The Arctic Data Center provides training in data science and data management. These are critical skills for the stewardship of data, software, and many other research products that are preserved at the Arctic Data Center. A goal of this center is to advance data archiving and promote reproducible science and data reuse.
This 5-day workshop, to be held in Santa Barbara, CA, Monday, October 19th – Friday, October 23rd, 2020 will provide researchers with an overview of best data management practices, data science tools and concrete steps and methods for more easily documenting and uploading their data to the Arctic Data Center.
Workshop topics will include:
- Arctic Data Center and NSF Standards and Policies
- Data Management Plans
- Effective data management for data preservation
- Storing and Preparing Data in Open Source Formats
- Stability, longevity, interoperability
- Publishing data at the Arctic Data Center
- Web-based submission
- Automating submission for large data sets
- Data and Metadata Quality
- Provenance for data and software
Space for this workshop is limited. Both early career and established researchers from the Arctic research community are encouraged to apply. Participants will be selected on the basis of their current research or work activities; their previous experience with open science practices, data management techniques and analysis methods; and their current or former opportunities to access training in these areas. We will prioritize applications from individuals currently funded through NSF Polar Programs.
Travel and Accommodation Support
Participants will receive support to cover the cost of an economy round trip airfare within the United States. Course participants will also be provided with accommodation in Santa Barbara for the duration of the course.
How to Apply
Applications are now closed.
2020 Application Timeline
February 1: Applications will open (application will be open for 11-12 weeks)
April 24: Applications are due for all applicants
Mid May: Applicant decisions will be made