This 5-day in-person workshop will provide researchers with an introduction to advanced topics in computationally reproducible research in python, including software and techniques for working with very large datasets. This includes working in cloud computing environments, docker containers, and parallel processing using tools like parsl and dask. The workshop will also cover concrete methods for documenting and uploading data to the Arctic Data Center, advanced approaches to tracking data provenance, responsible research and data management practices including data sovereignty and the CARE principles, and ethical concerns with data-intensive modeling and analysis. We welcome individuals participating in Arctic research with a wide variety of backgrounds, experience, and career stages to apply. However, this course is intended for Arctic researchers with a solid foundation in programming (R, python, or similar), who have a need to take their skills to the next level to maximize efficiency working with big datasets or running computing-intensive processes.
Topics include:
Scalable computing
Cloud computing concepts
Docker environments
Remote computing
Parallel processing and concurrency
Large data transfer, data staging
Data extraction
I/O efficiency
Apply by November 15, 2024! The workshop will be held in Santa Barbara, CA. at the National Center for Ecological Analysis and Synthesis https://www.nceas.ucsb.edu/ (NCEAS) from April 7-11, 2025. Limited support for travel is available. For more information, visit the course application: https://forms.gle/uBgk5DauVXG16cBz6. If you have any questions about the application or the course, please do not hesitate to reach out! We urge you to circle our workshops to colleagues you believe might benefit most from our learning materials.
Reminder: We opened applications for our virtual "Reproducible Approaches to Arctic Research Using R? workshop, which will close on September 27, 2024. If you would like to know more about this workshop, visit the application: https://forms.gle/Gvvv6hZYYi43YTQf7. For more details about our upcoming training opportunities, please visit our website: https://arcticdata.io/training/.