Data Management Plan (DMP)

Summary

This chapter explains how to use this guide effectively and provides tips for search, browse and navigation.

Learning outcomes
  • What is a Data Management Plan (DMP) and how it is structured
  • How to prepare the core section of a DMP

What is a Data Management Plan

A Data Management Plan (DMP) is a formal document that outlines how data will be handled during and after a research project.

Data Management Plans (DMPs) are documents normally required by research funders at the beginning, mid-point and end of research projects. A DMP is a living document that usually includes the following information:

  • How data is collected and generated
  • How data is used, elaborated and organised
  • How data, and data subjects, are protected
  • How data, code and ancillary elements are described and documented
  • How data is stored and secured during the project, and how long it will be retained
  • How dataset authorship and credit are assigned
  • How data is preserved
  • How, whether and under what terms, research data outputs can be shared

Every DMP is a unique document, grounded in the individual research project and it is the responsibility of the Principal Investigator (PI), or delegated person (e.g. the project manager, or member of the research team) to write and deliver a comprehensive, accurate and original data management plan.

It is important to update DMPs throughout the research project, incorporating new information about data collection, data generation, methodologies and relevant changes to the composition of the research team or consortium (for intenrnational collaborative projects).

Tip

Preparing a DMP at the beginning of a research endeavour is an important step in making sure that data are properly managed and curated, prepared for possible sharing and reuse, and in ensuring transparency and reproducibility of research. Early-stage researchers (like EUI PhD Researchers) are strongly invited to learn how to prepare a DMP, as they will be required when applying for future project funding.

Tip

The EUI Library can provide feedback and guidance. Please, reach out to the Research Data Librarian

Common elements of a DMP

The FAIR data principles - to make data findable, accessible, interoperable and reusable - must be considered during the preparation and revision of data management plans. Detailed information about these four principles is available in the grid below.

When preparing a DMP scholars can follow these prompts to address all core aspects:

Overview of research project
  • Provide an overview of the research project, indicating discipline/sub-discipline, inter-disciplinary scope, methodology and primary research questions
  • List the types of data being collected, generated, processed and used.
Anticipated data outputs and utility
  • Describe expected data outputs
  • Distinguish (where applicable) between (i) in-project data which will not be shared and (ii) post-project research dataset outputs (public or embargoed)
  • Describe expected public utility; eg. for researchers,
    policymakers, media organisations and the general public
  • Describe possible inter-disciplinary use
  • Describe data quality assurance processes.
Resources required for data collection, generation, processing and use
  • Provide a detailed description of data collection, generation, processing and use
  • Describe anticipated technical and research assistance
    requirements
  • Give an overview of the data management responsibilities of
    research project leaders, team members and (where applicable) institutional partners.
Data security, infrastructure and protocols
  • EUI members should write to the Data Security Officer to request the standard description of data security, infrastructure and protocols. This internal document describes layered physical and network security, authentication protocols and other elements for inclusion in data management plans.
Ethical considerations
  • Describe data protection measures in place to scrupulously guarantee that persons, families and households are not identifiable in any dataset outputs
  • EUI Ethics Committee review may be required before the collection and processing of personal data. Ethics Committee approval cannot be granted retroactively. EUI members should initiate the request for Ethics Review before collecting data.
  • Consultation with the EUI Data Protection Officer and/or Data Security Officer may be required (included in the Ethical Review).
  • Maintain a record of pre-existing data resources used in the project, and comply with copyright provisions.
  • Contact the Research Data Librarian for assistance.

FAIR principles in DMPs

Most funding programmes, including ERC and other Horizon Europe schemes, have embraced the principles and practices of Open Science, fostering the approach that data should always be “as open as possible and as closed as necessary”.

The FAIR principles have been adopted as a guiding rubric in DMPs to ensure that research data are effectively Findable, Accessible, Interoperable and Reusable. The FAIR principles also shaped the most recent version of the DMP templates for ERC and Horizon Europe.

