참고문헌

Bach, M. J. (1986). The design of the UNIX. RTM. Operating System Prentice Hall, 312–329.

Barrera, F. J., Brown, E. D. L., Rojo, A., Obeso, J., Plata, H., Lincango, E. P., Terry, N., Rodríguez-Gutiérrez, R., Hall, J. E., & Shekhar, S. (2023). Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: A systematic review. Frontiers in Endocrinology, 14, 1106625. https://doi.org/10.3389/fendo.2023.1106625

Boettiger, C. (2015). An introduction to docker for reproducible research. ACM SIGOPS Operating Systems Review, 49(1), 71–79. https://doi.org/10.1145/2723872.2723882

Brynjolfsson, E., Li, D., & Raymond, L. R. (2024). Generative AI at work. Science, 383(6684).

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS Inc, 9, 1–73.

Cleveland, W. S. (2001). Data science: An action plan for expanding the technical areas of the field of statistics. International Statistical Review, 69(1), 21–26.

Dolan, S. et al. (2023). Jq: Command-line JSON processor. https://stedolan.github.io/jq/

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.

GitHub. (2024a). Research: Quantifying GitHub copilot’s impact on developer productivity and happiness. https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/

GitHub. (2024b). The economic impact of the AI-powered developer lifecycle and lessons from GitHub copilot. https://github.blog/news-insights/research/the-economic-impact-of-the-ai-powered-developer-lifecycle-and-lessons-from-github-copilot/

Groskopf, C. et al. (2023). Csvkit: A suite of command-line tools for working with CSV data. https://csvkit.readthedocs.io/

Janssens, J. H. M. (2021). Data science at the command line: Facing the future with time-tested tools (2nd ed.). O’Reilly Media. https://datascienceatthecommandline.com/

Kernighan, B. W., & Pike, R. (1984). The unix programming environment. Prentice Hall.

Lála, J., O’Donoghue, O., Shtedritski, A., Cox, S., Rodriques, S. G., & White, A. D. (2023). PaperQA: Retrieval-augmented generative agent for scientific research. arXiv Preprint arXiv:2312.07559. https://arxiv.org/abs/2312.07559

Marwick, B., Boettiger, C., & Mullen, L. (2018). Packaging data analytical work reproducibly using r (and friends). The American Statistician, 72(1), 80–88.

Mason, H., & Wiggins, C. (2010). A taxonomy of data science. Dataists. http://www.dataists.com/2010/09/a-taxonomy-of-data-science/

McIlroy, M. D., Pinson, E. N., & Tague, B. A. (1978). Unix time-sharing system: foreword. Bell System Technical Journal, 57(6), 1899–1904.

McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub copilot. arXiv Preprint arXiv:2302.06590. https://arxiv.org/abs/2302.06590

Stack Overflow. (2024). Stack overflow developer survey 2024. https://survey.stackoverflow.co/2024/

Stanford HAI. (2024). The 2024 AI index report. Stanford University Human-Centered AI Institute. https://aiindex.stanford.edu/report/

The Nobel Foundation. (2024). The nobel prize in chemistry 2024. https://www.nobelprize.org/prizes/chemistry/2024/

Wilson, G., Capes, G., Devenyi, G. A., Koch, C., Silva, R., Srinath, A., Morris, C., Jackson, M., Boughton, A., Emonet, R., Gacenga, F., Nederbragt, L., csqrs, Irving, D., Becker, E. A., Deniz, F., Stimberg, M., Beagrie, R. A., McCloy, D., … Chhatre, V. (2019). swcarpentry/shell-novice: Software Carpentry: the UNIX shell, June 2019 (Version v2019.06.1). Zenodo. https://doi.org/10.5281/zenodo.3266823