Below are links to our partner groups on QUBES who have resources that are ready to implement in online settings. Note that many of these resources have adaptations that are also suitable for online courses. Check back often for updates!
Dendroclimatologists can reconstruct climate records further into the past than written records, by examining tree rings. Students "reverse-engineer" this process by considering growth rates of a tree species in response to various...
linear regression, graphing, data visualization, standardization, climate, tree rings, dendrochronology, introductory, Lab, Teaching material, High School, Undergraduate, Homework, Advanced, More than 1 hour
This module allows students to follow a researcher as she discovers new features of breast cancer tissue architecture, using 3D reconstructions of histological specimens.
graphing, data visualization, breast cancer, DCIS, histograms, centroid, sums of squares, introductory, Lab, Teaching material, High School, Undergraduate, Advanced
Students predict changes to tadpole morphology and coloration after considering characteristics of the predator species and the prey themselves then test their own hypotheses (typically with t-tests or ANOVA) by collecting novel data via image...
This module introduces students to leaf-cutter ants in the rainforests of Panama. Students derive their own research hypotheses regarding ant foraging or allometric scaling relationships.
leaf cutter ants, foraging, allometry, linear regression, graphing, data visualization, Resources @ AIMS, body size, introductory, Lab, Teaching material, High School, Undergraduate, Advanced, More than 1 hour, Extended Project
Avida-ED is an award-winning educational application developed at Michigan State University for undergraduate biology courses to help students learn about evolution and scientific method by allowing them to design and perform experiments to test hypotheses about evolutionary mechanisms using evolving digital organisms. Read more about using Avida-ED in a blog post by Jim Smith
The Biology Students Math Attitudes and Anxiety Program is a resource for educators who are looking to implement non-invasive techniques to change student attitudes and reduce anxiety towards math.
Biodiversity Literacy in Undergraduate Education - Data Initiative (BLUE Data) is a community of biodiversity, data, and education specialists who are working together to identify core biodiversity data competencies for undergraduates, develop strategies for integrating these competencies into the introductory biology curriculum, and build capacity for sustained development and implementation of biodiversity and data literacy education.
data in the classroom, process of science, biodiversity informatics, introductory, Lab, Online course, Teaching material, Lecture, High School, Undergraduate, Homework, Advanced, Majors, Non-majors, 1 Hour
This module uses digitized natural history and biodiversity collections to explore if, how, and whether disability can be studied in nonhumans as a form of biodiversity.
Adapted for online learning: In this lab, you will explore the physics of flight, the adaptations that make powered flight possible, and the evolution of powered flight in vertebrates and invertebrates.
data in the classroom, data visualization, introductory, Lab, Teaching material, Lecture, Undergraduate, Homework, Advanced, Majors, Non-majors, 1 Hour
data in the classroom, data visualization, digital resources, biodiversity informatics, biodiversity collections, introductory, Lab, Online course, Teaching material, Lecture, Undergraduate, Homework, Advanced, Majors, Less than 1 hour, Extended Project
Working collaboratively in teams of 2-4 researchers, students engage in targeted projects to address critical information and research needs using digital biodiversity data.
databases, data in the classroom, undergraduate research, digital collections, course-based research experiences (CUREs), biodiversity collections, introductory, Lab, Online course, Teaching material, Lecture, Undergraduate, Homework, Advanced, Majors, Less than 1 hour, Extended Project
databases, data in the classroom, biodiversity informatics, biodiversity collections, introductory, Lab, Online course, Teaching material, Lecture, High School, Undergraduate, Faculty, Homework, Graduate, Advanced, Majors, Non-majors, Less than 1 hour
data science, research ethics, data ethics, introductory, Lab, Online course, Teaching material, Lecture, High School, Undergraduate, Homework, Advanced, Majors, Non-majors, Less than 1 hour
Collections based research is a critical tool for organismal biology and biodiversity research. Yet natural history collections have a complicated past. This multi-class module examines the origins, problems, and current uses of collections.
In this module, students practice interpreting the relationship between variables before exploring changes in butterfly phenology through the use of natural history collections data.
Anna Monfils, Debra Linton, Libby Ellwood, Molly Phillips
Version: 1.0
In this module, students will be introduced to some emerging biodiversity data resources. They will be asked to think critically about the strengths and utility of these data resources and apply what they have learned to research question.
data in the classroom, biodiversity informatics, natural history collection, introductory, Lab, Online course, Teaching material, Lecture, Undergraduate, Homework, Advanced, Majors, Non-majors, More than 1 hour
A module to explore aggregated observation and collection information, through BISON, or Biodiversity Information Serving Our Nation (https://bison.usgs.gov), a tool for visualizing GBIF data.
Querying databases, data in the classroom, biodiversity informatics, natural history collections, introductory, Lab, Online course, Teaching material, Lecture, Undergraduate, Homework, Less than 1 hour
BSA is a nonprofit membership society. Their mission is to: promote botany, the field of basic science dealing with the study and inquiry into the form, function, development, diversity, reproduction, evolution, and uses of plants and their interactions within the biosphere.
Data Nuggets are free classroom activities, co-designed by scientists and teachers. They are an innovative approach to bring contemporary research and authentic data into the classroom. Data Nuggets include a connection to the scientist behind the data and the true story of their research process. Data Nuggets give students practice working with “messy data” and interpreting quantitative information. Students are guided through the entire process of science, including identifying hypotheses and predictions, visualizing and interpreting data, making evidence based claims, and asking their own questions for future research. Because of their simplicity and flexibility, Data Nuggets can be used throughout the school year, and across grades K-16, as students grow in their quantitative abilities and gain confidence. Data Nuggets have the potential to improve the understanding of science in society and help engage and motivate the next generation of scientists and engineers.
