2020 BIOME Institute Launch

The BIOME (Biology and Mathematics Educators) Institute is hosting a virtual “Summer Launch” that will provide a community of peers with the opportunity to share ideas and resources, brainstorm about plans, and initiate collaborations with the ultimate goal of improving student outcomes by piloting new Undergraduate Research Experiences (URE) or enhancing existing UREs.  The Institute will provide professional development opportunities and reduce barriers to change by providing faculty with information, resources, community, and guidance as they plan and implement new ideas. Following the Summer Launch, working groups will be formed to provide ongoing support, additional resources and information, and peer feedback.

Two NEON-related events will happen during the launch: 

1. A workshop hosted by NEON: "From NEON sites to your (online) Classroom - resources for Ecology Education in a Virtual World"  
2. A collaborative workshop hosted by EREN: "Distributed undergraduate research experiences in ecology for in-person or online learning: A partnership between EREN and NEON"

Summer Launch Details

The two-week Summer Launch will set the stage for the BIOME Institute in which participants will meet in working groups as they design an implementation plan for a new URE program or an enhancement to an existing URE program.  During the summer Launch, models of UREs at different institutions will be shared in a virtual sharing session, “Posters & Beyond.” Participants will have the opportunity to review the URE models and meet with presenters throughout the two-week period. In addition, two keynote speakers will present information relevant to shaping UREs, and participants will be introduced to a variety of resources appropriate for UREs in online introduction sessions.  After the launch, working groups will explore and review models of successful UREs and offer peer feedback.

NEON Events

Workshop: From NEON sites to your (online) Classroom – resources for Ecology Education in a Virtual World

Time: Monday, July 27, 3:00 - 4:30pm ET

"There has never been a greater need for online education, and the National Ecological Observatory Network (NEON) has been working to develop online resources specifically designed for independent, asynchronous learning. In this workshop we will explore the breadth of NEON educational resources through the lens of remote learning, including our Tutorials, Teaching Modules, and Science Videos. We will also do some live hand-on programming in R to access and explore NEON data. The goals for this workshop are to increase your awareness of NEON resources (documents and staff!), understand the workflow for working with NEON data in R, and to highlight how each of these can fit into an online curriculum.

The keystone of our remote learning resources are the Tutorials, which are designed to teach specific data science skills related to ecology and analyzing NEON data. These tutorials are often adapted, or used directly, by faculty who seek to integrate open data science into their classroom. Together, we will walk through one of these tutorials to show the step-by-step process of downloading and working with NEON data. Along the way we will highlight the common problems that students run into while working with these tutorials, and pragmatic solutions to use during synchronous or asynchronous teaching. We will then discuss the variety of topics that can be explored using these resources, from quantifying biodiversity, to analyzing LiDAR pointclouds in 3D, all of which can be supported by our videos available on YouTube. Finally, we will give an overview of our “turn-key” teaching modules available on QUBES hub, which include lesson plans, handouts, and extensions for employing NEON data within your classroom."

Visit the Workshop Page for more detais.

Workshop: Distributed undergraduate research experiences in ecology for in-person or online learning: A partnership between EREN and NEON

Time: Thursday, July 30, 3 - 4:30 pm ET

“The Ecological Research as Education Network (EREN) is an organization supporting the development of undergraduate research experiences in ecology that involve data collected by widely distributed collaborators and their students across multiple institutions. Core to EREN’s philosophy is that publication-quality ecological data can be collected by students in the undergraduate classroom and that data collection gains explanatory power when replicated and coordinated across geographic locations. EREN’s approach is similar in spirit to that of the National Ecological Observatory Network (NEON), an National Science Foundation (NSF) supported continental-scale observation facility collecting a variety of open-access ecological data across multiple sites. 

In this workshop, you will learn about a timely NSF-supported initiative by EREN and NEON to develop and disseminate ecological projects that may be adapted and used by faculty and students through in-person, hybrid, or fully online classrooms. Projects link data collected locally with analysis of existing nation-wide datasets. This approach allows students to gain experience with techniques and hands-on data collection, alongside experience with management and analysis of large datasets.

Two specific projects will be described in detail: a project focusing on the relationship between plants and people in anthropogenic habitats ("Plants in the Human-Altered Environment") and a project to better understand the distribution of pollinators along a rural-urban land-use gradient. We also will briefly describe additional forthcoming projects that engage students in the study of mosquitoes and lichens. Finally, we will describe a mechanism for the development and promotion of novel projects (the “Ecology CURE Incubator for Flexible Course Modes”), with financial support underwritten by the NSF.

Three break-out groups will facilitate detailed discussion of (1) the plants in the human-altered environment project, (2) the pollinator survey, and (3) new project ideas.”

Visit the Biome Institute Webpage for more details.