Data sharing & management

This involves organising, documenting, storing, and, where appropriate, sharing qualitative data so that others can understand and, if permitted, reuse it. Good practice includes creating clear metadata (information describing how, when, and by whom data were collected and organised), maintaining data quality, anonymising transcripts and sensitive materials to protect participants’ privacy, and depositing datasets in trusted repositories that follow the FAIR principles (Findable, Accessible, Interoperable, Reusable).

Effective data management helps preserve the richness and context of qualitative materials, strengthens transparency, and supports secondary analysis and teaching. As open research becomes a core expectation, many journals, funders, peer reviewers, and the Research Excellence Framework (REF) encourage or require researchers to make their data as accessible as ethical and legal constraints allow. Careful data management is therefore essential not only for meeting these expectations but also for enhancing the trustworthiness, integrity, and long-term value of qualitative research.

No Action (Qual.)

Data are not shared*, and no formal data management plan exists. Data are stored in inaccessible, inconsistent, or poorly organised formats. Ethical, legal, or contextual reasons for not sharing are neither documented nor considered.

* If you use publicly available datasets, clearly cite the source, and share the portion you used, your engagement already exceeds the No Action level.

Moving from No Action to Emerging in Data Sharing and Management (Qual.)

Begin by understanding why organising and managing qualitative data matters for transparency, ethical integrity, and long-term preservation.
Create a simple data management plan. Use free tools such as DMPOnline or the University of California’s DMPTool to outline how you will store, organise, and, where appropriate, share your data.
Organise your materials. Use clear folder structures and descriptive filenames for transcripts, fieldnotes, and consent forms so they are easy to locate and interpret later.
Document your data. Add a brief README file describing what the data contain, how they were collected, and any contextual details needed for interpretation.
Consider ethical sharing. If participant consent allows, explore secure repositories such as UK Data Service ReShare, QDR or Zenodo. If sharing is not possible, document the ethical, cultural, or legal reasons why.
Protect confidentiality. Review anonymisation guidance from the UK Data Service to remove identifying details while retaining meaning.
For an overview and checklist on managing and sharing qualitative data responsibly and efficiently, consult the UK Data Service Planning Guide.

Emerging (Qual.)

Data sharing occurs occasionally, typically within the research team or upon request. Some effort is made to organise materials such as transcripts and fieldnotes, but documentation and metadata are limited. Ethical considerations for sharing and long-term storage are acknowledged but not yet systematically addressed.

Moving from Emerging to Evolving in Data Sharing and Management (Qual.)

To move from Emerging to Evolving, strengthen how you plan, document, and ethically manage your qualitative data.
Update your data management plan. Use free tools such as DMPOnline or DMPTool to describe how you will store, organise, and, where appropriate, share your qualitative data.
Organise and describe your materials. Use clear folders and filenames for transcripts, fieldnotes, and consent forms. Add a README file that summarises how and when the data were collected, what they contain, and any contextual details needed for interpretation.
Plan ethical sharing. Check whether your consent forms allow sharing and under what conditions. If sharing is possible, explore trusted repositories such as UK Data Service ReShare or Zenodo. Use the re3data registry to find field-specific options.
Ensure confidentiality. Review anonymisation and pseudonymisation guidance from the UK Data Service to protect participants’ identities while retaining the integrity of the data.
Maintain secure storage and backups. Keep data in secure, backed-up locations with clear version control and documentation of any edits or redactions.

Evolving (Qual.)

Qualitative data are shared in secure or trusted repositories when consent and ethics allow. Basic documentation and metadata are provided to help others understand the context and content of the materials. A data management plan exists for some projects but is not applied consistently or reviewed throughout the research process.

Moving from Evolving to Sustained in Data Sharing and Management (Qual.)

To progress from Evolving to Sustained, make data management a consistent and integral part of every qualitative project.
Create and maintain a detailed plan. Use DMPOnline or DMPTool to develop and regularly update a data management plan describing how you will collect, store, organise, and, where appropriate, share qualitative data.
Document your materials clearly. Prepare a README file summarising how and when the data were collected, what they contain, and any relevant contextual information (e.g., setting, participants, or coding framework).
Plan ethical and secure sharing. Where participant consent allows, deposit data in trusted repositories such as UK Data Service ReShare or Zenodo. Use the re3data registry to identify discipline-specific repositories.
Protect participants’ privacy. Apply anonymisation or pseudonymisation using the UK Data Service guide while retaining the data’s contextual meaning. Record what changes have been made.
Ensure data quality and integrity. Use the UK Data Service quality checklist to confirm that your transcripts, fieldnotes, and related files are complete, well labelled, and clearly documented.
Use open and sustainable formats. Save files in accessible formats such as TXT, RTF, or CSV, and maintain clear version control and secure backups.
Clarify reuse conditions. Add an open licence that outlines how others can use your data ethically and responsibly.

Sustained (Qual.)

Qualitative data are managed and, where ethically and legally appropriate, shared in trusted repositories that follow the FAIR principles. Comprehensive metadata and documentation provide sufficient context for others to understand and, when permitted, reuse the data responsibly. Data management is embedded throughout the research workflow and reviewed regularly to ensure ethical integrity, quality, and compliance.

Guidance for Sustained Level in Data Sharing and Management (Qual.)

Congratulations on reaching this level of practice. You are consistently managing and sharing qualitative data to a high ethical and professional standard. As a next step, consider contributing to your research community by sharing your expertise and promoting responsible data practices. To maintain and further improve at this level, consider the following:

Support others. Offer workshops or training sessions using free materials such as the DataONE Education Modules or UK Data Service Training Resources. Focus on practical issues such as anonymisation, consent, and ethical sharing.
Shape institutional policy. Engage with your university’s open research or ethics committees to help strengthen guidance and support systems for managing and sharing qualitative data responsibly.
Monitor data use. Where your data are shared, track how they are accessed, downloaded, or cited using platforms such as OpenAIRE.
Cite data properly. Follow DataCite guidelines and include dataset citations in your papers, presentations, and reports.