Abstract

AbstractResearch data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP.

Highlights

  • Public funding agencies and research institutions are facing novel challenges related to the management of research outputs produced in Academia

  • This section is divided according to the main categories of the interview protocol, which are: grant application process (Section 4.1.1), quality of data management plans (DMPs), in Section 4.1.2, and challenges of research data management planning (RDMP) (Section 4.1.3)

  • Research data management planning (RDMP) has many ongoing challenges, which makes the evaluation of its soundness and effectiveness to generate reusable data a complex task

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Summary

Introduction

In recent years, governing bodies across the world have started to promote new open science policies and practices for managing scientific information. The following research question drives this study: What are the current challenges and practices in research data management planning? The goal of the analysis was to investigate whether current data management paragraphs reflect the ambition of producing reusable research data. Managers became aware of the potential business value of data stored in companies. It appeared that data was not fully integrated across information systems (IS) [10]. The ambition to align data systems with corporate needs is still actively pursued in new data management practices. Data management in academia has not been as thoroughly investigated as data management in businesses [4, 12]

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