We are building a knowledge-sharing community where environmental researchers can learn the latest data science skills and technologies, enabling their science to inform solutions more quickly and effectively.
Our trainings in synthesis and open science tools and methods build capacity among data-oriented researchers in academic institutions, government agencies, and NGOs and from all stages of their careers. We cover techniques and best practices for collaborative data science across a wide range of programming languages and skill levels.
Our programs and courses are open to any researcher or student, including those local to Santa Barbara, those traveling to Santa Barbara or those interested in hosting our trainers at their home institution.
For information on the type of opportunities that are available to you, explore our Programs and Courses sections or email training [at] nceas.ucsb.edu.
Learn: Workshops and Courses
Our courses are designed to be short, intensive introductions to a variety of data science topics, ranging from the basics of programming in a new language to advanced computing techniques.
Reproducible Research Techniques for Synthesis
The research landscape is changing. Researchers are increasingly engaging in collaboration across networks; open science includes not just open publication but also open data, software, and workflows; and technology is evolving in support of this new paradigm. This five-day workshop is designed to help researchers stay abreast of current best practices and initiatives and get started on acquiring good data science skills to maximize their productivity, share their data with the scientific community effectively and efficiently, and benefit from the re-use of their data by others.
Trainings at Your Institution
NCEAS leads fee-based trainings on-site at your institution to enable your group to learn shared practices together, encourage maximum participation and group cohesion, and eliminate travel time for you. Although the curriculum can be tailored to suit your needs, our courses generally cover:
- Data management and documentation
- Publishing data to open archives
- Literate and reproducible programming techniques
- Collaborative version control using git and GitHub
- An introduction to current and emerging data-science initiatives
Example course materials can be found here. For more information, email training [at] nceas.ucsb.edu.
Open Science for Arctic Research and Data Management
These trainings provide Arctic researchers with critical skills for the stewardship of data, software, and many other research products that are preserved at the Arctic Data Center. The trainings include an overview of best data management practices, data science tools, and concrete steps and methods for documenting and uploading data to the Arctic Data Center more easily. Support for this training series and its participants is provided by the National Science Foundation. Learn more >
Upcoming Trainings: October 7-11, 2019
Participate: Gain Experience
Through our environmental data science learning programs, we provide scientists experience and mentorship to build their skills and confidence, and to share and apply what they learn throughout their careers.
Openscapes: A Mentorship Program in Open Data Science
The Openscapes mentorship program empowers environmental scientists with open data science tools and grows the community of practice in their labs, departments, and beyond. Learn more >
Data Science Fellowship
The Data Science Fellowship is a 6 month, in-residence fellowship at NCEAS. Fellows work closely with data and informatics teams to solve data and software issues relating to environmental science. Learn more >
Access: Resources to Support Learning
The material NCEAS has developed for in-person courses can also be great reference material to use at your own pace. A complete archive of our training materials can be found here.
A selection of materials is included below.
Reproducible Research in R
2-day Workshop
Introduction to a set of tools used together in the R environment that make research more communicable and reproducible.
Topics:
- Literate analysis (RMarkdown)
- Code versioning (git/GitHub)
- Data tidying/reformatting (tidyr/dplyr)
- Data documentation and publishing
- Publication graphics (ggplot2, leaflet)
Arctic Data Center Training
5-day Workshop
A more in depth look of computing and analysis techniques for synthesis research.
Topics:
- Open data policies
- Literate analysis (RMarkdown)
- Code versioning (git/GitHub)
- Data tidying/reformatting (tidyr/dplyr)
- Data documentation and publishing
- Publication graphics (ggplot2, leaflet)
- Collaboration and team science
- Reproducibility and Provenance
Open Science for Synthesis
3-week Short Course
A more in extensive exploration of computing and analysis techniques and their application to synthesis research.
Topics:
- R for data manipulation, analysis, and visualization
- Linux/UNIX command line environment
- Meta-analysis and systematic reviews
- postgres databases and SQL
- Spatial analysis in R
- Parallel computing in R
Data Management Skillbuilding Hub
Learning Curriculum and Best Practices
Suite of ten customizable presentation files and supporting learning material focused on data management topics across all stages of the Data Life Cycle. The skillbuilding hub also comprises a database of best data management practices.
Topics:
- Why manage data
- Data sharing
- Data management planning
- Data entry and manipulation
- Quality control and assurance
- Protecting your data
- Metadata
- Data citation
- Analysis and workflows
- Legal and policy issues
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