Scalable and Computationally Reproducible Approaches to Arctic Research
Held in person at National Center for Ecological Analysis and Synthesis in Santa Barbara, CA.
March 25th – March 29th, 2024
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.
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
Application closes on December 22, 2023.
Reproducible Approaches to Arctic Research Using R
Held Virturally
February 26th – March 1, 2024
This 5-day remote workshop will provide researchers with an overview of best data management practices, data science tools for cleaning and analyzing data, and concrete steps and methods for more easily documenting and preserving their data at the Arctic Data Center. Example tools included R, Rmarkdown, and git/GitHub. This course provided background in both the theory and practice of reproducible research, spanning all portions of the research lifecycle, from ethical data collection following the CARE principles to engage with local stakeholders, to data publishing.
Topics include:
- Literate analysis (RMarkdown),
- data wrangling (tidyr/dplyr),
- data publishing,
- visualization (ggplot2/sf),
- code versioning (git),
- ethical data procedures (CARE principles)
Application closed on October 31st, 2023.
Fundamentals in Data Management for Qualitative and Quantitative Arctic Research
Held at National Center for Ecological Analysis and Synthesis, Santa Barbara, California
January 22nd – 26th, 2024
This 5-day in-person workshop will provide researchers with an overview of reproducible and ethical research practices, steps and methods for more easily documenting and preserving their data at the Arctic Data Center, and an introduction to programming in R. Special attention will be paid to qualitative data management, including practices working with sensitive data.
Example datasets will draw from natural and social sciences, and methods for conducting reproducible research will be discussed in the context of both qualitative and quantitative data. Responsible and reproducible data management practices will be discussed as they apply to all aspects of the data life cycle. This includes ethical data collection and data sharing, data sovereignty, and the CARE principles. The CARE principles are guidelines that help ensure open data practices (like the FAIR principles) appropriately engage with Indigenous Peoples’ rights and interests.
Topics include:
- Reproducible research 101
- Qualitative data management for reproducible research
- Definitions of data and best practices for management
- Data publishing
- Ethical data collection (CARE principles)
- Introduction to programming R
- Human subjects research considerations
- Reproducible survey workflows
- Text analysis in R
Applications are closed.
Eligibility
Space for alll workshops are 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.
How to Apply
Applicants should complete the online application form at the link above. The application form requests basic demographics in addition to information about research background and data science training and skills. Please also submit a 2-page Curriculum Vita in PDF format.