{
  "id": 14116,
  "url": "https://arxiv.org/abs/2601.14429",
  "title": "Measuring the State of Open Science in Transportation Using Large Language Models",
  "summary": "arXiv:2601.14429v2 Announce Type: replace-cross Abstract: Open science initiatives have strengthened scientific integrity and accelerated research progress across many fields, but the state of their practice within transportation research remains under-investigated. Key features of open science, defined here as data and code availability, are difficult to extract due to the inherent complexity of the field. Previous work has either been limited to small-scale studies due to the labor-intensive n",
  "authors": "Junyi Ji, Ruth Lu, Linda Belkessa, Liming Wang, Silvia Varotto, Yongqi Dong, Nicolas Saunier, Mostafa Ameli, Gregory S. Macfarlane, Bahman Madadi, Cathy Wu",
  "category": "research",
  "topics": "jobs-economy,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T04:00:00.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14116",
  "original_url": "https://arxiv.org/abs/2601.14429",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}