Whereas in quantitative research, the concept of open science has gained fairly high momentum in recent years, open science in qualitative research still has a long way to go. Several research data centers have been established, but the focus has been kept on the sharing of research data, neglecting other disiderata of the qualitative research process like coding schemas or memos. There does exist an infrastructure for the exchange of research data. For coding schemas there is no such infrastructure for exchange.

Nevertheless, there is a call for more transparency in qualitative research. Several articles  have been published calling for more transparency in qualitative research. The American Psychological Association (APA) released guidelines (Levit et al. 2018) how to describe qualitative research making it more traceable. These guidelines call not only for the description of the analysed research data, but also for the description of the data analysis and encourage researchers to provide information about their codes, whether coding was done deductive or inductive. Other work calls for the publication of the qualitative method, Data coding and first-order codes as well as data analysis and second or higher-order codes in order to make qualitative research reproducible (Aguinis & Solarino 2019).

This work focuses on the exchange of qualitative codes, which researchers use to analyse data, be it observation data or interviews. Codes are used according to the two methods, Grounded Theory and qualitative content analysis. All codes that are developed within this research are called a coding schema. In both methods, the coding schemas are an essential part of qualitative research in these methods, being either a tool for coding (qualitative content analysis) or the result of the coding (Grounded Theory) (Bücker 2020). Other literature compares rather the differences in the different coding activities; Saldana names 30 different ways of coding.

There is also the development of an exchange format for qualitative coding schemas from different QDA software led by the group REFI (Evers 2020). Since most of the QDA software is proprietary, it is not possible to exchange coding schemas or whole projects from one software to the next. The main goal of this initiative is to provide a format for exchanging and archiving coding schemas as well as project data. 

The exchange format does not provide a formal description of the coding schemas, yet alone a standard which information researchers have to provide about their codes.

The qualitative coding ontology (QualiCO) bridges this gap. QualiCO is an ontology to provide metadata and information for coding schemas as well as codes. It also incorporates information about other parts of research, which are needed to understand coding schemas: the study, the analysed data and publications.

Exchange and reusage of coding schemas bears a great potential for qualitative research. When coding schemas are published, the research becomes more transparent. Coding schemas can also be as a whole or partly reused in other research or used in teaching to give students examples. One other focus in the development of QualiCO was to minimize the effort of documentation, so we took care to only include information that can be filled out easily and that is clear to the researchers.

We used a participatory design and multi-method approach to create the ontology. We started with a requirement analysis, then used participatory design to develop a prototype and tested this prototype with two evaluations. In the requirements analysis, first a stakeholder analysis was conducted to identify the most relevant stakeholders in the field. This was not only researchers, but also developers of QDA-software, research data centers and scientific journals. However, our focus were qualitative researchers in educational research in Germany. Next, we conducted a participant observation to tap deeply into the field and see the research done in practice. Based on this, we did expert interviews (n=10) to broaden our research and talk to several stakeholders to get requirements for a first prototype. We also asked the participants to share their coding schemas with us and analyzed textbooks the researchers recommended. During the design phase, we first used a feedback round with qualitative researchers and one domain expert of a research data center (n=5) of paper prototypes of the ontology. We then presented the ontology in two stakeholder workshops. The last part of the design phase were again feedback rounds on a prototype of the ontology that has been implemented in Semantic Mediawiki, making it clickable and also containing prototype data (n=4). For the development of the prototype, we relied on Open Science standards like the FAIR data principles (Wilkinson et al. 2016) Lastly, we conducted two evaluations: in the first evaluation, we did user tests (n=20) to see whether researchers can re-use a coding schema based on the description with QualiCO. In this evaluation, our participant group consisted of qualitative researchers from the field of human-centered design in the US. We were thus able to bring in a new user group in order to see differences in the usage of methods in different fields and different countries. The last evaluation was a qualitative feedback interview with researchers from educational research and one editor of a scientific journal in Germany (n=10). We assessed the relevance of each item, the naming and the completeness of the whole ontology with regard to re-using and publishing coding schemas.

The main research outcome was the ontology QualiCO, which gives a set of metadata to describe coding schemas. QualiCO consists of several classes since participants articulated the need to have more information than just the codes to see whether a study fits their needs: publication, study, research data, the coding schema and the codes. The metadata in the class publication are kept simple and in line with the standard Bibo. The goal was to give basic information and link to the actual publication. For the study, the goal was to describe the institutional as well as the methodological background so users can see whether this was a large or small project and what theoretical literature was used. In the class research data, we followed DDI standards and the ontology of the research data center for education in Germany for the sake of compliance. This class contains metadata about how the data was obtained (e.g. interviews, observations) as well as information about sampling strategies and links to the original data. On the category coding schemas, we included information about the theoretical background of the study, the method used, how the coding schema was built and how the data was coded. Again, we included specific information for each method like information about inter-coder-reliability as well as the possibility to upload visualizations. On the level of codes, we included a code description, examples as well as information how often a code was used in the study and information about its relation to other codes. Since not all of these codes are equally important for both methods, only the description and one example are mandatory. The evaluation showed that QualiCO is suitable to support the researchers in different open science tasks like searching for and using qualitative coding schemas.

Aguinis, H, Solarino, AM. Transparency and replicability in qualitative research: The case of inter-views with elite informants. Strat. Mgmt. J. 2019; 40: 1291– 1315. https://doi.org/10.1002/smj.3015 Bücker, N. (2020). How to code your qualitative data—a comparison between grounded theory methodology and qualitative content analysis. In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research (Vol. 21, No. 1). Evers, J., Caprioli, M. U., Nöst, S., & Wiedemann, G. (2020, May). What is the REFI-QDA Stand-ard: Experimenting With the Transfer of Analyzed Research Projects Between QDA Software. In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research (Vol. 21, No. 2). Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report. American Psychologist, 73(1), 26-46. http://dx.doi.org/10.1037/amp0000151 Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., ... & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1), 1-9.