Dispatches From the Field: DCN Specialized Data Curation Workshops

L. Weaver1, Wanda Marsolek2, Tim Norris3, Mariah Kenney4, Amy Schuler5, Sophia Lafferty-Hess6


1University of Utah, 2University of Minnesota, 3University of Miami, 4Carnegie Mellon University, 5Cary Institute of Ecosystem Studies, 6Duke University

Introduction

The Data Curation Network (DCN) is a vibrant community of practice (Johnston et al., 2018) focused on curating datasets, promoting data curation best practices, and supporting evidence-driven research (Coburn & Johnston, 2020). DCN members are leaders in open science and collaborate with researchers (Burns et al., 2024) to support ethical data sharing in ways that are findable, accessible, interoperable and reusable (FAIR) (Data Curation Network, 2025b). Since its inception almost 10 years ago, the DCN has organized data curation workshops in a variety of venues, including in conjunction with conferences, in association with professional societies (Moore, 2018), and in collaboration with research institutions across the United States (2018-20 Workshops, 2020).  Throughout these activities, the DCN has prioritized making educational materials available for use and re-use within data curation and library communities (Data Curation Network, 2019).  DCN data curation materials are considered foundational for practitioners in the field (Lafferty-Hess, Erickson, Keshavarzian, Marsolek, Moore, et al., 2025; Long et al., 2024; Marsolek et al., 2023) and are widely available via the DCN’s website and/or GitHub:

In Spring-Summer 2025, the National Institutes of Health (NIH) sponsored three DCN curation workshops to support the professional development of this emerging community of data curators (Data Curation Network, 2025c). The first workshop focused on the foundations of the CURATED steps using well-established curriculum that has been taught to over 250 data professionals since 2017. The CURATED workshop was very well received, with one participant commenting:

“Overall, this was one of the best workshops I’ve attended in my library career. Even the PowerPoint was well-organized and engaging, with strong visuals and minimal verbatim reading. Y’all have something special here – thanks for having me!”

The next two workshops were part of a program of continued development of specialized curriculum materials and focused on curating computer code, simulation data, scientific images, and geospatial data (Data Curation Network, 2025d) and have been made available for reuse: 

Attendees were expected to be familiar with the seven step CURATE(D) process and the concepts of Findable, Accessible, Interoperable, and Reusable (FAIR) as a prerequisite (Coburn & Johnston, 2020), which allowed the workshop to focus on curation of specialized materials. The advanced curation workshops were first piloted in 2023 (Data Curation Network, 2025a) with a grant from the Institute for Museum and Library Services (IMLS).

Specialized Curation for Code and Simulations-based Research Workshop

On May 12-13, 2025, 17 participants from across the US met on the NIH campus in Bethesda MD to take a deeper dive into computationally intensive research practices.  The 2-day workshop offered hands-on activities to build participants’ skills around curating code and simulation-based research data. Participants had data curation backgrounds across a variety of geographic and institutional contexts, including university research groups, government research facilities, academic libraries, university data services, data curation consortia and healthcare. 

Seth Erickson and Greg Janée (University of California, Santa Barbara) led the discussions of code curation.  Seth piloted a novel virtual environment which hosted code, tools, and datasets for use in the workshop based on JetStream2. The workshop provided a community sharing good practices of what to look for as data stewards including file paths, source code file names, dependencies, documentation, and licensing. Attendees appreciated materials shared during the workshop, including a code licensing help guide and a code readme template:

Example: Readme Template (Code): https://doi.org/10.7298/h85a-qz91.2
Example: Licensing guide: https://choosealicense.com and https://choosealicense.com/appendix/
Example: Simulation Curation Rubric: https://zoidy.shinyapps.io/ModelDataRubric/ 

The discussion of code and research software illustrated the complexity of interaction between digital artifacts of the software stack, custom code, and data outputs. Facilitators L. Wynholds and Wanda Marsolek built upon the code module to transition to simulation-based data with interactive, hands-on activities. The mix of instruction and hands-on activities gave participants the opportunity to engage with the challenges presented by multilayered and multifaceted digital research artifacts stemming from computationally intensive research. For example, deciphering whether a dataset contains simulation data is not determined solely on file format or extension; documentation plays a decisive role in understanding how the dataset was generated and which parts should be prioritized for curation purposes. To illustrate this complexity, facilitators used the metaphor of puzzle pieces to engage participants to collaborate on puzzling together corresponding pieces of metadata to be able to tell the story of the data when the pieces were aligned. Participants appreciated the opportunity to connect, with one commenting:

“The content was valuable but being there in person and having the chance for organic personal interaction with peers, both during class time and outside of it, was really invaluable.” 

