Pre-start/Pre-award planning considerations

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» Guiding questions to identify research data management challenges
» Addressing research data management in grant proposals

It is beneficial to take research data management into consideration already ahead of starting a research project, particularly if applying for external funding. Early identification of aspects that may need attention or potentially time-consuming processes, will avoid delays in the project progress. Furthermore, costs related to data management efforts are considered eligible costs by many research funders. Some funders therefore ask to briefly outline data management as part of project proposals.

Do you know who you can approach with questions related to research data management? Perhaps data stewards or research advisors at your unit? For institutional support, which is often located at the library, and domain-specific support services see Get local/disciplinary support.

Data management considerations before starting on a project/ pre-award

Answering the following guiding questions during project planning can help you identify research data management aspects that are useful to investigate ahead of embarking on the project. Answering the questions in project planning phase can be seen as preparation for writing a more comprehensive data management plan (DMP) in the startup phase.

The guiding questions are also available as questionnaire in Data Stewardship Wizard.

Awareness of data management requirements

Discuss briefly which data sharing policies, guidelines or conventions may apply for your project.

Both institutions and research funders set requirements for research data management and sharing of research data. Furthermore, many scholarly journals require that data underlying scientific articles is made available. Investigating the submissions guidelines in relevant journals in your field can therefore be a good idea. In addition, there can be disciplinary conventions on how data should be shared with the community.

Read more about requirements by institutions and research funders and find links on the page Research data management and DMP requirements.

Investigating discipline-rooted resources and disciplinary support services can be helpful for identifying relevant community conventions. For more information, see the resources under Get local/disciplinary support.

Will ethical or legal aspects require attention?

Discuss briefly if there are legal or ethical aspects in the project that may require attention.

Some processes related to legal and ethical aspects in a research project require early planning, have implications for data management, and can be time-consuming. Not addressing them early enough may come with a risk of delaying or affecting the research project. Make sure that you are familiar with how these aspects are handled at your institution and how much time should be allowed for the respective processes.

In multi-partner projects, data ownership and responsibilities should be formalized in contracts or collaboration agreements. In international projects, the applicable legislation needs to be specified.

Highly relevant examples of aspects that may need consideration:

Collection/ processing of personal data
- Investigate need for data processor agreements or joint controllership agreements in multi-partner projects * Considerations regarding informed consent from participants, in particular in medical and health research
- Investigate need for Data Protection Impact Assessment (DPIA) if the project involves a “high risk” to people’s personal information
- Consult privacy guidelines and routines at your institution. For questions about processing of personal data contact your local data protection officer (DPO).
Export control regulations
Confidentiality/ commercial issues
Intellectual Property Rights/ IPR (including planned patenting, commercialization)
- For help with IPR aspects contact your local innovation advisor and/or technology transfer office
Ethical pre-approval (if e.g. conducting medical and health research, animal experiments)
- Investigate application routines * Creation of common ethical grounds in international projects
Protection of cultural heritage
Research on endangered species
Indigenous data governance
Responsible use of artificial intelligence
Consortium agreements or collaboration agreements in multi-partner projects
- For contract-related questions contact local research advisors or legal advisors at your institution

See guidance on the DMP chapter Legal and ethical aspects for additional information and links to institutional resources.

Information security: which level applies?

Research information is commonly classified into the following information security categories: open (green), restricted (yellow), confidential (red), strictly confidential (black). If not all data in the project classifies as open (green), information security must be considered at all project stages. Being aware of information security requirements helps identifying the correct storage solutions during the active phase of the research project and aids early planning for making project results available (openly or with restricted access). Institutions commonly have a storage guide giving examples how data are categorized and indicating which data can be stored where.

Please indicate the highest expected information security class in the project:

Open (green)
Restricted (yellow)
Confidential (red)
Strictly confidential (black)
NB! Dual use projects may follow the NATO security classification and have separate provisions

See guidance on the DMP chapter Storing and protecting data during the project for additional information and links to institutional resources.

Making data available

What data will the project collect or generate?

Briefly discuss what data the project will collect or generate.

Remember that research data is a broad term and includes all sorts of data used in research including observational and experimental data, surveys, registry data, simulations, and other data records. Also related project results such as research software/code should be included.

Read more about identifying data and approaches to research data management under Discipline-rooted approaches to research data management

Can all results be made (openly) available?

Some datasets cannot be made publicly available. Decisions to not share data should follow the principle “as open as possible, as closed as necessary”. Reasons may include protection of sensitive data, export control regulations, and intellectual property rights/confidentiality issues.

If data cannot be made available openly, consider if datasets still could be made available with restricted access.

Who else could be interested in using the data?

What application for your data inside or outside your own research field can you foresee? Will other researchers, public administration, industry or the general public be able to (re)use your results? In short: why is your data valuable? This question relates both to general reuse possibilities for this type of data and concrete plans to reuse the data from this project.

Resources and responsibilities

Who will be responsible for data management?

Will there be one/several dedicated person(s) taking care of research data management (RDM) in the project?

If the project plans to recruit or train dedicated staff, related costs should be included in the project budget.

Will research data management (RDM) in the project require additional resources? Consider costs for data storage, data processing, data archiving, expert support.

Many research funders consider list research data management as eligible costs in applications. Have costs for RDM been included in the project budget?

Please consult RDMkit and OpenAIRE resources for further information on RDM cost calculations.

Addressing research data management in grant proposals

Some research funders ask about Open Science and/ or data management planning already in grant applications. In this section you should demonstrate that you have reflected on how your research results can be made available including some practical aspects and how this will strengthen the impact of your project. You are not required to have a complete DMP at the proposal stage.

Some aspects that could be addressed in this section:

What data the project will reuse, produce or generate and who could be interested in using datasets and related research outputs (e.g. software, models, workflows) resulting from the project. Will the results e.g. be useful for researchers from the same discipline, researchers in another domain, public administration, industry or the general public?
How making data available will enable the anticipated impacts of the project, and is connected to the dissemination and exploitation strategy of the project
Demonstrate awareness of relevant concepts and terminology, e.g. FAIR principles
Provide examples of data types and metadata standards as well as research data archives where the data will be made available. This can be addressed specifically if already known, or with more generic phrases.
How data quality will be ensured
Outline how data management in the project will be organised and who will be responsible. Remember to include costs related to data management in your budget, if applicable.
Awareness of possible data management challenges or constraints particularly if handling large amounts of data or sensitive data.
If data cannot be made available openly, explain why. Describe relevant infrastructure to be used, e.g. for handling of sensitive data.
Familiarity with relevant guidelines and routines, e.g. institutional Open Science guidelines

Since 2023, The Research Council of Norway has incorporated assessment of Open Research under two subsections of the ‘Impact’ criterion: “Potential impact of the proposed research” (for example including making research data FAIR) and “Communication and exploitation” (for example including which archiving solutions will be used for research data).

The Research Council of Norway: Assessment of open science in grant applications

In Horizon Europe, Open Science practices are considered in the evaluation of proposals under the ‘Excellence’ and under the ‘Quality and efficiency of implementation’ assessment criterion. Open access to research outputs such as publications, data, software, models, algorithms, and workflows is considered mandatory practice and failure to address this will result in a lower evaluation score. Adoption of additional recommended Open Science practices can improve the evaluation score.