacities not sufficient, not adequate: certain data types are underrepresented


Data Infrastructure Need for action: coordination

joint point of access and overview to reduce complexity and enhance user-friendliness diversified services need to cooperate and need to be coordinated

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Researchers

deposit find

Qualitative data DIPF

Survey data GESIS

Assessment data IQB

reuse

www.forschungsdaten-bildung.de

FDB-project (German Network for Educational Data): Building a joint service

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1st funding period 2013-2016

http://www.forschungsdaten-bildung.de/


Data Infrastucture Need for action: coordination

joint point of access and overview to reduce complexity and enhance user-friendliness diversified services need to cooperate and to be coordinated

Coordinated infrastructure in the FDB-project: network architecture (technical, organizational, legal): roles and

responsibilities, shared software tool common metadata set (harmonization) common standards of data curation

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Data Infrastucture Need for action: expansion

build Sufficient and adequate capacities adequate services for data types so far underrepresented (qualitative

data, small scale studies data, etc.) services for „local data“: institutional repositories at universities and

research organizations low-threshold services

coordination selection and appraisal of data service level models

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added value service

?


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two pillars of open education data: Science and RDI educational researcher‘s tasks: motivation and capabilities data infrastructure‘s tasks: specialized services, joint and coordinated

services, sufficient capacities Future challenges:

- How to stimulate data reuse? Qualitative data ? -> Data quality - How to deal with the growing number of data?-> Selection and appraisal - How to build reliable services? -> Sustainability, long-term perspective,

professionalization of data curation activities - and many more…

Collaborative efforts needed!

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Forschungsdaten Bildung – German Network for Educational Data • Team DIPF: Doris Bambey, Alexia Meyermann, Maike Porzelt, Thomas

Schwager • Team IQB: Malte Jansen, Claudia Neuendorf, Lisa Pegelow • Team GESIS: Reiner Mauer, Marcus Eisentraut, Karoline Harzenetter

web: www.forschungsdaten-bildung.de E-Mail: [email protected] Phone: + 49 (69) 24708 - 311

Thank you for your attention !

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http://www.forschungsdaten-bildung.de/

mailto:[email protected]

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