https://aurora.auburn.edu/handle/11200/44142 Fri, 03 Apr 2026 21:30:41 GMT 2026-04-03T21:30:41Z https://aurora.auburn.edu/handle/11200/50747 A multi-strategic approach to locating institutional data deposits Fragmentation in the landscape of data sharing poses a challenge to institutional attempts to assess research output and compliance with grant requirements. Variation in disciplinary norms, individual practices, and metadata standards make it difficult to determine where, or whether, affiliated researchers deposit their datasets at project’s end. While commercial providers do offer paid services purporting to solve these issues, many institutions prefer to keep this work in-house for reasons of cost, accuracy, and fit with local objectives. Practical examples of dataset discovery projects, their successes and failures, are useful to decision-makers within research institutions weighing their options in this area. We report the results of a search for publicly accessible datasets produced by Auburn University researchers within the past ten years. This effort, a collaboration between two librarians and an undergraduate student, involved multiple strategies for finding data. Examples of such included searching databases for publications with associated data, searching generalist and specialist repositories directly, and utilizing the scholarly profiles (e.g. ORCID records) of known researchers of interest. Lists of federal grants received by Auburn-affiliated researchers were used to help identify and prioritize potential data depositors. This poster explores the rationale behind the various strategies along with their relative success in discovering data. As the purpose of the project was to iteratively develop a methodology, the roadblocks we encountered were as instructive as the successes of each strategy. We conclude with a discussion of planned next steps, such as discussions with institutional stakeholders, and a broader reflection on the challenges posed by inconsistent or absent affiliation metadata in the research reporting infrastructure. https://aurora.auburn.edu/handle/11200/50747 https://aurora.auburn.edu/handle/11200/50702 The Personal Touch: A Qualitative Dive into the Knowledge Networks of Librarian Goal: This work-in-progress conference presentation seeks to understand how librarians share knowledge and activate their social networks to solve job-related problems. Cross and Parker’s (2004) latent network view of access and awareness challenges serves as the foundation for surfacing both barriers and coping strategies. Methodology: Five open-ended responses were collected as part of an online survey of 280 professional librarians in 2019. Inductive coding is being used to identify and categorize lower-level concepts into overarching themes and a cohesive storyline (Corbin & Strauss, 2008). Data will be queried to identify themes that may be more relevant for individuals sharing certain demographic attributes (i.e., gender, ethnicity, age). Results: Findings will be submitted to a top library science journal in 2025 and communicated via narrative description, diagram, and cross-tabulation. https://aurora.auburn.edu/handle/11200/50702 https://aurora.auburn.edu/handle/11200/50670 No field? No problem! Enriching IR metadata via the DOI registrar Many DSpace institutional repositories rely on the Dublin Core metadata schema, limiting what can be described in the metadata records. This is true even when IRs locally adapt or extend Dublin Core. When IRs accept research data to help institutional personnel comply with public access mandates or journal requirements, this becomes a bigger issue. Rich metadata is crucial for data deposits not only to describe the data itself, thereby facilitating reuse, but also to ensure they are properly represented in the growing web of interconnected reporting systems that track research outputs. For example, dataset contributors should be linked to other entities through permanent identifiers such as ORCIDs, and bidirectional linkages to all related publications and software should exist. This talk describes an ongoing project at Auburn University Libraries to improve the metadata shared about data deposits in the institutional repository using its DOI registrar. As a member of DataCite, Auburn University utilizes its services to assign DOIs to items in the IR. The DSpace IR employs a modified Dublin Core metadata schema, but describing data using the more complex DataCite metadata schema would allow for much greater functionality. Since September 2023, a librarian and an undergraduate have been working together to decouple the local DC records from the DOI records in DataCite and create a process to feed more extensive metadata to the latter. This dual system permits information and relationships that cannot be represented in the repository's schema to be reported via the DOI public registration metadata. It will also enable DOIs to be reserved at the draft stage, a common request from researchers. Finally, this presentation will summarize the pros and cons of moving to a more complex workflow requiring greater manual intervention. https://aurora.auburn.edu/handle/11200/50670 https://aurora.auburn.edu/handle/11200/50669 Reusable for who? Discussing "data science ready" repository design Research data repositories are intended to make data FAIR - findable, accessible, interoperable, and reusable - but implementing these principles in practical terms entails deciding how to evaluate FAIRness. Recent interpretations of interoperability and reusability, in particular, have based their metrics of progress in implementation around facilitating machine processes. In other words, improving the “I” and the “R” of data in a repository translates into structuring data to be crawled and ingested by automated agents and incorporated as seamlessly as possible into aggregate datasets. This is referred to as “data science ready” research data, and some repositories are working towards this vision in consultation with computer scientists. This talk explores the implications of prioritizing the machine digestibility of research data in repository curation processes. What does this perspective imply about the relative value of datasets intended for reuse by researchers working manually to interpret, restructure, and analyze the data? About the fields of study that primarily produce data of that nature? There are tradeoffs in metadata structure and content when the intended “audience” of data is human vs. machine, and there are also risks associated with stripping research data of its context, as can easily happen when employing big data methodologies. Finally, potential impacts to the research ecosystem are considered. For instance, when data is automatically scraped at scale, what happens if the researchers that made it available miss out on attribution and citation of their work?Who will have oversight over later use, and perhaps misuse, of that data? How would the original creators know if this happened, and what could they do about it?These considerations are important because design choices that can shape the future of research practice should not go forward unexamined and unchallenged. https://aurora.auburn.edu/handle/11200/50669