04 Resources & Publications

We routinely collect resources for our community and have started to organize them here. Below you will find papers related to the challenges of integrating data science in academia. Our community originated with the Moore-Sloan Data Science Environments. We preserved content from the old MSDSE website HERE. Below we include papers and blogs from the MSDSE partners.

Don'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.

Don'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.

Advance your career through mentoring:

LEARN BEST PRACTICES FOR MENTORS.

EXPLORE RESOURCES FOR MENTEES.

Building a data science program? Check out:

2021 UPDATES FROM DATA SCIENCE INSTITUTIONS INCLUDING THEIR CHALLENGES

OUR FOUNDING MEMBERS BOOK.

Publications, Preprints, and White Papers

BY AND FOR ADSA

• 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 Initiative. PLoS 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 Network. Harvard 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. [pdf, supplementary 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

• 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]