Data science education is uniquely positioned to lead in diversity and inclusion in ecology. First, large ecological data sets and the software most often used to analyze this data are free and open source, which removes important barriers to conducting meaningful ecological research for many groups that are traditionally underrepresented in ecology. Next, data science education is often implemented in an interdisciplinary manner, outside of traditional discipline-based majors. As a result, a greater number of students are exposed to this field, including students who may not have initially identified an interest in ecology. Finally, data science skills are widely transferrable to a number of different career paths within ecology, which allows for a wider range of career options within ecology for students, and could possibly recruit and retain ecologists from underrepresented and diverse backgrounds through the career ranks in the field.
During this session presenters will share novel examples for promoting diversity and inclusion through data science education with ecological applications. The presenters have experience with a wide variety of educational settings and student populations. The session will generate ideas for meeting the 21st century challenge of training the next generation of ecologists to work with "big data".