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Data Management Plans (DMPs) are often required by organizations and funding agencies for research projects. One of the goals of DMPs is to capture how researchers plan to comply with some aspects of the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. When writing DMPs, taking into account community standards for managing and publishing research data can be a challenge for researchers. Community standards are often documented informally or communicated by word of mouth. The introduction of FAIR Implementation Profiles (FIPs) offers a structured way to capture such standards. This paper investigates with a user study, whether FIPs can serve as suggestions for aligning research data management with community standards. Through a customized interface with the related information extracted from FIPs as suggestions, we study whether participants take such suggestions into account when writing DMPs.

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Aligning Data Management Plans with Community Standards Using FAIR Implementation Profiles

  • 会议论文

  • 发表日期：

    2025年

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  • Navroop K. Singh,
  • Shuai Wang,
  • Angelica Maineri,
  • Tycho Hofstra,
  • Mark Bruyneel,
  • Stephanie van de Sandt,
  • Ronald Siebes,
  • Jacco van Ossenbruggen,
  • Tobias Kuhn

摘要

Data Management Plans (DMPs) are often required by organizations and funding agencies for research projects. One of the goals of DMPs is to capture how researchers plan to comply with some aspects of the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. When writing DMPs, taking into account community standards for managing and publishing research data can be a challenge for researchers. Community standards are often documented informally or communicated by word of mouth. The introduction of FAIR Implementation Profiles (FIPs) offers a structured way to capture such standards. This paper investigates with a user study, whether FIPs can serve as suggestions for aligning research data management with community standards. Through a customized interface with the related information extracted from FIPs as suggestions, we study whether participants take such suggestions into account when writing DMPs.