Open AccessAnnotation for transparent inquiry: Transparent data and analysis for qualitative research
Author(s) -
Sebastian Karcher,
Nicholas Weber
Publication year - 2019
Publication title -
iassist quarterly
Language(s) - English
Resource type - Journals
eISSN - 2331-4141
pISSN - 0739-1137
DOI - 10.29173/iq959
Subject(s) - annotation , transparency (behavior) , computer science , qualitative research , data science , research data , information retrieval , qualitative analysis , world wide web , qualitative property , data curation , sociology , artificial intelligence , social science , computer security , machine learning
How can authors using many individual pieces of qualitative data throughout a publication make their research transparent? In this paper we introduce Annotation for Transparent Inquiry (ATI), an approach to enhance transparency in qualitative research. ATI allows authors to connect specific passages in their publication with an annotation. These annotations provide additional information relevant to the passage and, when possible, include a link to one or more data sources underlying a claim; data sources are housed in a repository. After describing ATI’s conceptual and technological implementation, we report on its evaluation through a series of workshops conducted by the Qualitative Data Repository (QDR) and present initial results of the evaluation. The article ends with an outlook on next steps for the project.