Introduction
For decades, the open science movement has been building among quantitative researchers, aiming to share data and analysis as completely as possible. In part, this is meant to improve scientific transparency, so findings can be checked and verified (Open Science Collaboration 2015), but it also serves to provide a basis for evidence synthesis and meta-analysis (Palmer et al. 2016; Perrino et al. 2013), developing future studies (Bader and Finke 2017), and methodological training (Sobal 1982; Standing 2016).
Qualitative researchers have been much slower to adopt data and analysis sharing, for a few different reasons, including both technical and normative constraints. On the technical side, qualitative data has had limited standards, and analysis has often been conducted manually or with specialized software with limited compatibility with other software (Evers et al. 2020).
Perhaps more importantly, qualitative researchers tend to emphasize the role of both the context of data collection and the positionality of the researcher in the research process — in contrast to quantitative methods that often seek precisely to minimize the subjectivity of their methods and draw wide-ranging conclusions about entire populations (Karcher et al 2021, Mauthner and Parry 2009).
Moreover, qualitative data often presents challenges for protecting privacy when sharing data, because interviews, focus groups, and other qualitative methods provide rich detail that can make it easier to identify participants even if direct identifiers are removed, as well as often dealing with sensitive issues (Kirilova and Karcher 2017).
Despite these challenges, however, there is a small but growing movement to share and reuse qualitative and mixed methods data and analysis, helped along by recent technical developments. In the United Kingdom, archiving and sharing of qualitative data started in the late 1990s with the pioneering work of QualiBank, now part of the UK Data Archive (Corti 2000); in the United States, more recent work by ICPSR and the Qualitative Data Repository (QDR) (spurred, not unlike in the UK, by pressure from funding agencies to make funded data products widely available) has led to broader availability of appropriate infrastructure for sharing qualitative data (DuBois et al. 2023).
Developing Open Qualitative Curriculum
Libraries often find themselves the only campus units positioned to offer training in qualitative methods, data sharing and reuse, and qualitative data analysis software (QDAS), at least beyond courses in individual departments that are intended primarily for their own majors or graduate students. Given the range of responsibilities of data librarians, many of whom support data-driven research as only a small portion of their duties, there is a need for low- or no-cost, readily accessible and adaptable curriculum for this type of training.
Data repositories fill some of this need by offering training materials such as QDR’s Teaching Resources page (QDR 2021), but these are frequently limited to guides for data management, sharing, and reuse, with limited, if any, training in specific QDAS. Some software distributors offer structured training, in addition to manuals and user guides, but these tend to be recorded video demonstrations (and often require additional fees, such as the NVivo modules in Lumivero Academy; see Lumivero 2025). Video training is available freely through some professional organizations, such as the IASSIST Qualitative Social Science & Humanities Data Interest Group (iassistdata 2025). Such training, while valuable, lacks two key features of effective workshops for technology training - interactivity and responsiveness to participants’ interests and interventions. Moreover, many available tutorials focus on the most popular paid QDAS. Many universities offer interactive and evolving workshops through their libraries or other units, but these tend to be developed more or less independently, leading to a great deal of duplicate effort and gaps based on existing expertise of library teams.
A Fertile Ground
Over the last 5-10 years, several concurrent developments have enabled a dramatic change in how we teach computer-assisted qualitative methods. In addition to the increase in qualitative data archives discussed above, the REFI-QDA standard was launched in 2019 (qdasoftware.org; see also Evers et al. 2020), making it simple to move entire QDAS projects between a variety of packages for the first time. At the same time, several free, libre and open source (FLOSS) QDAS packages have debuted, most notably Taguette (Rampin and Rampin 2021) and QualCoder (Curtain 2025), with easier-to-use feature sets suited to a variety of project types, maintained well after their initial releases.
