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Research Data Management

Guide to creating and maintaining FAIR and Open Research data in the Humanities, Social Sciences and Cultural Heritage

The Digital Repository of Ireland (DRI) preserves and provides access to digital objects from cultural heritage institutions across Ireland, and works with research-producing institutions to provide support in disseminating and preserving research outputs in line with the internationally recognised FAIR data principles. The DRI is committed to promoting best practices in research data management, with DRI staff offering training and support on these topics to member institutions. The DRI also acts as the national node for the Research Data Alliance (RDA) in Ireland and is responsible for the support and promotion of RDA activities across Ireland.

  • Do you work for an Irish higher education institution with a mandate to share research data outputs? Learn more about how DRI membership can support the dissemination and preservation of research data.
  • Are you a researcher who is looking to learn more about research data management? Check out our advice below on How to Practise Good Research Data Management and download our DRI RDM Resource Pack for useful links and tools.

What are Research Data?

Research data can be broadly explained as:

[D]ata that are used as primary sources to support technical or scientific enquiry, research, scholarship, or artistic activity, and that are used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results. All other digital and non-digital content have the potential of becoming research data. Research data may be experimental data, observational data, operational data, third-party data, public sector data, monitoring data, processed data, or repurposed data. (CODATA, https://codata.org/rdm-terminology/research-data/).

Despite the enormous scope of activity identified here, there are many fields of research that still struggle to recognise when research materials, sources, or findings may also be data. It seems to be easier to call something data when it refers to fixed, objective, or otherwise observable information and harder when it is produced from the process of gathering, arranging, creating, or interpreting information. Yet in reality, research data are probably both what the researcher extracts from their sources and, conversely, brings to them. Research data are complex and context-dependent, sometimes comprising both the information itself and the systems, methods, people, and practices that surround it. So how is a researcher supposed to know when the information they are working with is also data? It may simply be that data are data when they are designed and produced to be data. “Data may exist only in the eye of the beholder: The recognition that an observation, artifact, or record constitutes data is itself a scholarly act.” (Borgman, 2012) DOI: 10.1002/asi.22634.

Why Research Data Management Matters

Researchers engaged in any kind of research inquiry are often interacting with data in some way – discovering, selecting, organising and interpreting – and there is a significant and growing burden of responsibility in ensuring the transparency and trustworthiness of the data that underpins the research process. Research data are valuable assets only insofar as they are properly managed. Well-managed data have the potential to advance research and scholarship while poorly managed data can hamper progress, waste research funding, and, at its worst, cause irreparable harm.

Whether or not all researchers recognise the role that data management plays in their research practice, chances are that they are making curatorial decisions around data collection, analysis, retention and sharing every day in order to meet institutional, funder, disciplinary, and community expectations. Those decisions are influenced by legal and ethical frameworks, publisher requirements, and an enormous variety of potential technologies, tools, and services that can either support or hinder their ability to utilise and share quality research outputs. Good data management planning ensures that decisions are made throughout the research process that support the research goals.

What does good data management do?

  • Data management relates all the sources of information to the context and purpose of the research inquiry, while also recording how that context evolves during the research process.
  • Data management shows the boundaries of the work (revealing what did and didn’t inform the research inquiry and how the information was understood to describe one thing and not another).
  • Data management empowers others to replicate the research or reuse the data in an entirely new way.
  • Data management allows others to properly credit both the research process and the conclusions.
  • Data management supports the long-term preservation of data.
  • Data management produces data that are FAIR: Findable, Accessible, Interoperable and Reusable.

How to Practise Good Research Data Management

Research Data Lifecycle diagramThere are quite a few formulas, workflows, and guidance documents available to help researchers visualise and implement good data management practices. Many of these resources begin with a diagram, like the one above, that summarises the potential stages of work, actions required, and roles that should be considered in the process of working with data, from planning to preservation. These diagrams are often circular in design, indicating that the research process is rarely linear and may involve multiple loops back through the lifecycle over the course of a project. A common theme connecting all of these resources is the importance of having clear controls over how research data are received, documented, amended or annotated, and even physically transferred, emailed, or otherwise copied across storage locations. Beginning with a Data Management Plan is a great way to ensure that best practices are applied throughout the research lifecycle.

Data Management Planning

A Data Management Plan (DMP) is a document that can help you to articulate the role that data will play in your research inquiry. It should outline how you will control the data you collect and/or create over the course of the project, as well as the roles and responsibilities of any collaborators or partners. It is a living document that you can return to at any time to guide decisions around what data ultimately needs to be shared and how you will do that. DMPs further help demonstrate transparency, openness and return on public investment by describing how the data can be made discoverable, accessible, and reusable.

Still not sure whether creating a DMP is right for you? The Australian Research Data Commons (ARDC) answers the question Why do I need a data management plan?

Tools for writing DMPs:

  • DMPonline is a UK-based online tool for writing DMPs. Create an account to gain access to a DMP template or browse examples of DMPs submitted by other researchers.
  • The DMPTool is the original free, open source application for creating DMPs. The Funder Requirements registry provides templates tailored to the particular information requested by certain funding bodies.
  • LIBER maintains a Data Management Plan Catalogue “to inspire researchers and others in the process of writing a Data Management Plan.”

DMP tips and advice:

For more useful tips and links to resources that can help you to fill out the sections of your DMP, download the DRI’s RDM Resource Pack

DRI is funded by the Department of Further and Higher Education, Research, Innovation and Science (DFHERIS) via the Higher Education Authority (HEA) and the Irish Research Council (IRC).

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