2016 Data Institute

When: June 20, 2016 - June 25, 2016
Location: Boulder, CO

2016 Data Institute theme: Remote sensing with reproducible workflows

NEON's Data Institutes provide critical skills and foundational knowledge for graduate students and early career scientists working with heterogeneous spatio-temporal data to address ecological questions. 

The 2016 Institute focused on remote sensing of vegetation using open source tools to promote reproducible science. The primary computing language was R.  This Institute was be held in Boulder, CO from 20-25 June 2016. For more information on the institute, view the 2016 NEON Data Institute page.

Key Dates 

  • Application Deadline: March 28, 2016 
  • Notification of Acceptance: April 4, 2016 
  • Tuition payment due by: April 18, 2016 
  • Pre-institute online activities: June 1-17, 2016 
  • Institute Dates: June 20-25, 2016

NEON’s Data Institutes provide critical skills and foundational knowledge for graduate students and early career scientists working with heterogeneous spatio-temporal data to address ecological questions. Learn more about NEON Data Institutes.

View all materials for the 2016 Data Institute here 

Registration Information

Registration for this event is now closed.


Institute Recap

The Institute was composed of three weeks of online activities followed by a week-long in-person course. 

Pre-institute activities: 2016 Data Institute participants were asked to complete a series of online activities for three weeks prior to the Institute that provided the foundational knowledge for everyone to succeed in the in-person portion.  Topics included how NEON collects data as well as reproducible workflow tools and techniques.


In-person course: The in-person phase of the Institute included guest lecturers and hands-on data-intensive activities, as well as individual/group activities and projects. The following topics were covered:

  • Introduction to hyperspectral remote sensing
  • Using & accessing the HDF5 file format
  • Remote sensing uncertainty
  • Data fusion (LiDAR with hyperspectral data)

Dialog content.