AIBS has announced a new webinar, Empowering 21st Century Learners through Biodiversity Knowledge - Resources for Online Learning, which will take place March 24, 2020 at 1:30pm ET.
Visit our YouTube page to watch a recording of this webinar.
Tuesday, March 24, 2020
1:30 - 2:30 PM Eastern Standard Time
Diane Bosnjak, Membership Manager, AIBS
Dr. Anna Monfils, Professor of Biology, Central Michigan University
The NSF-funded Biodiversity Literacy in Undergraduate Education Network (BLUE; biodiversityliteracy.com) has focused efforts on developing and disseminating exemplar educational materials, defining core biodiversity data literacy skills and competencies, and extending the network to engage with communities of scientists advancing similar initiatives. This presentation will showcase new resources and a course based undergraduate research exercise for undergraduates that provides opportunities for students to directly engage with digital data resources, facilitate data discovery and exploration, and create inclusive and culturally relevant research experiences.
The biodiversity sciences have experienced a rapid mobilization of data that has increased our capacity to investigate large-scale issues of critical importance in the 21st century (e.g., zoonotic disease transmission, climate change and its impacts on biology, sustainable resource management, impacts of invasive species, and biodiversity loss). Several initiatives are underway to aggregate and mobilize these biodiversity, environmental, and ecological data resources (iDigBio, NEON, GBIF, iNaturalist). Emerging initiatives such as the Extended Specimen Network are forming fostering the integration of data from various sources to answer new and complex questions. This requires a new set of skills for the 21st century biodiversity scientist, who is required to be fluent in integrative fields spanning evolutionary biology, systematics, ecology, geology, and environmental science and possess the quantitative, computational, and data skills to conduct research using large and complex datasets.