Abstract
Qualitative science methods have largely been omitted from discussions of open science. Platforms focused on qualitative science that support open science data and method sharing are rare. Sharing and exchanging coding schemas has great potential for supporting traceability in qualitative research as well as for facilitating the reuse of coding schemas. In this study, we present and evaluate QualiCO, an ontology to describe qualitative coding schemas. Twenty qualitative researchers used QualiCO to complete two coding tasks. In our findings, we present task performance and interview data that focus participants’ attention on the ontology. Participants used QualiCO to complete the coding tasks, decreasing time on task, while improving accuracy, signifying that QualiCO enabled the reuse of qualitative coding schemas. Our discussion elaborates some issues that participants had and highlights how conceptual and prior practice frames their interpretation of how QualiCO can be used.
Highlights
The basic ideas behind open science are probably centuries old and are present in the earliest scientific letters and in the establishment of journals for sharing research results
We evaluate QualiCO, an ontology for qualitative coding schemas, to understand how the ontology might improve the work of open science
The goal of this study is to demonstrate the usefulness of the system and understand how to improve QualiCO to fit the needs of qualitative researchers
Summary
The basic ideas behind open science are probably centuries old and are present in the earliest scientific letters and in the establishment of journals for sharing research results. Since roughly 2000, discussions of what constitutes open science have grown [1]. Some clear components of open science include the sharing of scientific data, sharing and explaining scientific methods, as well as sharing research results. For many open science efforts the accessibility of data is considered the primary means of openness across multiple fields of knowledge, people, and institutions as well as the validation of results. According to the Open Science Collaboration (OSC), prior research “should not gain credence because of the status of authority of their originator but by the replicability of their supportive evidence” [2]. In the midst of the ongoing replication crisis it is important to be able to have access to prior data as well as clearly described methodologies for collecting, cleaning, manipulating and analyzing such data
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