NEON offers week-long Data Institutes that provide critical skills and foundational knowledge for graduate students and early career scientists working with heterogeneous spatio-temporal data to address ecological questions.
Each Institute has a different data theme/topic in which participants gain theme-specific skills and knowledge and apply them to ecological data from many different sources including NEON. In addition, all Institutes emphasize and teach core data science skills with a focus on reproducible methods including:
- Code documentation, sharing and reproducible workflows.
- Using version control (GitHub) to support collaborative method development and to backup work.
- Documenting and publishing methods and workflows.
Each Data Institute consists of three main components:
- Pre-Institute Materials: Pre-institute activities consisting of self-paced online lessons and/or online group meetups that are designed to provide participants with the required working knowledge necessary to come prepared into the Institute (expect 1-5 hrs of material depending on your experience with the week’s topic).
- Institute: During the Institute, information is presented in several formats, including presentations by topic experts, group coding activities, and small-group project work.
- Culmination Project: The Institute culminates in a small group-designed capstone research project pulling together and applying the skills from the Institute.
“The NEON Data Institute gave us the tools to work with novel ecological data. With our own knowledge of the domain combined with NEON data and tools, we are in a position to ask novel ecological questions that will advance the field of ecology beyond what has been traditionally possible.”
-- Sarah Graves, University of Florida
“Ecology increasingly depends on "big data" and remote sensing and scientists need the skills necessary to work with this data and to inform their hypotheses. NEON does an amazing job at helping scientists learn how to work with and use a suite of data and data products.”
-- Jeff Atkins, Virginia Commonwealth University
“The course offered a comprehensive overview of best practices for managing and analyzing remote sensing data, and how to make data analysis workflows well-documented, collaborative, and reproducible.”
-- Robert Paul, University of Illinois at Urbana-Champaign
Data Institute Materials
To view the materials for specific Data Institutes, visit the Workshops search page and filter on Data Institutes.