The Basics

Learning outcomes

• Understand the basic concepts in Research Data Management

Research Data

Research data is all information intentionally collected, observed, or generated to validate research findings and substanciate scientific claims.¹

Research data play a foundational role in Social Science and Humanities research, providing the scientific evidence for confirming hypotheses, backing claims and, more in general, substanciating research.

Data can be quantitative or qualitative in nature and take the forms of tables, text, images, audio/video recordings, archival material and other sources or physical evidence.

Research Data Management (RDM)

Research Data Management (RDM) is an umbrella term covering how you organise, structure, store, and care for the digital information generated or used during a research project.²

RDM practice usually includes:

• Planning in advance how your data will be managed
• Documenting working practices
• Considering how information will be handled on a day-to-day basis
• Making decisions on what happens to data in the long term – after the project concludes
• Preparing for data to be preserved
• Considering whether the information may be reproducible]

Data should be carefully managed throughout the duration of the research project. Particular attention should be given to data input, quality control, dataset design, data protection, data security and backup during the project. At an early stage, scholars should define dataset structure, file structure and variable naming. Documentation and codebooks should be updated throughout the research project.

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1. The European Commission, under their Q&A on Open Science requirements for funded projects defines research data as “the information (facts or numbers) collected to be examined and considered, and to serve as a basis for reasoning, discussion or calculation.” https://rea.ec.europa.eu/open-science_en

2. We adopted the definition from Research Data Oxford https://researchdata.ox.ac.uk/about-data-management]