Writing a DMP is most useful to you - it will save time and effort and make the research process easier.
A Data Management Plan (DMP) is a essentially an outline for what you're going to do with your data throughout the lifecycle of your research project. You may not have thought of everything, but by planning you can dodge major problems of data loss and you can foresee what you'll need to do to share the data at the end of your research. For example you can get the costs upfront for the services that you'll need so you can put that into your grant proposal so that the funders will pay it meaning you won't be stuck without the services that you need. DMPs support the researcher in thinking about all the relevant aspects of data management from the very beginning of a research project.
Note: Your DMP is a document that will change over time - they are working documents and should be updated as the project progresses or if there are any significant changes to the initial project plan.
In Horizon 2020 there was a strong emphasis on making research data FAIR (Findable, Accessible, Interoperable and Re-usable) this should be reflected in the DMP. The programme has been succeeded by Horizon Europe, the EU’s key funding programme for research and innovation.
Taking ARGOS as default DMP tool, which offers DMP templates that match the demands and suggestions of the Guidelines on Data Management in Horizon 2020, OpenAIRE have a useful guide on how to create a Data Management Plan. ARGOS simplifies the writing, management, validation, monitoring and maintenance of Data Management Plans.
Essential elements:
DMPs are very individual. Some funders specify a specific template for their DMPs (there are varying types) however if one is not provided you can checkout the examples below.
Please note: Not all elements or questions will be relevant to your research, use these examples as a starting point to help you structure your DMP.
When creating DMP that involves personal data, it is essential to incorporate measures to ensure compliance with GDPR requirements. The DMP should outline how personal data will be handled throughout its lifecycle, from collection to storage, sharing, and disposal.
Here's some considerations:
Creating a research Data Management Plan (DMP) at the start of the research project is the easiest way to open up research data and save time collecting, describing and analysing data. Effective management and documentation of research data means you can verify your results, replicate the research, and provide access to data. Funding agencies are progressively requiring data to be made publicly available and requiring the execution of a DMP.
Plan for data management as your research proposal is being developed (for funding agency, dissertation committee, etc.).
Outline the processes and resources for the entire data life cycle. Start with the project goals (outputs, outcomes, etc.), include a description of the data that will be compiled, and how the data will be managed and made accessible.
Consider the formats and types of data you will generate during the lifecycle of a research. Organizing your files with consistent and descriptive names can make your data easier to understand, share, and preserve.
It is important to collect data in such a way as to ensure its usability later.
Before collecting data, think carefully about how your data will be created. What types of data will you produce? What data formats will you use? How much data will you collect?
You also need to think about how to manage the accompanying research records: correspondence; grant applications; ethics applications; technical reports and appendices; research publications; signed consent forms
How will the data be processed, what software will be used, algorithms, workflows etc.
After data is collected, it must be processed to make it usable. This could include tasks such as integrating data from various sources, converting data from one format to another, and applying procedures for validation or quality control. All data processing steps should be thoroughly documented to ensure the results can be replicated from the original data.
Data analysis is the phase where raw research data is examined to generate the insights that form the foundation of the study’s findings, which will later be documented and shared through research outputs.
It’s important to record the tools and techniques used in the analysis process; any code developed for analyzing or visualizing data may also need to be preserved and made accessible to support the research outcomes.
Think about how you will share your research data at the end of the research. Many funders and publishers require research data to be made publicly available, where this is possible.
Are you allowed to share your data? What factors might restrict you being able to share your data? Are there other people apart from you (the creator) who have the right to see or use the data? Are you opting-out of sharing some of the research data and if so why?
Do you plan to deposit your data in a data repository, if so which one?
How will you safeguard that your data will remain accessible and usable in the long term? Who owns the data?
Consider that ongoing support of a particular file format is not definite, some formats may not be supported in the future. For preservation purposes, it is suggested you use open file formats where possible.
ARGOS - An open and collaborative platform developed by OpenAIRE to facilitate Research Data Management (RDM) activities concerning the implementation of Data Management Plans.
DMPTool - Create data management plans that meet institutional and funder requirements. The DMPOnline tool provides a free template for the drafting of your Horizon2020 DMP.
DMPOnline - helps you to create, review, and share data management plans that meet institutional and funder requirements.
DS Wizard - Create Smart Data Management Plans for FAIR Open Science.
Zenodo - the default publishing platform in OpenAIRE.