What is collaboration? I prompted ChatGPT to create an image illustrating collaboration, and this is what it produced:

I would guess that most people would conjure up something similar if asked to mentally visualize collaboration: a group of people, in the same physical space, working together. Direct, face-to-face collaboration is indeed an important way to partner and act collectively. But for libraries, another form of collaboration may be at least as important—and impactful. It is rooted in the concept of a collective collection: “the combined holdings of a group of libraries, analyzed and possibly managed as a unified resource.”

OCLC Research has produced a considerable body of work focused on defining, describing, and thinking through the implications of collective collections. An important strand of these studies examines collective collections in the context of shared print collections, in which groups of libraries work collaboratively to steward their collective print holdings. Most recently, we released the OCLC Research report Making Shared Print Work, which gathers community insight on workflows, data, and tools supporting collective stewardship of print collections, along with perceived gaps and opportunities that, if addressed, could strengthen the future of shared print programs. This report is part of OCLC Research’s Stewarding the Collective Collection project.

Shared data powers informational collaboration

One finding we reported in the study was that, as a practical matter, many shared print collections are distributed across a network of local collections, rather than physically consolidated into one collection. Aggregation of these local collections into a collective collection occurs through a layer of data and services that sits over the distributed collections, knitting them together into a data construct and allowing them to be analyzed and managed as a cohesive whole.

A related finding from Making Shared Print Work is that data is the key to delivering value to shared print programs:

Accurate and comprehensive data is essential for effective stewardship of collective collections, such as those managed by shared print programs. Monographic shared print programs involve six core workflow categories, with collection analysis, metadata management, and verification being the most data-driven—and in some cases, the most time-intensive—activities. The importance of data to shared print workflows is amplified by the fact that these programs primarily operate as distributed collections, requiring extensive coordination of holdings, retention, and bibliographic data across multiple partner libraries. (p. 7)

In these circumstances, it is not necessarily collaborators seated in the same room that drive successful shared print partnerships, but rather, informational collaboration: collective action powered by shared information that informs local and group decision-making.

The importance of informational collaboration was reinforced again and again in our Making Shared Print Work study. The perspective we gathered from interviews and focus groups revealed the primacy of data-driven workflows in shared print programs, underscoring the role of data as the connective tissue linking distributed local collections into an overarching collective collection. Case in point: the most frequently mentioned shared print workflow by our interviews was collection analysis.

Collection analysis, at its core, is about turning bibliographic and holdings data into actionable insights. Detailed knowledge of the size, scope, and salient features of a library collection—or a collective collection—leads to informed decision-making across a wide range of stewardship activities: from weeding and storage planning, to ensuring the fit and relevance of the collection to user needs, to redressing gaps in representation and diversity in legacy holdings. To this, we can add a number of shared print-specific considerations, such as choosing to make print retention commitments within the local collection or even identifying rare or last copies of publications within the context of a group’s collective print holdings.

Informational collaboration through collection analysis

Informational collaboration fuels this type of data-driven collection analysis in a shared print context. Sharing data about local collections in the partnership builds a clearer picture of collective print holdings, which, in turn, allows for better informed decision-making at the group level, but also at the local level, where knowledge of the size, scope, and features of the collective collection provides a contextual backdrop against which local decisions can be made.

Retention commitments are a great example of how this works in practice. A recent OCLC Research study examined print retention commitments registered in OCLC’s WorldCat database. A retention commitment—an assurance that a library will continue to retain and steward a particular print volume in its collection—is vital intelligence that informs the local retention decisions of other libraries. Informational collaboration occurs when the retention commitment is registered in WorldCat: when this information is shared and analyzed across a group of libraries, each can make local de-accessioning decisions based on the assurance that at least one copy of the publication will remain available.

Data-driven analysis supported by informational collaboration helps libraries keep books in collections as well. The Statewide California Electronic Library Consortium (SCELC) launched a pilot shared print program in 2016. A group-wide analysis of collective print holdings, produced using OCLC’s GreenGlass collection analysis tool, revealed the surprisingly low rate of overlap across the partner collections, with a large percentage of the collective collection consisting of publications held by only one or two member libraries. Informational collaboration in the form of sharing information about local print holdings through the GreenGlass analysis led to actionable intelligence for the group members: knowledge of the high incidence of rare or unique holdings within the group informed and optimized group-wide retention commitment strategies.

The importance of informational collaboration through collection analysis and other forms of data-driven analysis was underlined further in our Making Shared Print Work study when interviewees indicated that more was needed within shared print programs and beyond. For example, practitioners we spoke to noted a lack of systematic coordination across shared print programs, resulting in inefficiencies and duplication of effort that only become evident when the full landscape of shared print collections is taken into account. More sharing of data across shared print programs—in other words, more informational collaboration—could improve decision-making and coordination of resource allocations across the full spectrum of collective print stewardship efforts.

Data and tools are collaborative infrastructure

Collaboration requires collaborative infrastructure, the scaffolding upon which partnership can be established, sustained, and encouraged to thrive. For face-to-face collaboration—people working together in the same room—collaborative infrastructure might take the form of meeting spaces, committees, governance policies, and so forth.

Collaborative infrastructure is also needed for informational collaboration, but the nature of that infrastructure is different: databases, data exchange mechanisms, and data analysis tools that create the actionable intelligence that informs local and collective decision-making. Think of WorldCat, a database of shared information about library collections around the world. Consider also analysis tools like OCLC’s GreenGlass and Choreo Insights. Taken together, these resources—data and tools—create opportunities for informational collaboration in shared print and beyond.

Shared print programs illustrate how collaboration in libraries increasingly depends on informational collaboration that links distributed local collections into a collective collection through shared data and services. The infrastructure needed to support informational collaboration, like databases and analytic tools, complements the data-driven workflows that support shared print as well as other forms of collection stewardship. Informational collaboration provides the foundation for successful, sustained partnerships that help libraries achieve greater efficiencies and impact through scale.

The post Data-driven workflows and the art of informational collaboration appeared first on Hanging Together.

Funding and resourcing, technology, staffing, community needs and expectations—the pace of change library leaders now need to navigate and lead their organizations through is nothing short of breathtaking. Trends that took years to evolve now demand responses and strategic planning within months, or even days. Grounding those choices in rigorous, in-depth research remains essential.

At the same time, library decision-makers benefit from collective wisdom and insights shared among peers. Knowing how others are responding to similar pressures can help leaders calibrate their strategies and avoid reinventing the wheel. When those insights are confined to personal or regional networks, the limited perspective can restrict leaders’ views of how priorities and decisions are shifting.

OCLC Research leadership insights: Real-time insight for real-world decisions

This tension between the need for deeply researched guidance and the demand for timely, real-world insight creates a gap for the field. Library leaders need to understand not only which frameworks and models exist for long-term decision-making that are supported by our traditional research efforts, but also how their peers are responding to rapidly changing conditions right now.

To help fill this gap, OCLC Research is expanding its approach to gathering and sharing knowledge with a new series of pulse surveys focused on library leadership priorities. These quick, timely surveys aim to gather information on the decisions library leaders are making on a variety of critical topics shaping the future of librarianship.

A complementary approach to longstanding research practices

These short surveys are designed to capture high-level snapshots of the decisions library leaders make in the moment on subjects critical to the field, such as community engagement tactics and the use and implementation of new technologies, including AI. They are intentionally brief, both to respect leaders’ time and to enable us to respond quickly to emerging issues.

This approach does not replace the in-depth, foundational research OCLC Research is known for. Rather, it adds another dimension to it.

Our long-form research projects will continue to provide thoughtful frameworks, deep analysis, and foundational guidance for operational decision-making and long-term innovation. Leadership insights surveys complement that work by:

• Broadening the range of topics we can address, especially those that are evolving quickly
• Expanding the pool of voices contributing insight, drawing from library leaders across regions and library types
• Capturing change as it happens, and tracking how priorities and decisions shift over time

Together, these approaches create a more layered understanding of the field, combining depth with immediacy.

