Data Management Plan
The fulfilment of these guidelines has a positive impact on the evaluation of the project, right from the proposal stage.
There are several free training courses that can help you better understand the need for and how to make a data management plan:
- Skills4 EOSC - FAIR-by-Design Methodology
- NAU/FCCN - Essentials of Research Data Management
- RDNL - Essentials 4 Data Support
Yes, funding agencies request the release of the data sets that support publications, but there is no obligation to share the remaining sets. In addition, it is possible that initially the availability of some datasets is planned, but this decision can be modified later, especially if these data fit into the exceptions foreseen, such as the possibility of commercial exploitation. In such situations, the decision must be justified in the DMP.
Funding agencies recognise valid grounds for not disclosing research data, such as when the project does not generate data, when the data is subject to commercial exploitation, for reasons of confidentiality, privacy, national security, or if the sharing of data compromises the central objective of the project. This is why the European Commission has adopted the principle "As open as possible, as closed as necessary".
The preservation of research data is essential to guarantee its integrity and long-term accessibility. The following repositories are recommended:
Institutional Repository:
The Lusófona Scientific Repository (https://recil.ensinolusofona.pt/) is a digital service that brings together the scientific work produced at the Lusófona Group, making the Group's scientific output publicly and universally available.
Thematic Repositories:
Some repositories focus on specific types of data, such as geospatial data, genetic data, among others. These repositories can offer a more suitable infrastructure for certain types of data, also making them more accessible to peers (e.g. BioData.pt - biological data; APIS - social information data).
Specialised Data Centres:
Some specialised data centres offer data preservation services to the scientific community (e.g. Zenodo; POLEN).
It is important to consider data preservation policy, long-term accessibility, security, metadata requirements and the specific needs of your research and community when choosing the best repository.
The following tools are suggested to support the creation and management of DMPs:
- ARGOS: ARGOS is a tool that supports automated processes for creating, managing, sharing and linking PGDs with the corresponding research results. As well as integrating with funding bodies and research projects, ARGOS allows predefined templates to be used when creating a PGD. It also offers the flexibility to create new models parameterised according to the specifications of the institution, project, funding body, etc.
- DMPonline: The DMPonline tool integrates a wide variety of project funders, making it easier for researchers to organise the data collection process. It allows the different versions of the project to be edited, updated and shared between researchers. It also makes it possible to export data in various formats (pdf, docx, csv, html, etc.) at each stage of the project.
These platforms allow DMPs to be inserted according to the models established by the funding organisations (e.g. FCT, Horizon Europe ect).
However, as the specific requirements for DMPs can vary between funders and organisations, it is always a good idea to consult the funder's specific guidelines.
When a DMP should be drawn up varies depending on the context of the project:
As part of a funded project:
- First version: During the funding application process or within the first 6 months of the project, as required by the funder.
- Updates: Whenever significant changes warrant or new data sets are added. In the middle or final stages of the project.
- Evolving Nature: The DMP is not static; it evolves, gaining more precision and substance over the course of the project, as not all data or potential uses may be clear from the outset.
Within the research units:
- First version: At the time of the funding proposal, in line with the institutional regulations for research data management.
- Support for drawing up the DMP and its maintenance: The UID must support the researcher in drawing up and managing the DMP, providing conditions for the development of the DMPs of the projects associated with it.
- Evolving Nature and Curation: The DMP is not static; it evolves, gaining more precision and substance over the course of projects, since not all data or potential uses are clear from the outset. The institution must take responsibility for managing the preservation and curation of the data even after the end of the project, as stipulated in each PDG.
Common requirements include drawing up a DMP and making research data available in open access wherever possible. This requirement covers the data needed to validate the results in scientific publications, as well as other data arising from the project as specified in the DMP.
European Commission - European Data Strategy:
The European Commission advocates access to data validating scientific publications, as well as making available all other data associated with the project, in order to maximise access to and re-use of data generated by research projects. However, projects may not share data, either at the proposal stage or during execution, subject to justification in the project's PDG. This can happen in the following scenarios:
- If the project does not generate or collect data;
- In cases of conflict with the protection of results, especially if commercial or industrial exploitation is expected;
- When making data openly available jeopardises the achievement of the project's main objective;
- In situations of conflict with confidentiality obligations;
- In situations of non-compliance with national security obligations;
- In violation of personal data protection rules.
FCT - Policy on making data and other results of scientific research funded by FCT available.
The creation of a DMP, its subsequent processing, as well as its sharing and availability, are essential requirements in the context of Open Access. The guidelines established by the European Union (EU), through the European Research Council (ERC), and by projects funded by Horizon 2020, consider this criterion to be desirable, providing specific tools for its implementation. Encouraging the dissemination of research data under Horizon 2020 is done through Open Research Data (ORD), in line with the FAIR principles, which advocate that data should be findable, accessible, interoperable and reusable.
