Claire M. Curry, Eugene Albin, bobby reed, Amanda Schilling, Beth Tweedy, and Kristi Wyatt

Plain Language Workshop Descriptions

How to Attract Participants from all Disciplines

Claire M. Curry is a science librarian at the University of Oklahoma, email: cmcurry@ou.edu. Eugene Albin, formerly of the University of Oklahoma, is an independent researcher and taxonomist, email: joey.albin@gmail.com. bobby reed is an instructor of computer science at Oklahoma City University, email: breed@rose.edu. Amanda Schilling is a STEM librarian at the University of Oklahoma, email: amanda.schilling@ou.edu. Beth Tweedy, formerly of the University of Oklahoma, is a STEM librarian at the University of California, Davis Library, email: bntweedy@ucdavis.edu. Kristi Wyatt is an emerging technologies librarian at the University of Oklahoma, email: kwyatt@ou.edu.

© 2024 Claire M. Curry, Eugene Albin, bobby reed, Amanda Schilling, Beth Tweedy, and Kristi Wyatt

At the University of Oklahoma (OU) Libraries, we offer a variety of research workshops each semester, many focusing on file and data management and analysis. We find that STEM field practitioners usually recognize they need these skills and saw themselves in the wording we advertised for the workshops. However, with our libraries’ strong emphasis on digital scholarship and digital humanties, we wanted to make sure these skills reached everyone who needed them. To increase the impact of research and data workshops and help workshop organizers make their event descriptions understandable and appealing to a wider audience, volunteers representing varied disciplinary backgrounds in our library system met to develop plain-language guidelines. The three key principles for our plain-language guidelines are: make workshop goals obvious, supplement jargon with explanations, and use broad or discipline-agnostic descriptors so people recognize relevance to their work. We describe the process we used to converge on these principles, describe the review process for new workshop descriptions, and show a “before and after” example. While the process and advice we provide are specific for our data-focused workshops, the principles could be applied to ensuring broad audiences and marketing for any type of workshop description.

How It Started

The OU Libraries began offering Software1 and Data2 Carpentries workshops to the OU community in 2013.3 The original workshops used the Carpentries’ provided workshop descriptions. The Carpentries workshops are developed by people experienced in teaching to a broad audience and their workshops are maintained by the user community, ensuring adjustments to wording and advertising are made based on feedback gathered from attendees.

In 2019, after onboarding a cohort of new librarians at OU Libraries with science, technology, engineering, and mathematics (STEM) backgrounds,4 the new STEM librarians began expanding the workshop offerings5 and writing our own descriptions. While we imitated the style of the Carpentries descriptions, we often struggled to make the descriptions for the workshops attractive to people outside of STEM. This was even the case when the tools and concepts covered were of potentially broad interest, such as organizing research project files on computers.

In 2021, the research tools and data workshops around OU Libraries went from being occasionally, but not always, coordinated by individual divisions to being coordinated formally by a standing committee for research workshops with representation from each division. This coordination provided an opportunity to discuss the barriers to generating workshop interest among different disciplines, particularly to potential participants from the humanities and social sciences. We chose to convene an additional informal working group to create guidelines to widen the appeal of workshop descriptions.

Identifying Barriers to Interest

The working group chair requested volunteer participation from people in multiple public-facing disciplines within OU Libraries. The chair also made targeted requests to individuals who asked clarifying questions in unrelated workplace meetings. These individuals tended to question assumptions and ask for definitions or clarification, while not talking over other participants, which the chair felt would be valuable for a group where back-and-forth discussion would give better results.

To orient the working group, the chair asked people to focus on pre-defined goals for workshop descriptions, specifically what should be in the one-to-three-sentence summary for each workshop and how skills and objectives should be described.

Next, the chair chose three existing workshop descriptions to represent a range of topics. During each of these 10-minute discussions, the chair asked the working group to brainstorm and point out specific concerns with the existing description. As the moderator, the chair kept track of the time and noted people’s concerns. The chair did not address, explain, or defend any of the wording in existing descriptions. Refraining from personal comment was key, as the chair was one of the original authors, and such explanations could have derailed or discouraged commenting.

