Presenter: Emilio Mayorga (Applied Physics Laboratory, University of Washington)
Description:
OceanHackWeek has taken place for four years (2018-2021) bringing data science and oceanography practitioners together with the goal of broadening skills in data science throughout the international oceanography community through a combination of intensive tutorials and real world, collaborative coding projects pitched by participants. Participants represent a broad cross-section of the oceanography community, from non-oceanographic undergraduate students investigating a further career in oceanography to graduate students, postdocs, researchers, staff and technicians in academia, government, non-profits and private industry. Tutorials often touch on state-of-the-art tools in Big Data and data science today, leading participants through workflows with Pangeo, machine learning, and various data access mechanisms. Participants have actively engaged in cutting edge package development in Python and R, among other topics. We will present on outcomes and feedback from these events, highlighting the positives along the way and lessons learned on how to run a hackweek/hackathon event that develops data science skills.
More Information: https://oceanhackweek.github.io
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Full list of Authors
- Emilio Mayorga (University of Washington Applied Physics Laboratory)
- Matthew Biddle (Integrated Ocean Observing System)
- Filipe Fernandes (Integrated Ocean Observing System)
- Chelle Gentemann (Farallon Institute)
- Derya Gumustel (University of Washington Applied Physics Laboratory)
- Alex Kerney (Gulf of Maine Research Institute)
- Jane Koh (University of Washington eScience Institute)
- Wu-Jung Lee (University of Washington Applied Physics Laboratory)
- Paige Martin (Australian National University)
- Catherine Mitchell (Bigelow Laboratory for Ocean Sciences)
- Thomas Moore (CSIRO Climate Science Centre, Australia)
- Nicholas Record (Bigelow Laboratory for Ocean Sciences)
- Amanda Tan (University of Washington)
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OceanHackWeek - A Collaborative Model For Expanding Data Science Proficiency In Oceanography
Category
Scientific Session > OD - Ocean Data Science, Analytics, and Management > OD12 Big Data for a Big Ocean 2022
Description
Presentation Preference: Poster
Supporting Program: None
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