Since the 1990s, advancements in big data and information technology have increasingly driven data-centric research in the field of Library and Information Science (LIS). To assess the influence of this data-driven research paradigm on the LIS discipline, this study conducts a fine-grained analysis to uncover the evolutionary trends of research methods within the domain.
arXiv:2607. 20328v1 Announce Type: cross Abstract: This study empirically analyzed generative AI as an emerging discovery pathway to academic library resources.
By Hae Min Kim, Stacy Stanislaw
The global development of Library and Information Science (LIS) is influenced by various factors such as the economy, society, culture, discipline, tradition, and more. Consequently, the research methods of LIS vary greatly among countries.
arXiv:2511. 14007v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) raises expectations of substantial increases in rates of technological progress, but such anticipations are often not connected to detailed ground-level studies of AI use in innovation processes.
By John P. Nelson, Olajide Olugbade, Philip Shapira, Justin B. Biddle
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.
By Luigi Sigillo, Matteo Silvestri, Francesco Tabaro, Rajat Bhatnagar, Syed Irtaza Mubashar, Matt Jeffryes, Daljit Nijjer, Vittorio Perera, Ola Spjuth, Julio Saez-Rodriguez, Melissa Harrison, Fabio Petroni
arXiv:2608. 09964v1 Announce Type: cross Abstract: The Brazilian Conference on Intelligent Systems (BRACIS) is the main national venue for Artificial Intelligence research in Brazil, hosted by the Brazilian Computer Society since 2012 and publishing work from institutions across the country.
By Thales Sales Almeida, Giovana Kerche Bon\'as, Thiago Laitz, Jo\~ao Guilherme Alves Santos, Hugo Abonizio, Roseval Malaquias Junior, Marcos Piau, Celio Larcher, Ramon Pires, Rodrigo Nogueira
arXiv:2607. 16845v1 Announce Type: new Abstract: Scientists at European XFEL conduct experiments that generate very large and complex datasets.
By Tim Fuchs, Luca Gelisio, Steffen Hauf, Walid Maalej
Modelpedia is an automated, LLM-assisted framework that extracts and organizes findings about AI models from published papers into a searchable public catalog. It links each finding to the relevant model, dataset, method, and concept, and has already extracted over a thousand findings from ICLR 2024 and 2025 papers. The authors invite the community to explore, contribute to, and build on this open catalog, positioning model findings as a shared foundation for the meta‑science of AI.
By Franciszek Bernat (Centre for Credible AI, Warsaw University of Technology), Dawid P{\l}udowski (Centre for Credible AI, Warsaw University of Technology), Micha{\l} Jan W{\l}odarczyk (Centre for Credible AI, Warsaw University of Technology), Luca Longo (University College Cork), Jianlong Zhou (University of Technology Sydney), Andreas Holzinger (Human-Centered AI Lab), Riccardo Guidotti (University of Pisa, ISTI-CNR), Wojciech Samek (Technical University of Berlin, Berlin Institute for the Foundations of Learning and Data), Przemys{\l}aw Biecek (Centre for Credible AI, University of Warsaw)
arXiv:2607. 17242v1 Announce Type: cross Abstract: Pretrained machine learning (ML) models help developers build ML-intensive software systems without training models from scratch.
By Md Erfan, Ahmed Ryan, Md Rayhanur Rahman
The article outlines four practical applications of AI for PhD students: locating relevant citations, consolidating code snippets, fact‑checking research claims, and preparing for the thesis defence. It highlights how AI tools can streamline the research process and improve the quality of academic work.
By Conor O'Sullivan
arXiv:2606. 31366v1 Announce Type: cross Abstract: Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate.
By Chenyao Ma, Di Zhang, Weibo Gong, Wei Du, Rui Su, Yuhang Chen, Kan Xu, Huan Gu, Limin Li, Piao Ma, Zhenghao Li, Hao Li
The paper investigates how software engineering researchers approach machine learning in their work, reviewing research, review, and education practices. It finds that while many researchers follow data collection, model training, and evaluation routines, only a minority adopt recommended practices such as hyperparameter tuning. Common challenges include data handling, evaluating non‑functional properties, and integrating human expertise, and education often relies on hands‑on activities alongside traditional methods.
By Anamaria Mojica-Hanke, David Nader Palacio, Denys Poshyvanyk, Mario Linares-V\'asquez, Steffen Herbold