Why is Research Data Management strongly related to Open Science?


One of the major challenges for Open Science therefore concerns the opening up of data. But to be effective, data opening must go hand in hand with good data management. In order to be reusable, research data must indeed be rigorously processed (e.g. it must be well documented, described by metadata and recorded in open formats).



There is no simple definition for Research Data Management because it depends on many factors such as the specificity of the project, type of data and others. However, the definition below makes it quite clear what Research Data Management is.


Research data management (or RDM) is a term that describes the organization, storage, preservation, and sharing of data collected and used in a research project. It involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions). It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data in a repository for long-term archiving and access). Source: https://pitt.libguides.com/managedata



And as stated in the Guidelines on FAIR Data Management in Horizon 2020, “Good data management is not a goal in itself, but rather is the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse by the community after the data publication process”.


Benefits of RDM and sharing

The benefits of good data management and openness are numerous! In a few points:

  • New requirements and opportunities for researchers
    • Researchers can better promote their research and be cited, as the data enter the scientific publishing process (data repository, publication of data papers).
    • Data sharing may be a condition for obtaining funding for scientific projects or for the publication of an article.
  • New perspectives for science
  • Making data available offers a better guarantee against scientific fraud.
  • Sharing data requires the adoption of good data management practices (describing data, documenting them, making them sustainable, etc.), which improves the quality of research work.
  • The cost of creating, collecting and processing data can be very high. Reusing existing data rather than recreating makes research profitable, accelerates innovation and the return on investment in Research and Development.
  • The creation of databases allows data mining (Text Data Mining), extraction, cross-checking and the construction of visualizations. These new processes make it easier to initiate new research initiatives and their interdisciplinary nature.
  • The deluge of digital data (Big Data) is having an impact on the way scientific research is carried out. We talk about Data Driven Science, an approach that automates discoveries by harnessing the power of computers to find correlations among large amounts of data.
  • A better use of public money and a return for society
    • Publicly funded research must be open to all. Opening up data makes research more transparent, builds citizens’ trust and enables them to get involved (e.g. in citizen science).
    • The data generated by Open Data and Big Data provide a field for scientific research, which in turn can inform society about its most recent developments.

Picture source : https://www.openaire.eu/rdm-elearning-and-fair-data-software-things-top-10