ASU Library Research Data Repository

Metrics

104,420 Downloads

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.

Ready to share your data? Visit the ASU Research Data Repository home to learn more and request a consultation.

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

71 to 80 of 12,734 Results

PLOS_One_Manuscript_Dataset.xlsx 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.csv 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 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...
CLV_1-10_action1.png Mar 18, 2026 - Climate Adaptation Actions for Historic Structures at Cape Lookout National Seashore: Optimal Preservation Model Maps PNG Image - 7.6 MB - MD5: 2026207354ae1b64bc95809a81ecadaf Data Adaptation actions for Cape Lookout Village (2025-2035, Action Preference Model)
CLV_1-10_action3.png Mar 18, 2026 - Climate Adaptation Actions for Historic Structures at Cape Lookout National Seashore: Optimal Preservation Model Maps PNG Image - 7.6 MB - MD5: 43cb48ac397ce9da97f71e232cdd627e Data Adaptation actions for Cape Lookout Village (2025-2035, Public Planning Priority Model)

PLOS_One_Manuscript_Dataset.xlsx

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 -

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 -

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 -

Data Collection Guidelines.pdf

Mar 19, 2026 - A Corpus of Artificial Intelligence Policies from US R1 Research Universities

Adobe PDF - 146.1 KB -

R1_university_ai_policies.csv

Mar 19, 2026 - A Corpus of Artificial Intelligence Policies from US R1 Research Universities

Tabular Data - 19.3 KB - 9 Variables, 133 Observations -

ReadMe.txt

Mar 19, 2026 - A Corpus of Artificial Intelligence Policies from US R1 Research Universities

Plain Text - 4.3 KB -

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...

CLV_1-10_action1.png

Mar 18, 2026 - Climate Adaptation Actions for Historic Structures at Cape Lookout National Seashore: Optimal Preservation Model Maps

PNG Image - 7.6 MB -

Data

Adaptation actions for Cape Lookout Village (2025-2035, Action Preference Model)

CLV_1-10_action3.png

Mar 18, 2026 - Climate Adaptation Actions for Historic Structures at Cape Lookout National Seashore: Optimal Preservation Model Maps

PNG Image - 7.6 MB -

Data

Adaptation actions for Cape Lookout Village (2025-2035, Public Planning Priority Model)

Add Data

Share Dataverse

Link Dataverse

Reset Modifications