Effective information retrieval (IR) depends on accurate relevance classification. But when the criteria are subjective or underspecified, small variations in classification can cause consequential shifts in retrieval results. The potential for such variability becomes critical for institutions when they use IR for research assessment. Retrieval instability can lead to relevant literature being overlooked, hindering a comprehensive understanding of the research landscape, and potentially undermining the validity of subsequent analyses and decisions.

The Virginia Tech University Libraries (VTUL) Digital Library Platform (DLP) hosts digital collections that offer our users access to a wide variety of documents of historical and cultural importance. These collections are not only of academic importance but also provide our users with a glance at local historical events. Our DLP contains collections comprising digital objects featuring complex layouts, faded imagery, and hard-to-read handwritten text, which makes providing online access to these materials challenging. To address these issues, we integrate AI into our DLP workflow and convert the text in the digital objects into a machine-readable format. To enhance the user experience with our historical collections, we use custom AI agents for handwriting recognition, text extraction, and large language models (LLMs) for summarization. This poster highlights three collections focusing on handwritten letters, newspapers, and digitized topographic maps. We discuss the challenges with each collection and detail our approaches to address them. Our proposed methods aim to enhance the user experience by making the contents in these collections easier to search and navigate.

Traditional archival practices for describing electronic theses and dissertations (ETDs) rely on broad, high-level metadata schemes that fail to capture the depth, complexity, and interdisciplinary nature of these long scholarly works. The lack of detailed, chapter-level content descriptions impedes researchers’ ability to locate specific sections or themes, thereby reducing discoverability and overall accessibility. By providing chapter-level metadata information, we improve the effectiveness of ETDs as research resources. This makes it easier for scholars to navigate them efficiently and extract valuable insights. The absence of such metadata further obstructs interdisciplinary research by obscuring connections across fields, hindering new academic discoveries and collaboration. In this paper, we propose a machine learning and AI-driven solution to automatically categorize ETD chapters. This solution is intended to improve discoverability and promote understanding of chapters. Our approach enriches traditional archival practices by providing context-rich descriptions that facilitate targeted navigation and improved access. We aim to support interdisciplinary research and make ETDs more accessible. By providing chapter-level classification labels and using them to index in our developed prototype system, we make content in ETD chapters more discoverable and usable for a diverse range of scholarly needs. Implementing this AI-enhanced approach allows archives to serve researchers better, enabling efficient access to relevant information and supporting deeper engagement with ETDs. This will increase the impact of ETDs as research tools, foster interdisciplinary exploration, and reinforce the role of archives in scholarly communication within the data-intensive academic landscape.

As research institutions increasingly commit to supporting the United Nations’ Sustainable Development Goals (SDGs), there is a pressing need to accurately assess their research output against these goals. Current approaches, primarily reliant on keyword-based Boolean search queries, conflate incidental keyword matches with genuine contributions, reducing retrieval precision and complicating benchmarking efforts. This study investigates the application of autoregressive Large Language Models (LLMs) as evaluation agents to identify relevant scholarly contributions to SDG targets in scholarly publications. Using a dataset of academic abstracts retrieved via SDG-specific keyword queries, we demonstrate that small, locally-hosted LLMs can differentiate semantically relevant contributions to SDG targets from documents retrieved due to incidental keyword matches, addressing the limitations of traditional methods. By leveraging the contextual understanding of LLMs, this approach provides a scalable framework for improving SDG-related research metrics and informing institutional reporting.

Metadata is crucial for the accessibility, interoperability, and long-term usability of digital objects such as Electronic Theses and Dissertations (ETDs). In large-scale academic repositories, poor metadata quality can significantly impede the discovery and use of resources. This study addresses persistent issues of incomplete and inconsistent ETD metadata collected from U.S. university libraries. However, directly applying machine learning-based error detection and correction models may introduce unwanted errors due to the imperfection of these models. We propose an ETD metadata improvement system (ETDMIS) that mitigates the problem by integrating metadata validation and a version control mechanism. Our system was applied to a dataset of 100,000 U.S. ETDs, resulting in substantial improvements in metadata quality. Scalability was demonstrated by processing the entire dataset efficiently. The original and the enhanced metadata for the 100,000 ETDs are publicly accessible at https://github.com/lamps-lab/ETDMiner/tree/master/Meta100K.

Theses and dissertations record the work of graduate students and are typically a requirement at the culmination of the graduate degree. Thus, they contain important information that reflects a graduate student’s exploration of their research topic. Although print submission was commonplace early on, most universities now require students to submit an electronic version. The electronic document referred to as an ETD henceforth has become the primary way of submitting, storing, and distributing graduate work. Millions of such documents have been created in the past two decades. They are maintained and stored by university libraries, digital repositories, and other academic publishing companies. These online repositories have increased access to such documents. Nonetheless, these documents fail to meet the needs of researchers, who find it challenging to find and access knowledge from such long documents. The worldwide ETD collection has increased in volume to become what is known as ‘scholarly big data’. Apart from the text body, these documents contain a myriad of other pieces of knowledge like tables, figures, definitions, literature reviews, and references. There is a growing demand amongst researchers across various domains to make this collection of scholarly documents more computationally driven. We use ideas from natural language processing, information retrieval, and machine learning to excavate knowledge from this rich information source. In this paper, we examine some of the challenges we face, identify some key areas of exploration, and discuss our methods to mitigate the challenges.

In this work, we report our progress on building a collection containing over 450k Electronic Theses and Dissertations (ETDs), including full-text and metadata. Our goal is to close the gap of accessibility between long text and short text documents, and to create a new research opportunity for the scholarly community. For that, we developed an ETD Ingestion Framework (EIF) that automatically harvests metadata and PDFs of ETDs from university libraries. We faced multiple challenges and learned many lessons during the process, that led to proposed solutions to overcome/mitigate the limitations of the current data. We also described the data that we have collected. We hope our methods will be useful for building similar collections from university libraries and that the data can be used for research and education.