Attendees left the workshop with experience in curating real-world datasets and had the opportunity to collaborate with other participants to understand the complexities in working with these datasets and/or associated code.  Using examples of real-world datasets introduced participants to the complex challenges identifying which elements are most essential for curation. 

Specialized Curation for Scientific Images and Geographic Information Systems (GIS)

Following the Code and Simulation Data workshop, a second cohort of 17 participants convened on the NIH campus on July 14-15, 2025, for a two-day workshop focused on curation for GIS and scientific images.  Members of the cohort represented government agencies, including the United States Geological Survey (USGS) and NIH, nonprofits including the National Geographic Society, librarians from research institutions, and data curators. 

The combined focus on GIS and scientific images brought a rich synergy to the workshop materials. Tim Norris and Kelly Grove (Florida State University) led the GIS instruction on the first day. Day 1 introduced the complexities of working with GIS data and common challenges curators face stewarding geospatial data for research. Participants appreciated the exercises with QGIS, commenting:

“I always enjoy the hands-on exercises. For me, GIS software always seemed so intimidating. Being introduced to it in this way made it seem less so and I’m excited to practice with it more.” 

On Day 2, Mariah Kenney and Amy Schuler led the instruction for curating scientific images. They focused on information structures behind the curation of scientific images, emphasizing key descriptive metadata elements for microscopy data (Ivey et al., 2024) and related data types suggested by best practices, such as instrument identifiers (FASEB, 2023). Activities were designed to equip attendees with the tools to explore and manipulate a wide variety of scientific images, and to facilitate discussion on how metadata enhances the value and usability of image datasets. Both sections of the workshop emphasized how open-source software tools can be leveraged to curate these specialized data types.

As the instructional modules finished up, the workshop turned towards an interactive activity. The participants and instructors broke into groups to discuss boots-on-the-ground practices in the participants’ areas of interest: how to start a GIS-based data repository; what metadata elements should be required by funding agencies for GIS data; and how generative AI can be leveraged for data curation. The ensuing conversations were rich with shared experience to help fill some knowledge gaps. At the same time, participants identified other areas that may be ready for further inquiry or development of novel curation practices. This wonderful group helped instructors refine the curriculum as it was taught for the second time. The instructors noticed marked improvement in both the delivery and reception of the materials since the first workshops in 2024, and participants expressed hope that there will be further teaching opportunities to improve the curriculum materials and contribute to the DCN vision of elevating data curation best practices:

“These are important workshops and would love to see more types of advanced workshops in specialized data.” 

Conclusion

The advanced curation workshops closed with a sense of accomplishment and intense knowledge sharing. Feedback from workshop attendees was highly positive (Lafferty-Hess, Erickson, Keshavarzian, Marsolek, Molls, et al., 2025). Workshop participants left eager to apply their new knowledge and share out the experience with workplace colleagues and broader data curation networks. The workshop agenda, lesson plans, and slides for the workshops are available through the DCN’s GitHub webpage (Data Curation Network, 2025d) and are archived with the University of Minnesota’s digital collection of DCN workshop curriculum materials (Data Curation Network, 2025a).  We encourage readers to incorporate these educational resources into their data curation instructional modules and to consider participating in future DCN workshops as they become available.

Acknowledgments

These workshops were supported by the National Institutes of Health Data Management Center of Excellence (DMCOE) project “Expanding Data Curation Education” in partnership with MITRE and developed with support from the Institute for Museum and Library Services (IMLS) grant RE-252343-OLS-22.

The Code/Simulation workshop used Jetstream2 resources at Indiana University, utilizing allocation OTH240004 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296

References

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Coburn, E., & Johnston, L. (2020). Testing Our Assumptions: Preliminary Results from the Data Curation Network. Journal of eScience Librarianship, 9(1), Article 1. https://doi.org/10.7191/jeslib.2020.1186

Data Curation Network. (2019). Share your subject, functional or data expertise to help extend library curation capacity. Data Curation Workshops. https://sites.psu.edu/dcnworkshops/

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Data Curation Network. (2025b). Participate in the DCN. Participate in the DCN. https://datacuration.network/participate/

Data Curation Network. (2025c). Professional development for an emerging data curator community. Upcoming Workshops. https://datacuration.network/workshops/

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