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Satoshi Amagai, Melissa Csikari, Gabriela Hamerlinck, (2018), "HHMI BioInteractive Faculty Mentoring Network", Online, : Online, June, . Cited by:
Kaitlin Bonner, Arietta Fleming-Davies, Kristine Grayson, Raisa Hernández-Pacheco, Gabriela Hamerlinck, (2018), "DIG into Data Faculty Mentoring Network", Online, : Online, June, . Cited by:
Gabriela Hamerlinck, Deborah Rook, (2018), "Investigating socio-environmental issues with data", Online, : Online, January, . Cited by:
Megan A. Jones, Kristine Grayson, (2018), "NEON Faculty Mentoring Network", Online, : Online, January, . Cited by:
Ben Galluzzo, Maria Luteman Hernandez, Jeremy M Wojdak, (2017), "Math Modeling Faculty Mentoring Network", Online, : Online, September, . Cited by:
Teresa Mourad, Elizabeth H Schultheis, Melissa Kjelvik, Gabriela Hamerlinck, (2017), "LDC - DataNuggets Faculty Mentoring Network", Online, : Online, September, . Cited by:
Jennifer Hanselman, Tara Holmberg, Michelle Ann Fisher, Gabriela Hamerlinck, Deborah Rook, (2018), "InTeGrate Faculty Mentoring Network", Online, : Online, January, . Cited by:
Erin N. Bodine, Carrie Diaz Eaton, Gabriela Hamerlinck, Jeremy M Wojdak, (2017), "The Discrete Faculty Mentoring Network - Approachable Modeling without Calculus", Online, : Online, November, . Cited by:
Teresa Mourad, Gabriela Hamerlinck, (2018), "ESA Data Discovery Faculty Mentoring Network", Online, : Online, February, . Cited by:
J. Phil Gibson, Paul Beardsley, Gabriela Hamerlinck, Nicole Chodkowski, Deborah Rook, (2018), "Plants by the Numbers Faculty Mentoring Network", Online, : Online, January, . Cited by:
Holly D Gaff, Gabriela Hamerlinck, Jeremy M Wojdak, (2017), "Beanbag Biology Faculty Mentoring Network", Online, : Online, October, . Cited by:
Melissa Csikari, Satoshi Amagai, Gabriela Hamerlinck, (2017), "HHMI BioInteractive Faculty Mentoring Network 2017", Online, : Online, June, . Cited by:
Bill Morgan, Sam S Donovan, Hayley Orndorf, Sabrina Robertson, Elizabeth Ryder, Michael Sierk, Anne Rosenwald, Liz Dinsdale, Eric Triplett, Mark A. Pauley, William Tapprich, (2018), "Development of the NIBLSE Learning Resource Collection and Incubators", International Society for Computational Biology, : Chicago, Illinois, July, . Cited by:
Joe Dauer, Robert Lee Mayes, (2018), "Quantitative Modeling by Biology UnderGraduate Students", University of Houston, : Houston, Texas, February, . Cited by:
Harless, Jacob, LaMar, Drew, (2017), "Developing Open Source Plugins for the Educational Gateway QUBEShub: Expanding Community Reach and Building a Robust Open Education Resource Ecosystem", The 12th Gateway Computing Environments Conference, : Ann Arbor, Michigan, October, . Cited by:

We present work on development of (1) a component to record, track and display the multitude of partners and professional organizations involved in the QUBES mission, and (2) a component to bring open-education resource development to HubZero and QUBES

Morgan, William, Donovan, Sam, Orndorf, Hayley, Ryder, Elizabeth F., Sierk, Michael, Wright, Robin, Rosenwald, Anne, Dinsdale, Elizabeth, Triplett, Eric W., Pauley, Mark A., Tapprich, William, (2017), "Incubators: A community based model for improving the usability of bioinformatics learning resources", 2017 American Society for Cell Biology/European Molecular Biology Organization Meeting, : Philadelphia, Pennsylvania, December, . Cited by:

The Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE) is an NSF-funded Research Coordination Network that aims to establish bioinformatics as an essential component of undergraduate life sciences education. As part of that effort, NIBLSE is working to make existing bioinformatics learning resources more accessible to non-specialists and to increase their use across undergraduate biology courses. To this end, NIBLSE has partnered with the Quantitative Undergraduate Biology Education and Synthesis (QUBES) project and CourseSource to develop and implement a novel model for supporting the refinement, publication, and dissemination of high quality bioinformatics teaching resources. NIBLSE Incubators are small, short-lived, online communities that work with an existing learning resource to (1) improve its usability across diverse life sciences classrooms, (2) introduce and teach important bioinformatics learning outcomes, and (3) move the learning resource toward publication and broader dissemination. Incubator pilots have highlighted the opportunities and challenges of this approach and led to the refinement of the development process. NIBLSE and QUBES are supported by grants from the National Science Foundation (DBI 1539900 and DUE 1446269, respectively).

