04 Resources & Publications

Below you will find resources and products developed by the ADSA community, in discussion about ADSA, and content from teh Moore-Sloan Data Science Environments - the predecessor to ADSA.

Newsletter iconDon't forget to also check out our Data Science Community Newsletter for all the latest news in the world of data science and our Community Blog for deeper dives into stories from our community.

 

Building a data science program? Check out:

Education icon UPDATES FROM DATA SCIENCE INSTITUTIONS INCLUDING THEIR CHALLENGES
THE ADSA MEMBER BOOK

ADSA Publications and Products

the cover of the ADSA-USRSE career guidebook

ADSA-USRSE Career Guidebook

ADSA teamed with US-RSE to create guidance for hiring, supporting, and retaining data scientists and research software engineers in academia

group photograph of attendees at the ADSA US-RSE career guidebook workshop

Taxonomy for Masters Programs

Explore the taxonomy of competencies for masters level degree programs, developed by the ADSA community

screen capture of the Stages of the research process as described in the Data Science Ethos website

Data Science Ethos

The Data Science Ethos offers a more true-to-life view of how ethics and human contexts can be integrated into the research process

groundcover with purple and blue clusters of flowers on a green roof in Boston

Framework for Data Science Phases

The ADSA community developed this framework that describes common phases for data science initiatives - moving from inception to sustainability

cover of the program book for the ADSA Annual Meeting in 2023

Proceedings of the 2023 ADSA Annual Meeting

ADSA launched a proceedings for our annual meeting in 2023 - keep an eye out here for future issues!

Join a Working Group

ADSA creates products and publications through a community driven process. Learn more about joining or proposing a working group or product here.

