Biology subjects

Farrell, K. J.

Publications and source records attributed to Farrell, K. J..

2 recordsLinked to original sources

bioRxiv2021

Training Data: How can we best prepare instructors to teach data science in undergraduate biology and environmental science courses?

There is a clear and concrete need for greater quantitative literacy in the biological and environmental sciences. Data science training for students in higher education necessitates well-equipped and confident instructors across curricula. However, not all instructors are versed in data science skills or research-based teaching practices. Our study sought to survey the state of data science education across institutions of higher learning, identify instructor needs, and illuminate barriers to teaching data science in the classroom. We distributed a survey to instructors around the world, focused on the United States, and received 106 complete responses. Our results indicate that instructors across institutions use, teach, and view data management, analysis, and visualization as important for students to learn. Code, modeling, and reproducibility were less valued by instructors, although there were differences by institution type (doctoral, masters, or baccalaureate), and career stage (time since terminal degree). While there were a variety of barriers highlighted by respondents, instructor background, student background, and space in the curriculum were the greatest barriers of note. Interestingly, instructors were most interested in receiving training for how to teach code and data analysis in the undergraduate classroom. Our study provides an important window into how data science is taught in higher education as well as suggestions for how we can best move forward with empowering instructors across disciplines.

scientific communication and education↗

bioRxiv2019

Including variability in air temperature warming scenarios in a lake simulation model highlights uncertainty in predictions of cyanobacteria

Despite growing evidence that climate change will increase temperature variability and the frequency of temperature extremes, many modeling studies that analyze the effects of warming scenarios on cyanobacteria in lakes examine uniform warming temperature scenarios without including any variability. Here, we used the one-dimensional hydrodynamic General Lake Model coupled to Aquatic EcoDynamics modules (GLM-AED) to simulate 11 years of nitrogen-fixing and non-nitrogen-fixing cyanobacterial biomass in Lake Mendota (Madison, WI, USA). We developed climate scenarios with either uniform (constant) warming or variable warming based on random sampling of daily air temperatures from either a normal or Poisson probability distribution. We found that while the median cyanobacterial biomass among repeated simulations for each of the years was similar regardless of whether or not air temperature variability was included in the climate scenarios, the randomly-sampled air temperature distribution scenarios exhibited much greater variability in the year-to-year cyanobacterial biomass estimates. Our results suggest that including temperature variability in climate scenarios may substantially change our understanding of how climate warming may alter cyanobacterial blooms over yearly to decadal time scales. To more effectively predict the range of possible future cyanobacterial dynamics, both the magnitude and variability of warming must be considered when developing climate scenarios for lake modeling studies.

ecology↗