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 2017: Remote Sensing with Reproducible Workflows
Our 2017 Institute focused on remote sensing of vegetation using open source tools and reproducible science workflows – the primary programming language was Python. This Institute was held at NEON headquarters in June 2017.
The Institute consists of three weeks of online activities followed by a week-long in-person course.
Pre-institute activities: Participants complete a series of online activities for three weeks prior to the Institute that provided the fundemental knowledge for everyone to succeed in the in-person portion. Topics include how NEON collects data as well as reproducible workflow tools and techniques.
In-person course: The in-person portion of the Institute includes guest speakers on specific topics and hands-on data-intensive activities, as well as several individual/group activities and projects. The following topics will be covered:
- Day 1 - Using HDF5 & Intro to Using Hyperspectral Remote Sensing Data
- Day 2 - Automating Workflows & Intro to Using LiDAR Data
- Day 3 - Uncertainty in Remote Sensing Data
- Day 4 - Hyperspectral Remote Sensing of Vegetation
- Classification of Spectra
- Tree crown mapping
- Vegetation biomass calculations
- Day 5 - Individual & Small Group Applications w/ Instruction
- Day 6 - Presentations of Individual Applications
Who should attend?
Are you interested in heterogeneous ecological, biological and remote sensing data? The Institute is geared towards graduate students and early career scientists with some programming experience who want to develop critical skills and foundational knowledge for working with heterogeneous spatio-temporal data to address ecological questions. Qualified applicants are required to have some prior basic experience in the Python programming environment (or experience in another programming environment and willing to learn Python). All participants must bring their own laptop to participate in the hands-on data activities.
How to Apply
Applications for the 2017 Remote Sensing with Reproducible Workflows Data Institute have closed.
Tuition for the course is $750. Tuition includes all instruction as well as lunches, snacks, and coffee/tea each day of the course. Read the logistics page for more information.
The application primarily consists of answering multiple choice questions pertaining to your background using different data and tools in addition to a short statement of why you want to participate in the Data Institute.
If you have any questions, please contact us.
Key 2017 Dates
- Application Deadline: 10 March 2017
- Notification of Acceptance: late March 2017
- Tuition payment due by: mid April 2017
- Pre-institute online activities: June 1-17, 2017
- Institute Dates: June 19-24, 2017
Data Institute Logistics
Read here for more information on the logistics of the 2017 Data Institute.
Data Institute 2016
Learn more about the 2016 Data Institute.