Computer Programming Skills for Environmental Sciences

Valle, Denis; Berdanier, Aaron B.

doi:10.1890/0012-9623-93.4.373

Cited by 20 publications

(24 citation statements)

References 26 publications

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“…Regardless of their future careers, ecology undergraduates will likely need to manipulate and visualize large, heterogeneous datasets, which are now ubiquitous in government, industry, and academia (Mellody, ). Increased data science, systems thinking, and quantitative skills are seen as increasingly essential in the workforce (Valle & Berdanier, ), and increased computational literacy and programming skills will prepare current students for diverse 21 st century careers. Consequently, teaching ecology undergraduates computational literacy will not only advance ecology as a discipline, but will also help build an informed workforce and electorate that is prepared to work with and interpret a wide range of big data.…”

Section: Potentialmentioning

confidence: 99%

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Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Farrell

Carey

2018

Ecology and Evolution

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Environmental research requires understanding nonlinear ecological dynamics that interact across multiple spatial and temporal scales. The analysis of long‐term and high‐frequency sensor data combined with simulation modeling enables interpretation of complex ecological phenomena, and the computational skills needed to conduct these analyses are increasingly being integrated into graduate student training programs in ecology. Despite its importance, however, computational literacy—that is, the ability to harness the power of computer technologies to accomplish tasks—is rarely taught in undergraduate ecology classrooms, representing a major gap in training students to tackle complex environmental challenges. Through our experience developing undergraduate curricula in long‐term and high‐frequency data analysis and simulation modeling for two environmental science pedagogical initiatives, Project EDDIE (Environmental Data‐Driven Inquiry and Exploration) and Macrosystems EDDIE, we have found that students often feel intimidated by computational tasks, which is compounded by the lack of familiarity with software (e.g., R) and the steep learning curves associated with script‐based analytical tools. The use of prepackaged, flexible modules that introduce programming as a mechanism to explore environmental datasets and teach inquiry‐based ecology, such as those developed for Project EDDIE and Macrosystems EDDIE, can significantly increase students’ experience and comfort levels with advanced computational tools. These types of modules in turn provide great potential for empowering students with the computational literacy needed to ask ecological questions and test hypotheses on their own. As continental‐scale sensor observatory networks rapidly expand the availability of long‐term and high‐frequency data, students with the skills to manipulate, visualize, and interpret such data will be well‐prepared for diverse careers in data science, and will help advance the future of open, reproducible science in ecology.

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Section: Potentialmentioning

confidence: 99%

“…Increased data science, systems thinking, and quantitative skills are seen as increasingly essential in the workforce (Valle & Berdanier, 2012), and increased computational literacy and programming skills will prepare current students for diverse 21 st century careers.…”

Section: P Otentialmentioning

confidence: 99%

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Farrell

Carey

2018

Ecology and Evolution

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“…Any user familiar with these two languages should have no difficulty picking up the BUGS language and the code can often be less complex, as in our case where bootstrapping was necessary (Appendices 1-3). Finally, there has been recent focus to include programming skills into the curriculum of environmental science programs (Valle and Berdanier 2012). As more environmental scientists become comfortable with programming, taking advantage of Bayesian approaches will become much more within reach.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Estimation of Age and Length at 50% Maturity

Doll

Lauer

2013

Trans Am Fish Soc

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Fish age and length at 50% maturity are used extensively in the management of exploited fish populations. These parameters are historically estimated using logistic regression models (e.g., frequentist inference) for individual yearclasses and often fail to converge or result in insignificant results when a small sample size is used. The sample-size problem motivated us to evaluate whether a hierarchical logistic regression model fit using frequentist inference or Bayesian inference, could improve our ability to fit these models. Our objective was to compare Bayesian and frequentist inference for estimating age and length at 50% maturity to determine whether the models produced similar values. To make this evaluation, we used a long-term data set of Yellow Perch Perca flavescens from southern Lake Michigan. Frequentist inference of the year-class-specific models resulted in significant results when sample size was sufficiently large, a result that occurred in 76% of the models. The hierarchical model produces estimates of age (or length) at 50% maturity for all year-classes using both frequentist and Bayesian inference. However, Bayesian inference of the hierarchical model resulted in more precise parameter estimates and provided the complete posterior distribution in one seamless and easy approach, and the computation time was 78% to 83% faster. We suggest that a hierarchical model fit using Bayesian inference of age (or length) at 50% maturity is an improvement over frequentist interference methods by providing more information about the population of interest, particularly when sample sizes are limited.