Powered by OCLC’s global membership network

The value of these leadership insights depends on scale. OCLC is uniquely positioned to engage a broad, global network of libraries and library leaders representing diverse viewpoints. This allows us not only to collect perspectives from beyond individual professional networks but also to share results with the field quickly and widely.

The outcomes will be intentionally concise: scannable, easy-to-digest summaries that surface patterns, contrasts, and emerging directions. Think of them as snapshots—ephemeral by design—that help illuminate how decisions are being made today, while also building a record of how those decisions evolve over time.

What this means for library leaders

For library leadership, this new format offers another way to stay oriented in a fast-moving environment:

• Insight into how peers are prioritizing and responding to shared challenges
• Timely information that can inform near-term decisions
• A broader field-level perspective that complements local experience

By adding pulse surveys to our toolkit, OCLC Research is expanding the breadth and increasing the pace of the insights we provide, while remaining grounded in the thoughtful, evidence-based work that has long supported libraries’ strategic and operational decision-making.

We see this as one more way to help library leaders make sense of complexity, learn from one another, and move forward with confidence. Our first pulse survey, focused on AI innovation & culture in libraries, will be fielded with US library leaders in early March 2026.

Subscribe to Hanging Together, the blog of OCLC Research, for updates on the survey series and to follow our latest work.

The post Listening to library leaders: Surveys capture real-time perspectives shaping decisions across the field appeared first on Hanging Together.

“Ghost kitchens” are pop-up restaurants geared entirely toward food delivery. They typically rent space in traditional restaurants to prepare food, take orders online, and deliver them to the doorstep via delivery apps like DoorDash or Uber Eats. Ghost kitchens proliferated during the COVID pandemic, which for a time practically extinguished dine-in food service. Restaurants of all descriptions needed to restructure their operations to scale up food delivery as their main service model; ghost kitchens were the extreme example, with the entire service model built around delivery.

The story of ghost kitchens is one of a specific business sector—restaurants—retooling traditional operational structures and service models to meet changing conditions in the marketplace. Gary Pearce, Director, Academic Services in the Monash University Library, touched on a similar theme in a recent OCLC Research Library Partnership webinar, describing how the Library reimagined its operational and service models to scale up research support capacities and better address institutional needs and priorities. As with ghost kitchens, Monash sought to reimagine its services in response to changing imperatives—specifically, the need to deliver research support at scale, within the confines of prevailing budgetary limitations. This situation will surely resonate with other research libraries, and there is much to be learned from Monash’s experiences and innovative solutions.

Retooling operational structures and service models

Academic Services is one of three portfolios at Monash University Library. To address the need to scale research support services and align more closely with stakeholder needs, Academic Services shifted from a traditional liaison librarian model organized on disciplinary lines to a functional specialization approach based on library expertise. This change moved away from multiple teams providing duplicate services to specific disciplines, in favor of agile, project-based service teams that work across disciplines.

A key aspect of Monash’s approach is the creation of a new Library Business Partner role, whose chief responsibility is strategic relationship management with senior leadership in a specific academic area. The Library Business Partner serves as a conduit for two-way communication between the Library and its academic stakeholders: on the one hand, communicating library messaging to the academic unit, and on the other, gathering intelligence and feedback on the unit’s needs and mobilizing capacity within the Library’s service teams to address them.

Pearce provided a rich description of how this retooling of operational structures and service models was conceived and implemented. Here are a few of the themes that emerged from his discussion:

• Acknowledging relationship management as a dedicated role: A key innovation was the creation of the Library Business Partner role to manage outreach and engagement with academic units. The Library Business Partner represents the entire Library and therefore can provide a comprehensive view of Library capacities, as well as expedite responses to stakeholder needs. Separating relationship management from service delivery facilitated a shift from a reactive, transactional model to a more proactive, two-way partnership.
• Emphasizing a culture of agility: Building a service model that was both scalable and responsive led Monash to adopt an agile approach. Academic Services implemented a matrix organizational structure in which staff have fixed reporting lines with flexible membership across multiple service teams—including research support. Staff have the option to rotate across teams to deepen expertise and experience. Work is divided between “business as usual” work and project work, the latter of which can be scaled up or down as needs and resource availability dictate. While this new operational structure could pose challenges to long-standing professional identities tied to traditional service models, it also opens up new pathways to leverage existing areas of expertise and develop new ones.
• Close attention to change management: The new operational structures were a significant departure from previous models. In recognition of this, the development and implementation processes were characterized by consultation, transparency, and communication, including a series of consultative visits to peer institutions facing similar challenges in adapting service development and delivery; presentations to stakeholder groups; regular updates to staff, along with clear milestones and timelines; open channels for questions and feedback; and planning for professional development needs.     

These are just some of the key themes that provide the foundation for Monash Library’s story of transformation, scalability, and responsiveness.

Managing implementation is crucial

Pearce’s presentation elicited many questions from the audience in attendance. Collectively, the questions reflected a keen interest in the implementation aspect of the shift to new operational structures and service models, touching on issues like:

• Stakeholder response and buy-in: how researchers and staff reacted to service model changes; channels for communication and feedback
• Staffing implications: impact of restructuring on staffing counts; work allocations between “business as usual work” and project work; cross-training opportunities
• Strategic relationship management: interest in the details of how the new Library Business Partner role works in practice

The audience’s interest in these topics highlight that a shift from a traditional/subject-focused service model to a functional/specialization model requires attention to both structural innovation and the staffing and stakeholder reactions to significant organizational change.

Additional reading

Pearce’s webinar intersects with several OCLC Research studies that complement some of the themes from the emerging from the service model transformation experience. First, check out our work on social interoperability, which we define as the creation and maintenance of working relationships between individuals and organizational units within an institution. Our report describes strategies and tactics that can help strengthen social interoperability skills—an essential element of roles like Monash’s Library Business Partner. In addition, OCLC Research’s forthcoming work on the Library Beyond the Library—an operational principle that emphasizes the importance of the library engaging with the broader institutional environment through strategic alignment, collaboration, and storytelling—connects with Monash’s ambitions to retool its service model to better align with institutional research needs and priorities.

Ready to dive deeper? Listen to the full recording

The webinar and subsequent Q&A offered a richly informative look behind the scenes of a major shift in operational structures and service models to better address the needs of stakeholders. If you didn’t have a chance to join us for the live webinar, please take some time to view the recording. Many thanks to Gary Pearce for sharing his perspective with all of us!

The post Scaling research support at Monash University Library appeared first on Hanging Together.

Many libraries and archives are homes for oral history programs. Oral histories add voice, personality, richness, and depth, and are a natural complement to other types of collections. While these narratives can bring significant value to collections, supporting oral history production can present special challenges. Oral history programs can be composed of complex, multi-stage projects that require thoughtful planning, coordination, and even innovative approaches to capture, preserve, and make accessible valuable cultural and historical narratives.

In a Works in Progress webinar hosted by the OCLC Research Library Partnership (RLP) on 21 October 2025, practitioners from the University of Washington and Montana State University shared their experiences developing and implementing effective oral history program workflows, which now includes best practices for balancing human expertise with emerging AI technologies.

Conor Casey, Head of the Labor Archives of Washington at University of Washington Libraries Special Collections, shared his experiences based on over 13 years of evolved practices with collaborative workflows and scalable project management. The Montana State University team—Jodi Allison-Bunnell, Head of Archives and Special Collections; Emily O’Brien, Metadata and Mendery Specialist; and Taylor Boyd, Metadata & Collection Support Technician—discussed their experiments using two different tools to generate oral history abstracts.

This blog post summarizes the webinar’s key insights, but you can watch the full recording right here.

The Labor Archives of Washington: Shared stewardship in practice

The Labor Archives of Washington, founded in 2010 as a partnership between the Harry Bridges Center for Labor Studies and UW Libraries Special Collections, demonstrates how community-founded archives can scale oral history programs through strategic collaboration. Despite never having more than two full-time staff members (who also have other duties and responsibilities), the archive has completed close to 200 interviews across multiple projects by leveraging a collaborative model that designates clear roles for all players.