Dahlquist, Kam D, Aikens, Melissa L, Dauer, Joseph T, Donovan, Samuel S, Diaz Eaton, Carrie, Callender Highlander, Hannah, Jenkins, Kristin P, Jungck, John R, LaMar, M Drew, Ledder, Glenn, Mayes, Robert L, Schugart, Richard C, (2017), "An invitation to modeling: building a community with shared explicit practices", PeerJ Preprints, : September, (DOI: 10.7287/peerj.preprints.3215v1). Cited by:

Models and the process of modeling are fundamental to the discipline of biology, and therefore should be incorporated into undergraduate biology courses. In this essay, we draw upon the literature and our own teaching experiences to provide practical suggestions for how to introduce models and modeling to introductory biology students. We begin by demonstrating the ubiquity of models in biology, including representations of the process of science itself. We advocate for a model of the process of science that highlights parallel tracks of mathematical and experimental modeling investigations. With this recognition, we suggest ways in which instructors can call students’ attention to biological models more explicitly by using modeling language, facilitating metacognition about the use of models, and employing model-based reasoning. We then provide guidance on how to begin to engage students in the process of modeling, encouraging instructors to scaffold a progression to mathematical modeling. We use the Hardy-Weinberg Equilibrium model to provide specific pedagogical examples that illustrate our suggestions. We propose that by making even a small shift in the way models and modeling are discussed in the classroom, students will gain understanding of key biological concepts, practice realistic scientific inquiry, and build quantitative and communication skills.

Hamerlinck, Gabriela, Kidder, Kevin E., LoRe, Sondra, Hale, Alison N., Bishop, Pamela, Jenkins, Kristin, Donovan, Sam (2017), "Professional development in quantitative biology and its relationship to promoting scholarly teaching", Ecological Society of American Annual Conference 2017, : August, (DOI: 10.25334/Q4T95S). Cited by:
Donovan, Sam, LaMar, M. Drew, (2017), "Building a gateway between classrooms and data science using QUBESHub", Gateways 2017, : Ann Arbor, Michigan, October, (DOI: 10.6084/m9.figshare.5483692.v1). Cited by:

This paper addresses the gap between the practice of biological science and biology education as it pertains to data science and quantitative literacy. We discuss one way to address this gap through the development of a new web application called BioRadiant, an open-source R Shiny application that will be deployed on the scientific gateway QUBES (Quantitative Undergraduate Biology Education and Synthesis).

Monfils, Anna, Linton, Debra, Phillips, Molly, (2017), "Using Natural History Collections to Increase Biodiversity Literacy in Undergraduate Education", 4th Life Discovery - Doing Science Education Conference, : Norman, Oklahoma, October, . Cited by:

A new initiative entitled Biodiversity Literacy in Undergraduate Education (BLUE) aims to bring together communities of biodiversity, data, and education specialists to identify core undergraduate data competencies and standards, delineate learning progressions and develop effective strategies for sustained development and implementation of biodiversity and data literacy education. BLUE participants have also brought some of these strategies into practice by developing example activities.In this workshop, we will present a completed BLUE resource, Angiosperm Reproduction, and Coevolution, in which students use publicly available digitized natural history collections data to analyze spatial co-occurrence of pollinators and the plants they pollinate. Using various data layers and digital resources, students explore the global patterns of species occurrence in a geographic context and form hypotheses related to the interdependence of bats and agave. Specific questions are asked regarding the ecology, distribution, and conservation of both the plant and animal species. Students are asked to propose research questions in a geographic context. The lab is currently being evaluated at Central Michigan University as part of the introductory biology curricula.This module, along with others, is part of a QUBES-Hub Faculty Mentoring Network (FMN). We will also introduce other modules under development, demonstrate the module’s design process, and talk about the ongoing evaluation. Finally, the presenters will explain how participants can get involved in both BLUE and FMNs (

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