Skip to content

Resources

Each lifecycle section carries its own Selected Learning Resources list. The full bibliography of works cited throughout the handbook is generated below from refs.bib.

References

  1. Anna Alber, L. Briggs, Paul Brunk, M. Joshi, A. Kamble, A. Kefi, Timothy Middelkoop, Semir Sarajlic, Ana Marija Sokovic, Jeffrey Valdez, and Ying Zhang. Ai project facilitation guidance for research computing and data (RCD) professionals. In Practice and Experience in Advanced Research Computing 2025: The Power of Collaboration. ACM, 2025. doi:10.1145/3708035.3736061. ↩

  2. Patrick Schmitz, Scott Yockel, Claire Mizumoto, Thomas Cheatham, and Dana Brunson. Advancing the workforce that supports computationally and data intensive research. Computing in Science & Engineering, 23(5):19–27, 2021. doi:10.1109/MCSE.2021.3098421. ↩

  3. Timothy J. Boerner, Stephen Deems, Thomas R. Furlani, Shelley L. Knuth, and John Towns. ACCESS: advancing innovation: NSF's advanced cyberinfrastructure coordination ecosystem: services and support. In Practice and Experience in Advanced Research Computing 2023 (PEARC '23), 173–176. ACM, 2023. doi:10.1145/3569951.3597559. ↩

  4. Katia Bulekova, Carolyn Casler, Erik Deumens, Jeremy Frumkin, Jill Gemmill, Karen Green, Kathryn Kelley, Glen MacLachlan, Michael Navicky, Alana Romanella, H. Birali Runesha, Semir Sarajlic, Dan Stanzione, and Kim Wong. The dynamic state of AI in research computing. 2024. Unpublished. doi:10.13140/RG.2.2.30956.58244. ↩

  5. James J. Donlon. The national artificial intelligence research institutes program and its significance to a prosperous future. AI Magazine, 45(1):6–14, 2024. doi:10.1002/aaai.12153. ↩

  6. Glenn A. Bowen. Document analysis as a qualitative research method. Qualitative Research Journal, 9(2):27–40, 2009. doi:10.3316/QRJ0902027. ↩

  7. National Artificial Intelligence Research Resource Task Force. Strengthening and democratizing the U.S. artificial intelligence innovation ecosystem: an implementation plan for a national artificial intelligence research resource. Technical Report, National Science Foundation and White House Office of Science and Technology Policy, January 2023. URL: https://www.ai.gov/wp-content/uploads/2023/01/NAIRR-TF-Final-Report-2023.pdf. ↩

  8. Jason Bates. U.S. NAIRR pilot brings cutting-edge AI resources to researchers. National Science Foundation, 2024. URL: https://www.nsf.gov/science-matters/us-nairr-pilot-brings-cutting-edge-ai-resources-researchers. ↩

  9. Campus Research Computing Consortium (CaRCC). AI facilitation interest group. \url https://carcc.org/ai-facilitation-ig/, 2025. URL: https://carcc.org/ai-facilitation-ig/. ↩

  10. Campus Research Computing Consortium (CaRCC). AI facilitation materials working group. \url https://carcc.org/ai-facilitation-materials-working-group/, 2025. URL: https://carcc.org/ai-facilitation-materials-working-group/. ↩

  11. Benjamin Laufer, Sameer Jain, A. Feder Cooper, Jon Kleinberg, and Hoda Heidari. Four years of FAccT: a reflexive, mixed-methods analysis of research contributions, shortcomings, and future prospects. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22), 401–426. ACM, 2022. doi:10.1145/3531146.3533107. ↩

  12. Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19), 220–229. ACM, 2019. doi:10.1145/3287560.3287596. ↩

  13. Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. Datasheets for datasets. Communications of the ACM, 64(12):86–92, 2021. doi:10.1145/3458723. ↩

  14. National Institute of Standards and Technology (NIST). Artificial intelligence risk management framework (AI RMF 1.0). Technical Report NIST AI 100-1, National Institute of Standards and Technology, January 2023. URL: https://www.nist.gov/itl/ai-risk-management-framework, doi:10.6028/NIST.AI.100-1. ↩

  15. Lorenn P. Ruster. Responsible AI practices: histories, definitions, barriers and future directions. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8(3):2227–2241, 2025. doi:10.1609/aies.v8i3.36708. ↩

  16. M. Joshi, A. Alber, L. Briggs, J. Garcia Mesa, A. Kamble, L. Michael, T. Middelkoop, S. Sarajlic, A. Sokovic, J. Valdez, and Y. Zhang. The ai project lifecycle: implementation strategies and tools. 2026. doi:10.5281/zenodo.19121611. ↩