Joanna Schroeder
Data Services Librarian
Boston College
https://orcid.org/0000-0003-1514-569
Introduction
In the fall of 2024, I made a career transition from data scientist to data services librarian at Boston College (BC) in the Libraries’ Digital Scholarship Group (DSG). I was asked in my interview if I had ever worked with humanities data. At the time the answer was no, and honestly, I hadn’t ever considered what humanities data even was. Since then, through collaboration with my teammates, I’ve learned a lot about digital scholarship, digital humanities, and how these fields intersect with data librarianship and data science. This editorial reflects on how working as part of a digital scholarship team reshaped my understanding of data librarianship as an instruction-driven, collaborative, and cross-disciplinary profession.
Context
Before BC, I worked as a research data scientist in an academic research lab. I knew I wanted to work more closely with students in a research support role for my next step, which led me to a library data services position. I completed my MLIS part-time, took on more data management-focused responsibilities at work, and learned more about data librarianship to prepare myself for a smooth transition. Unsurprisingly, institutional differences mean that no two data librarian positions are identical. Traditionally, data librarianship emphasizes research data management (Semeler et al., 2019; Xia & Wang, 2014). The ability to consult on data management plans isn’t the only necessary skill, though; technical skills, like coding or data visualization, and soft skills like teamwork and communication are also important (Federer, 2018). As data science has grown and other disciplines become increasingly data-driven, data literacy has become a central focus (Bauder, 2021; Kerns, 2025; Koltay, 2017).
I came into data librarianship with some of these core skills. I worked on grant-funded teams, collaborated with stakeholders, and mentored student research projects. The greatest challenge for me wasn’t to acquire new skills, but to redefine my professional identity. As a data scientist I might work intensively on three projects in a year, but as a data librarian I might have one hundred consultations, each requiring me to quickly assess needs, communicate effectively, and then step back so that others can carry the work forward. The shift from project ownership to high-volume, instruction-driven engagement marked the biggest change for me.
I also quickly learned that research data management (RDM) was not a central part of my new position. Despite its prominence in the profession, and my own efforts to expand into data management work while preparing for the transition, RDM is largely handled by other units on campus. The structure of my role reflects the influence of digital scholarship, which has shaped data services toward instruction, collaboration, and project-based work rather than traditional research data management support. While digital scholarship is often associated with technology and digital methods, instruction is also at the core of service offerings in library digital scholarship centers (Hensley & Bell, 2017). Necessary skills for digital scholarship align with data librarianship: blending technical skills with communication, instruction, and relationship-building (Federer, 2018; Kerns, 2025; King, 2018). Digital scholarship itself is also evolving as a field, which creates pressure to balance technology, space, and staffing to support emerging research methods (Hensley & Bell, 2017; Hurrell, 2019).
In this changing environment, shared understanding is essential for effective collaboration across disciplinary backgrounds. Before starting this role, I expected to work primarily with STEM faculty and students, but instead found myself providing substantial support for the humanities. Most of my colleagues on the DSG have a digital humanities background, which is the application of digital and computational methods in traditionally humanistic research (Walsh et al., 2022). My initial unfamiliarity with digital humanities isn’t uncommon; my colleagues describe having to explain or correct assumptions about their field often. I’ve come to see that digital humanities and data science have a lot of overlap: using methods like visualization, text analysis, and critical approaches (Bauder, 2021; Koltay, 2017; Walsh et al., 2022). My colleagues also remark that, because digital humanities often operates with fewer institutional resources than data science, it can foster creativity and resourcefulness. These shared methods and values make digital humanities and data librarianship a natural fit within a digital scholarship center, and the structure of the DSG has enabled collaborations that are both creatively and technically fulfilling.
Examples of Collaboration
Instruction in the Digital Humanities Certificate Program
One major collaborative effort within the DSG is instruction for BC’s Graduate Digital Humanities Certificate Program, a joint initiative between the Libraries and the English and History Departments. My DSG colleague is the Digital Humanities Certificate Coordinator, and, in addition to teaching the project-based Capstone course, he teaches and delegates lesson-specific instruction responsibilities to the rest of the DSG.
My role focuses on teaching data modeling in the humanities. Early on, I didn’t appreciate why my colleagues called humanities data messy and complex; now I better understand this perspective. Typically, a humanities data project requires extensive conceptual work to define units of analysis, rather than relying on pre-defined variables. For example, an English student may need to define character traits from literary text, while an economics student can rely on established measures like unemployment or gross domestic product, and a data science student may work with a toy dataset largely detached from conceptual meaning. Any of these assignments has merit given a set of learning outcomes, but it means digital humanities students often spend more time grappling with what data represents.
Developing a deeper understanding of digital humanities has made me a more effective and empathetic instructor. Because humanities students work from the inside out to define data and underlying concepts, they more readily surface the biases introduced by human decision-making in research. While students may initially lack confidence with digital methods, their processes build distinctly valuable skills in critical data science. In this context, my role as a librarian is less about providing technical solutions and more about scaffolding student thinking. I help them articulate assumptions, define categories, and translate conceptual questions into structured data models.
Research Collaboration on the Catholic Almanacs Project
Members of the DSG also collaborate on research initiatives, including the Catholic Almanacs project, which aims to create a structured database of institutions and people documented in nineteenth-century Catholic almanacs. Co-led by a Digital Scholarship Specialist, the project engages undergraduate and graduate students across the full data lifecycle: from data entry and validation to database development.