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“…However, despite its obvious importance and conspicuous integration into many areas of biology, computer science is still viewed as an obscure field that has, thus far, permeated into only a few of the biology curricula across the nation. A national survey has shown that lack of computational literacy in environmental sciences is the norm rather than the exception [Valle & Berdanier (2012) Bulletin of the Ecological Society of America, 93,[373][374][375][376][377][378][379][380][381][382][383][384][385][386][387][388][389]. In this article, we seek to introduce a few important concepts in computer science with the aim of providing a context-specific introduction aimed at research biologists.…”

Section: Introductionmentioning

confidence: 99%

“…As such, a basic understanding of computer science is becoming an essential part in training the next generation of data-enabled biologists, not only as a tool during the inevitable integration of computer science in biology, but also to foster productive interactions in the new era of multidisciplinary and large-scale biology. While an increasing number of undergraduate and graduate programmes are including bioinformatics, programming or computer science in their curricula, precious few students seem to understand the principal computational concepts underlying the tools they use on a regular basis in their research (Pevzner & Sharmir 2009;Valle & Berdanier 2012).Computer science is a discipline that lays the theoretical foundations for the systematic study of processes that describe and transform information (Brookshear & Brookshear 2003). Its applications are widespread and encompass, among other topics, the design of computer systems, algorithms, artificial intelligence and data mining.…”

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confidence: 99%

Demystifying computer science for molecular ecologists

Belcaid

Toonen

2015

Molecular Ecology

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In this age of data-driven science and high-throughput biology, computational thinking is becoming an increasingly important skill for tackling both new and long-standing biological questions. However, despite its obvious importance and conspicuous integration into many areas of biology, computer science is still viewed as an obscure field that has, thus far, permeated into only a few of the biology curricula across the nation. A national survey has shown that lack of computational literacy in environmental sciences is the norm rather than the exception [Valle & Berdanier (2012) Bulletin of the Ecological Society of America, 93,[373][374][375][376][377][378][379][380][381][382][383][384][385][386][387][388][389]. In this article, we seek to introduce a few important concepts in computer science with the aim of providing a context-specific introduction aimed at research biologists. Our goal was to help biologists understand some of the most important mainstream computational concepts to better appreciate bioinformatics methods and trade-offs that are not obvious to the uninitiated. IntroductionNext-generation sequencing (NGS) technologies have produced a substantial decrease in the cost and the complexity of generating sequence data and are allowing researchers to tackle questions that were not previously possible. Along with this remarkable progress in data acquisition, parallel advances in computational sciences, such as in machine learning and high-performance computing, are allowing researchers to answer complex biological problems using creative computational and quantitative techniques. For example, it is now possible to identify germline and somatic variants in thousands of individuals at reasonable costs, by employing powerful algorithms to analyse data sets generated using reduced-representation methods, such as RAD-seq (Marx 2013;Pabinger et al. 2014). The scale and the complexity of these new problems require computational infrastructures beyond those typically available in a traditional molecular ecology laboratory, and the computational background required to mine these data sets can be a major handicap. As such, a basic understanding of computer science is becoming an essential part in training the next generation of data-enabled biologists, not only as a tool during the inevitable integration of computer science in biology, but also to foster productive interactions in the new era of multidisciplinary and large-scale biology. While an increasing number of undergraduate and graduate programmes are including bioinformatics, programming or computer science in their curricula, precious few students seem to understand the principal computational concepts underlying the tools they use on a regular basis in their research (Pevzner & Sharmir 2009;Valle & Berdanier 2012).Computer science is a discipline that lays the theoretical foundations for the systematic study of processes that describe and transform information (Brookshear & Brookshear 2003). Its applications are widespread and encompass, amo...

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Computer Programming Skills for Environmental Sciences

Cited by 20 publications

References 26 publications

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Bayesian Estimation of Age and Length at 50% Maturity

Demystifying computer science for molecular ecologists

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