Papers on Piloting Computational Networks:

  1. “Developing a Framework to Enable Collaboration in Computational Archival Science Education”, Richard Marciano, Gregory Jansen, and William Underwood, Paper presented at 2019 Society of American Archivists (SAA) Research Forum, 2, 2019, Austin, TX and accepted for publication in 2020. See: https://www2.archivists.org/sites/all/files/Research_Forum%20_2019_Marciano_final.pdf
  2. “Establishing an International Computational Network for Librarians and Archivists”, iConference 2019 Blue Sky Papers series, Richard Marciano, Victoria Lemieux, Mark Hedges, Yoichi Tomiura, Katuu Shadrack, Jane Greenberg, William Underwood, Katrina Fenlon, Adam Kriesberg, Mary Kendig, Greg Jansen, Phil Piety, David Weintrop, Michael Kurtz. See: http://hdl.handle.net/2142/103139, IDEALS Institutional Repository.
  3. Archives, Access, and Artificial Intelligence, Working with Born-Digital and Digitised Archival Collections, Edited by Lise Jaillant, Bielefeld University Press, Sep. 2021,See: https://cup.columbia.edu/book/archives-access-and-artificial-intelligence/9783837655841. Afterword by Richard Marciano, “Towards a new Discipline of Computational Archival Science”.
  4. “Establishing a Research Agenda for Computational Archival Science through Interdisciplinary Collaborations between Archivists and Technologists”, P. Nicholas, R. K. Gnanasekaran, L. Perine, A. Hill, R. Marciano, SAA 2020 Research Forum (under review).

Papers on Applying Computational Thinking to Library & Archival Science:

  1. “Introducing Computational Thinking into Archival Science Education”, William Underwood, David Weintrop, Michael Kurtz, Richard Marciano, 2018 IEEE International Conference on Big Data, Jan. 24, 2019, pp.2761-2765. See: https://ai-collaboratory.net/wp-content/uploads/2020/03/1.Underwood.pdf
  2. “Reframing Digital Curation Practices through a Computational Thinking Framework”, Richard Marciano, et al., 2019 IEEE International Conference on Big Data, Dec. 11, 2019, Los Angeles, CA. See: https://ai-collaboratory.net/wp-content/uploads/2020/04/ReframingDC-UsingCT_final.pdf
  3. “Computational Thinking in Archival Science Research and Education”, William Underwood, Richard Marciano, 2019 IEEE International Conference on Big Data, Dec. 11, 2019, Los Angeles, CA. See: https://ai-collaboratory.net/wp-content/uploads/2021/03/Underwood.pdf
  4. Computational Treatments of the Legacy of Slavery (CT-LoS) ‘Reasserting Erased Memory’”, L. Perine, K. Gnanasekaran, P. Nicholas, A. Hill, R. Marciano, 2020 IEEE International Conference on Big Data, Dec. 12, 2020, Atlanta, GA. See: https://ai-collaboratory.net/wp-content/uploads/2020/11/Perine.pdf.

Papers on Jupyter Notebooks for Library and Archival Science:

  1. “Automating the Detection of Personally Identifiable Information (PII) in Japanese-American WWII Incarceration Camp Records”, Richard Marciano, William Underwood, Mohammad Hanaee, Connor Mullane, Aakanksha Singh, Zayden Tethong, 2018 IEEE International Conference on Big Data, Jan. 24, 2019, pp.2725-2732. See: https://ai-collaboratory.net/wp-content/uploads/2020/03/1.Underwood.pdf
  2. Piloting Data Science Learning Platforms through the Development of Cloud-based interactive Digital Computational Notebooks, Rajesh Kumar Gnanasekaran, and Richard Marciano, submitted to ISGC2021, International Symposium on Grids and Clouds. Submitted May 15, 2021. Academia Sinica, Taipei, Taiwan.

Papers on Machine Learning for Library and Archival Science:

  1. “Digital Curation to Support Machine Learning”, Teddy Randby, Richard Marciano, 2020 IEEE International Conference on Big Data, Dec. 11, 2020, Atlanta, GA. See: https://ai-collaboratory.net/wp-content/uploads/2020/11/Randby.pdf.
  2. “Using AI and ML to Optimize Information Discovery in Under-utilized, Holocaust-related Records”, K Strigel Carter, A Gondek, W Underwood, T Randby, R Marciano, Submitted to Journal of AI and Society on April 30, 2021.
  3. Special Issue: Technology and Records Management: Disrupt or Be Disrupted? Guest Editorial, Julie McLeod, Richard Marciano, pp.125-127. See: https://www.emerald.com/insight/content/doi/10.1108/RMJ-07-2020-057/full/pdf?title=guest-editorial.

Papers on use of AI in Library and Archival Science:

  1. Underwood, B., Marciano, R., Laib, S., Apgar C., Beteta, L., Falak, W., Gilman, M., Hardcastle, R., Holden, K., Huang, Y., Baasch, D., Ballard, B., Glaser, T., Gray, A., Plummer, L., Diker, Z., Jha, M., Singh, and A., Walanj, N., “Computational Curation of a Digitized Series of WWII Japanese-American Internment,” IEEE Big Data 2017’s 2nd Computational Archival Science (CAS) Workshop, Boston, MA, Dec. 13, 2017. See: https://ai-collaboratory.net/wp-content/uploads/2020/04/Underwood-CAS-2017.pdf
  2. “Digital Curation of a World War II Japanese-American Incarceration Camp Collection: Implications for Sociotechnical Archival Systems”, Richard Marciano, Myeong Lee, William Underwood, Sandra Laib, Zeynep Diker, and Aakanksha Singh, DigitalHeritage2018, San Francisco, Oct. 27, 2018 (part of the Digital Solutions for Heritage Archives & Collections session). See: https://ai-collaboratory.net/wp-content/uploads/2020/04/DigitalHERITAGE_2018_paper_220.pdf

This grant is funded by the Institute of Museum and Library Services (IMLS) through a
Laura Bush 21st Century Librarian Program (LB21)
National Digital Infrastructures and Initiative (NDII) Project grant: RE-246334-OLS-20.