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:
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:
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