Skip to main content
2023 ESA Annual Meeting Main banner
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.
Back

Contributed Poster

Session: Education

PS 26-118 - Creating inclusive spaces for environmental data science education

Tuesday, August 8, 2023
5:00 PM – 6:30 PM PDT
Location: ESA Exhibit Hall
Abstract: As the ecological and environmental sciences become more data intensive, there are growing demands for training designed to help early-career scientists level-up their technical data and analytics skills. The paths to acquiring the interdisciplinary sets of skills required to work with increasingly large and complex environmental data are frequently filled with a variety of obstacles, particularly for members from historically marginalized groups. We present models for Environmental Data Science (EDS) education that help meet the need for data-intensive training for life, earth, and environmental scientists from a diversity of backgrounds and career stages, including skills related to remote sensing, coding, and programming. Led by researchers at Earth Lab and the NSF-funded Environmental Data Science Innovation & Inclusion Lab (ESIIL), at the University of Colorado Boulder, this study will present findings from surveys of students enrolled in the Earth Data Analytics Professional Graduate Certificate Program (EDA), and the NSF-funded Earth Data Science Corps (EDSC). Over five years, these two programs have helped a diverse cohort of professionals (n = 96) gain fundamental EDS skills for career advancement, and built capacity to teach and learn EDS at other institutions catering to minority communities, including Tribal Colleges and Universities and Hispanic Serving Institutions. Students enrolled in these programs learn how to find and work with data to answer questions in the context of applied ecological and environmental science using the Python programming language. Students then apply their new skills by designing and completing culturally-relevant projects that have focused on topics including characterizing vegetation using NEON remote sensing data, emerald ash borer detections in Minnesota cities, bird behaviors within the Prairie Pothole Region, and more. All lessons are taught using a free and open curriculum, which provides widely accessible teaching and learning materials (earthdatascience.org). Using Likert surveys and a Rasch modeling approach, we demonstrate that students from these programs show significant growth in their self-reported comfort and confidence with different data science skills (p < 0.001), as well as a deeper understanding of core disciplinary ecological and environmental science concepts. In the coming months, we will launch the ESIIL Stars internship and ESIIL Data Short Course, modeled after Earth Lab’s EDSC and EDA programs. Inclusive programs such as these are integral to innovation in data-intensive education and empowering the next generation of diverse ecological and environmental data scientists.