DESCRIPTION

Sharing qualitative data means making it available to others for examination and reuse. As VandeVusse et al. note, '[t]he most common form of shared qualitative data is interview and focus group transcripts, but qualitative data can be shared in a wide range of formats including [...] images, audio, and audio-visual materials, scanned historical documents, field notes, and observations' (2022, 182). 'Sharing' is a continuum from making data available in a repository with specific access requirements, to publishing it openly in a repository for anyone to access and reuse for any purpose. In some instances, data may also be shared via discipline-specific collaborative online platforms such as the Platform for Experimental, Collaborative Ethnography (PECE) in cultural anthropology. Making data available only on request is not considered a meaningful form of sharing.

Sharing data, including qualitative data, is an expectation of many research funders, including, in the UK, the ESRC and Wellcome Trust, two of the main funders of research in the social sciences. The benefits of qualitative data sharing include enabling critical scrutiny and thereby improving research quality; maximising the dataset's value and impact; facilitating training of new qualitative researchers; and allowing peer and public oversight (Du Bois et al., 2018, 384-5).

Expectations around qualitative data sharing have, however, been controversial, with drawbacks highlighted both within and beyond discipline-specific debates such as that surrounding the Data Access & Research Transparency (DA-RT) initiative in political science. Ethical issues raised include concerns about participant safety (Class et al., 2021, 3), especially as the sensitivity of topics may change over time (Prosser et al., 2024, 655); concerns about reinforcing rather than addressing stigma in sensitive contexts, including via appropriation of open data by AI (Field, 2025); and concerns about extractivism (Majic, 2018). Epistemological issues raised include the context-dependency of qualitative data, which may render it less meaningful when separated from its immediate context (Broom et al., 2009; Chauvette et al., 2019; Feldman and Shaw, 2019; Mauthner et al., 1998). Lamb et al. have highlighted the epistemic inappropriateness to qualitative research of notions of data sharing for replication (2024, 659), while others have further criticised qualitative data sharing expectations as imposing a quantitative/positivist framework on qualitative work (Cramer, 2015; Mauthner et al., 1998; McLeod and O'Connor, 2021; Prosser et al., 2023; Tripp, 2018; Vuckovic Juros, 2022). Finally, researchers including Tsai et al. (2016) have highlighted practical challenges, including the time and labour demands of preparing qualitative data for sharing.

These challenges have themselves been contested. Regarding ethical difficulties, Bishop (2009) suggests that a focus solely on protecting participants neglects a consideration of researchers' duties to the scholarly community and broader publics, while Campbell et al. (2022) demonstrate the possibility of trauma-informed sharing protocols that protect participants and minimise the risk of further harm. Studies have also highlighted a high level of willingness among participants to share data (Cummings et al., 2015; Kuula, 2011; Mozersky et al., 2020; VandeVusse et al., 2022). In response to concerns about the meaningfulness of qualitative data that is detached from its original context, critics including Bishop (2009) and Moore (2007) emphasise the extent to which all analysis is a (re)contextualisation of data from a given standpoint, unsettling the distinction between primary and secondary data usage.

As qualitative data sharing and reuse become more common, attention has focused on the specific practices needed to enable it. These may include de-identification, which should be planned carefully in advance (Mannheimer et al., 2019) and address both direct and indirect identifiers, with strategies including 'broadening categories or partially reducing content' (Kirilova & Karcher, 2017, 4). For a detailed worked example of deidentification strategies in the context of highly sensitive data, see Campbell et al., 2023; for further information on de-identification, see the UK Data Service and Qualitative Data Repository guidance. Where it is not practically possible to deidentify the entirety of a dataset, a sample may be shared. Another vital mechanism in qualitative data sharing is informed consent, which should involve a verbal script explaining to participants what data sharing means (Gowie et al., 2024) and may entail offering a range of data sharing and confidentiality options (Kirilova and Karcher, 2017, 4; Kaiser, 2009, 1638), including recognising that participants may wish to be named. Several scholars have also highlighted the value of ongoing or multi-stage consent processes (Cummings et al., 2015; Cutcliffe and Ramcharan, 2002; Kaiser, 2009) and the role of member checking or member sharing as part of a staged consent protocol (Karhulahti, 2023). Finally, researchers should consider the appropriateness or necessity of access levels, which can be applied at the individual file (e.g. transcript) level in repositories such as the Qualitative Data Repository (QDR) and include options from fully open access, to access to logged-in users who agree to use the data for academic purposes only, to more restricted routes that may be appropriate where there is greater potential for misuse.

Guidance on how to prepare and share a qualitative dataset is frequently provided by repositories, though Antes et al. (2018) observe variability in the topics covered. Useful information regarding data preparation is provided in the QDR and UKDS guidelines for depositors (see 'Further Reading'). In general, qualitative data sharing should adhere to the FAIR principles (Findable, Accessible, Interoperable and Reusable) in order to make data 'as open as possible, as closed as necessary' and to maximise their usefulness. Data's findability is enhanced by sharing via a repository that grants a DOI. If the data are being shared according to a funder mandate, a particular repository may be specified; if not, researchers may select a field-specific or qualitative-focused repository (see the Re3Data registry for field-specific suggestions), or a generalist repository like Figshare or Zenodo. To enhance the accessibility of the shared data, researchers should where possible use open and accessible file formats. This also improves interoperability - the extent to which others can engage meaningfully with the data and integrate them as appropriate into their project workflows. The principle of reusability is further supported by the presence of a README file to support interpretation, and a licence (for example, from the Creative Commons suite) to clarify the conditions under which the data can be reused. Data should be fully documented to increase reusability (Faniel et al. 2019), including provenance information and complete metadata in line with disciplinary standards. Particularly when the data relate to marginalised communities or groups, researchers should also consider and where relevant apply the CARE principles (Collective benefit, Authority to control, Responsibility, Ethics) to ensure data are handled and shared in accordance with community interests (see the separate entry on this topic).

References

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