Reproducible Research Techniques for Synthesis
A five-day immersion into widely adopted R-based tools for open science
The research landscape is changing. Are you changing with it?
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.
Taught as an in-person or remote event, this five-day immersive 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.
This course is taught in partnership with DataONE.
Curriculum at a Glance
- Metadata - what is it and how to write a quality data description
- Data modeling - tidy data for efficient access and storage
- Data publishing, citation, and credit
- Data munging with R tidyverse
- Working collaboratively - git and GitHub
- Writing functions in R
- Building packages for publishing reproducible research
- Literate analysis with RMarkdown
- Publishing analytical web pages with GitHub pages
- Data visualization with ggplot and leaflet
Who Should Attend
This opportunity is for researchers from across career stages and sectors who want to gain fundamental data science skills that will improve their reproducible research techniques, particularly for the purposes of synthesis science. This workshop focuses on using R as the primary programming language. Participants should be familiar with basics of programming in R. If you are unsure of your ability or do not know R and want to learn, please email firstname.lastname@example.org for more information and pre-course preparation recommendations.
Register for an Upcoming Course
This event will take place virtually.
Space is limited, and applications are accepted on a rolling, first-come, first-served basis. If the minimum number of registrants is not met by the cutoff date, the session may be canceled. In this case, participants can choose to attend a future session or receive a refund. Participants may continue to register for the workshop after the cutoff date if the course minimum has been met.
Location for in-person courses: NCEAS, 735 State Street #300, Santa Barbara, CA 93101
Remote course delivery occurs via zoom.
*Includes: 5 days of hands-on instruction. Does not include: travel, lodging costs, breakfast or dinner.
How We Teach
We teach a core set of data science skills and concepts through the hands-on application of modern tools (such as R and git), short-thematic lectures, and paired discussion and Q&A techniques. Participants will come away with a broad understanding of how to make their work more reproducible, transparent, and communicable through every step of the research process. By utilizing tools on real datasets, we will mimic real scientific workflows, preparing participants to apply what they learn to their own research.
Our agenda will include both technical and non-technical sections. We build into our agenda dedicated practice time to ensure participants get ample opportunity to apply their skills, in addition to open blocks of time where participant-suggested topics can be taught, or existing topics can be explored more deeply.
Whether teaching as part of an in-person environment or via remote technology, we emphasize a hands-on approach to learning, breaking into small groups to facilitate application and comprehension.
Meet Our Instructors
With their diverse backgrounds and extensive hands-on experience doing synthesis science, our trainers bring years of experience in environmental data science, data management, collaboration, and open science to our workshops. Each workshop has 2-3 trainers in the room at all times; while one trainer teaches, others help debug errors and answer questions during hands-on exercises, ensuring no participant is left behind.Instructor Bios
This workshop will take place in person at NCEAS, in Santa Barbara, California. Located on a downtown thoroughfare, the NCEAS office is a convenient walking distance from local hotels, restaurants, wineries, and of course the beach. Established in 1995, NCEAS was the first synthesis science center in the world. We pioneered the movement toward this collaborative approach to science and have helped build a community of scientists around it. NCEAS has also been a leader in data science infrastructure and software development, supporting preservation and stewardship of data through repositories such as the Knowledge Network for Biocomplexity and Arctic Data Center, and as partners in DataONE, a federated network of data repositories. NCEAS has hosted many training workshops since its inception, such as the 3 week long Open Science for Synthesis, 2 day courses for Alaska salmon researchers, and week long courses for Arctic researchers in addition to numerous short workshops at domain society meetings in collaboration with DataONE.
Cancellations made before the deadline will receive a refund subject to a 10% fee. Cancellations made after this date are not eligible for a refund. If a session needs to be cancelled by the organizers due to insufficient number of participants, all registrants will be notified on the day after the cancellation deadline. In this instance, a full refund will be issued, or registrants will be provided with a credit that can be applied to another session date within 12 months.
For November 2020 course, cancel by October 12, 2020
In-person Event Logistics
NCEAS secures a reduced rate for local hotels. Check back closer to the registration deadline for housing options. Participants may also find affordable housing options on AirBnB.
NCEAS is located in downtown Santa Barbara, 15 minutes driving from the Santa Barbara regional airport. Most visitors to NCEAS choose to stay within walking distance to NCEAS, and utilize ride-sharing apps (Lyft/Uber) for transport to and from the airport. Santa Barbara also has a bus system, in addition to an Amtrak stop.