globalchange  > 气候变化与战略
DOI: 10.1073/pnas.1717196115
论文题名:
Hack weeks as a model for data science education and collaboration
作者: Huppenkothen D.; Arendt A.; Hogg D.W.; Ram K.; VanderPlas J.T.; Rokem A.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2018
卷: 115, 期:36
起始页码: 8872
结束页码: 8877
语种: 英语
英文关键词: Data science ; Education ; Interdisciplinary collaboration ; Reproducibility
Scopus关键词: adult ; article ; career ; data analysis ; education ; female ; human ; human experiment ; immersion ; literacy ; male ; reproducibility ; self report ; educational model ; information dissemination ; interdisciplinary education ; science ; university ; Humans ; Information Dissemination ; Interdisciplinary Studies ; Models, Educational ; Science ; Universities
英文摘要: Across many scientific disciplines, methods for recording, storing, and analyzing data are rapidly increasing in complexity. Skillfully using data science tools that manage this complexity requires training in new programming languages and frameworks as well as immersion in new modes of interaction that foster data sharing, collaborative software development, and exchange across disciplines. Learning these skills from traditional university curricula can be challenging because most courses are not designed to evolve on time scales that can keep pace with rapidly shifting data science methods. Here, we present the concept of a hack week as an effective model offering opportunities for networking and community building, education in state-of-the-art data science methods, and immersion in collaborative project work. We find that hack weeks are successful at cultivating collaboration and facilitating the exchange of knowledge. Participants self-report that these events help them in both their day-to-day research as well as their careers. Based on our results, we conclude that hack weeks present an effective, easy-to-implement, fairly low-cost tool to positively impact data analysis literacy in academic disciplines, foster collaboration, and cultivate best practices. © 2018 National Academy of Sciences. All Rights Reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163667
Appears in Collections:气候变化与战略

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作者单位: Huppenkothen, D., Institute for Data-Intensive Research in Astrophysics and Cosmology, Department of Astronomy, University of Washington, Seattle, WA 98195, United States, Center for Data Science, New York University, New York, NY 10003, United States, Center for Cosmology and Particle Physics, Department of Physics, New York University, New York, NY 10003, United States, University of Washington eScience Institute, Washington Research Foundation Data Science Studio, University of Washington, Seattle, WA 98105, United States; Arendt, A., University of Washington eScience Institute, Washington Research Foundation Data Science Studio, University of Washington, Seattle, WA 98105, United States, Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA 98105-6698, United States; Hogg, D.W., Center for Data Science, New York University, New York, NY 10003, United States, Center for Cosmology and Particle Physics, Department of Physics, New York University, New York, NY 10003, United States, Max-Planck-Institut für Astronomie, Heidelberg, D-69117, Germany, Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, United States; Ram, K., Berkeley Institute for Data Science, University of California, Berkeley, CA 94720, United States, Berkeley Initiative in Global Change Biology, University of California, Berkeley, CA 94720, United States; VanderPlas, J.T., University of Washington eScience Institute, Washington Research Foundation Data Science Studio, University of Washington, Seattle, WA 98105, United States; Rokem, A., University of Washington eScience Institute, Washington Research Foundation Data Science Studio, University of Washington, Seattle, WA 98105, United States

Recommended Citation:
Huppenkothen D.,Arendt A.,Hogg D.W.,et al. Hack weeks as a model for data science education and collaboration[J]. Proceedings of the National Academy of Sciences of the United States of America,2018-01-01,115(36)
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