After the discussion, we had a 10-minute period in which we identified three themes, summarized below in the “Plain Language Guidelines” section. Any secondary concerns that had been brought up in the discussions, such as concision in writing, were not included in these workshop-specific guidelines for how to describe the topics.

After the meeting, the chair further expanded on these themes in a summary bullet-point document6 that could be shared with workshop organizers and with the working group to confirm agreement. For the three descriptions that we used as examples in the meetings, the chair asked the original authors of the descriptions to make the requested revisions and provide the new versions to the working group. Then working group members were sent the revised description for their suggested revisions and comments in two media: email or internal instant messaging channels. The chair facilitated this process with reminders to working group members and workshop organizers.

Plain Language Guidelines

We ultimately converged on three principles: make workshop goals obvious, supplement jargon with explanations, and use broad or discipline-agnostic descriptors so people see the workshops as relevant to their discipline.

Make Workshop Goals Obvious

“Burying the lede” is a term for hiding the key point instead of featuring it in your communication. Our group discovered in all three of our examples that the original writers tended to provide too much background and contextual information on the workshop. This information can be a part of the presentation, if needed, and wholly removed if it is found to be unimportant to the learners. Instead of using up scarce copy space on context, we focused descriptions on goals so learners can assess the utility and applicability of the content for themselves.

Supplement Jargon with Explanations

Jargon is specialized language to convey more meaning in fewer words. Jargon can be helpful for writing within a discipline but a barrier to understanding outside of a discipline. Software and research tools are often described by the jargon of their field of origin. Some keywords or software names may be useful for people searching for specific skills or tools, so not all specialized terms should be eliminated. However, we suggest that workshop writers balance jargon with additional wording, parentheticals, or sentences explaining the specialized terms. In this way, those seeking a specific tool as well as those who need help but may not know the specialized terminology can find and be interested by the workshop descriptions.

Use Broad or Discipline-Agnostic Descriptors

Even the terms used for the people, processes, and outcomes of academic research vary between fields and may unintentionally discourage people from participating in workshops. We suggest either using universal terms or including a mix of humanities, social sciences, and STEM terms for the people, processes, and outputs involved in the research process. These lists are derived from personal experience working with people in these fields and from reading proposal guidelines from the National Endowment for the Humanities, National Endowment for the Arts, National Institutes of Health, and National Science Foundation.

Broader Terms for Attendees

The most generalized terms for that we found in documentation or discussion across humanities, social sciences and STEM were faculty, staff, postdocs, graduate students, and undergraduate students (an additional benefit to describing people by classification is that it clarifies to all groups that they are part of a given workshop’s audience, as we have noticed staff and undergraduates are particularly hesitant to assume they can attend.) Terms that we found in common for humanities, social sciences, and STEM for process and output included projects, analysis, and research.

Humanities and Social Sciences Terms

Humanities and social sciences tend to label people involved in research as “practitioners” or “scholars.” Words for process and output include outcomes, creative activities, digital expression, digital tools, and digital humanities.

STEM Terms

STEM tends to describe people involved in research as “researchers” or “principal investigators” (PIs). Process and output words included data analysis, workflows, coding, scripting, programming, and data visualization.

Before and After Example

As an example of the process in action, the beginner workshop we revised was “Using Computers for Scholarly Research.” The original description mentions specific tools that are not used in the workshop and uses terms for researchers that are likely not general enough to be register to humanities and social sciences researchers, despite being a target audience.

Before Version

Do you want to use your computer for tasks that are more powerful than email and web searches in your research? This introductory workshop is targeted toward researchers who are interested in using tools like R, Python, Bash, GIS, or spreadsheets, but would like more basics about how these tools work in general.