Scaling through partnership and project management

Conor Casey emphasized that success stems from recognizing institutional strengths and distributing work accordingly. The archive provides infrastructure, tools, and preservation expertise; community partners bring connections to narrators, scholarly expertise, and cultural knowledge essential for developing interview questions that are meaningful within the user base.

The Labor Archive’s workflow success relies on project management tools, including Asana templates, Google Drive file management, and comprehensive documentation. As an example of how documentation and project management tools work to ensure a smooth experience, Casey stressed the importance of “baking in” permissions, requiring signed release forms from participants before embarking on a project to enable stewardship of a given collection. These tools also ensure quality and consistency in metadata.

Ensuring that all parties are on the same page is also vitally important. Casey advised that clear roles and deadlines to keep the project on track, and that project charters or MOUs can be very helpful when collaborating between organizations and with multiple parties, even within organizations.

Even with AI promise, transcription remains a human-centered activity

Beginning in 2018, the Labor Archives began experimenting with AI-aided transcription using tools like Otter.ai, Maestra, and Descript. These tools are not one-size-fits-all solutions and depend heavily on project needs. When AI is employed, Casey’s workflow diagrams revealed a crucial insight: human intervention appears at nearly every stage of the AI-aided process.

Despite the promise of AI in some cases, a professional transcriptionist is still faster and more accurate for certain projects. “Professional transcribers may still be more efficient than just AI,” Casey noted. “You are still going to have to do a lot of work to correct, tag, and conform AI to transcription style guides.”

Accessibility is core to mission

Casey also discussed the implementation of many accessibility features, such as including transcriptions and captioned media for all interviews. Casey positioned accessibility not as an add-on compliance requirement, but as central to the archival mission. The archive proactively implemented transcription and captioning before Washington State’s 2017 mandate for all new online projects, viewing these activities as extensions of intellectual access rather than a burden.

Montana State University: AI to support accessibility needs

The Montana State University team faced a specific challenge: creating abstracts for approximately 350 oral history recordings in their Trout and Salmonid collection to meet ADA Title II accessibility requirements. The MSU library staff were very much encouraged by leadership to embark on a journey of learning around AI tools, so experimenting with tools to learn (and employing exploration and curiosity along the way) was a natural step for the team and the framework for their presentation and discussion overall.

These field recordings from around the world presented unique challenges, including:

• Multilingual interviews conducted with interpreters
• Variable field recording quality
• Videos that had been edited to include interviewer questions only on text intertitles

Jodi Allison-Bunnell spoke about using Claude.ai to generate 200-word abstracts from full transcripts produced in Trint. The team found that Claude generally produced coherent abstracts. However, several limitations emerged:

• Missing context: Because interviewer questions were only shown on intertitle slides, Claude struggled to provide complete context
• Factual error: If proper names and technical terms were incorrect in the transcript, they were also in the abstract; the transcript had to be thoroughly reviewed to avoid promulgating errors
• Over-inference: When given insufficient content, Claude would sometimes infer more about subjects than was supported by the source material and required a human with good knowledge of the transcript to intercept and correct the imbalance

The cataloger perspective: Quality and workflow integration

Emily O’Brien and Taylor Boyd assessed the abstracts from a metadata creation standpoint, focusing on whether AI-generated abstracts contained sufficient information for accurate cataloging without requiring catalogers to repeat work.

For this specific use case, Trint consistently outperformed Claude, Gemini, and ChatGPT. However, caution should be applied before declaring a clear and persistent winner in an area where tools and technology are evolving quickly. The team found that Claude abstracts generated months apart showed significant quality differences; this finding demonstrates that it is difficult to make static judgments about tools in this quickly evolving space.

O’Brien and Boyd also emphasized a vital point in assessing the accuracy and efficacy of AI-generated abstracts that will support metadata creation: unless AI-assisted humans have prior learned experience with creating abstract-level metadata, they make lack the ability to assess the quality of AI outputs.

Despite limitations, the team found significant time savings. Reading a transcript and writing an abstract from scratch took, on average, 1-2 hours, while reading a transcript, running it through Claude or Trint, and assessing and correcting the result took an average of 30 minutes. However, Allison-Bunnell also highlighted the need to maintain oversight over tools (including changes in terms of service) and to budget time to review workflows that might be impacted by those changes, as well as to develop, implement, and review AI use policies on an ongoing basis. Ultimately, time savings may be shifted across positions in ways that aren’t initially evident.

Lessons learned and workflow implications

Both presentations emphasized that effective workflows supported by AI require human oversight at multiple stages. The Labor Archives workflows reflect human intervention points throughout the process, while the Montana State team stressed that professional experience in the relevant domain is essential before AI tools can be effectively evaluated or implemented.

Successful oral history workflows cannot be separated from organizational context, resources, and mission. The Labor Archives’ collaborative model works because of their community-focused mission and partnership infrastructure. Montana State’s approach is grounded in their need to meet specific accessibility requirements and also in their deep collection strengths supported by corresponding curatorial and community knowledge. Both institutions demonstrated how accessibility considerations drive innovation rather than constrain it.

Looking forward

These presentations illustrated that effective oral history workflows require thoughtful integration of human expertise, technology, and collaborative partnerships. AI tools can enhance efficiency and accessibility, but they work best when implemented with a clear understanding of their limitations and within robust frameworks that center human knowledge.

The key insight across both presentations was that technology should amplify human-centered values and good project design, not replace them. Successful oral history programs leverage innovation in service of their core mission: preserving and providing access to irreplaceable cultural narratives. These presentations demonstrate the value of taking a stance of curiosity, exploring, and sharing our experiments and lessons learned as we navigate the integration of new technologies with traditional archival practice. We would love to hear about your experiments in this area!

—

Special thanks to Conor Casey, Jodi Allison-Bunnell, Emily O’Brien, and Taylor Boyd for generously sharing their insights and experiences. For more resources on oral history workflows, including templates and project management tools, view the slides and watch the full webinar recording from the event page.

The post Building effective workflows for oral history projects: Collaboration, structure, and AI innovation appeared first on Hanging Together.

We associate Halloween with many supernatural creatures, including werewolves. However, these werewolves have a long history in folklore predating their association with Halloween. Werewolves have been featured in literature for thousands of years, including the novel Satyricon by Petronius. Werewolves remain popular in modern fiction as well as in films and television series. Unlike vampires, who frequently headline supernatural narratives, werewolves seem to be more commonly featured in supporting roles within the genre, like the werewolves Jacob in The Twilight Saga and Wayne in Hotel Transylvania. If you enjoy a good werewolf story, read on for a description of some resources that will leave you howling for more—all available in WorldCat, of course!

Metamorphoses

The myth of King Lycaon is not the earliest werewolf story but is an important exemplar of the wolf transformation being used as a punishment. In Metamorphoses, the Roman poet Ovid describes how the god Jupiter transformed Lycaon into a wolf as a punishment for attempting to trick the disguised Jupiter into eating human flesh. Several 16th-century editions of Metamorphoses include illustrations of this myth, but one of the best may be from an incomplete project by Dutch engraver Henrick Goltzius to provide 300 illustrations for Metamorphoses. Fifty-Two Engravings Illustrating Ovid’s Metamorphosis contains illustrations he completed, including one depicting the Lycaon myth. For readers who love etymology, yes, there is a connection between King Lycaon’s name and lycanthrope. The word lycanthrope derives from two Greek words: λύκος (wolf) and ἄνθρωπος (man).

The Phantom Ship

Most werewolves in stories were male until the 1839 publication of Frederick Marryat’s Gothic novel The Phantom Ship, which is available to read online at HathiTrust. In chapter 39, the character Hermann Krantz tells a story about a mysterious woman named Christina who married his father and they discovered could transform into a white wolf. It is noteworthy that Christina came from Transylvania, which would be the setting of the 1897 novel Dracula. This chapter has been published as a short story called “The White Wolf of the Hartz Mountains” in anthologies such as Terrifying Transformations: an Anthology of Victorian Werewolf Fiction, 1838-1896.

This is a horror story with Christina beating her stepchildren in her human form and killing Krantz’s siblings in her wolf form. Like many supernatural creatures described in Gothic fiction, the female werewolf in The Phantom Ship is evil rather than the complex depictions of cursed humans in later literature.

