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Share, discover, and cite research data from Arizona State University.

The ASU Research Data Repository is a platform for ASU-affiliated researchers to share, preserve, and publish research data in a way that ensures long-term accessibility, usability, and citation. Our team supports researchers in preparing high-quality, well-documented datasets that comply with funder and publisher data sharing requirements.

Curated by ASU Library data professionals, the repository enables global discovery of research data through permanent digital identifiers (DOIs). It is powered by the open-source Dataverse application developed by Harvard University and complements the KEEP Institutional Repository to support the full range of ASU research outputs.

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1 to 10 of 12,668 Results
Replication Data for: State and federal policies and school meal participation: A descriptive analysis from Arizona
Apr 7, 2026
Raval, Shreya; Martinelli, Sarah; Dykstra, Tatum; Ohri-Vachaspati, Punam, 2026, "Replication Data for: State and federal policies and school meal participation: A descriptive analysis from Arizona", https://doi.org/10.48349/ASU/S6OQPS, ASU Library Research Data Repository, V1, UNF:6:WuS56aMtT7xjEkGEdAgewA== [fileUNF]
This analytical dataset was created using data provided by the Arizona Department of Education (ADE) and the National Center for Education Statistics (NCES). It includes school meal participation rates for Arizona public and charter schools from July 2022 through June 2024. A data dictionary will be included to describe all variables and facilitate...
Data_Dictionary_PLOS_One.tab
Apr 7, 2026 - Replication Data for: State and federal policies and school meal participation: A descriptive analysis from Arizona
Tabular Data - 2.4 KB - 4 Variables, 29 Observations - UNF:6:GZhoeNcI21MNFq9uhU0oUQ==
Documentation
PLOS_One_Manuscript_Dataset.tab
Apr 7, 2026 - Replication Data for: State and federal policies and school meal participation: A descriptive analysis from Arizona
Tabular Data - 14.7 MB - 29 Variables, 67673 Observations - UNF:6:6iDXzqiGrY9dru3VDRh5Mg==
Data
README_PLOS_One.txt
Apr 7, 2026 - Replication Data for: State and federal policies and school meal participation: A descriptive analysis from Arizona
Plain Text - 4.4 KB - MD5: 91b49bfb6dcd113ddec78bf6c0bc5b3c
Documentation
A Corpus of Artificial Intelligence Policies from US R1 Research Universities
Mar 19, 2026 - ASU Library Unit for Data Science
Abbasov, Namig; Mehta, Hetavi Dilip; Dwivedi, Vishnu; Abhiramacheri, Vaibhav; Panda, Abhipsa; Rathod, Mohak Narendrakumar; Chandrasekhar, Tejas; Ndegwa, Martin Mwangi; Zhang, Fan; Mysore Srinidhi, Chinmayi; Patel, Puravkumar; Tang, Wei Chieh; Bhatia, Sargun; Vongsenekeo, Tylor; Batra, Garima; Huang, Yaqing, 2026, "A Corpus of Artificial Intelligence Policies from US R1 Research Universities", https://doi.org/10.48349/ASU/VOYGW9, ASU Library Research Data Repository, V1, UNF:6:f3nmKdlJQZ3f7roPbZUNCw== [fileUNF]
This corpus contains official artificial intelligence (AI) policy documents issued by R1 universities across the United States. It includes guidelines on AI use in teaching and learning, academic integrity, research practices, data privacy, governance, and ethical considerations. The collection reflects how institutions are responding to generative...
corpus.zip
Mar 19, 2026 - A Corpus of Artificial Intelligence Policies from US R1 Research Universities
ZIP Archive - 477.7 KB - MD5: a1f884daebd9d78837148cf8ba1a46f2
Data Collection Guidelines.pdf
Mar 19, 2026 - A Corpus of Artificial Intelligence Policies from US R1 Research Universities
Adobe PDF - 146.1 KB - MD5: f26a3cfc18b6e506940ae0f00cc76ba4
R1_university_ai_policies.tab
Mar 19, 2026 - A Corpus of Artificial Intelligence Policies from US R1 Research Universities
Tabular Data - 19.3 KB - 9 Variables, 133 Observations - UNF:6:f3nmKdlJQZ3f7roPbZUNCw==
ReadMe.txt
Mar 19, 2026 - A Corpus of Artificial Intelligence Policies from US R1 Research Universities
Plain Text - 4.3 KB - MD5: 8fb08d24222d5e94471761717771c487
Climate Adaptation Actions for Historic Structures at Cape Lookout National Seashore: Optimal Preservation Model Maps
Climate Adaptation Actions for Historic Structures at Cape Lookout National Seashore: Optimal Preservation Model Maps
Mar 18, 2026
Xiao, Xiao; Seekamp, Erin; Li, Peizhe, 2026, "Climate Adaptation Actions for Historic Structures at Cape Lookout National Seashore: Optimal Preservation Model Maps", https://doi.org/10.48349/ASU/5XXFLT, ASU Library Research Data Repository, V1
The dataset includes the outputs from the Optimal Preservation Model- A decision support tool of adaptation actions for historical structures in Cape Lookout National Seashore under the low budget scenario. The maps are 10-year outputs of adaptation actions applied to each historical structure in Portsmouth Village and Cape Lookout Village and stru...
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