The BEDE Network is a motivated community of scientist/educators dedicated to integrating and normalizing data science approaches in undergraduate biology and environmental science curricula. Our goal is to use data science education to make the life and environmental sciences more accessible, transparent, reproducible, and relevant. 

   Return to BEDE Net Overview

 

Steering Committee:

  • Matthew Aiello-Lammens (Pace University)
  • Erika Crispo (Pace University)
  • Nathan Emery (Michigan State University)
  • Kelly O'Donnell (Macaulay Honors College, City University of New York)
  • Sarah Supp (Denison University)
  • Sam Donovan (University of Pittsburgh)
  • Kait Farrell (University of Georgia)
  • Drew Kerkhoff (Kenyon College)
  • Laura Broughton (Bronx Community College)

 

BEDE Network Goals and Objectives

  • BEDE Network is an organization connecting data science practitioners with educational specialists to improve student learning of data science in undergraduate biology and environmental science.  Our mission incorporates pedagogical best practices and community-driven expertise into inclusive undergraduate curricula and course design.
  • BEDE Network empowers undergraduate biology instructs with the necessary skills to teach concepts and methods used by data scientists, and also equips instructors with best practices pedagogical techniques to effectively teach data science to undergraduate students.

 

QUBES Project

The mission of the Quantitative Undergraduate Biology Education and Synthesis (QUBES) project is to improve learning opportunities for all students enrolled in undergraduate biology courses by reflecting the centrality of quantitative approaches in modern biology [more info]. One of the ways that QUBES promotes quantitative approaches is by partnering with existing projects and communities to facilitate the discovery and use of their materials [more info]

 

BEDE Net / QUBES Collaboration

BEDE Net and QUBES have joined forces to:

  1. Provide a platform for sharing pedagogical materials.
  2. Host discussions about best practices in data science education.
  3. Connect members and build a larger community.