While The Phantom Ship lacks the popularity of other Gothic novels like Dracula and Frankenstein, the contribution of the female werewolf character paved the way for another female werewolf in my favorite modern werewolf story, Bitten by Kelley Armstrong. The novel’s main character is Elena Michaels, the only known female werewolf. Elena struggles to reconcile her human and werewolf sides in this novel and its sequels. I don’t want to spoil the story for those of you interested in reading the novel, so I’ll just say that Elena stands out in the pack.

Teen Wolf

The motion picture Teen Wolf, directed by Rod Daniel and starring Michael J. Fox, may not be a horror film classic, but this coming-of-age comedy provides a bold twist to the typical werewolf story. High school student Scott Howard’s lycanthropy is an asset instead of a curse. As a werewolf, Howard uses his superior athletic abilities to help his basketball team win games, and he becomes very popular. With popularity comes arrogance, and thus Howard learns to be himself and work with his teammates. As a child of the 80s, I remember this film fondly for its simplistic storytelling. Also, unlike other supernatural movies from the same time period, this movie never gave me nightmares.

There is something for everyone in werewolf stories—horror, comedy, bildungsroman, and paranormal romance. If your Halloween plans are not already made, consider enjoying a good werewolf tale. Happy Halloween!

The post Werewolves in WorldCat appeared first on Hanging Together.

“No library stands alone,” remarks Valerie Horton, a longtime director of library consortia. “Library cooperation goes back to the 1880s and is a long-standing tenet of the profession. Collaboration is strongly rooted in most of our current activities.” Horton goes on to suggest a number of reasons why this is so: professional networking, access to more resources, sharing expertise, prestige, and, of particular significance, economies of scale, with the promise of cost savings and the ability to reallocate resources to emerging priorities.

Horton’s observations were published in 2015, but they still resonate today as key drivers for libraries to join consortia and engage in other forms of partnership. However, collaboration comes at a cost: the direct costs of staff and other resources required to participate, as well as the indirect cost of losing some autonomy (for more on the collaboration/autonomy trade-off, see the “Coordination Spectrum” in the 2019 OCLC report Operationalizing the BIG Collective Collection: A Case Study of Consolidation vs Autonomy). Choosing to collaborate therefore becomes a strategic decision requiring careful evaluation of costs and benefits.

Prospective collaborative partners are perhaps all too cognizant of the costs of collaboration; it is therefore important to have an equally clear articulation of the benefits, especially in areas where libraries have a long history of acting autonomously. Partnership should feel not like a sacrifice of independence, but like a strategic advantage that independent action cannot match. Tina Baich, director of the Eastern Academic Scholars Trust (EAST), a shared print collaboration of more than 150 institutions, underscored the importance of clarifying the value of collaboration in a recent presentation. One key pillar of EAST’s strategy to support its organizational vision is to “Enhance the ongoing value of membership,” which includes “Communicating the value of membership” to current and prospective members.  Another pillar is “Advocate on behalf of members,” which involves efforts to “Make the case for shared print.” In both cases, there is a need to understand and communicate the motivations (and ultimately, the potential benefits) for institutions to partner around print stewardship.

Insights from shared print

Recent evidence gathered by OCLC Research offers concrete examples of what these compelling motivations look like in practice in the context of shared print programs.

Stewardship of print collections is an excellent illustration of an activity that libraries have traditionally carried out at local scale, but for which they are now adopting collaborative approaches at group scale. What are the motivations exerting sufficient gravitational force to pull libraries away from long-standing local-scale approaches? We touched on this question in the project Stewarding Collective Collections: US and Canadian Perspectives on Workflows, Data, and Tools for Shared Print, in which we gathered insights from staff at shared print programs and participating institutions on a number of topics, including the motivations for managing print monographs collectively—an important area of library collaboration.

For this project, we spoke to a total of 37 people through focus groups and individual interviews, gathering perspective from individuals from institutions participating in monographic shared print efforts—deans/directors, AULs for collections, collections librarians/strategists, metadata librarians, and resource sharing librarians. We also talked with staff from monographic shared print programs—primarily program managers—from a number of North American partnerships.

What we found

Our interviewees discussed key reasons for participating in or operating shared print programs. The most frequently mentioned were institutional roles and histories, including previous institutional experiences in collaborative collection stewardship—or collaboration of any kind. According to our interviewees, institutions that have previously invested themselves in some form of collective action are often more open to opportunities for new collaborations, such as shared print. This is especially true when members participate in consortium-sponsored shared print programs. Past experiences and established collaborative infrastructure within the consortium build trust and confidence in collective endeavors. We have described this elsewhere as acquiring an option to collaborate, where past collaborative efforts create the option to engage in future collaborative opportunities.

Several interviewees also mentioned that their status as the only or the largest institution in the region led to a sense of obligation to participate in shared print efforts.

The second most frequently mentioned incentive to join shared print programs was management of physical space—the opportunity to reduce the print collection’s physical footprint in the library, releasing space for other uses. Access to shared capacities (storage facilities, technical systems, aggregated data) was another top response, highlighting a desire for greater efficiency beyond what is achievable through local-scale implementations. Moreover, shared approaches may be the only feasible option for some institutions to obtain these capacities.

Next on the list of key drivers for participating in shared print efforts was access to holdings beyond the local collection. This response touches on a key principle behind collective collections—the idea that managing holdings collectively not only makes stewardship more efficient, but also removes barriers to discovery, delivery, and ultimately, greater use. Another incentive was the opportunity for greater decision support: using collective collection analysis to inform local collection decision-making, such as weeding, moving materials to off-site storage, and acquisition strategies.

The next two drivers mentioned by our interviewees rise above local interests to touch on opportunities to advance the common good. One is recognition of a collective responsibility to steward the print published record, an objective that can only be truly achieved by the combined efforts of many institutions working toward this common purpose. Related to this, interviewees also mentioned a desire to safeguard last or rare copies of print publications, which often can only be identified at scale, where the individual distinctiveness of local print book collections aggregates into a rich and diverse long tail of rare or even unique materials within the collective collection.

Interviewees also highlighted the importance of collection characteristics as another important factor impacting engagement in shared print efforts. Institutions that specialize in collecting in certain subject areas—like the arts or medicine—can become important strategic partners in broader, multi-institutional shared print efforts, in which specialized collections complement the holdings of other institutions. It is the broader context of a collective collection that amplifies the visibility of these local strengths.

Perhaps surprisingly, direct mention of cost savings—lowering the cost of managing print monographs through collaboration—was relatively infrequent among our interviewees. This suggests that institutions often enter shared print partnerships for non-economic reasons, although some motivations, such as shared capacities, may have implicit cost considerations.

Data-driven analysis is key for incentivizing collaboration

While not comprehensive of all incentives, these findings are indicative of some of the major factors that drive participation in shared print programs. The golden thread that runs through many of them is the importance of data-driven analysis to highlight and clarify untapped opportunities to create, expand, or optimize shared print efforts.

Want to know how participation in a shared print program will help release space for new uses? Data-driven analysis can clarify the extent of redundancy across a group’s collective print monograph holdings, identifying candidate materials for deaccessioning or removal to off-site storage. Are you interested in how the characteristics of your local print collection stack up against those of your shared print partners? Data-driven analysis can help identify potential complementarities and unique strengths, providing strategic intelligence to inform local collection development. Wondering if your collection contains hidden gems—rare or even uniquely held materials—that warrant special stewardship attention? Data-driven analysis of group- or even global-scale holdings can answer that question.

Aggregated data, combined with analytic tools that can turn it into actionable intelligence, is essential for identifying and communicating the key motivations for participation in shared print programs. These analytical approaches create the evidence needed to show how participation in collective stewardship efforts around print holdings creates value across the partnership. In doing so, they help libraries choose their collaborations strategically, through evidence-based evaluation of the incentives to join. And for shared print programs, this approach sharpens the benefits, makes them more visible, and transforms collective stewardship from an ideal into a demonstrable asset.

