Upcoming courses at SMSC: GIS in R, Animal Movement Analysis in R, and Computer Vision Methods in Ecology

[NZCBI-SCBI Training]

Greetings!

 

We wanted to announce our schedule of upcoming graduate/professional courses at SMSC. If you have additional questions after reviewing the course web page, please contact: SCBITr...@si.edu.

 

GIS in R: Fundamentals and applications for ecologists - ONLINE

Website: https://smconservation.gmu.edu/programs/graduate-and-professional/gis-ol/

Dates: August 25 – October 19, 2025

Cost: 600.00 USD (full scholarships available for eligible applicants)

Deadline: July 14, 2025

 

Description: This 8-week online course is designed to introduce R users to the expanding range of tools available for working with spatial data in R. More and more, it is feasible to conduct all processing and manipulation of spatial data, as well as generation of publication-quality maps, inside the R environment. Learning to do this effectively provides huge advantages to those already working in the R environment, including seamless integration between spatial and non-spatial data, as well as a lack of reliance on costly, menu-based GIS programs. The course will teach participants how to import, project, and manipulate both vector/shape and raster data and will cover a wide range of available functions. The primary R packages used in the course will be sf and terra, with some use of sp and raster. The course closely integrates tidyverse functions with GIS tools to develop efficient and intuitive coding. Content will also focus on visualizing spatial data in R with both static and dynamic maps, as well as with web applications in RShiny. See the course webpage for a complete list of course topics. This is course is ideal for those already working in the R environment who are looking to expand their R skills to include working with spatial data. Those without any background in R should work to become familiar with basic R programming before attending. Some pre-course preparation work will be provided to help toward building this familiarity.

 

Format: This course is taught in an asynchronous format over 8 weeks, with at least 2 weekly opportunities for live feedback and instructor interaction over Zoom. This format allows for maximum flexibility for participants juggling complicated work schedules, classes and other commitments.

 

Animal Space Use and Movement Analysis - ONLINE

Website: https://smconservation.gmu.edu/programs/graduate-and-professional/animal_movement-ol/

Dates: October 20 – December 14, 2025

Cost: 500.00 USD (full scholarships available for eligible applicants)

Deadline: August 18, 2025

 

Description: This 8-week online course teaches the latest approaches for the summary and analysis of animal movement data in the R environment. Course material focuses on the application of these data to research questions involving home ranges, movement behavior, habitat selection, and connectivity. The goals of the course are to: 1) teach the core themes and concepts underpinning animal movement behavior and the determinants of animal space use; 2) familiarize participants with the range of tools available to import, clean, summarize, visualize and analyze animal movement data in R and; 3) expose participants to the challenges and potential biases inherent in movement data, and how to address them at the design and analytical stage. See the course webpage for a complete list of course topics. This is course is ideal for graduate students and professionals already familiar with how to interact with spatial data in R, but looking to apply those skills to more advanced analytical approaches specific to animal tracking data. Class lessons and exercises will use real animal tracking datasets, and will integrate tidyverse functions to maximize coding efficiency. Additional packages covered in the course include ctmm, amt, adehabitatLT, adehabitatHR, moveHMM, gdistance and others.

 

Format: This course is taught in an asynchronous format over 8 weeks, with at least 2 weekly opportunities for live feedback and instructor interaction over Zoom. This format allows for maximum flexibility for participants juggling complicated work schedules, classes and other commitments.

Computer Vision Methods for Ecology

Website: https://smconservation.gmu.edu/programs/graduate-and-professional-2/cv4ecology/

Dates: January 12-30, 2026

Cost: 3,562.00 USD 

Deadline: June 6, 2025

Description: Computer vision (CV) is significantly accelerating ecology research by automating the analysis of raw images and video from camera traps, drones, and satellites. While ecologists often have training in statistics and programming, they are rarely exposed to the software engineering, machine learning and CV skills necessary to analyze big sets of visual data on their own. This course is an intensive 3-week workshop designed to fill this educational gap, with a mission is to empower ecologists to efficiently process their existing data, design new studies around CV, and scale their research to larger datasets. The course is geared to early career scientists and graduate students and will teach the rudiments of computer vision and how to train and evaluate computer vision models on their own data to help answer specific ecological research questions. Students will leave with a working tool, a grasp of the underlying concepts, and the ability to tackle diverse ecological problems with computer vision. Participants will also develop a network of computer vision researchers with whom they can engage and collaborate. All work will be conducted in Python and students will learn to use the VSCode integrated development environment to access remote compute, train CV models, and evaluate results. Participants can expect access to GPU-accelerate cloud compute for the duration of the course.

Format: CV4E a three-week, in-person, full-immersion course, composed of classroom training and hands-on projects with one-on-one mentorship. Participants will arrive with data they have collected to address an ecological research question. Leading up to the class, instructors will help students get their data prepared for the CV techniques they will experiment with. Across the three weeks, students will listen to lectures, hear from guest speakers, participate in group activities, and have dedicated work sessions in small groups with a dedicated instructor. 

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