Data Champions Guide for Research Data Management
  • Data Champions Program
    • πŸ”΅Data Champions at reNEW
    • πŸ”΅License and Reusability
    • πŸ”΅Contact Details
  • RDM Resources
    • 🟒What is RDM?
      • 🟒RDM Checklist
      • 🟒Research Project Process
    • 🟒Labguru Support
    • 🟒Research Portal
    • 🟒RDM Portal
  • Elixir Resources
    • 🟠What is Elixir?
      • 🟠RDM Guide for Life Sciences (Elixir)
      • 🟠RDM Kit for Life Sciences (Elixir)
      • 🟠RDM Kit for BioImaging (Elixir)
      • 🟠RDM Kit for Bioinformatics (Elixir)
      • 🟠RDM Kit for Data Repository (Elixir)
  • Organizing Your Data
    • 🟣Batch Renaming
    • 🟣File and Folder Tips
      • 🟣File and Folder Tips I
      • 🟣File and Folder Tips II
      • 🟣File and Folder Tips III
      • 🟣File and Folder Tips IV
      • 🟣README File Template
  • EOSC
    • πŸ”΄European Open Science Cloud
  • DMP Resources
    • 🟑DMP
      • 🟑DMP Planning
      • 🟑Data Management Plans
    • 🟑DMP Templates
      • 🟑EU Grants
        • 🟑EU Grants
          • 🟑Horizon Europe DMP
          • 🟑Horizon 2020 DMP
          • 🟑ERC DMP
        • 🟑Marie Curie Program
          • 🟑Implementation Guide for Marie Curie Fellows
          • 🟑Marie Curie Fellows Website
        • 🟑Horizon Europe DMP
        • 🟑Horizon 2020 DMP
        • 🟑ERC DMP
      • 🟑Genomics
        • 🟑10X scRNA Sequencing
        • 🟑Bulk RNA Sequencing
        • 🟑ChiPseq ATAC Sequencing
        • 🟑CUT and RUN/CUT and TAG Sequencing
        • 🟑Whole Genome Sequencing
      • 🟑UCPH
    • 🟑DeiC DMP Online
  • Information Videos
    • 🟒Information Videos
      • 🟒Organize Your Data
      • 🟒OMERO Plus Demo - Glencoe Software
      • 🟒reNEW Labguru Training Video 1
      • 🟒reNEW Labguru Training Video 2
      • 🟒Horizon Europe DMP - Webinar
    • 🟒eLearning RDM
  • Open Science
    • 🟠FAIR Principles
    • 🟠FAIR Principles - MP4
    • 🟠Open Science - 8 Pillars
  • Biomedical Repository
    • βšͺBiomedical Data Repositories
      • βšͺGuidance: Biomedical Repositories I
      • βšͺGuidance: Biomedical Repositories II
      • βšͺPLOS Guidance: Biomedical Repositories III
  • reNEW Websites
    • 🟣reNEW Websites
      • 🌎reNEW Website
      • 🌎reNEW Connect
      • 🌎reNEW KUnet
      • 🌎reNEW Genomics
      • 🌎reNEW Imaging
      • 🌎reNEW DanGPU
      • 🌎reNEW Flow Cy
    • 🟣Research Groups
      • 🟣Aragona Group
      • 🟣Brickman Group
        • Brickman Group
      • 🟣Jensen Group
      • 🟣Kirkeby Group
      • 🟣Little Group
      • 🟣Sedzinski Group
      • 🟣Zylicz Group
  • GDPR Resources
    • πŸ”΄Data Protection Agency
    • πŸ”΄GDPR for Researchers
    • πŸ›‘GDPR Resouces
    • πŸ›‘GDPR - Project Like Mine
  • UCPH IT Resources
    • 🟀Archive vs. Backup
    • 🟀UCPH HPC Guide
    • 🟀UCPH Data Storage
    • 🟀UCPH Research IT
  • Infographics
    • 🟠FAIR Principles
    • 🟠Open Science Pillars
    • 🟠Research Process
    • 🟠RODMM Framework
    • 🟠UCPH HPC Quick Guide
  • reNEW RDM Blog
    • 🟑Monthly Blog
      • 🟑Disclaimer
      • 🟑Blog Post - May 2024
      • 🟑Blog Post - June 2024
      • 🟑Blog Post - July 2024
      • 🟑Blog Post - Aug 2024
      • 🟑Blog Post - Sept 2024
      • 🟑Blog Post - Oct 2024
      • 🟑Blog Post - Nov 2024
    • 🟑License and Acknowledgements
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  1. RDM Resources

What is RDM?

Data Champions Program

Research Data Management (RDM) is a systematic approach to organizing, storing, preserving, and sharing research data throughout its lifecycle. It is essential for ensuring that data remains accessible, reliable, reusable, and compliant with institutional and funding requirements. Effective RDM involves implementing policies, standards, and best practices that govern how research data is managedβ€”from the moment they are collected until their long-term preservation and accessibility.

Key Components of RDM:

A well-designed RDM strategy encompasses multiple aspects, including:

  • Data Collection & Documentation: Ensuring that data is collected systematically, with appropriate metadata and contextual information that make them interpretable in the future.

  • Data Storage & Security: Establishing secure, scalable, and reliable storage solutions to protect data from loss, corruption, or unauthorized access.

  • Backup & Preservation: Implementing redundant backup strategies to prevent data loss and ensuring long-term preservation using sustainable storage infrastructure.

  • Data Sharing & Access Control: Defining clear access policies that allow authorized users to access and reuse data while safeguarding sensitive information.

  • Compliance & Governance: Aligning data practices with institutional, national, and international standards, as well as fulfilling funding agency mandates.

The Role of the FAIR Principles in RDM:

An effective RDM strategy is built upon the FAIR principles, which ensure that data remains valuable and reusable over time:

  • Findable: Research data should be easily discoverable through persistent identifiers (e.g., DOIs) and rich metadata that describe their content and origin.

  • Accessible: Data should be retrievable by authorized users through well-defined access protocols, ensuring security and compliance with ethical and legal guidelines.

  • Interoperable: Data should be formatted to facilitate integration across different platforms, disciplines, and research domains, using standardized metadata and standard vocabularies.

  • Reusable: Data should be well-documented, versioned, and licensed to enable reproducibility and future use by different researchers or research communities.

Why RDM Matters:

Effective Research Data Management is essential for:

  • Enhancing Research Integrity: Well-managed data improves transparency, accuracy, and reproducibility, fostering trust in scientific findings.

  • Facilitating Collaboration: Organized and accessible data enables seamless sharing among researchers, institutions, and disciplines.

  • Maximizing Impact: Properly curated data allow for broader reuse and citation, increasing the visibility and longevity of research contributions.

  • Ensuring Compliance: Many funding agencies, institutions, and publishers now require researchers to follow strict RDM guidelines for ethical and responsible data stewardship.

Conclusion:

Implementing strong RDM practices is no longer optional but necessary in modern research environments. By aligning with FAIR principles and adhering to best practices in data collection, documentation, storage, and sharing, researchers safeguard their work, foster innovation, and contribute to the advancement of Open Science.

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Last updated 2 months ago

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