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Data Management Plans: a Resource to Shape Institutional Data Management Services

Authors : Willeke de Haan, Veerle Van den Eynden

At KU Leuven, a university in the Flemish region of Belgium, data management plans have become an important resource to drive and shape the development of data management support, services, and training. With 8,000 researchers and 7,000 PhD students in fundamental and applied research across a comprehensive range of disciplines, KU Leuven is the largest university in Belgium.

Public research funding is provided by the federal and regional governments, mainly via the Research Foundation Flanders (FWO) and via research funding allocated to universities based on excellence criteria through the Special Research Fund (BOF) and the Industrial Research Fund (IOF).

Since 2018, FWO and BOF-IOF incorporated data management into their policies, requiring researchers to submit Data Management Plans (DMPs) to their institutional research office. Since then, the number of DMPs that are developed each year has increased exponentially, from 150 in 2018 to nearly 700 per year now.

The Research Coordination Office at KU Leuven decided to review all DMPs to provide feedback to ensure high-quality plans. To manage the submission, monitoring, review, and preservation of this volume of DMPs efficiently, an online platform was developed that is integrated with the university’s research information systems.

Initially, the focus of the DMP review was on supporting the development of DMPs, as this was a new concept for researchers. The review process has significantly improved the quality of DMPs. Later, support shifted to provide advice on best practices in data management. Reviews of over 2600 DMPs provide a rich source of information to develop services and training.

Based on findings from DMP reviews, the IT department developed an interactive storage guide; ethical and legal compliance in research projects can be monitored; new data management training modules are developed; and a collection of example DMPs has been developed. In addition, the growing DMP collection is a rich source of information on researchers’ data practices, providing the baseline information to develop further services. Future plans include implementing artificial intelligence in DMP reviews to automate problem detection and exploring machine-actionable DMPs for an institutional data register.

URL : Data Management Plans: a Resource to Shape Institutional Data Management Services

DOI : https://doi.org/10.2218/ijdc.v19.i1.1051

Catégories
EN

Advancing research data publishing practices for the social sciences: from archive activity to empowering researchers

Authors : Veerle Van den Eynden, Louise Corti

Sharing and publishing social science research data have a long history in the UK, through long-standing agreements with government agencies for sharing survey data and the data policy, infrastructure, and data services supported by the Economic and Social Research Council.

The UK Data Service and its predecessors developed data management, documentation, and publishing procedures and protocols that stand today as robust templates for data publishing.

As the ESRC research data policy requires grant holders to submit their research data to the UK Data Service after a grant ends, setting standards and promoting them has been essential in raising the quality of the resulting research data being published. In the past, received data were all processed, documented, and published for reuse in-house.

Recent investments have focused on guiding and training researchers in good data management practices and skills for creating shareable data, as well as a self-publishing repository system, ReShare. ReShare also receives data sets described in published data papers and achieves scientific quality assurance through peer review of submitted data sets before publication.

Social science data are reused for research, to inform policy, in teaching and for methods learning. Over a 10 years period, responsive developments in system workflows, access control options, persistent identifiers, templates, and checks, together with targeted guidance for researchers, have helped raise the standard of self-publishing social science data.

Lessons learned and developments in shifting publishing social science data from an archivist responsibility to a researcher process are showcased, as inspiration for institutions setting up a data repository.

URL : Advancing research data publishing practices for the social sciences: from archive activity to empowering researchers

DOI : doi:10.1007/s00799-016-0177-3