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Living in, with and around the longhouses at Strøby Toftegård: A biographical approach to longhouses from the Late Iron and Viking Age
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Bronzealderens sidste ofring?
No full textPræsentation af et velbevaret Hallstatt-sværd med detaljer af jern, fundet som del af depotfundet Egedalfundet
Navigating Data Science and Artificial Intelligence Integration in Library and Information Science:Insights from Four National Libraries
No full textThis chapter examines the integration of artificial intelligence (AI) and datascience in library and information science, using insights from four national libraries: the British Library, the National Library of France, the Royal Library of Belgium, and the Royal Danish Library. The integration of these technologies represents a transformative shift, enabling libraries to automate tasks, enhance user experiences, and optimize operations. This study adopts a qualitative approach, drawing on in-depth interviews with key personnel and analyses of strategic documents to explore the challenges and opportunities posed by AI. The findings highlight critical organizational issues such as resistance to change, cross-departmental collaboration, resource allocation, and the need for skill development. The chapter proposes actionable strategies for addressing these challenges, including fostering collaboration, developing flexible funding models,and investing in targeted training programs. By analyzing the implementation of AI in national libraries, the study offers a comprehensive understanding of how these institutions can navigate the complexities of digital transformation and position themselves as leaders in the evolving landscape of information science.Keywords: Artificial Intelligence, data science, library innovation, national libraries, digital transformation, organizational change, qualitative research, automation, resource allocation, skill developmen
Vom "Paradepferd" zum Sorgenkind:Dänemark unter deutscher Besatzungsherrschaft 1940-1945
No full textRapid analysis of shipworm attack - a novel digital tool for assessing shipworm damage (RANDA)
Get PDFShipworm (Teredo navalis) poses a significant threat to wooden structures and archaeological artifacts in marine environments, leading to extensive biodeterioration. Quantifying shipworm damage remains challenging due to the limitations of traditional assessment methods, which are often invasive, time-consuming, and/or subjective. In this study, we introduce RANDA (Rapid Analysis Digital Tool), a software designed to assess shipworm damage severity through image-based analysis of entry holes on wood surfaces, with the potential for non-destructive application. Data from 232 T. navalis entry holes and tunnels revealed a strong correlation between entry hole size and tunnel volume, suggesting that surface analysis can approximate internal damage. When applied to eight test panels, RANDA degradation estimates differed by -0.69±1.05 % from weight loss analysis and by 2.74 ± 1.67 % from tunnel volume measurements obtained via CT imaging. For hole classification, the RANDA model achieved a precision of 80 % and a recall of 81 %. While currently validated under controlled conditions, this method offers a rapid and reliable alternative to conventional approaches and represents an important step toward a non-destructive in situ assessment tool.Shipworm (Teredo navalis) poses a significant threat to wooden structures and archaeological artifacts in marine environments, leading to extensive biodeterioration. Quantifying shipworm damage remains challenging due to the limitations of traditional assessment methods, which are often invasive, time-consuming, and/or subjective. In this study, we introduce RANDA (Rapid Analysis Digital Tool), a software designed to assess shipworm damage severity through image-based analysis of entry holes on wood surfaces, with the potential for non-destructive application. Data from 232 T. navalis entry holes and tunnels revealed a strong correlation between entry hole size and tunnel volume, suggesting that surface analysis can approximate internal damage. When applied to eight test panels, RANDA degradation estimates differed by -0.69±1.05 % from weight loss analysis and by 2.74 ± 1.67 % from tunnel volume measurements obtained via CT imaging. For hole classification, the RANDA model achieved a precision of 80 % and a recall of 81 %. While currently validated under controlled conditions, this method offers a rapid and reliable alternative to conventional approaches and represents an important step toward a non-destructive in situ assessment tool
