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How Data Curation Enables Epistemically Responsible Reuse of Qualitative Data
Author(s) -
Sebastian Karcher,
Dessislava Kirilova,
Christiane Pagé,
Nicholas Weber
Publication year - 2021
Publication title -
the qualitative report
Language(s) - English
Resource type - Journals
ISSN - 2160-3715
DOI - 10.46743/2160-3715/2021.5012
Subject(s) - reuse , data curation , qualitative research , data sharing , qualitative property , computer science , skepticism , data science , sociology , knowledge management , epistemology , management science , engineering ethics , social science , engineering , medicine , philosophy , alternative medicine , pathology , machine learning , waste management
Data sharing and reuse are becoming the norm in quantitative research. At the same time, significant skepticism still accompanies the sharing and reuse of qualitative research data on both ethical and epistemological grounds. Nevertheless, there is growing interest in the reuse of qualitative data, as demonstrated by the range of contributions in this special issue. In this research note, we address epistemological critiques of reusing qualitative data and argue that careful curation of data can enable what we term “epistemologically responsible reuse” of qualitative data. We begin by briefly defining qualitative data and summarizing common epistemological objections to their shareability or usefulness for secondary analysis. We then introduce the concept of curation as enabling epistemologically responsible reuse and a potential way to address such objections. We discuss three recent trends that we believe are enhancing curatorial practices and thus expand the opportunities for responsible reuse: improvements in data management practices among researchers, the development of collaborative curation practices at repositories focused on qualitative data and technological advances that support sharing rich qualitative data. Using three examples of successful reuse of qualitative data, we illustrate the potential of these three trends to further improve the availability of reusable data projects.

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