My role focuses on enabling large-scale, student-driven data entry while enforcing shared standards that make the data interoperable and reusable. Using tools such as Google Apps Script and the googlesheets4 R package, I balance flexibility in student workflows with consistency in schema, naming conventions, and controlled vocabularies. A key contribution of mine has been the development of a person ID pipeline that updates twice daily, allowing records to be linked across hundreds of Google Spreadsheets. These standardized records can also be benchmarked against external sources, such as historical records and Census data, to assess consistency and improve overall data quality. By preparing the data properly at entry, the project produces a dataset that is both usable for humanities inquiry and robust enough for evaluation, while remaining sustainable and scalable for student participation. This work also reflects core data librarian practices: enforcing standards for consistency and interoperability and documenting workflows so the data remain accessible, understandable, and reusable for future research.
Creating Datasets from Special Collections
Some of my favorite collaborative projects involve creating datasets from special collections. We’ve partnered with colleagues at BC’s Burns Library, who identify materials suitable for dataset production, enabling exploration of the content and analysis in relation to other collection objects.
The objects we’ve encoded so far focus on historic Boston. One is a police captain’s log book from the North End (1854-1859), which students used to record monthly crime statistics and examine trends in arrest categorization ( Figure 1).
Students also extracted structured information from records of St. Elizabeth’s Hospital School of Nursing (1895-1917), and highlighted patterns in immigration and specialization choices during the early professionalization of nursing ( Figure 2).
Student workers contribute to both the data modeling and entry, with guidance from DSG staff. They then demonstrate the datasets’ utility through summary statistics and visualizations, giving them a tangible sense of impact. Like the Catholic Almanacs project, these collaborations benefit from multiple perspectives and disciplines, and their bite-sized scope allows students to complete a meaningful project within a summer or single semester. While most student workers major in the humanities, some come from social sciences, and occasionally STEM. The partnership with Burns ensures the materials are relevant to community interests and suitable for structured analysis. Overall, these projects exemplify digital scholarship in action, guiding students to apply technical skills, critical thinking, and but collaborative problem-solving to create meaningful, public-facing resources.
Conclusion
I’ve thoroughly enjoyed being a data librarian on a digital scholarship team. In many ways, I’m still a data scientist (“data librarian” AND “data scientist”), and I’ve found this role incredibly technically fulfilling. I didn’t realize the world of digital scholarship existed until this role, and now, I see it offers both fascinating subject matter and opportunities to explore coding, data modeling, and analysis in ways I never expected. I look forward to contributing to our growing project pipeline, turning unique collections into structured datasets and providing students with hands-on, project-based learning. Ideally, these datasets will support student research across disciplines, fostering the kind of collaboration that truly embodies digital scholarship.
Acknowledgments
Thank you to our DSG student workers, particularly Duygun Ruben, Faris Lahham, Munir Paviwala, Yuchen Xiong, and Yrvicca Paul, colleagues at Burns Library, and to my ever-collaborative DSG teammates, Ashlyn Stewart, Antonio LoPiano, Dave Thomas, and Melanie Hubbard for showing me the ways of digital humanities and digital scholarship.
Works Cited
Bauder, J. (Ed.). (2021). Data Literacy in Academic Libraries: Teaching Critical Thinking with Numbers. American Library Association.
Federer, L. (2018). Defining data librarianship: A survey of competencies, skills, and training. Journal of the Medical Library Association, 106(3). https://doi.org/10.5195/jmla.2018.306
Hensley, M. K., & Bell, S. J. (2017). Digital scholarship as a learning center in the library: Building relationships and educational initiatives. College & Research Libraries News, 78(3), 155–158. https://doi.org/10.5860/crln.78.3.9638
Hurrell, C. (2019). Aligning the Stars: Understanding Digital Scholarship Needs to Support the Evolving Nature of Academic Research. Partnership: The Canadian Journal of Library and Information Practice and Research, 14(2). https://doi.org/10.21083/partnership.v14i2.4623
Kerns, H. (2025). Data Please!: Expanding the Role of Libraries in Data Science through Digital Scholarship. Journal of eScience Librarianship, 14(1). https://doi.org/10.7191/jeslib.961
King, M. (2018). Digital Scholarship Librarian: What Skills and Competences are Needed to be a Collaborative Librarian. International Information & Library Review, 50(1), 40–46. https://doi.org/10.1080/10572317.2017.1422898
Koltay, T. (2017). Data literacy for researchers and data librarians. Journal of Librarianship and Information Science, 49(1), 3–14. https://doi.org/10.1177/0961000615616450
Semeler, A. R., Pinto, A. L., & Rozados, H. B. F. (2019). Data science in data librarianship: Core competencies of a data librarian. Journal of Librarianship and Information Science, 51(3), 771–780. https://doi.org/10.1177/0961000617742465
Walsh, J. A., Cobb, P. J., De Fremery, W., Golub, K., Keah, H., Kim, J., Kiplang’at, J., Liu, Y., Mahony, S., Oh, S. G., Sula, C. A., Underwood, T., & Wang, X. (2022). Digital humanities in the iSchool. Journal of the Association for Information Science and Technology, 73(2), 188–203. https://doi.org/10.1002/asi.24535
Webster, J. (2019). Digital Collaborations: A Survey Analysis of Digital Humanities Partnerships Between Librarians and Other Academics. Digital Humanities Quarterly, 13(4). http://www.digitalhumanities.org/dhq/vol/13/4/000441/000441.html
Xia, J., & Wang, M. (2014). Competencies and responsibilities of social science data librarians: An analysis of job descriptions. College & Research Libraries, 75(3), 362–388.
