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Research Data Management: DMP

 

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.

 

A DMP is a document that describes:

  • What data will be created
  • What policies will apply to the data
  • Who will own and have access to the data
  • What data management practices will be used
  • What facilities and equipment will be required
  • Who will be responsible for each of these activities.

 

In Horizon 2020 research projects should aim to make research data FAIR (Findable, Accessible, Interoperable and Re-usable) this should be reflected in the DMP.

 

The Research Data Lifecycle

Research Data Lifecycle

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.

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.

Writing a DMP is most useful to you - it will save time and effort and make the research process easier. 

Checklist

To make sure you cover the essential elements of a DMP, we recommended you use a checklist that incorporates best practice in the planning of data management and funder requirements. 

Essential elements:

  • Data description and collection or re-use of existing data
  • Documentation and data quality
  • Storage and backup during the research process
  • Legal and ethical requirements, codes of conduct
  • Data sharing and long-term preservation
  • Data management responsibilities and resources

 

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.

 

Template Examples:

 

 

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.

 

Tools

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.


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