Best practices for documenting social sciences research data
| Dates: | 17 November 2025 |
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| Times: | 10:00 - 11:30 |
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| What is it: | Workshop |
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| Organiser: | Cathie Marsh Institute for Social Research |
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| How much: | Free |
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Data documentation is essential to make sure that well-organised and well-documented research data can be produced from our research projects. This workshop provides an overview of different types of documentation depending on data type (e.g. primary or secondary, survey or transcripts), as an essential part of implementing good data management in research projects, with a focus on optimising data sharing.
In this session we will explore the following:
The critical role of data documentation in research integrity and data sharing, for both current and future research projects.
How to develop comprehensive documentation for various data types, incorporating practical examples and templates that can be adapted to your projects.
An overview of metadata: what it is, its importance in making your data discoverable and understandable, and the standards to follow for effective data sharing.
During this free 90-minute online workshop, participants will learn the foundational principles and practical strategies for creating robust data documentation that enhances the accessibility, usability, and longevity of research data. Effective documentation is not a one-size-fits-all process; it varies significantly across different types of data and the workshop is tailored to address these nuances, ensuring researchers can apply best practices relevant to their specific data types.
The workshop will conclude with a dedicated Q&A session.
Presenter: Maureen Haaker. Maureen has worked with the UK Data Service for over 12 years supporting researchers and institutions in managing, preserving, and sharing qualitative data ethically and effectively. She runs training sessions on research data management, advises on best practices in data curation and anonymisation, and has worked on special projects, including a UKRI-funded project exploring uses of synthetic data in Trusted Research Environments (TREs). She is co-editor of the IASSIST Qualitative Data Special Interest Group and a member of the DDI Working Group developing metadata standards for qualitative data.
Level: Introductory
Experience/knowledge required: Basic understanding of research methods is recommended but not required.
Target audience: Academic and non-academic data professionals such as researchers, academic and professional services staff, data managers, data stewards and others keen on improving their data documentation practices.
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