After this workshop, learners should be able to:

• explain what a computer program does
• see commonalities in programs such as run, stop, and exit commands/buttons and menu items
• understand how to make experimenting with a new computer program safe for their files

Comments

Comments in the initial meeting focused on vocabulary, discipline inclusivity, and further explaining why someone would attend.

• “Do you want to”: marketing-speak that hides the goal and the audience
• “More powerful”: too judge-y
• Expand task terms to “research, teaching, and service”
• Humanities tends to assume “this isn’t for me”
• Tool names too specific; add data analysis tools or spreadsheets as a supplement
• Name research as a process; add scholar as a noun for people conducting the work

After Version

This introductory workshop is targeted toward scholars, practitioners, and researchers who are interested in using data analysis and digital humanities tools in projects for research, teaching, and service. This workshop provides more basics about how these tools work in general, allowing you to consider if new tools are right for your research process.

After this workshop, learners should be able to:

• explain what a computer program does
• see commonalities in programs such as run, stop, and exit commands/buttons and menu items
• understand how to make experimenting with a new computer program safe for their files

Implementation

After the initial revisions, all workshop facilitators converted the remaining dozen workshop descriptions to the new, plain-language versions over a four-month period. Some descriptions were accepted as is, others received editing to meet the guidelines. In more recent semesters, whenever someone creates a new workshop, we offer to send their descriptions to the group. Most organizers accept and have commented on the usefulness of the process.

Having a set group to make these changes took away the problem of the same people writing and editing the events. Having multiple people in the working group reduces workload on members as only a few need to comment each time. Generally, description revisions are done before each semester, leaving several weeks for members to comment before the given deadline. We have so far received one rush request (with less than a week until advertising), but with a pool of eight working group members, we still had two people available to provide comments even in this short time frame.

Conclusions

After the 2022 implementation of plain-language guidelines for OU Libraries’ workshops, our descriptions have been more understandable both by workshop participants and liaison librarians choosing which workshops to advertise to their departments, based on formal and informal feedback conducted. With the success of the guidelines so far, our internal workshops coordination committee plans to continue using these principles to guide ongoing revisions to workshops and in writing new topic descriptions. In addition to successfully addressing the primary need of making our research workshops more accessible and visible to a broader range of potential participants across academic disciplines, these language guidelines also serve to improve our ability to “build, deliver, and sustain services, expertise, and scholarly information that reflects the broad research and learning needs of our diverse and evolving communities,” one of OU Libraries’ stated, guiding principles.

Acknowledgments

Thanks to additional working group members (alphabetical by last name) Karie Antell, Logan Cox, Kendall Morgan, Lina Ortega, and Patrick Wright for their participation in this ongoing project. Thanks to Mark Laufersweiler for suggesting the term “plain language” for the title of our working group. Sarah Robbins encouraged the documentation of our process for the wider library community and provided feedback on the manuscript.

Notes

1. Greg Wilson, “Software Carpentry: Lessons Learned,” F1000Research 3 (January 28, 2016), https://doi.org/10.12688/f1000research.3-62.v2.
2. Tracy K. Teal et al., “Data Carpentry: Workshops to Increase Data Literacy for Researchers,” International Journal of Digital Curation 10, no. 1 (February 18, 2015): 135–43, https://doi.org/10.2218/ijdc.v10i1.351.
3. “Software and Data Carpentry | OU Libraries,” accessed October 4, 2023, https://libraries.ou.edu/content/software-and-data-carpentry.
4. Sarah Robbins et al., “Recruiting, Hiring, & On-Boarding Non-MLS Liaison Librarians: A Case Study,” Library Leadership & Management 36, no. 1 (March 14, 2022), https://doi.org/10.5860/llm.v36i1.7490.
5. “University Libraries On-Request Workshops | OU Libraries,” accessed October 5, 2023, https://libraries.ou.edu/content/university-libraries-request-workshops.
6. Claire M. Curry, “Plain Language Guidance” (OSF, May 12, 2022), https://osf.io/pn6wy/.