arXiv:2204. 09333v3 Announce Type: replace-cross Abstract: In recent years, with the rapid growth of Internet data, the number and types of scientific and technological resources are also rapidly expanding.
By Bowen Yu, Yingxia Shao, Ang Li
arXiv:2204. 08476v2 Announce Type: replace-cross Abstract: In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly.
By Changwei Zheng, Zhe Xue, Meiyu Liang, Feifei Kou, Zeli Guan
arXiv:2204. 04883v2 Announce Type: replace-cross Abstract: With the advent of the cloud computing era, the cost of creating, capturing, and managing information has gradually decreased.
By Yue Wang, Zhe Xue, Ang Li
arXiv:2607. 21327v1 Announce Type: cross Abstract: Bibliometric indicators - citation counts, h-indexes, co-authorship networks - have long anchored science, technology, and innovation (STI) analytics, yet suffer from temporal lag, semantic shallowness, and an inability to capture the non-linear dynamics of contemporary knowledge ecosystems.
By Muhsen Hammoud
The paper introduces the Scientific Contribution Graph, a large-scale resource that extracts 6 million scientific contributions from 655 k open-access papers across multiple disciplines and links them with 36 million prerequisite edges. It frames automated technological roadmapping as the task of identifying contributions and their prerequisites, and presents a new scientific prerequisite prediction task where models forecast which existing technologies enable future discoveries. The authors report that current models achieve a 0.48 MAP score on temporally-filtered backtesting, indicating rapid progress in this area.
By Peter A. Jansen
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)