Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This repository contains the replication package for the article "Rapid Publication Growth and Limited Collaboration Centrality in Türkiye-Affiliated Artificial Intelligence Research, 2000–2025" accepted to the Journal of Information Science Theory and Practice (JISTaP). The package provides the analysis code and derived outputs needed to reproduce the tables and figures reported in the article. It includes: code/ — Python scripts for data collection from the OpenAlex API and for all analyses (collaboration-network construction, methodological and thematic classification, FP-Growth association-rule mining, citation analysis, and robustness checks). output/ — the derived datasets (processed CSV tables) that generate the article's tables and figures. README.md — environment requirements, the OpenAlex API query, and the order in which to run the scripts. The raw OpenAlex records are not redistributed here. They are openly available under a CC0 waiver and can be reconstructed directly from the OpenAlex API using the concept filter Artificial Intelligence (C154945302), the date range 1 January 2000 to 31 December 2025, and the document-type and DOI filters described in the article and in the data-collection script. Supplementary materials are published with the article by the journal. Version 3. This version accompanies the accepted version of the article. The six single-corpus figures for core journals, methodological themes and application contexts (figure_08 to figure_13 in versions 1 and 2) were combined into three two-panel figures following an editorial suggestion at the proof stage; code/build_final_figures.py gained a panel_pair() function for this purpose. BASE_DIR resolution was corrected in build_final_figures.py and build_final_tables.py, so that the shipped output/ directory is located both in the packaged layout and in a flat working tree. The derived data tables are unchanged from v2; only the figure files and these two scripts differ. Table 1 and several narrative values in the article were corrected during final proof verification against the outputs already contained in this package. These corrections changed no file in this deposit.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6