Research data management

The management of research data is an integral part of the research process and aims to make it as efficient as possible.

It takes into account the entire data life cycle as well as the global context of the research (ethical, informational, IT/technical, legal, etc. issues). In addition, it ensures compliance with the current requirements of universities, public funders, scientific editions and current regulations and legislations.

The way in which data are managed varies according to the type of research project (individual or collective, national or international, public or private funder, etc.) and according to the data themselves (their nature, type, mode of collection and production, processing, analysis, etc.).

Good practices for managing research data include :

•     Develop a Data Management Plan to plan data management
•     Collect and organize your data
•     Store and secure your data
•     Archive and share your data

Managing your data allows you to :

• Save time : by planning your needs via a Data management plan you will save time, resources and the quality of your research.
• Increase the impact of your research : by sharing your data, your number of citations and future collaborations will potentially increase.
• Preserve your data : by depositing your data in a repository that complies with FAIR principles, you guarantee the durability of your work, its possible reuse and recovery.
• Maintain the integrity of your data : by managing and documenting your data throughout your project, you act in a responsible and transparent manner.
• Meet donor requirements : many funders require a repository of research data from the projects they fund.
• Facilitating new discoveries : Sharing your data promotes innovation and the creation of new data.
• Supporting the openness and dissemination of knowledge : disseminating scientific knowledge allows the creation of new knowledge.

Generally, the management of research data is the responsibility of the researchers who collect and produce the data.

However, many other people are involved in the research process to ensure data integrity, quality or security. It is therefore crucial to clearly define and assign, at the very beginning of the project, the roles and responsibilities of the various stakeholders, such as :

• Team members involved in the analysis, collection, creation, aggregation or processing of data, for example.
• External collaborators and partners for specific tasks (e. g. translation, transcription).
• Support staff (e.g., administration, research project management).
• Data center that provides tools and solutions for data backup, security and storage.
• Information specialists (e. g. archivists, librarians, data curators/managers) responsible for access, processing, availability, long-term preservation and enhancement.
• Legal advisors for legal aspects (e. g. patents, contracts, copyright and intellectual property).
• University leadership for the adoption of a general framework, specific policy, strategic vision and resources for implementation.
• Trainers in support of researchers and doctoral students.

The costs associated with data management and sharing vary from one research project to another and are sometimes very significant. Therefore, it is important to anticipate them in advance when planning the project and, to some extent, try to reduce them as much as possible.

These costs include both data management and sharing activities (e.g. anonymization/desidentification, file conversion, documentation, cleaning, transcription, etc.), in terms of time and resources (human, financial, material, etc.). Unfortunately, there is no absolute method for calculating the costs intrinsically related to the nature of the research project and the data. As an indication, according to the High Level Expert Group on the European Open Science Cloud (European Commission), about 5% of research expenditure should be devoted to data management.

In concrete terms, the cost assessment is carried out in the Data Management Plan or when applying for funding/subsidies from a donor.

For more information, see our storage & security pages.

Swiss National Science Foundation (SNSF)

The costs of making research data collected, observed or generated with SNSF funding available are eligible under the conditions set out in the RE art. 2.13.

It should be noted that the costs charged to a grant must relate to the storage of data that have a thematic link with the research funded by the SNSF.

As a general rule, a maximum of CHF 10,000 can be charged per grant. The costs must already be taken into account when submitting the request.

Horizon 2020

Eligibility for reimbursement during the duration of the project is defined in the standard grant agreement, mainly in Art. 6 (eligible and ineligible costs), and more precisely in clause 6.2.D.3.