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Attending to the Cultures of Data Science Work Cover

Attending to the Cultures of Data Science Work

By: Lindsay Poirier  
Open Access
|Apr 2023

References

  1. 1Barker, M, Wilkinson, R and Treloar, A. 2019. The Australian Research Data Commons. Data Science Journal, 18(1): 44. DOI: 10.5334/dsj-2019-044
  2. 2Benjamin, R. 2019. Race after Technology: Abolitionist Tools for the New Jim Code. Cambridge, UK: Polity Press. DOI: 10.1093/sf/soz162
  3. 3Bezuidenhout, L, Drummond-Curtis, S, Walker, B, et al. 2021. A school and a network: CODATA-RDA Data Science Summer Schools Alumni Survey. Data Science Journal, 20(1): 10. DOI: 10.5334/dsj-2021-010
  4. 4Borgman, CL. 2012. The conundrum of sharing research data. Journal of the American Society for Information Science and Technology, 63(6): 10591078. DOI: 10.1002/asi.22634
  5. 5Bowker, GC and Star, SL. 1999. Sorting Things Out: Classification and Its Consequences. Cambridge, MA: MIT Press. DOI: 10.7551/mitpress/6352.001.0001
  6. 6Boyd, KL. 2021. Datasheets for datasets help ML engineers notice and understand ethical issues in training data. Proceedings of the ACM on Human-Computer Interaction 5(CSCW2), Article 438: 127. DOI: 10.1145/3479582
  7. 7Capadisli, S. 2016. Deprecating owl:sameAs. Available at: https://lists.w3.org/Archives/Public/semantic-web/2016Apr/0002.html (Last accessed 6 February 2023).
  8. 8Carroll, SR, Garba, I, Figueroa-Rodríguez, OL, et al. 2020. The CARE principles for Indigenous data governance. Data Science Journal, 19(1): 43. DOI: 10.5334/dsj-2020-043
  9. 9Chun, WHK. 2021. Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition. Cambridge, MA: MIT Press. DOI: 10.7551/mitpress/14050.001.0001
  10. 10danbri. 2017. Add a publictoilet type #1624. schemaorg/schemaorg. Available at: https://github.com/schemaorg/schemaorg/issues/1624 (Last accessed 9 January 2023).
  11. 11Denton, E, Hanna, A, Amironesei, R, et al. 2021. On the genealogy of machine learning datasets: A critical history of ImageNet. Big Data & Society, 8(2). DOI: 10.1177/20539517211035955
  12. 12D’Ignazio, C and Klein, LF. 2020. Data Feminism. Cambridge, MA: MIT Press. DOI: 10.7551/mitpress/11805.001.0001
  13. 13Dominik, M, Nzweundji, JG, Ahmed, N, et al. 2022. Open Science—For whom? Data Science Journal, 21(1): 1. DOI: 10.5334/dsj-2022-001
  14. 14Dourish, P. 2014. No SQL: The shifting materialities of database technology. Computational Culture, 4. Available at: http://computationalculture.net/article/no-sql-the-shifting-materialities-of-database-technology (Last accessed 25 May 2016).
  15. 15Edwards, P, Mayernik, MS, Batcheller, A, et al. 2011. Science friction: Data, metadata, and collaboration. Social Studies of Science, 41(5): 667690. DOI: 10.1177/0306312711413314
  16. 16Eubanks, V. 2018. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: St. Martin’s Press.
  17. 17Forsythe, DE. 1993. Engineering Knowledge: The Construction of Knowledge in Artificial Intelligence. Social Studies of Science, 23(3): 445477. DOI: 10.1177/0306312793023003002
  18. 18Gebru, T, Morgenstern, J, Vecchione, B, et al. 2020. Datasheets for datasets. arXiv:1803.09010 [cs]. Cornell University. Available at: http://arxiv.org/abs/1803.09010 (Last accessed 24 January 2021).
  19. 19Geertz, C. 1973. Thick description: Towards an interpretive theory of culture. In: Geertz, C (ed.), The Interpretation of Cultures. New York: Basic Books, pp. 336.
  20. 20Gownaris, NJ, Vermeir, K, Bittner, M-I, et al. 2022. Barriers to full participation in the open science life cycle among early career researchers. Data Science Journal, 21(1): 2. DOI: 10.5334/dsj-2022-002
  21. 21Halpin, H, Hayes, PJ, McCusker, JP, et al. 2010. When owl:sameAs isn’t the same: An analysis of identity in linked data. In: Patel-Schneider, PF, Pan, Y, Hitzler, P, et al. (eds.), The Semantic Web—ISWC 2010. Lecture Notes in Computer Science 6496. Heidelberg, Germany: Springer Berlin Heidelberg, pp. 305320. DOI: 10.1007/978-3-642-17746-0_20
  22. 22Horst, AM, Hill, AP and Gorman, KB. 2022. Palmer archipelago penguins data in the palmerpenguins R package-an alternative to Anderson’s irises. R Journal, 14(1): 244254. DOI: 10.32614/RJ-2022-020
  23. 23Klump, J. 2017. Data as social capital and the gift culture in research. Data Science Journal, 16: 14. DOI: 10.5334/dsj-2017-014
  24. 24Kouper, I, Scheidt, LA and Plale, BA. 2021. Fostering interdisciplinary data cultures through early career development: The RDA/US Data Share Fellowship. Data Science Journal, 20(1): 2. DOI: 10.5334/dsj-2021-002
  25. 25Laine, H. 2017. Afraid of scooping—Case study on researcher strategies against fear of scooping in the context of open science. Data Science Journal, 16: 29. DOI: 10.5334/dsj-2017-029
  26. 26Noble, SU. 2018. Algorithms of Oppression. New York: NYU Press. Available at: https://nyupress.org/9781479837243/algorithms-of-oppression (Last accessed 2 May 2019).
  27. 27Poirier, L. 2019. Classification as catachresis: Double binds of representing difference with semiotic infrastructure. Canadian Journal of Communication, 44(3). DOI: 10.22230/cjc.2019v44n3a3455
  28. 28Ribes, D, Hoffman, AS, Slota, SC, et al. 2019. The logic of domains. Social Studies of Science, 49(3): 281309. DOI: 10.1177/0306312719849709
  29. 29Ronallo, J. 2012. HTML5 Microdata and Schema.org. The Code4Lib Journal, 16. Available at: http://journal.code4lib.org/articles/6400?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+c4lj+(The+Code4Lib+Journal) (Last accessed 15 October 2014).
  30. 30Seaver, N. 2017. Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2). DOI: 10.1177/2053951717738104
  31. 31Star, SL. 1991. The sociology of the invisible: The primacy of work in the writings of Anselm Strauss. In: Strauss, AL and Maines, DR (eds.), Social Organization and Social Process: Essays in Honor of Anselm Strauss. Piscataway, NJ: Transaction Publishers, pp. 265283.
  32. 32van Panhuis, WG, Paul, P, Emerson, C, et al. 2014. A systematic review of barriers to data sharing in public health. BMC Public Health, 14(1): 1144. DOI: 10.1186/1471-2458-14-1144
  33. 33Wong, M, Levett, K, Lee, A, et al. 2022. Development and governance of FAIR thresholds for a data federation. Data Science Journal, 21(1): 13. DOI: 10.5334/dsj-2022-013
Language: English
Submitted on: Jan 10, 2023
Accepted on: Feb 6, 2023
Published on: Apr 3, 2023
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Lindsay Poirier, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.