A Data Management Plan is a structured document that describes what research data is created, how it will be managed during and after the project and the different responsabilities. It enlists the set of actions that will be taken to produce, analyse, store, preserve and share the data generated by a research.
It is written at the beginning of a research project and it forces you to think about the data you will produce before you have started working on it: thinking about data in advance helps to manage it better. The time spent planning will come back to you in terms of reliability, accessibility and understandability of the data you have produced.
Indeed, a DMP not only describes every step of your research accurately in terms of methoodgy, instruments used and preservation measures, but it also forces you to reflect on the type of data you are going to create, if you can use data provided by others and if there are any copyright issues that you should take into consideration, along with measures for guaranteeing data quality, security and preservation. All this considered, a Data Management Plan is a living document which should be updated whenever the need arises when changes in the research project are being made; thus, it is like a compass to guide you in your own research project.
Importantly, the European Commission has made DMPs mandatory for researchers who have been awarded with an EU research grant (MSCA, ERC, etc.) from the launch of the Horizon Europe programme in 2021, thus enhancing the mandatory open access to publications and open science principles applied throughout the programme. Despite this compulsory feature for funded projects, the writing of a DMP (both for those who have a EU grant and for those who do not) should not be seen as a bureaucratic obligation, because writing a good DMP guarantees proper data management according to the FAIR principles and can be a truly useful guideline for your own benefit. Indeed, always bear in mind that any research project (even more so a complex or large research project) has greater success if it is well-organised and managing research data and specific procedures improves our understanding of data enahces its quality and security.
Planning your DMP: things to keep in mind
- Give yourself a complete overview of all your research materials, the data you will collect or create, along with methodologies and tools.
- Discover if others have already created data that you can reuse: take note if you use data created by others (secondary data), since this brings relevant copyright implications.
- Check any kind of restriction you might encounter: use the scheme on the right to help you and to know who you can contact.
- If you are working with research teams and/or external partners ensure that you have all the necessary agreements in place to clarify the responsabilities and rights on the data produced.
- Think about the terms of access and reuse: indeed, there is a lot of un-FAIR data around, since creating open research data is not simply about publishing it in open access.
USEFUL LINKS:
- Sensitive data and research
- How to anonymise your data
- DMP in pratice
- Planning for projects with human data
- Questions to support you when writing your DMP
- Consult examples of DMPs
- ARGOS, OpenAire's tool for writing DMPs
- Other useful tools for writing a DMP
Writing your DMP: the content of the document
Once you have conceived the previous issues, you are ready to start writing your Data Management Plan. Importantly, if your funder has a DMP template (such as HORIZON Europe DMP Template) it is mandatory that you use it; otherwise you can choose the template you prefer (even UNIMI has its own one). Please, bear in mind that not all DMPs are the same, since different DMP formats and templates exists, but they all have common content and share the same basic features:
1. Data description and collection or re-use of existing data
- How will new data be collected or produced and/or how will existing data be re-used (importantly, keep in mind disciplinary practices for data management in the SSH and in STEM/LS)?
- What data (for example the kinds, formats, and volumes) will be collected or produced?
- Who (for example role, position, and institution) will be responsible for data management (e.g. data steward, data manager, PI)?
2. Making your data FAIR
- What resources (for example financial and time) will be dedicated to data management and ensuring that data will be FAIR?
- What metadata and contextual documentation (for example the methodology of data collection, the way of organising data, and/or a readme file) will accompany data?
- What methods, software, tools will be needed to access and use the data?
- How will the application of a unique and persistent identifier (such as a DOI) to each dataset be ensured?
- What are the naming conventions and domain standards used (give an explicative example)?
3. Data quality
- What data quality control measures will be used?
- How will the integrity of the research data be guaranteed?
4. Data accessibility, security and preservation
- How will data and metadata be stored and backed up during the research process?
- How will data security and protection of sensitive data be taken care of during the research? And how it will be (eventually) anonymised?
- How and when will data be shared? Are there possible restrictions to data sharing or embargo reasons? If yes, why?
- What will be the license associated to data?
- How will data for preservation be selected, and where will data be preserved long-term (e.g. a data repository)?
5. Legal and ethical aspects
- If you are working with human or animal data, do you have all the necessary authorisations and agreements?
- If personal data are processed, how will compliance with legislation on personal data and on data security be ensured?
- How will legal issues, such as intellectual property rights and ownership, be managed? What legislation is applicable?
- How will possible ethical issues be taken into account, and codes of conduct followed?
Once you have written your DMP, pay attention to the fact that some funding institutions (including universities) may carry out a formal assessment of the document that you submit, in some cases giving candidates a grade: at this link you can consult possible DMP evaluation frameworks. Importantly, UNIMI does not provide a written assessment of DMPs, as its policy on this is to provide support throughout for developing the DMP and to ask candidates to modify their DMP when necessary, also with the support of the university staff in the review of the latest version of the DMP.
Thus, if you have been awarded a funded grant and you are required to write a DMP (or you simply think that it can be useful for your research project) write to us for supporting you in writing and reviewing it. We also hold an annual training programme for first-year PhD students (and their supervisors) in the effective drafting of a DMP: contact us at dmp.phd@unimi.it for more information.