FAIR data refers to data that is managed in accordance with the FAIR principles - Findable, Accessible, Interoperable and Reusable. These principles aim to guide the management of research data, facilitating its location, access, interoperability and reuse. Each letter of the FAIR acronym represents a set of 15 principles.
- FINDABLE
Assigning a persistent unique identifier to (meta)data
Describing the data with detailed metadata
Registering or indexing the (meta)data in a searchable resource
Including the identifier in the metadata
- ACCESSIBLE
The (meta)data can be retrieved via its identifier, using a standardised communications protocol.
The communications protocol is open, free and universally implementable
The communications protocol allows for an authentication and authorisation procedure when necessary.
Metadata remains accessible even if the data is no longer available.
- INTEROPERABLE
(Meta)data uses a formal, accessible, shared and widely applicable language to represent knowledge.
(Meta)data uses vocabularies that follow the FAIR principles.
(Meta)data includes qualified references to other (meta)data.
- REUSABLE
(Meta)data has a plurality of precise and relevant attributes.
The (meta)data is made available with a clear and accessible licence to use the data.
The (meta)data is linked to its provenance.
The (meta)data complies with relevant standards of the disciplinary community.
The FAIR principles act as guidelines, not standards. They outline essential qualities or behaviours to optimise data reuse, highlighting the importance of elements such as description and citation.
A DMP comprises several essential elements to ensure an efficient and ethical approach to data management throughout the lifecycle of a research project. Some of the key components include:
What data will be created or collected?
Clear identification of the types of data that will be created or collected during the project.
How will the data be created or generated?
A detailed description of how the data will be created, generated or collected.
What methods and standards will be used to process the data?
Definition of the methods and standards used to process the data, including cleaning, transformation and analysis processes.
What methods and standards will be adopted for handling data throughout the process?
Definition of the procedures relating to data handling, i.e. all procedures relating to deposit in a repository, transfer, or safe and efficient reuse of the data throughout the research project.
What documentation or metadata will be integrated into the data?
Specification of the documentation or metadata that will be integrated into the data to facilitate understanding and future reuse.
How will ethical issues be dealt with?
Indication of the strategies used to deal with ethical issues related to the collection, (re)use and dissemination of the data.
How to deal with copyright and intellectual property issues?
Detailed information about the copyright and intellectual property issues associated with the data (https://grupolusofona.sharepoint.com/sites/Click/administracao/Ordens%20de%20Servio/Forms/OS.aspx?FilterField1=Ano&FilterValue1=2023&FilterType1=Text&FilterDisplay1=2023&FilterField2=Entidade&FilterValue2=COFAC&FilterType2=Choice&FilterDisplay2=COFAC&id=%2Fsites%2FClick%2Fadministracao%2FOrdens%20de%20Servio%2FOS%5FCOFAC%5F2023%5F079%2Epdf&viewid=b48e1dbb%2D1b10%2D4f7c%2D8ba4%2D6f9891caf54d&q=Propriedade%20intelectual&parent=%2Fsites%2FClick%2Fadministracao%2FOrdens%20de%20Servio&parentview=7).
How will the data be stored and backed up during the project?
Outline of the file formats and procedures adopted to ensure secure data storage and backup during the project.
What are the levels of data access and security?
Identification of data access levels (e.g. restricted to the Institution, completely open access, etc.) and security measures implemented.
How will the data be maintained and preserved after the end of the project?
Outline a detailed plan for data preservation, including file formats and storage strategies.
What is the long-term data preservation plan?
Definition of a detailed plan for long-term data preservation, including identification of curation processes.
What data will be made available in Open Access?
Determination of which data will be made Open Access and in what form.
How will the data be shared?
Strategies for sharing the data, including platforms and formats.
Are there guidelines on restrictions or open access to the data?
Identification of restrictions or open access to data, where applicable.
Who is responsible for data management?
Clear designation of the person or team responsible for ongoing data management (DPO).
What resources are needed to implement the DMP?
Estimate of the human, financial and technological resources needed to implement the DMP.
A Data Management Plan (DMP) is a formal document that defines the life cycle of the data generated or collected in a research context. It covers various aspects, from the creation or collection to the processing of data during and after a research project. It identifies how the data will be created and documented, who will have access to it, how it can be (re)used and where it will be stored and/or preserved.
DMPs are dynamic documents that adapt and evolve as the research progresses. They are crucial for efficient research data management, as they provide a comprehensive understanding of the data and the circumstances in which it was generated. This approach to data management makes it possible to reuse and replicate data, thus contributing to a more robust scientific system.