Qualitative Data Management and Analysis within a Data Repository

Abstract

Data repositories can support secure data management for multi-institutional and geographically dispersed research teams. Primarily designed to provide secure access, storage, and sharing of quantitative data, limited focus has been given to the unique considerations of data repositories for qualitative research. We share our experiences of using a data repository in a large qualitative nursing research study. Over a 27-month period, data collected by this 15-member team from 83 participants included photos, audio recordings and transcripts of interviews, and field notes. The data repository supported the secure collection, storage, and management of over 1,800 files with data. However, challenges were introduced during analysis that required negotiations about the structure and processes of the data repository. We discuss strengths and limitations of data repositories, and introduce practical strategies for developing a data management plan for qualitative research, which is supported through a data repository.

Towards Open Science for the Qualitative Researcher: From a Positivist to an Open Interpretation

“…However, as researchers explore new possibilities for praxis, they should be careful to keep in mind the intrinsic characteristics of qualitative researchwhich may not be as clear-cut as one might think (Aspers & Corte, 2019). In any case, qualitative research in an Open Science perspective presents a clear opportunity for deep reflection and methodological reconsideration, in line with Antonio et al (2020)'s work and with UNESCO (2020)'s draft recommendation for Open Science. review) and inclusivity (e.g.…”

“…In opposition, some scholars hold that data context does not preclude data reuse, and that qualitative data are fit for secondary analysis whether the context of the new study is akin to that of the original study or not. Others argue that attaching detailed metadata to datasets (Bishop, 2009; Jones et al, 2018) would allow subsequent researchers to treat each dataset, or dataset chunk, as a unique case and decide the extent to which each one could be shared and reused, depending on the nature of the data (Chauvette et al, 2019; Roulston, 2019), data-protection regulations and deeper methodological questions (Antonio et al, 2020). Despite this active and evolving debate on qualitative data sharing, however, no studies have yet suggested a framework for the development of qualitative researchers’ understanding of Open Science and how this translates into their praxis.…”

“…It then 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. Finally, 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)?…”

Transparent, Open, and Reproducible Prevention Science

“…Qualitative researchers can aspire to share materials like detailed memos, codebooks, and information on inter-rater reliability (Lorenz & Holland, 2020). Qualitative researchers are also demonstrating ways in which data management plans can be developed to share various forms of data-such as photos, audio recordings, interview transcripts, and field notes-in an ethically and legally appropriate manner (Antonio et al, 2020). Prevention scientists could contribute empirical examples to the nascent but dynamic literature on making qualitative research more transparent and open (Kapiszewski & Karcher, 2021).…”

Tracing the footsteps of open research data in China

“…The main function of the research data repository is to provide data storage and open data sharing services for scientific researchers, academic groups, the public and other target users (Antonio et al, 2020; Chen & Wu, 2017; Cragin et al, 1926). Therefore, the data repositories mostly formulate data policies from the perspective of terms and conditions of the platform.…”