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Osuva on Vaasan yliopiston avoin julkaisuarkisto. Osuva sisältää Vaasan yliopiston omat julkaisut, opinnäytteet ja tieteellisten artikkeleiden rinnakkaistallenteet. Osuvaan sisältyy julkaisujen viitetietoja, tiivistelmiä ja kokotekstejä. Sähköisten arkistokokoelmien sisältö ei ole luettavissa verkossa.
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Viimeksi tallennetut
The Retrocatalography Project: Combining AI with the Microsoft Power Platform at the Royal National Library of Belgium
Schumann, Anna-Lea; Mergel, Ines; Mergel, Ines (toim.); Schmidt, Carsten (toim.) (Springer, 2025)
The Royal National Library of Belgium (KBR) collects all Belgian publications and safeguards the country’s cultural and historical heritage. Given the increasing volume of records and the existence of records that are only catalogued on paper cards, there emerged a need for an AI-based solution. In response, KBR initiated a project called Retrocatalography, collaborating with the external service provider Initum. This partnership resulted in the development of an AI-based cataloguing solution which was created in just 10 days. Built on the Microsoft Power Platform, this low-code application is capable of gathering information from a book’s cover, archiving it, and verifying the details. In addition, KBR developed the Allez app in-house to scan library records. Volunteers use the app by assisting in the verification process to ensure accuracy of the records data. The AI-based solution went live in October 2022, with strong support from top management of the library. Along the way, KBR encountered several challenges, including staff shortages, time constraints for integrating the system into daily operations, and various technical difficulties. Overall, the AI-based solution transforms the workflow of librarians at KBR. It has made cataloguing books more efficient, ultimately enhancing the quality of the library services.
Assessing Nineteenth-Century Library Collections with the “Living with Machines” Project at the British Library, United Kingdom
Kühler, Justus; Mergel, Ines; Mergel, Ines (toim.); Schmidt, Carsten (toim.) (Springer, 2025)
As part of its commitment to innovation and to improve the accessibility of the library’s collection of resources from the long nineteenth century, the British Library ran the “Living with Machines” project between 2018 and 2023. The Library’s project uses Artificial Intelligence (AI) technologies to improve access to historical collections. The library faced the challenge of making the extensive collections easier to interact and more accessible to users. The project was initiated to explore how AI and machine learning (ML) would impact historical research, with a particular focus on the impact of mechanisation in the nineteenth century. The project team was gathered from 2018 to July 2019. The project involved over 5500 volunteers and engaged the public through crowdsourcing tasks. The collaboration spanned various disciplines, including data scientists, historians and research software developers. The British Library also reached out to the public to ask for their help in commenting on the artefacts. Collaboration challenges arose when staff did not spend enough time understanding each other’s disciplines. According to the interviewees, librarians will need a solid grounding in “information organisation” when it comes to working with AI solutions and outputs.
Exploring Computational Descriptions for Metadata Creation for E-Books at the Library of Congress, United States of America
Schumann, Anna-Lea; Kühler, Justus; Mergel, Ines; Mergel, Ines (toim.); Schmidt, Carsten (toim.) (Springer, 2025)
The Library of Congress’ (LoC) project “Exploring Computational Description” (ECD) is investigating the use of machine learning (ML) to create metadata for e-books that have not yet been catalogued. In-house the LC Labs carried out this initiative with the U.S. Programs, Law, and Literature Division and an external vendor. An initial budget of $250,000 from the National Digital Trust Fund was allocated for this experimental AI endeavour, prompted by a massive backlog of e-books. During the first project phase, five ML models were evaluated, and in the second project phase, human-in-the-loop prototypes that offer machine-generated terms to librarians were introduced. The integration of AI at the LoC has the potential to enhance cataloguing efficiency by automating repetitive tasks, thereby allowing librarians to focus more on intellectual tasks. At the same time, the project faced several challenges, including ensuring the reliability of AI-generated records, copyright concerns, and managing potentially harmful language in older texts used for training the models. Improving the accuracy of these models remains essential and depends on access to extensive digital data. However, human expertise remains crucial for ensuring high quality, and librarians need to develop a foundational understanding of ML to leverage these technologies effectively. The aim of the project is to develop innovative approaches that contribute to improving library practices.
Recommendations for the AI Implementation in Libraries
Mergel, Ines; Kühler, Justus; Schumann, Anna-Lea; Schmidt, Carsten; Mergel, Ines (toim.); Schmidt, Carsten (toim.) (Springer, 2025)
AI implementation is part of the digital transformation efforts of National Libraries. To understand what the challenges are and derive recommendations for other libraries, we researched the AI implementation. Based on 90 interviews with library and AI experts, we identified ten European National Libraries and included the Library of Congress and the British National Library in our case analysis. Here, we derive the challenges that National Libraries face when implementing AI technologies. Based on these insights, we provide recommendations for libraries that plan to also implement AI solutions in their organisations. This chapter focuses on sustainable governance structures that ensure long-term strategic alignment of funding and implementation goals, the promotion of technical independence of international providers through internal competence and upskilling efforts, and the involvement of information specialists and users to ensure adoption and acceptance of AI tools. Furthermore, we highlight the importance of collaborative and interdisciplinary approaches as well as transparency, responsible data management, and ethical AI.
LAI-Derived Atmospheric Moisture Condensation Potential for Forest Health and Land Use Management
Lin, Jung-Jun; Arslan, Ali Nadir (MDPI, 2025)
The interaction between atmospheric moisture condensation (AMC) on leaf surfaces and vegetation health is an emerging area of research, particularly relevant for advancing our understanding of water–vegetation dynamics in the contexts of remote sensing and hydrology. AMC, particularly in the form of dew, plays a vital role in both hydrological and ecological processes. The presence of AMC on leaf surfaces serves as an indicator of leaf water potential and overall ecosystem health. However, the large-scale assessment of AMC on leaf surfaces remains limited. To address this gap, we propose a leaf area index (LAI)-derived condensation potential (LCP) index to estimate potential dew yield, thereby supporting more effective land management and resource allocation. Based on psychrometric principles, we apply the nocturnal condensation potential index (NCPI), using dew point depression (ΔT = Ta − Td) and vapor pressure deficit derived from field meteorological data. Kriging interpolation is used to estimate the spatial and temporal variations in the AMC. For management applications, we develop a management suitability score (MSS) and prioritization (MSP) framework by integrating the NCPI and the LAI. The MSS values are classified into four MSP levels—High, Moderate–High, Moderate, and Low—using the Jenks natural breaks method, with thresholds of 0.15, 0.27, and 0.37. This classification reveals cases where favorable weather conditions coincide with low ecological potential (i.e., low MSS but high MSP), indicating areas that may require active management. Additionally, a pairwise correlation analysis shows that the MSS varies significantly across different LULC types but remains relatively stable across groundwater potential zones. This suggests that the MSS is more responsive to the vegetation and micrometeorological variability inherent in LULC, underscoring its unique value for informed land use management. Overall, this study demonstrates the added value of the LAI-derived AMC modeling for monitoring spatiotemporal micrometeorological and vegetation dynamics. The MSS and MSP framework provides a scalable, data-driven approach to adaptive land use prioritization, offering valuable insights into forest health improvement and ecological water management in the face of climate change.