Skip to content
#artificial intelligence Dataset Open access

Policy-event dataset on the environmental governance of AI and data-centre infrastructure

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This record contains Supplementary Data 1–7 of the associated article on the environmental governance of artificial-intelligence and data-centre infrastructure: the final event-level policy corpus (Supplementary Data 3, Supplementary Table S3), the full-sample reliability records (Supplementary Data 1, Supplementary Table S1), the fixed-frame search protocol and query log (Supplementary Data 2, Supplementary Table S2), the macro-category and jurisdiction-level distributions (Supplementary Data 4, Supplementary Table S4), the analytical specifications and resampling settings (Supplementary Data 5, Supplementary Table S5), the illustrative-case source register (Supplementary Data 6, Supplementary Table S6) and the numerical source data underlying Figs. 1–4 (Supplementary Data 7). The final corpus comprises 114 retained policy events (Dataset B); Dataset A comprises the 63 retained events of the screened baseline. The event classifications describe coded policy orientations and governance approaches. They do not measure realized environmental outcomes. Base-map boundaries used for Fig. 1 are from Natural Earth (public domain) and are not redistributed here. Reproduction code for the reported statistics is deposited as a separate software record under an MIT licence: https://doi.org/10.5281/zenodo.22863248

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

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...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.

Jiaqi Xue, Meng Zheng, Yebowen Hu et al. · 109 citations · ⚡8

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.