2023Unpublished venueRequires access

Research Data Management

Tibor Koltay

Open publisher page 1 citations

Abstract

This chapter explains the main aspects and the importance of being familiar with research data management (RDM), stressing the centrality of the research data lifecycle and the importance of data sharing, whereby the ideas of the FAIR principles are characterized as a main driver of reuse. The importance of data management plans is emphasized as well. RDM processes such as adding metadata, citing, retrieving, and curating datasets are portrayed. The need for cooperating between disciplinary researchers and different data professionals, such as data librarians and data curators, is highlighted. Moreover, it is underlined that a number of educational programs to data science also contain data management.

About this research paper

What this paper is about

This chapter explains the main aspects and the importance of being familiar with research data management (RDM), stressing the centrality of the research data lifecycle and the importance of data sharing, whereby the ideas of the FAIR principles are characterized as a main driver of reuse. The importance of data management plans is emphasized as well. RDM processes such as adding metadata, citing, retrieving, and curating datasets are portrayed. The need for cooperating between disciplinary researchers and different data professionals, such as data librarians and data curators, is highlighted. Moreover, it is underlined that a number of educational programs to data science also contain data management.

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Available abstract

This chapter explains the main aspects and the importance of being familiar with research data management (RDM), stressing the centrality of the research data lifecycle and the importance of data sharing, whereby the ideas of the FAIR principles are characterized as a main driver of reuse. The importance of data management plans is emphasized as well. RDM processes such as adding metadata, citing, retrieving, and curating datasets are portrayed. The need for cooperating between disciplinary researchers and different data professionals, such as data librarians and data curators, is highlighted. Moreover, it is underlined that a number of educational programs to data science also contain data management.

Key concepts: RDM, Metadata, Data management plan, Centrality, Data sharing, Data management, Reuse, Research data

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