CyberTraining: Implementation: Medium: Collaborative Research: Computational and Data-Centric Ecology Training

Clarkson University, Potsdam NY

Investigators

Abstract

While the scientific community has ever cheaper and more rapid access to large amounts of data with respect to ecology and other issues, the training necessary to take full advantage of these large data streams has not kept up. This project will create an online learning and community platform known as Data4Ecology to support the integration of computing, statistics, and data science into undergraduate ecology curriculum and courses. To do so, this proposal will build a freely accessible website enabling students and others in ecology and allied STEM disciplines to develop the skills in computational and statistical reasoning necessary to address problems related to ecology and data science. This project, Data4Ecology, will develop and deploy a website to curate, assemble and embed open educational and learning resources that will serve as training for a multitude of undergraduate students in ecology and environmental science at various college and universities. In turn, this project will integrate computing, statistics, and data science into an ecology curriculum. The project will build and make accessible collaboration and community resources while providing professional development and disseminating project products. The project will serve the larger scientific community by providing researchers and educators with a dedicated repository of data-centric ecology data, resources, and curriculum. Further, the learning resources from this project will be ported to STATS4STEM, an NSF-funded project that collects educational resources on data, computing, and statistics for use by educators and students at many levels, thus exposing users beyond the project to an expanded pool of ecology-based data-centric learning resources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.