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Data Management Plan (DMP)

Data Management Plan (DMP)

A Data Management Plan (DMP) is recommended, or sometimes required, in conjunction with the research data.

Through this tool, researchers plan the collection, storage, description and dissemination of their research data and metadata according to the so-called FAIR Principles.

By allowing traceability, availability, authenticity, citability and appropriate storage of data and also taking into account ethical and security aspects, they ensure and regulate their future use.

The DMP is conceived as a living document because it follows the entire life cycle of the data, allowing to save time and resources through efficient data management.

It is important to develop a Data Management Plan (DMP) at the very start of the research, it must be reviewed and revised over the course of the project, it also must be updated in its subsequent versions and when the characteristics of the data or the approach to managing them change.

The DMP helps researchers manage their own data, fulfill funder requirements, and it supports the use of the data by others when it is shared.

Usually the principal investigator has the responsibility of drafting the DMP.

There are many templates available on the Internet for the drafting of the DMP, but often the funding programmes themselves will provide a template to be filled in.

It usually has several sections, such as:

  • administrative information on the research project
  • a description of the research data being created or re-used in the project
  • an overview of how the research data will be collected and managed
  • security measures in the processing of data during the project
  • management of potential issues concerning ethics, management of personal and sensitive data, confidentiality and privacy requirements
  • the storage and sharing of the data used in the scientific publications resulting from the project
  • archiving and access to data after project completion
  • management of the data care documentation
  • identification of the responsibilities involved in data production and management

It is advisable to archive data in archives or in institutional data repositories; where these exist, in disciplinary-specific repositories used by the various scientific communities, or in multidisciplinary repositories such as Zenodo which is managed by CERN, Dryad or Figshare.

Most are free of charge up to a certain size of dataset, and through databases such as re3data.org and OpenDOAR it is easy to find the most suitable repository for your data.

However, it will be necessary to verify that the selected repository meets certain requirements, in particular the following:

  • must have public governance
  • must guarantee the long-term storage of data
  • must support open licenses, such as Creative Commons
  • must be compliant with standard metadata requirements of international aggregators such as OpenAIRE
  • must assign a persistent identifier to data sets (DOI, Handle, URN)
  • must allow cross-linking with scientific publications
  • must manage the deposit of updated versions of the same data set linked together (versioning)

Along with the data, you should submit documentation and instructions (read-me files) for the tools and software used to generate and process the data.

The following descriptive metadata must be deposited with the data:

  • author(s) and contributor(s)
  • title
  • date of publication
  • abstract
  • references to funding, if any
  • citation of publications to which they refer, if any
  • distribution license
  • level of access
  • any embargo period.

Depositing software, for example on GitHub, and protocols,for example on Protocols.io would also be good practice.

Software must use the appropriate license, such as GNU or MIT licenses.

Other licenses are available on the Open Source Initiative website.

Zenodo Dryad Figshare Re3data OpenDOAR Creative Commons OpenAIRE GitHub Protocols.io Licenze GNU Licenza MIT Open Source Initiative