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Research Data Management (RDM): Home

What is research data management?

Research data management (RDM) refers to the processes applied throughout the lifecycle of a research project to guide the collection, documentation, storage, sharing, and preservation of research data. RDM practices are directly related to responsible research and can help researchers save resources by ensuring that their data is complete, understandable, and secure. RDM practices also follow the policies of institutions and funding agencies that seek to protect their investments. All areas of research can make the most of research data when it is accessible, shared, reused, and repurposed. (Groupe d'experts sur la formation en GDR de Portage, 2019)

Data management allows for:

  • mitigating the risk of data loss by securely storing the data;
  • enhancing the capacity to prove and validate research results;
  • ensuring the integrity of research data;
  • avoiding costly repetition of data collection;
  • preventing accidental violations of privacy and ethical legislation;
  • providing opportunities for collaboration with other researchers;
  • facilitating processing and analysis through effective organization of files and data;
  • ensuring compliance with the expectations and policies of funding agencies, institutions, or journal publishers regarding research data. (UQAM Library Services, 2019).

Research data is a primary source material that supports research projects, academic studies, or artistic works. It can be experimental data, observational data, operational data, third-party data, public sector data, monitoring data, processed data, or reused data. It can be used as evidence to validate results.

All other digital and non-digital content has the potential to become research data.

The main recommendations for researchers regarding the management of their research data are:

  • Backup raw data separately from the data you are working on;
  • Backup your active data according to the 3-2-1 rule (see the Storage and Backup tab);
  • Describe your data: include metadata that describes all the data you produce (see the Documentation and metadata tab);
  • Preserve and share your data: upload final data to a data repository (see the Data Repositories tab).

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References

Digital Research Alliance of Canada (2019). Research Data Management. Retrieved from https://portagenetwork.ca/wp-content/uploads/2019/08/Introduction_RDM_Aug2019_EN.pdf

Service des bibliothèques de l'UQAM (2019). Gestion des données de recherche. Retrieved from https://uqam-ca.libguides.com/gestion-des-donnees-de-recherche