Publications, Preprints, and White Papers

BY AND FOR ADSA
  • Stephanie Shipp, Donna LaLonde & Wendy Martinez (2023). Making Ethical Decisions Is Hard!, CHANCE, 36:4, 42-50, DOI: 10.1080/09332480.2023.2290955.
  • A Framework for Phased Development of Academic Data Science. (2023). Academic Data Science Alliance. https://adsa.pubpub.org/pub/ds-development-framework
  • Van Tuyl, Steve (Ed.). (2023). Hiring, Managing, and Retaining Data Scientists and Research Software Engineers in Academia: A Career Guidebook from ADSA and US-RSE. Zenodo. https://doi.org/10.5281/zenodo.8329337
  • Crall, A., Bolduc, S., Ho, K., Parker, M., Tsosie, K., Washington, T., Diaz Eaton, C., Lunch, C., Jordan, K., Santistevan, C., & Woodley, L. (2023). Identifying Assets and Collaborative Activities to Support Student Success in Environmental Data Science at Minority Serving Institutions. Zenodo. https://doi.org/10.5281/zenodo.8231167
  • Boenig-Liptsin, M., Tanweer, A., Edmundson, A. (2022). Data Science Ethos Lifecycle: Interplay of ethical thinking and data science practice. Journal of Statistics and Data Science Education. [DOI]
  • Luba Katz. (2021). The impact of COVID-19 pandemic on three data science communities. Abt Associates. Zenodo. [DOI]
  • Parker, M.S., Burgess, A.E., Bourne, P.E. (2021). Ten Simple Rules for Starting (and Sustaining) a Data Science InitiativePLoS Comput Biol 17(2): e1008628. [DOI]
  • Kolaczyk, E. D., Lee, M. M., Liu, J., & Parker, M. S. (2021). We Need a (Responsible!) Data Science Rapid Response NetworkHarvard Data Science Review. [DOI]
  • Katz, L. (2020). Careers of Data Scientists: Report from 13 Academic Institutions. Abt Associates. [pdf]
  • Cragin, M. and Kloefkorn, T. (2020). The 2018 Data Science Leaders Summit - Organizational Structures Panel Report. [DOI]
  • Katz, L. (2019). Academic Data Science Centers in the United States: A Study of 20 Universities. Abt Associates. [DOI]
  • Erickson, L.C., Carson, C., Aikat, J., Davis, S. & Janeja, V. (2019). The 2018 Data Science Leaders Summit - Ethics Panel Report [DOI]
FROM THE MOORE-SLOAN DATA SCIENCE ENVIRONMENTS
  • Geiger S, DeMasi O, Culich A, Zoglauer A, Das D, Hoces de la Guardia F, Ottoboni K, Fenner M, Varoquaux N, Barter R, Barnes R, Stoudt S, Dorton S, van der Walt S. 2019. Best Practices for Fostering Diversity and Inclusion in Data Science: Report from the BIDS Best Practices in Data Science Series. [DOI]
  • Muilenburg, J. and Ruttenberg, J. 2019. New Collaboration for New Education: Libraries in the Moore-Sloan Data Science Environments. Research Library Issues, no. 298: 16–27. [DOI]
  • Steeves V, Rampin R, Chirigati F. 2019. Reproducibility, Preservation, and Access to Research with ReproZip and ReproServer. LIS Scholarship Archive (2019, December 11). [link]
  • The University of Washington’s eScience Institute “Reflections on five years of the Moore-Sloan Data Science Environment” April 10, 2019
  • Geiger RS, Gonzalez-Beltran A, Haines R, Hetherington J, Holdgraf C, Mueller H, O'Reilly M, Petricek T, Van der Plas J. 2018. So you want to start a data science institute? Achieving sustainability. Software Sustainability Institute Blog. [link]
  • Geiger, R.S., Mazel-Cabasse, C., Cullens, C., Norén, L. Fiore-Gartland, B. Das, D. Brady, H. 2018. Career Paths and Prospects in Academic Data Science: Report of the Moore-Sloan Data Science Environments Survey [DOI]
  • Geiger RS, Sholler D, Culich A, Martinez C, Hoces de la Guardia F, Lanusse F, Ottoboni K, Stuart M, Vareth M, Varoquaux N, Stoudt S, van der Walt S. 2018. Challenges of Doing Data-Intensive Research in Teams, Labs, and Groups: Report from the BIDS Best Practices in Data Science Series. [link]
  • Huppenkothen D, Arendt A, Hogg DW, Ram K, VanderPlas JT, Rokem A. 2018. Hack weeks as a model for data science education and collaboration. Proceedings of the National Academy of Sciences Sep 2018, 115 (36) 8872-8877. [pdfsupplementary materials]
  • Katz, L. 2018. Evaluation of the Moore-Sloan Data Science Environments, Final Report. Abt Associates. [DOI]
  • The Moore-Sloan Data Science Environments. 2017. Creating Institutional Change in Data Science [DOI]
  • Tanweer A, Fiore-Gartland B, Aragon C. Impediment to insight to innovation: understanding data assemblages through the breakdown–repair process. Information, Communication & Society 2016; 19:6, 736-752. [DOI]
  • Rokem, A., Aragon, C., Arendt, A., Fiore-Gartland, B. Hazelton, B., Hellerstein, J., Herman, B., Howe, B., Lazowska, E., Parker, M., Staneva, V., Stone, S. 2015. Building an urban data science summer program at the University of Washington eScience Institute, Bloomberg Data for Good Exchange Conference, September 28, New York City, NY. [DOI]
SEE ALSO:
  • The BIDS Best Practices in Data Science Series [link]
  • BIDS 2018-2019 Annual Report [link]

FROM OUR PEER COMMUNITIES
  • Hanson B, Stall S, Cutcher-Gershenfeld J, Vrouwenvelder K, Wirz C, Rao Y (Douglas), Peng G. Garbage In, Garbage Out: Mitigating Risks and Maximizing Benefits of AI in Research. Nature 623, 28-31 (2023). [DOI]
  • Cohen J, Katz DS, Barker M, Chue Hong N, Haines R, Jay C. The Four Pillars of Research Software Engineering. IEEE Software 2020. [DOI]
  • Katz DS, McHenry K, Reinking C, Haines R. 2019. "Research Software Development & Management in Universities: Case Studies from Manchester's RSDS Group, Illinois' NCSA, and Notre Dame's CRC," 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science), Montreal, QC, Canada, 2019, pp. 17-24. [DOI]
  • Rawlings-Goss R.  Data Science Careers, Training, and Hiring: A Comprehensive Guide to the Data Ecosystem: How to Build a Successful Data Science Career, Program, or Unit. 2019. Springer. [link]
  • Katz DS, Allen G, Barba LA et al. The principles of tomorrow's university [version 1; peer review: 2 approved]. F1000Research 2018, 7:1926 [DOI]
  • Wilson G. Software Carpentry: lessons learned [version 2; peer review: 3 approved]. F1000Research 2016, 3:62 [DOI]