An opportunity to dedicate time to creating training resources with some of these new tools arose when the University of California, Los Angeles (UCLA) selected Open Qualitative Science as part of its new Library Carpentry lesson development cohort (UCLA Libraries 2025; for Library Carpentry see The Carpentries 2025), funded by the Institute of Museum and Library Services (IMLS). We selected Taguette to use for the initial lesson because it combined stable cross-platform compatibility with a clean and easy-to-learn interface. The Taguette lesson was developed to provide basic training in qualitative data reuse, qualitative data analysis and reasoning, and data sharing (Porter 2024).
Taguette also has downsides, however, three of which proved significant enough to consider developing a parallel lesson for a different QDAS: Taguette only works with a limited number of text-based datafile types, it has limited (codebook-only) compatibility with REFI-QDA data, and it provides no tools for mixed methods analysis of qualitative data through queries. As part of a larger IMLS-funded project to expand QDR’s infrastructure to take full advantage of REFI-QDA, the opportunity arose to develop such a lesson using QualCoder (Porter and Karcher 2025), a less intuitive but more full-featured open-source QDAS, once QualCoder reached development maturity (less bugs, out of the box support for all major operating systems, etc.).
Both lessons, as developed, follow three interconnected guiding purposes:
Provide OA training for FLOSS QDAS that is widely discoverable and has support for ongoing development and maintenance
Integrate QDR data and REFI-QDA standards deeply into QDAS training where possible, not as separate offerings (which are inevitably less attractive to busy students and faculty looking to learn they minimum they need to complete their analysis)
Use the live-coding/live-demonstration philosophy to integrate experiential learning with the tool, repository, and standard that equips learners for their own projects
Pilot Workshops and Outcomes
Once initial development of the curricula was complete, a series of pilot workshops were taught between Fall 2024 and Summer 2025 to test and refine the lessons for a variety of audiences. Each curriculum was taught twice as a three-hour hybrid or virtual workshop through a research-intensive university’s professional development network, with a multi-disciplinary audience of primarily graduate students and faculty, though they were also open to undergraduates, staff, and community members. In addition, QualCoder was offered at two professional conferences for library and archive professionals, the Research Data Access and Preservation Summit (RDAP) 2025 (virtual) and the annual meeting of the International Association of Social Science Information Service & Technology (IASSIST) 2025 (in-person).
All pilot workshops were delivered using a participatory live coding approach (Nederbragt et al. 2020). In live coding, a team of 2 or more co-instructors takes turns demonstrating skills in real-time, with participants performing the same or similar actions, supporting practice-based active learning (Brown et al. 2023). They do so by using their own computers and software, thus ensuring the learning environment closely matches their likely research environment compared to working in custom web or containerized (i.e. Docker) environments. Instructors not actively teaching make themselves available to respond to learner questions or errors in real-time, whether in the classroom or via Zoom chat. Pilot workshops differed from traditional Carpentries workshops in two ways: there were no helpers other than the co-instructors and they were delivered individually rather than as part of a more comprehensive 2-day workshop covering multiple software packages.
The two initial university pilots of the Taguette and QualCoder workshops were each attended by approximately 30 participants and taught by the lesson developers (individually or in collaboration). Few participants responded to post-workshop survey invitations, but informal feedback indicated that the workshops were helpful in choosing software, learning philosophies and methods of qualitative coding, and finding and using secondary data. At least five participants requested follow-up consultations to support Taguette or QualCoder implementation in new or ongoing research projects.
The RDAP workshop was attended by around 50 virtual participants. The workshop was co-taught by the lesson developers. An additional RDAP facilitator was present throughout the session, monitoring the chat and available to provide Zoom support. RDAP solicits standardized feedback for all workshops, which was completed by 13 participants. 11 out of 13 participants rated the workshop as 4 or 5 on a 5-point scale for “I found the workshop engaging and useful.” Positive qualitative feedback emphasized the “hands on” nature of the workshop as well as the “examples and activities.” Several participants emphasized that they valued that the software introduced (QualCoder) was open source. Constructive comments focused particularly on pacing, highlighting both the general difficulty in providing differentiated instruction in large workshops and, more specifically to this workshop, a challenge on whether to emphasize the qualitative coding or the software-related elements of the workshop.