The importance of data-driven analysis is a key finding of our Stewarding the Collective Collection project. Our conversations with interviewees made clear that shared print is first and foremost a data-driven activity—collecting, organizing, and analyzing data about groups of collections. Shared print collections are often decentralized across many local collections, rather than existing as physically consolidated collections; in this sense, they exist, for all practical purposes, as constructs in data. Reliable data and analytical tools are therefore indispensable for successful shared print programs.

This finding was echoed in another OCLC Research study, which explored collaboration opportunities for specialized art research libraries. This study found:

“Collaboration is an important strategy for art libraries as they seek sustainability in a dynamic environment. . . . This report uses bibliographic, holdings, and ILL data to document potential opportunities for collaborative activity around art research collections. Indeed, our study of the proxy art research collective collection indicates . . . that art libraries bring a sizable group of rare or unique materials to the table that are not in other collections. And this creates demonstrable value: our study of ILL transactions found that most ILL transactions involving art libraries were for materials not owned by the borrowing institution. This is a classic case of value created through collaboration—specifically, resource sharing broadens the scope of the local collections of all partners.”

Like our shared print findings, this analysis identifying opportunities for art libraries to collaborate was conducted under the auspices of a research project. Fortunately, libraries now have the capacity to carry out similar analyses themselves, using WorldCat-powered tools like Choreo Insights and GreenGlass. So, when the question “Why collaborate?” is posed, we have answers. Evidence is always more persuasive than exhortation.

Valerie Horton notes that, “in the end, the reason so many libraries join together is to achieve more than any library can achieve on its own. The era of the library consortia is not ending; instead it is set for a transformation as technology has removed many of the physical barriers to collaboration that distance formerly created.” Data-driven technologies, like collection analysis tools, are yet another transformation that diminishes the barriers to collaboration—in this case, by clarifying the incentives to participate in collective stewardship efforts like shared print.

Stay tuned for more findings from the Stewarding the Collective Collection Project!

Many thanks to the participants in our interviews and focus groups whose insight is shared in this post!

The post Autonomy to alliance: Unpacking the motivations for library partnership appeared first on Hanging Together.

This is the fourth post in a short blog series on what we learned from the OCLC RLP Managing AI in Metadata Workflows Working Group. This post was co-authored by Rebecca Bryant and Annette Dortmund.

Artificial intelligence (AI) holds significant potential for improving metadata workflows, offering tools to enhance efficiency, improve discovery, and address long-standing challenges in libraries. Yet, as with any transformative technology, AI adoption requires thoughtful consideration of its limitations, ethical implications, and impact on professional practice. The key is finding the right balance—one that leverages AI’s capabilities while maintaining the quality and professional standards that libraries depend on.

From April to June 2025, the OCLC Research Library Partnership (RLP) convened the Managing AI in Metadata Workflows Working Group. This working group brought together metadata managers to explore how AI could be integrated into cataloging, special collections, and institutional repository workflows. Across these discussions, librarians and archivists expressed both enthusiasm and caution about AI adoption, and a set of cross-cutting themes emerged—insights that extend beyond specific workflows and highlight the opportunities and challenges of responsible AI adoption in libraries.

This blog post—the final of a four-part series—synthesizes key themes, including the critical importance of metadata quality, the need for ethical standards and transparency, the evolving roles of metadata professionals, and the responsibility to adopt sustainable AI practices. These insights, combined with emerging best practices from organizations like OCLC, point toward a future where AI enhances rather than replaces human expertise in metadata work.

Quality and reliability of metadata is essential

A fundamental theme across all discussions was the critical importance of metadata quality. Working group participants consistently stated that creating records using AI is counterproductive if resources are not accurately described or if users are misdirected. This emphasis on quality isn’t a barrier to AI adoption—it’s a framework for responsible implementation.

Several quality considerations emerged repeatedly:

• Hallucinations that introduce false information into catalog records
• Inconsistent outputs from identical inputs, undermining reliability
• Unreliable confidence scores that don’t always accurately reflect the quality of AI-generated content
• Entity recognition failures where AI-generated results might look syntactically correct but fail to identify the right person, place, or organization

However, these challenges are driving productive innovations rather than insurmountable barriers. OCLC’s approach to AI-powered de-duplication in WorldCat demonstrates how quality concerns can be addressed through hybrid approaches that combine AI efficiency with human expertise. OCLC has worked closely with the cataloging community to help validate its machine learning model’s understanding of duplicate records in WorldCat. To date, OCLC has removed more than 9 million duplicate records from WorldCat as a result of this AI model, which we continue to test and refine. The process includes conservative decision-making protocols and human oversight for complex cases, showing how AI can scale quality work rather than compromise it.

These developments are driving productive conversations about human oversight processes, quality control checkpoints, and training approaches that help staff effectively evaluate AI outputs—and that are already yielding practical solutions.

Contextual and cultural knowledge gaps exist

One of the most significant limitations identified by the working group involves AI’s current struggle with contextual and cultural knowledge. Participants noted practical challenges, such as AI transcription systems converting “MARC” to “Mark” or “nomen” to “Newman” in recordings with technical terminology. More broadly, AI systems often lack the deep contextual understanding needed for community-specific terminology or cultural nuances that don’t appear in general training databases.

Rather than viewing these as permanent limitations, the library community is actively addressing them. These challenges highlight an important opportunity: the need for more specialized, task-specific AI tools rather than general-purpose models. OCLC’s experiments with subject analysis and classification prediction demonstrate this approach in action. By grounding AI models in high-quality library metadata—specifically WorldCat data—OCLC is developing tools that understand library contexts better than general-purpose models.

This specialized approach also reinforces the continuing value of librarians’ and archivists’ deep collections knowledge and cultural expertise, positioning AI as a tool that extends rather than replaces professional judgment.

Evolving professional roles and skills: Enhancement, not replacement

Participants expressed genuine interest in AI as a tool for increasing efficiency and freeing metadata specialists from repetitive work to focus on more complex and specialized tasks. At the same time, thoughtful questions emerged about professional development and skill maintenance in an AI-enhanced environment.

Key considerations include how to ensure that new professionals develop foundational skills traditionally gained through tasks like brief record creation—skills that become essential for effectively evaluating AI outputs later in their careers. Experienced catalogers wondered whether spending more time reviewing than creating might impact their ability to identify subtle errors or handle complex materials that require human insight.

These discussions highlight the importance of designing AI implementations as enhancements to human expertise rather than replacements, ensuring that professional development pathways remain robust while leveraging AI’s potential to handle volume and routine tasks. OCLC’s approach exemplifies this philosophy. OCLC’s AI de-duplication project doesn’t, for instance, doesn’t eliminate human oversight but refocuses it where expertise matters most. As noted by Bemal Rajapatirana, “This approach to de-duplication is not about reducing the role of people—it’s about refocusing their expertise where it matters most. Catalogers can focus on high-value work that connects them to their communities instead of spending hours resolving duplicate records.”

Real world library examples already demonstrate this potential. The University of Calgary Library successfully redirected 1.5 FTE of staff time to more strategic, higher-level tasks following the implementation of its AI chatbot, showing how AI can create space for the uniquely human aspects of library work rather than diminishing professional roles.

Ethical considerations and standards: Building transparency into practice

Working group members identified several important ethical considerations, with data provenance and transparency emerging as particularly crucial. Participants emphasized the need to track when and how AI contributes to metadata, both for quality control purposes and transparency.

For example, in a case study where AI was given a finding aid and asked to provide headings for personal names that were verified against the LC Name Authority File, the tool provided headings that looked correctly formulated (e.g., “Bukowski, Charles, 1920-1994” with dates added ), and AI even claimed that they were verified, but actually they were not the correct authorized headings (“Bukowski, Charles”). In this type of case, the provision of provenance information indicating that the heading was AI contributed could trigger human review for quality control.

OCLC has responded to community questions about data provenance for AI-generated metadata by updating WorldCat documentation and providing guidance through programs like AskQC Office Hours. OCLC’s Bibliographic Formats and Standards (BFAS) now includes instructions for recording AI-generated metadata in bibliographic records in section 3.5. Readers may also find it useful to consult the August 2025 AskQC Office Hours session.

