Open qualitative research comes with a set of challenges that are unique to it. Qualitative data in particular are often difficult to share and reuse, as they often embedd a unique set of personal information linked to the research participants. Data protection laws across countries define clear rules and limitations under which personal information can be collected and used for various purposes.3 However, there are few simple practices that can be uptaken to provide some degree of openness and transparency to qualitative research.

Documenting the research process

Providing detailed information on the research process, the methodology and the set up of the qualitative study helps with contextualising and documenting the research approach. This constitutes a set of information that can help enhancing the transparency of the overall research, and allows for reproducing the study in similar contexts. It also enhance the ability of other researchers to recontruct the research process and review it, even when access to the primary qualitative data underlying it is not possible.

Releasing metadata

Even if the qualitative data collected and used as the basis of a research study cannot be openly shared and distributed, a defined set of metadata could be released on the web to help researchers in understanding the

Reflexivity and Positionality statements

Complementing a detailed methodological documentation with information on the researchers’ own specific approach, predisposition, knowledge background and preconditions to the study per se and with the participants to the study. This can be addedd in the research output, be it a scientific paper or a project report. This step is important to highlight natural predispositions or potential bias towards particular ways to analyse and interpret qualitative data, and therefore generate insights. This provide an added layer of transparency in enabling a better understanding of the interpretation of concepts and statements that might facilitate the reproducibility of the study, even when the underlying data are not released openly.

Anonymisation vs Pseudonymisation

There is a difference that is worth considering when processing qualitative data for the purpose of making them open to some degree. Below, official definitions from the European Commission 4,5,6 are provided for clarification.

Anonymisation

Anonymisation is defined by the European Commission as: “the process of creating anonymous information, namely, information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable”4, where personal data are defined as information that is related to “an identified or identifiable natural person”5

Pseudinymisation

Pseudonymisation is defined by the European Commission as: “the processing of personal data so that they can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person.”6

Discussing the importance of Open Science with research participants

Asking for more permissive consent in the use and share of qualitative data at the beginning of the study. Engaging research participants in the study itself, and allow them to contribute to the broader science field. Driving a positive discussion about the value of open science and open data for qualitative research, as part of the research study itself.

Managing a range of degrees of openness

Remember there is a wide range of openness that can be implemented. Each situation might demand a different approach. Some studies might be so specific and circumstantial that do not allow for much of the reuse and collaboration that open science allows.

Useful resources