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, an acronym that stands for:
- 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.
Thanks to R.D. No. 155 of 20 February 2024, the Politecnico di Torino has provided itself with its own University Policy for the management of research data, which continues the path taken by the Politecnico towards Open Science, started with the Policy for Open Access to Publications in 2019.
With this Policy, the Politecnico di Torino recognises the fundamental role of data produced during research activities for the advancement of knowledge and the relevance of their management for maintaining the values of quality and integrity of scientific research.
The Policy aims to raise awareness and encourage research staff to responsibly manage data throughout the research lifecycle according to the principles of Open Science ("as open as possible as closed as necessary") and the FAIR principles, providing guidelines for the management, sharing and long-term preservation of research data.
- more robust guarantees about the integrity, authenticity and reproducibility of the research
- minimise the risks of losing your data
- increase research efficiency
- prevention of duplication of effort in research
- meet the requirements of funding bodies
- increased possibilities for collaboration, through comprehensible and reusable data
- valorisation of all research outputs (e.g. software, datasets, protocols...), in addition to publications, for research evaluation purposes