The the era of big data in the earth sciences is here and learning how to effectively use oceanographic remote-sensing data, both in the cloud and on your computer, is a core skill for modern fisheries science and management. Learning how to access cloud-based data, visualize these data, use these data in models, and use the tools of modern reproducible and collaborative science is the main goal of these hackday events. Through these events, participants will gain experience with assessing remote-sensing data in the cloud, R and RStudio, Python and Jupyter notebooks, and collaborating with Git and GitHub.
- Learn how to discover and use oceanographic remote-sensing data
- Familiarize participants with using remote-sensing data in R and Python with code.
- Obtain hands-on experience in using remote-sensing data for various science applications.
- Learn by working together on a small group project/task.
- What is a hack event? See the description of hackweeks on the University of Washington eScience institute website:
A hackweek is a participant-driven workshop that blends data science education, community building, and project work over a short period of time (one to two weeks). The events are highly immersive and allow participants to work directly with data science professionals to co-shape projects and educational outcomes. Hackweeks often help individuals and teams engage more effectively in open and reproducible science. - eScience Institute, University of Washington
- All tutorials and examples are developed openly and will be publicly available during and following the event. Participants will strengthen their practice of open science, using open source code and collaborating on their projects with course peers.
This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project content is provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.
This content was created by U.S. Government employees as part of their official duties. This content is not subject to copyright in the United States (17 U.S.C. §105) and is in the public domain within the United States of America. Additionally, copyright is waived worldwide through the CC0 1.0 Universal public domain dedication.
Currently most of the content is from the NASA Openscapes AGU 2023 workshop https://github.com/NASA-Openscapes/2023-Cloud-Workshop-AGU
Permissive Re-Mix/No Attribution Needed: You may reuse the content in this repository---excluding the NMFS Open Science logo and any NOAA logos---in any way you like. You do not need permission. You do not need to give attribution, but if you use large parts of tutorials or content it is polite to give acknowledgement of the source. Please check each NMFS Open Science repository for its reuse statement.