Abstract
This reflection by a qualitative researcher stems from a concrete experience with data handling in a funded research project. The researcher followed Open Research Data guidelines and found optimal solutions to pseudonymise data, but this later evolved into a deep epistemological questioning on praxis. During the first phase of the project, a tailor-made software was developed with help from librarians and an IT professional to automate the pseudonymisation of the 150 data chunks generated by 16 students, 3 tutors and 3 decision makers. In the second phase of the project, this experience sparked questions about the meaning of such data handling and interpretations of Open Science, which led the researcher to suggest a framework for the professional development of qualitative researchers in their understanding of Open Science. The article raises awareness of normative frameworks in institutional data handling practices and calls for active contributions to defining qualitative research in an Open Science perspective, particularly taking as a reference the recent draft recommendation by UNESCO (2020)
Highlights
Open Science is a new approach to the entire scientific process based on transparency, collaboration, new ways of disseminating knowledge and evolving interactions between science and society (de la Fuente, 2017-19; Ramjoue, 2015)
The article opens a new line of epistemological questioning, namely, beyond the focus on data sharing (Antonio et al, 2020), what could the praxis of qualitative researchers look like in an Open Science perspective (UNESCO, 2020)?
The main question subsequently became: ‘Beyond data handling, what could the praxis of qualitative researchers look like in an Open Science perspective?’
Summary
Open Science is a new approach to the entire scientific process based on transparency, collaboration, new ways of disseminating knowledge and evolving interactions between science and society (de la Fuente, 2017-19; Ramjoue, 2015). The article explains how the SoTL approach was used to guide the researcher’s reflections It uses the case study of a Swiss National Science Foundation (SNSF) project on Open Education research methodology teaching to illustrate how qualitative data were handled and a tailor-made software designed and developed to automate pseudonymisation. Data are deidentified for several reasons: (1) researchers must comply with national and European ethical data-protection laws like the GDPR; (2) public funding institutions strongly recommend that researchers publish their datasets on a data repository and (3) an increasing number of journals require researchers to upload their dataset with their article This practice invites researchers to make the entire iceberg of their research visible and Open Access, including their dataset, DMP, instruments and article (Tennant et al, 2019) – whereas until recently, only the tip of the iceberg (i.e. the article) was required to be published. It is recommended that, when making these decisions, researchers consider their research question and the requirements it imposes in terms of detailed information, as well as the effort readers would have to make in order to re-identify pseudonymised information
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