Questions also arose about the lifecycle of AI-generated metadata: When does AI-generated content become simply “cataloger-reviewed content,” similar to copy cataloging workflows? How do we balance transparency with practical workflow considerations? These discussions reflect the library community’s commitment to responsibly working through the practical implications of new technologies.

Environmental awareness and responsibility

Participants expressed concerns about AI’s environmental impacts, indicating a preference for less energy-intensive solutions when they prove similarly effective. Rather than viewing this as a barrier, metadata managers identified a need for accessible information about the environmental impact of different AI applications, enabling informed decision-making and meaningful conversations with their teams about responsible implementation choices.

OCLC’s approach to AI development reflects this environmental consciousness. The WorldCat de-duplication model is designed to be computationally efficient, reducing unnecessary resource use while maintaining high-quality results. As Rajapatirana explains, “by optimizing AI’s footprint, we ensure that de-duplication remains cost-effective and scalable for the long term.” This environmental consciousness reflects the library community’s broader commitment to sustainability and responsible technology adoption, suggesting opportunities for training and information sharing about library AI energy impacts.

Conclusion

The concerns and opportunities described in this blog post reflect a community that is actively thinking through the implications of an emerging technology, rather than simply adopting it. The clearly articulated need for specialized AI tools, quality frameworks, and ethical guidelines is driving innovations that address current limitations.

Working group participants’ emphasis on maintaining professional expertise while leveraging AI’s capabilities suggests a thoughtful approach to technology integration that preserves what makes library work valuable while enhancing its impact.

The RLP Managing AI in Metadata Workflows working group provided the opportunity for metadata managers to identify important implications for AI usage in metadata workflows. This blog series distills those insights, and we hope that these observations will offer useful guidance to the library community as it collectively navigates technological change.

NB: As you might expect, AI technologies were used extensively throughout this project. We used a variety of tools—including Copilot, ChatGPT, and Claude—to summarize notes, recordings, and transcripts. These were useful for synthesizing insights for each of the three subgroups and for quickly identifying the types of overarching themes described in this blog post.

The post Striking the right balance: Opportunities and challenges of AI in metadata workflows appeared first on Hanging Together.

This is the third post in a short blog series on what we learned from the OCLC RLP Managing AI in Metadata Workflows Working Group.

Institutional repositories (IRs) play a critical role in preserving and showcasing the research outputs of universities and other organizations. However, managing metadata for these repositories presents unique challenges, largely due to user creation of metadata during the deposit process. Researchers and students generally care less about metadata quality than libraries and may rush through deposit to satisfy what they may see as an academic or funder mandate. Legacy metadata in repositories can also accumulate inconsistencies, errors, and gaps over time, further complicating workflows.

Today’s blog post—the third in a four-part series— summarizes findings from the “Institutional Repositories” workstream of the OCLC RLP Managing AI in Metadata Workflows Working Group, which was comprised of:

• Michael Bolam, University of Pittsburgh
• Helen Williams, London School of Economics

The group met five times between April and July 2025, with four meetings focusing on the opportunities for AI to make IR metadata workflows more efficient and productive. They also thoughtfully considered potential implementation barriers and implications for preserving professional skills and job satisfaction. As with most new technologies, the use of AI in repository workflow contexts presents both opportunities and trade-offs. The key is balancing open-mindedness about AI’s potential with a realistic assessment of current capabilities and institutional readiness.

Core workflow opportunities for AI

The working group identified two critical IR workflow areas where AI could be a useful tool:

• Deposit processes (including metadata creation)
• Legacy metadata clean-up and management

From these contexts, we identified several opportunities where AI-powered tools could boost efficiency and productivity.

Improving the self-deposit experience

Working group members discussed many ways that AI might improve the IR deposit process, most of which focused on addressing incomplete or erroneous metadata at submission. In both self-deposit and mediated deposit workflows, users often fail to supply complete and accurate metadata because students and researchers find metadata creation burdensome and time-consuming. Examples where AI tools might ameliorate this problem include:

• Subject classification suggestions based on full-text analysis
• Abstract generation for materials lacking summaries
• Basic metadata extraction from uploaded files (or campus system information) to pre-populate repository records

By streamlining file processing and metadata extraction and ensuring completeness, the use of AI tools can potentially reduce the burden on researchers during self-deposit and on repository staff managing mediated deposits. AI tools could be used to scan full-text files and extract certain metadata elements to pre-populate or enrich repository records.

Enhancing legacy metadata

Working group members suggested that there is significant potential for metadata improvement on the back end, and identified the following opportunities:

• Managing complex entity relationships. Repositories have persistent problems in disambiguating author names and affiliations, especially for research outputs that involve multiple authors who may be affiliated with other institutions. AI tools could help cluster names and affiliations, suggest connections to persistent identifiers like ORCID, ISNI, and WorldCat Person Entities, and provide more linkages between authors, institutions, and research outputs.
• Enriching and correcting legacy data. Existing repository records can harbor a number of accumulated problems, including missing data, inconsistent metadata practices and standards, and anomalies introduced by system migrations and other technological changes. AI tools could support automated scanning of repository records to identify gaps and inconsistencies, and either correct them automatically or flag them for staff review. It could also suggest enrichments, such as abstracts, for materials that lack them.
• Publication status tracking. Research outputs in institutional repositories are often preprints or other materials that are involved in formal publication processes elsewhere. AI tools could conduct automated checks for changes in the publication status of “in press” materials and make appropriate changes to repository records as needed.

This is certainly not an exhaustive list of opportunities, but it points to core workflow challenges and operational pain points that AI tools could potentially address—especially incomplete or inaccurate metadata provided at the time of deposit by researchers who find metadata creation onerous, and who are, in any event, likely not skilled metadata practitioners. Poor metadata negatively impacts activities like compliance reporting, research impact assessments, and the discoverability of institutional research outputs. AI tools could help repository staff redirect their time from manual metadata management to more strategic, high-value activities.

Making the most strategic use of AI in IR workflows

Our discussions revealed some principles for maximizing the value of AI in repository workflows.

Focus on the front end of the IR workflow: Our discussions revealed that the most successful AI implementations in IR workflows would focus on early intervention during the deposit process. Supplying missing metadata or correcting inaccuracies at the time of deposit is more efficient than later remediation.

This suggests that prioritizing AI integration on “front-end” deposit support rather than “back-end” cleanup may be the optimal approach. Another suggestion to help smooth integration of AI support into repository workflows was to prioritize processes that might require less human review.

Make a critical assessment of the value added by AI: A key theme that emerged from our discussions was that AI integration requires careful consideration of its benefits. Does AI actually save time, or does it simply shift work to other parts of the workflow? Many identified problems may be solved with non-AI approaches—such as scripting, enhanced system features, or workflow redesign—rather than requiring AI technologies. What is the necessary quality threshold AI tools must achieve to equal or surpass existing metadata practices, such as traditional authority control or linked data methods?

Addressing these questions will help repository staff determine whether AI integration offers genuine value and whether expected benefits justify the costs of implementation and use.

Open questions about AI

The working group surfaced several considerations specific to repositories. One touched on a lack of clarity about the library’s authority to modify metadata created by users during the deposit process, which raises questions about depositor control over AI-generated content. Furthermore, AI solutions must address the need to restrict access to certain deposits, such as doctoral dissertations that include potentially patentable information; these materials, for example, may not be suitable for use as training data for AI models.

Several other issues emerged, including the need for more training and skills development, concerns about the loss of professional skills, the importance of ensuring quality metadata, and the necessity of maintaining human expertise in the loop.

The subgroup identified several key takeaways for the institutional repository community moving forward:

• Real-world examples are essential. The community needs concrete case studies that document actual workflows and demonstrate measurable time savings and improved outcomes, not just theoretical possibilities.
• Technical guidance must be accessible. Best practices for AI implementation need to be communicated in language that nontechnical metadata teams can understand and act upon.
• Ethical frameworks are needed. The community would benefit significantly from practical guidelines for the responsible use of AI, specifically tailored to repository metadata creation.
• Strategic focus matters. Rather than pursuing AI for efficiency alone, repository leaders should align implementations with clear strategic objectives, such as improving discovery and enabling research intelligence.

