Research Data Management

Research Data Management (RDM) refers to the process of organizing, storing, preserving, and sharing data collected and used in a research project. It encompasses the planning and implementation of practices throughout the data lifecycle—from creation and processing to archiving and reuse. To address these challenges, Research Data Management (RDM) has recently gained importance as a key topic in the research environment.

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RDM has many advantages, presented below, but also faces many challenges, as described here.

Why managing your Reaserch Data ?

  • Rigorous planning of research stages.
    Indeed, the Data Management Plan is a crucial stage in research data management.
  • Improved data management practices.
    Currently, different problems exist:

    • File content is generally poorly documented.
    • Data theft or loss is common (loss of computer, USB key, or hard drive; accidental deletions).
    • Files get corrupted over time; software and data can become obsolete.
    • If multiple copies exist, it is sometimes difficult to identify the correct one because file naming and organisation are often not standardised.
    • Colleagues leave with data or without having documented it, and the knowledge is lost.
    • Lack of transparency in research practices. RDM principles link data to the results they produced, thereby promoting research reproducibility and transparency.
  • Access data collected by other researchers in other places.
    More broadly, sharing research data allows researchers to access valuable datasets that they do not need to collect themselves. Ultimately, research data can also be shared with civil society.
  • Controlled data sharing.
    We do not share everything. RDM makes it possible to take all legal precautions, such as respecting participant rights or industry sponsorship contracts—for example, via access restrictions and embargo periods, as is already the case for open access to research articles.
  • Rigorous and systematic research data citation:
    • Research project data constitute a major output. It is therefore imperative to cite data and articles to credit authors for their work’s impact.
    • Linking publications to data provides evidence for results.
    • A unique identifier permanently establishes the citation and must be flexible enough to meet the expectations of each research field while being common enough to apply universally (e.g. DOI for article citations).
  • Project visibility improves:
    We cite data when we use them.
  • Certain analyses can be performed by other researchers:
    Data are reused more frequently, which increases the number of publications and boosts return on investment for both researchers and funding organisations.
  • Some funders make it mandatory; some publishers make it a condition of publication; and most journals increasingly demand linking results to data.
  • Data storage and guaranteed access for research partners working on the same database.
  • Research transparency:
    Allows for the reproduction of results and improves research integrity (see above).
  • Creation of new research projects and new questions by pairing several databases,
    which promotes new methods and enables testing of alternative hypotheses.

 

Useful resources

Science Europe (2021). Practical Guide to the International Alignment of Research Data Management https://www.scienceeurope.org/our-resources/practical-guide-to-the-international-alignment-of-research-data-management/

 

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