Research data management concerns the organisation, archiving, exchange and storage of data collected or generated during research activities.
This involves the development of methods and protocols for collecting, documenting, storing, analysing and sharing of data, as well as the security and preservation of data over time.
Effective research data management processes are essential to ensure the reliability and quality of research results, to promote researcher collaboration and to enable the re-use of data for future research. Furthermore, such management plays a key role in meeting ethical and regulatory requirements related to data privacy and confidentiality as well as data protection.
In summary, research data management aims to optimise the value of research data by ensuring that they are well documented, secure and easily accessible and by enabling reliable verification of results.
Many funding bodies demand a responsible, transparent and as open as possible management of the data and results produced by the research they support.
This translates into the production of documents certifying the correct handling of data according to FAIR principles:
- Findable
- Accessible
- Interoperable
- Reusable
FAIR principles are nowadays becoming standards in the reasearch data management and the main document for defining data management strategies is the Data Management Plan (DMP).
On these pages you will find support and information on both the FAIR principles and the DMP.
- ensure the integrity and reproducibility of your research
- ensure the authenticity, completeness and credibility of your research data
- minimise the risks of losing your data
- increase research efficiency
- prevent waste of resources and energy by allowing others to reuse your data
- meet the requirements of funding bodies
- improve collaboration with others by making your data understandable
- have other results (e.g., software, datasets, protocols, etc.) in addition to publications recognised for research evaluation purposes