Conclusion

The most important takeaway from our discussions was the critical role of the human element of AI integration. This includes maintaining “human in the loop” oversight for AI-generated content as well as addressing the professional development and job satisfaction needs of repository staff. As repositories develop AI strategies, they must strike a balance between the promise of AI-powered automation and the essential role of human mediation in metadata creation and management.

NB: As you might expect, AI technologies were used extensively throughout this project. We used a variety of tools—including Copilot, ChatGPT, and Claude—to summarize notes, recordings, and transcripts. These were useful for synthesizing insights for each of the three subgroups for quickly identifying the types of overarching themes described in this blog post.

The post Examining the role of AI in institutional repository workflows appeared first on Hanging Together.

This is the second in a short blog series on what we learned from the OCLC RLP Managing AI in Metadata Workflows Working Group.

Archives and special collections contain a wide range of resource types requiring different metadata workflows. Resources may be described in library catalogs, digital repositories, or finding aids, and the metadata can vary greatly because of platforms, collections priorities, and institutional policies. Providing online access and discovery for these unique resources presents an ongoing challenge because of inconsistent or incomplete metadata and new digital accessibility standards. AI presents new possibilities for providing access to unique resources in archives and special collections, where it may be used for data—like captions and transcriptions—relying on the strengths of large language models (LLMs).

This blog post—the second in our series on the work of the OCLC Research Library Partnership (RLP) Managing AI in Metadata Workflows Working Group—focuses on the “Metadata for Special and Distinctive Collections” workstream. It shares current uses of AI by members, insights on assessing whether AI is suitable for a task, and open questions about accuracy and data provenance.

Participants

This workstream brought together metadata professionals from diverse institutions, including academic libraries, national archives, and museums. Their collective expertise and the use cases they shared provided valuable insights into how AI tools can address the unique challenges of special and distinctive collections. Members of this group included:

Integration in existing tools

Participants primarily described using tools already available to them through existing licensing agreements with their parent institution. While this works for proof-of-concept experimentation, these ad hoc approaches do not scale up to production levels or provide the desired increases in efficiency. Participants expressed that they want integrated tools within the library workflow products they are already using.

Using multiple tools is a long-standing feature of metadata work. In the days of catalog cards, a cataloger might have a bookcase full of LCSH volumes (i.e., the big red books), LCC volumes, AACR2, LCRIs, a few language dictionaries, a few binders of local policy documents, and, of course, a typewriter manual. Today, a cataloger may have four or five applications open on their computer, including a browser with several tabs. Working with digital collections compounds this complexity, requiring additional tools for content management, file editing, and project tracking. Since AI has already been integrated into several popular applications, including search engines, metadata managers hope to see similar functionality embedded within their existing workflows, potentially reducing the burden of managing so many passwords, windows, and tabs.

Entity management

Many metadata managers, including our subgroup members, dream of automated reconciliation against existing entity databases. This becomes even more important for archives, which often contain collections of family papers with multiple members with the same names. A participant observed that URIs are preferable for disambiguation due to the requirement to create unique authorized access points for persons using a limited set of data elements. The natural question then becomes, “How can AI help us do this?”

Yale University’s case study explored this question, noting that it used AI in combination with many other tools, as using an LLM for this work would have been prohibitively expensive. The technology stack is shared in the entity resolution pipeline and includes a purpose-built vector database for text embeddings. The results included a 99% precision rate in determining whether two bibliographic records with different headings (e.g., “Schubert, Franz” and “Schubert, Franz, 1797-1828”) referred to the same person and did not make traditional string match errors that occur when identical name strings refer to different persons. This case study demonstrated how AI could be effectively used in combination with multiple tools, but it may also require technical expertise beyond that of many librarians and archivists.

Readiness and need

All participants indicated some level of organizational interest in experimenting with AI to address current metadata needs. Due to distinct workflows and operations common in special collections and archives, there were fewer concerns about AI replacing human expertise than in the general cataloging subgroup.

We identified three factors influencing their willingness to experiment with AI:

• Traditional divisions of labor
• Quantity of resources to be described
• Meeting accessibility requirements

Traditional divisions of work

In archival work, item-level description elements, such as image captions and transcripts, have often been done selectively by volunteers and student workers rather than metadata professionals due to the volume of items and the lack of specialized skills needed.* For example, the United States’ National Archives and Records Administration (NARA) relies on its Citizen Archivist volunteer program to provide tagging and transcription of digitized resources. Even with these dedicated volunteers, NARA uses AI-generated descriptions because of the extensive number of resources. However, NARA’s volunteers provide quality control on the AI-generated metadata, and the amount of metadata generated by AI ensures that these volunteers continue to be needed and appreciated.

Quantity of resources

Archival collections may range from a single item to several thousand items, resulting in significant variation in the type and level of description provided. Collection contents are often summarized with statements such as “45 linear feet,” “mostly typescripts,” and “several pamphlets in French.” However, when collections are digitized, more granular description is required to support discovery and access. The workflow at NARA is a good demonstration of how an archive uses AI to provide description at a scale that is not feasible for humans. Many archivists have been open to the idea of using AI for these tasks because the quantity of resources meant that detailed metadata was not possible.

Meeting accessibility requirements

Accessibility is a growing priority for libraries and archives, driven by legal requirements such as the ADA Title II compliance deadline in the US. For digital collections, this may mean providing alt text for images, embedded captions and audio descriptions for video recordings, and full transcripts for audio recordings.

A participant observed that, in their experience with AI-generated transcripts, AI does well transcribing single-language, spoken word recordings. However, the additional nuances with singing and multiple-language recordings are too complex for AI. This provides a natural triage for audio transcript workflows in their institution.

Creating transcripts of audio recordings is time-consuming, and archives have largely relied on student workers and volunteers for this work. Many institutions have a backlog of recordings with no transcriptions available. Thus, using AI for transcripts enables them to meet accessibility requirements and increase discovery of these resources.

Challenges and open questions around the use of AI

While AI offers opportunities, the group also identified several challenges and open questions that must be addressed for successful implementation. Metadata quality and data provenance were the top issues emerging for special and distinctive collections.

Assessing metadata quality

What is an acceptable error rate for AI-generated metadata? Participants noted that while perfection is unattainable, even for human catalogers, institutions need clear benchmarks for evaluating AI outputs. Research providing comparative studies of error rates between AI and professional catalogers would prove valuable for informing AI adoption decisions, but few such findings currently exist. High precision remains critical for maintaining quality in library catalogs, as misidentification of an entity will provide users with incorrect information about a resource.

The subgroup also discussed the concept of “accuracy” in transcription. For instance, AI-generated transcripts may be more literal, while human transcribers often adjust formatting to improve context and readability. An example from NARA showing a volunteer-created transcription and the AI data (labeled as “Extracted Text”) illustrates these differences. The human transcription moves the name “Lily Doyle Dunlap” to the same line as “Mrs.”, but the AI transcribes line by line. While the human transcriber noted untranscribed text as “[illegible],” the AI transcribed it as “A.” Neither reflects what was written, so both could be described as not completely accurate. Unlike cataloging metadata, there has never been an expectation that transcriptions of documents or audiovisual records would be perfect in all cases for various reasons, including handwriting legibility and audio quality. One participant characterized their expectations for AI-generated transcripts as “needed to be good, but not perfect.”

One case study used confidence scores as a metric to determine whether the AI-generated metadata should be provided to users without review. Confidence scores provide a numerical value indicating the probability that the AI output is correct. For example, a value of over 70% might be set as a threshold for providing data without review. Because confidence scores are provided by the models themselves, they are as much a reflection of the model’s training as its output.

Providing data provenance

Data provenance—the story of how metadata is created—is a critical concern for AI-generated outputs. Given the risk of AI “hallucinations” (generating incorrect or fabricated data), it is important to provide information to users about AI-created metadata. Working group members whose institutions are currently providing such data provenance shared their practices. NARA indicates that a document transcript is AI-generated using the standard text “Contributed by FamilySearch NARA Partner AI / Machine-Generated” (see this example for extracted text of a printed and handwritten document).

