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Authors: Kirsty Wallis, Thomas Kaarsted, Simon Worthington, Alisa Martek and Dragana Janković.

Library Infrastructures and Citizen Science
Section Editor Kirsty Wallis
v1.0, 2023
Series: Citizen Science for Research Libraries — A Guide

DOI: 10.25815/tz0x‑m353


FigureResearch data lifecycle. RDM toolkit. (Jisc 2021)

By Dr James Houghton & Dr Christiana McMahon (ORCID iD: 0000-0002-9330-2686), University College London.

Article DOI: 10.25815/f3wr-we72

Research data management refers to guidelines and best practices that guide researchers through the process of planning a project, undertaking research, publishing their findings, and archiving their outputs. These concepts are intended to help researchers and other scientists successfully run their projects whilst meeting ethical and legal requirements for handling data or evidence, using IT services effectively to prevent data loss and mitigate other risks, and ensure the integrity and transparency of research. Institutions, funding agencies, and publishers all have expectations regarding data management.

Most funding agencies now expect a data management plan (DMP) to be written as part of a project proposal. Funders want to know data will be properly looked after and there will be a maximum return on their investment. Institutions and publishers also have expectations regarding data retention and availability for reproducibility and transparency purposes and that other potential users of the data can reuse and repurpose these to help maximise their research potential. As an example, the Wellcome Trust has a policy covering data, software and other materials with funders more generally requiring DMPs such as Horizon Europe.

Effective data management policy and practice encourages individuals to carry out activities routinely performed as part of a project, but in a more structured and rigorous way. Ideally this good practice needs to be applied before the project has started so that important decisions can be identified and any risks to the project and data or evidence, are sufficiently mitigated. Jisc and The UK Data Archive provide useful guidance on managing projects:

Research data management policy and practice form a part of the much wider issue of open science and scholarship — something that is fostering change across academia. A guide to data and open science is available from UCL.


Infobox: Common Issues

Aims and objectives are unique to each individual research project, so are data management plans — tailoring the plans to suit the needs of specific projects means that plans can end up looking very different from each other. Nonetheless, common issues include:

  • Backing up data and securing evidence during the lifespan of the project. Guards against data loss and other acts, both inadvertent and malicious.

  • Assuring data quality and that evidence is gathered consistently; especially if the work is being done by multiple persons.
  • Complying with data protection legislation when working with living human participants. Bear in mind that you may be subject to international data protection laws collaborating with research teams outside your home country.
  • Adhering to data sharing agreements and ensuring that all parties involved are satisfied before data or materials are moved from organisation to another.
  • Ensuring data and metadata are properly archived at the end of a project so that information is not lost.
  • Choosing an appropriate platform to share data, so others can make use of the data or evidence, findings and other outputs of the research.



Jisc. 2021. “Research Data Management Toolkit.”
User Type
  • Educator/museum
  • Researcher/research institution
Resource type
  • Collecting and processing data
  • Digital tools
Research Field