The workshop at IASSIST was taught by one lesson developer together with an expert instructor not involved in the course creation to 8 in-person participants. Given both lower attendance and one hour of additional time, facilitators added two components not included in the published course: 1) frequent pauses to share tips and experiences for teaching QualCoder with the audience (mainly data librarians) and 2) a short discussion and demonstration of the AI features included in QualCoder since version 3.7. While there is significant disagreement about AI use in qualitative research (e.g., Davison et al. 2024), workshop participants expressed curiosity about the functionality, indicating it may be wise to consider including some discussion of AI in future workshops. Due to the lengthy initial set-up and required API keys for AI services, we would recommend a brief demonstration over hands-on activities for most contexts.
Overall, the workshop was found to be very effective in its current form. To allow personalized instruction, the number of participants may be limited to about 20, with clear advertisement of the principal goal of the workshop (e.g., learning QualCoder or Taguette, learning computer-assisted qualitative coding). The data used in the course can also be substituted with relative ease if participants have a particular background or expertise, with the Carpentries Workbench infrastructure (Davey et al. 2025a, 2025b, 2025c) providing user-friendly tools for adapting lesson length and content.
Ongoing Use and Development
Both lessons are designed to be teachable by any instructor with a basic understanding of qualitative research and Carpentries teaching methodologies (Brown et al 2023). The QualCoder lesson is now in beta status, having been taught multiple times by both the authors and other instructors. Taguette has still only been taught by the author and thus remains in alpha (The Carpentries 2025), but additional pilot testing is underway. We intend for both to eventually become a stable part of the Library Carpentry suite of lessons. The availability of open data, standards, and tools finally make it viable to develop and teach a Carpentries lesson on computer assisted qualitative data analysis. As all Library Carpentry lessons, this lesson is licensed under a creative commons attribution license and can be freely used, copied, and remixed as long as credit is given to its creators — as part of official Carpentries instruction or in self-organized workshops. Both QualCoder and the data at QDR are also free to use, so integrating the lesson into a library’s curriculum requires no licenses or other financial commitments.
As part of QDR’s work on QDAS data, a wider variety of REFI-QDA formatted data is expected to become available at QDR and beyond over the next 12 months.
Conclusion
Collaboratively developing lessons on qualitative research and software using The Carpentries framework and infrastructure has exposed two key challenges of adapting approaches primarily intended for training in quantitative tools to more diverse approaches.
First, the live coding framework (Nederbragt et al 2020), while adaptable to non-coding live demonstration, relies in part on the ability of learners with different experience levels and computing speed to continue viewing code after it is run, something which is generally impossible for mouse- and menu-driven commands. Given this limitation, successfully running inclusive trainings that leave no learners behind may require more co-instructors or instructional assistants per learner than traditional live coding workshop to help learners resolve errors or catch up.
Second, the lessons’ guiding purposes proved ambitious in seeking to simultaneously train novice users on software, qualitative research methodology, OA and data reuse. Particularly in the case of QualCoder, keeping the workshop short enough to be taught during a term when most potential learners are teaching or enrolled in classes that can create schedule conflicts, necessitated simplifying or omitting major areas of functionality. This is, however, consistent with Carpentries philosophy, which emphasizes building literacy in core tool functionality over complete training for a single research workflow. In this way, learners build knowledge, practice skills, and internalize positive affect that enable them to continue learning on their own. In the future, the authors intend to develop optional add-on modules for teaching mixed methods analysis and embedded AI tools with QualCoder for situations where more time is available.
It is of course our hope that these lessons will be widely adopted within and beyond libraries for qualitative research software training. Beyond that, however, we believe these lessons and this paper provide two additional springboards for future improvement. They provide the first openly licensed curriculum using both FLOSS software and open data for maximum transparency and flexibility. Finally, the insights gleaned from observation and feedback as they are delivered can be used by both librarians and educators in diverse disciplines to teach qualitative research skills more effectively, regardless of the software and data sources they use.
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