OCLC recognizes the importance of this issue to the community and is providing support in these ways:

• Updated WorldCat documentation:
  Section 3.5 of the Bibliographic Formats and Standards (BFAS) now includes guidance on recording AI-generated metadata.
• AskQC Office Hours webinar:
  The August 2025 session focused on providing data provenance in bibliographic records, including AI use cases.
• Collaboration on principles and best practices:
  OCLC is participating in the Program for Cooperative Cataloging’s Task Group on AI and Machine Learning for Cataloging and Metadata to develop guiding principles and best practices for using AI in metadata work.

Conclusion

Metadata professionals have a long-standing interest in the use of automation to provide and improve metadata, and AI joins macros, controlling headings, and batch updates as the latest technology tool in this effort. Our subgroup’s case studies demonstrated that AI tools can be used in special collections workflows in cases where AI is well-suited to the metadata needed. The most compelling applications involved transcribing documents and recordings, where AI capabilities, such as automatic speech recognition (ASR) and natural language processing (NLP), make it a good fit for such tasks.

NB: As you might expect, AI technologies were used extensively throughout this project. We used a variety of tools—Copilot, ChatGPT, and Claude—to summarize notes, recordings, and transcripts. These were useful for synthesizing insights for each of the three subgroups and for quickly identifying the types of overarching themes described in this blog post.

*It is worth noting that the labor available to national and university archives includes volunteers and student workers, whereas a smaller stand-alone archive like a historical society would not have access to so many human resources.

The post Exploring AI uses in archives and special collections: Integration, entities, and addressing need appeared first on Hanging Together.

This is the first post in a short blog series on what we learned from the OCLC RLP Managing AI in Metadata Workflows Working Group. This post was co-authored by Merrilee Proffitt and Annette Dortmund.

Libraries face persistent challenges in managing metadata, including backlogs of uncataloged resources, inconsistent legacy metadata, and difficulties in processing resources in languages and scripts for which there is not staff expertise. These issues limit discovery and strain staff capacity. At the same time, advances in artificial intelligence (AI) provide opportunities for streamlining workflows and amplifying human expertise—but how can AI assist cataloging staff in working more effectively?

To address these questions, the OCLC Research Library Partnership (RLP) formed the Managing AI in Metadata Workflows Working Group earlier this year. This group brought together metadata managers from around the globe to examine the opportunities and risks of integrating AI into their workflows. Their goal: to engage collective curiosity, identify key challenges, and empower libraries to make informed choices about how and when it is appropriate to adopt AI tools to enhance discovery, improve efficiency, and maintain the integrity of metadata practices.

This blog post—the first in a four-part series—focuses on one of the group’s critical workstreams: primary cataloging workflows. We share insights, recommendations, and open questions from the working group on how AI may address primary cataloging challenges, such as backlogs and metadata quality, all while keeping human expertise at the core of cataloging.

The “Primary Cataloging Workflows” group was the largest of our three workstreams, comprising seven participants from Australia, Canada, the United States, and the United Kingdom. Participants represented institutions in primarily English-speaking countries in which libraries may lack needed capacity to provide metadata for resources written in non-Latin scripts like Chinese and Arabic.

Motivations: shared (and persistent) needs

Working group members are turning to AI to help solve a set of familiar cataloging challenges that result from a combination of resource constraints and limited access to specific skills. These challenges include:

• Increasing cataloging efficiency
• Improving legacy metadata
• Obtaining assistance with resources in certain scripts where expertise is limited

Members of the working group assessed both the capabilities and limitations of AI tools in addressing these challenges by examining specific tools and workflows that could support this work.

Increasing cataloging efficiency

Backlogs of uncataloged resources prevent users from discovering valuable resources. Even experienced, dedicated staff are unable to keep up with the amount of resources awaiting description. AI offers the potential to address this problem by streamlining and accelerating the cataloging workflow for these materials. The working group identified key use cases of backlogs, including legal deposits, gifts, self-published resources, and those lacking ISBNs.

Copy cataloging is critical to addressing backlog issues, but the key challenge here is to identify the “best record.” Working group participants discussed how AI could streamline these workflows by automating record selection based on criteria such as the number of holdings or metadata completeness.

When original cataloging is required, AI-generated brief records for these materials can enable them to appear in discovery systems earlier, accelerating the process of making hidden collections discoverable and supporting local inventory control. This approach addresses the immediate need for discovery while allowing records to be completed, enriched, or refined over time.

Improving legacy metadata

Legacy metadata may contain errors, inconsistencies, or outdated terminology, which hinders discovery and fails to connect users with relevant resources. AI could assist with metadata cleanup and enrichment, reducing manual effort while maintaining high standards. This was an area where working group members had not experimented directly with AI tools, but could imagine a number of use cases, including:

• Identifying and replacing outdated terms in existing metadata
• Using AI tools to flag duplicates, diacritic errors, or anomalies to streamline cleanup efforts and improve data quality
• Suggesting additional metadata fields or descriptions to enhance discovery
• Supplying matching headings from local authority files to existing authorized headings or validated entities

Improving metadata quality, including reducing the number of duplicate records, has also been an area where OCLC has devoted considerable effort, including the development and use of human-informed machine learning processes, as illustrated in this recent blog post on “Scaling de-duplication in WorldCat: Balancing AI innovation with cataloging care.”

Providing support for scripts

Language and script expertise is a long-standing cataloging issue. In English-speaking countries, this manifests as difficulty describing resources written in languages using non-Latin scripts and those that are not often taught in local schools. AI tools could assist with transliteration, transcription, and language identification, enabling the more efficient processing of these materials. Some tools lack the basic functionality or support for specific, required languages. Even when AI tools confidently provide transliteration, human expertise is still very much required to evaluate AI-generated work. A library looking to AI to fill an expertise gap for these languages faces a double challenge of not fully trusting AI tools and also lacking access to internal language skills to effectively evaluate and correct its work.

Working group members brainstormed ways to address the needs in this situation. Research Libraries collect resources in dozens or even hundreds of languages to support established academic programs. Although the library may lack direct access to language proficiency, this expertise may be abundant across campus, with students, faculty, and researchers who are experts in the languages for whom hard-to-catalog resources are selected. These campus community members could help address a specific skill gap and safeguard the accuracy of AI-assisted workflows, while fostering community involvement and ensuring that humans are in the loop. In implementing such a program, libraries would need to create an engagement framework that includes rewards and incentives—such as compensation, course credit, or public acknowledgment—to encourage participation.

Open questions around the use of AI

Unsurprisingly, as with any new technology, opportunities come paired with questions and concerns. Metadata managers shared that some of their staff expressed uncertainty about adopting AI workflows, feeling they need more training and confidence-building support. Others wondered whether shifting from creating metadata to reviewing AI-generated records might make their work less engaging or meaningful.

Metadata managers themselves raised a particularly important question: If AI handles foundational tasks like creating brief records—work that traditionally serves as essential training for new catalogers—how do we ensure new professionals still develop the core skills they’ll need to effectively evaluate AI outputs?

These are important considerations as we explore the implementation of AI tools as amplifiers of human expertise, rather than replacements for it. The goal is to create primary cataloging workflows where AI manages routine tasks at scale, freeing qualified staff for higher-level work while preserving the meaningful aspects of metadata creation that make this field rewarding.

Conclusion

While not a panacea, AI offers significant potential to address primary cataloging challenges, including backlogs, support for scripts, and metadata cleanup. By adopting a pragmatic approach and emphasizing the continued relevance of human expertise, libraries can leverage AI with care to address current capacity issues that will make materials available more easily and improve discovery for users.

NB: As you might expect, AI technologies were used extensively throughout this project. We used a variety of tools—including Copilot, ChatGPT, and Claude—to summarize notes, recordings, and transcripts. These were useful for synthesizing insights for each of the three subgroups for quickly identifying the types of overarching themes described in this blog post.

The post Backlogs and beyond: AI in primary cataloging workflows appeared first on Hanging Together.