Here following a few elements that Principal Investigators and Project Managers have to consider when addressing the FAIR principles in their DMP:

Making data Findable
  • Identify the repository where data outputs will be preserved and, where possible, made openly available; eg. the EUI research
    repository, Cadmus.
  • Describe how folders, files, variables and versions will be
    consistently named to aid discovery
  • Datasets should be assigned accurate and consistent metadata to
    aid findability and machine-retrievability (Section 6.c)
  • Datasets should have persistent, unique digital object
    identifiers (eg. handles generated by the EUI research
    repository, Cadmus).
Making data Accessible
  • Distinguish between in-project data which will remain closed (or will be destroyed) after the project; and data outputs which will be preserved and (where possible) openly shared after the project
  • For restricted data, give reasons for restricted status (eg.
    personal data protection and/or database copyright compliance)
  • For restricted data the terms and conditions for access should be explicitly stated (even if these might be revised as the research progresses)
  • For sharable data, the status ‘open’ (accessible immediately) or ‘embargoed’ (accessible at a later date) should be assigned
  • Describe data protection and anonymisation measures in detail
  • Describe the software packages/tools required to use the data
  • Metadata records are indexed in the EUI research repository,
    Cadmus, which provides machine-readable metadata for indexing by search engines and application programming interfaces (eg. Google Scholar, OpenAIRE and CORE).
Making data Interoperable
  • Provide details of any standard controlled vocabularies (ontologies, thesauri and taxonomies) from the relevant discipline (social sciences, humanities, ethnography &c.)
  • Variable schemas should be readable by standard software packages to facilitate data interoperability
  • Standard metadata and naming conventions should be used
  • The EUI research repository, Cadmus, uses web standards (the underlying DSpace software adopts standards for embedded micro-data and the OAI-PMH interoperability protocol) providing both human- and machine-readable interfaces to search, discover and access reposited data.
Making data Reusable
  • Open data generated by the research project should be made available under an open licence that clearly states reuse conditions - either the Creative Commons Attribution International license: CC-BY International[^49] or the Creative Commons Public Domain Universal license: CC0
  • Supporting documentation and codebooks (PDF/A documents and ‘readme.txt’ files) should be reposited with the data to make the observations comprehensible and reusable by other scholars and stakeholders
  • Provide information about any tools or instruments necessary to reuse or verify the data (software, algorithms, routines, models &c.)
  • Note any data embargo period, where applicable.

EUI supporting documents

The following supporting materials should be consulted during the preparation of EUI DMPs:

Data management planning tools

Online tools can be used to prepare structured data management plans - complying with EU Horizon Europe, European Research Council, and other science funder DMP requirements.

  • DMPonline[^53] (maintained by the Digital Curation Centre) can be accessed via ‘Create Account’ at the upper right of the DMPonline homepage. For organisation enter ‘other’. Select the science funder's template. The principal investigator (P.I.) should be identified in the data management plan. For research teams, the P.I. can assign DMP sharing rights by entering colleagues’ email addresses and assigning the status of ‘co-owner’, ‘editor’ or ‘read only.’ Enter project details (title, abstract, &c.) and click on ‘Initial DMP’ in the top menu. Complete the sub-fields to generate the data management plan. A Word document can be generated. If the science funder provides a DMP template, the text generated in DMPonline can be transferred into the funder's template.

  • The Research Data Management Organiser (RDMO)[^54] is an online tool available in German and English to support research data management. RDMO is maintained by the Deutsche Forschungsgemeinschaft (DFG).

  • Argos[^55] is an online DMP creation tool maintained by OpenAIRE. Create an account on the Argos platform, click ‘Launch Wizard’ and follow the step-by-step instructions.

  • The DMP Evaluation Rubric[^56] developed by Science Europe, provides core criteria which can be used by principal investigators and project managers to evaluate data management plans.

  • FAIRsharing is a data support service of the Data Readiness Group and the Bodleian Library, University of Oxford, providing resources for data and metadata standards and research data management.[^57]

  • The F-UJI Automated Fair Data Assessment Tool[^58] maintained by the Fostering FAIR Data Practices In Europe project, helps scholars comply with the FAIR Guiding Principles for scientific data management that ensure data are - and remain - findable, accessible, interoperable and reusable (FAIR).

  • FAIR-Aware[^59] maintained by the Data Archiving and Networked Services Institute is an online checklist tool to help researchers build compliance with the FAIR data principles.

  • The DMP Catalogue[^60] maintained by LIBER, provides examples of research data management plans.