Minor modifications were made to this Data Nugget in order to use it in a fully online version of the introductory, non-majors botany course, Plants and People.
This publication contains the adaptations I made to the Data Nuggets module in order to use it with an online, introductory, non-majors botany class. I used this with a unit on natural and artificial selection in plants.
The Data Incubator Group focuses on promoting quantitative education through expanding the adoption of data-centric resources and facilitating large-scale collaboration on approaches to classroom implementation
This resource seeks to provide a quantitative platform by which students can be taught about interactive effects using the TIEE module created by Little (2018). Key to the adaptation is the use of a Shiny app to create figures and R output.
Students investigate the role of olfaction and infochemicals on bird foraging behavior through three different quantitative modules. In this module students create graphs, generate statistics, draw conclusions, and apply their knowledge.
Identifying, mapping and understanding the magnitude and the spatial prevalence of the Sexually Transmitted Diseases (STDs) such as chlamydia and gonorrhea diseases. Analyzing the spatial trends of the disease patterns at National, State and...
Resources @ ESA - TIEE, Disease ecology, fmn, DIG, human ecology and behavior, bacterial infections, spatial and temporal spread of disease, Geographic Information System (GIS), introductory, Lab, Online course, Teaching material, Lecture, Undergraduate, Advanced, More than 1 hour
This module explores the effects of host density on contact rates and transmission of pathogens or mutualist symbionts using a hands-on simulation, a computer agent-based model in NetLogo, and an authentic ecological dataset.
Earth Lab capitalizes on the data deluge from space and other platforms to accelerate science, reduce environmental risk, and train a new generation of earth data scientists. The Earth Lab Earth Analytics Education Initiative provides undergraduate, graduate, and professional students as well as online learners around the globe with core in-demand technical skills at the intersection of earth and environmental science and data science. All work is guided by a commitment to open and reproducible science, open education and expanding the reach of earth data science education to students across varying academic, professional, socio-economic and geographic dimensions.
Earth analytics is an intermediate, multidisciplinary course that addresses major questions in Earth science and teaches students to use the analytical tools necessary to undertake exploration of heterogeneous ‘big scientific data’.
There are a suite of powerful open source python libraries that can be used to work with spatial data. Learn how to use geopandas, rasterio and matplotlib to plot and manipulate spatial data in Python.
data analysis, data, data types, data visualization, data science, earth science, python, remote sensing, Geographic Information System (GIS), data science education, computer programming, wildfire, earth anlaytics, Online course, Teaching material, Undergraduate, Faculty, Graduate, More than 1 hour, Open Educational Resources (OER)
data analysis, data, data types, data science, earth science, python, remote sensing, github, version control, data science education, computer programming, wildfire, earth analytics, v, geographic information systems, Online course, Teaching material, Undergraduate, Faculty, Graduate, More than 1 hour, Open Educational Resources (OER)
data analysis, data types, data visualization, data science, earth science, R programming language, remote sensing, Geographic Information System (GIS), data science education, computer programming, wildfire, earth analytics, Online course, Teaching material, Undergraduate, Faculty, Graduate, More than 1 hour, Open Educational Resources (OER)
Introduction to Earth Data Science is an online textbook for anyone new to open reproducible science and the Python programming language. There are no prerequisites for this material, and no prior programming knowledge is assumed.
data analysis, data, data types, data visualization, data science, earth science, python, remote sensing, Geographic Information System (GIS), data science education, computer programming, wildfire, earth analytics, Online course, Teaching material, Undergraduate, Graduate, Extended Project, Open Educational Resources (OER)
Earth analytics is an advanced, multidisciplinary course that addresses major questions in Earth science and teaches students to use the analytical tools necessary to undertake exploration of heterogeneous ‘big scientific data.’
data analysis, data, data types, data visualization, data science, earth science, R programming language, remote sensing, Geographic Information System (GIS), data science education, computer programming, wildfire, earth analytics, Online course, Teaching material, Undergraduate, Faculty, Graduate, Extended Project, Open Educational Resources (OER)
The Earth Analytics Bootcamp is a three-week introductory-level course taught by instructors in Earth Lab and is a part of the Professional Certificate in Earth Data Analytics - Foundations at CU Boulder.
data analysis, data, data types, data visualization, data science, earth science, python, remote sensing, Geographic Information System (GIS), data science education, d, computer programming, wildfire, earth analytics, Online course, Teaching material, Undergraduate, Faculty, Graduate, Extended Project, Open Educational Resources (OER)
Growth in large ecological datasets and large environmental synthesis projects has resulted in the need for a diverse workforce with technical data science skills. A variety of organizations support underrepresented groups entering the data science field through training, mentoring, and networking opportunities. However, many of these initiatives have been developed in isolation, limiting opportunities for an exchange of ideas and lessons learned. The Environmental Data Science Inclusion Network (EDSIN) is intended to strengthen initiatives across existing alliances and organizations to recruit and retain individuals from underrepresented groups in data science careers.
The Ecological Society of America (ESA) is a nonpartisan, nonprofit organization of scientists founded in 1915 to promote ecological science and improve the quality of ecology education at all levels.
This module is a modification of the Beckstead et al. module in TIEE Volume 7. It includes a meta-cognition based model and self reflection, no statistics, and is organized to be done in 1-2 lab/in-class periods (no advance reading, no homework).
This is a modification of the Linton et al. module in TIEE Volume 13. Students use data from digitized museum records of butterfly specimens in a modification with no homework (lab/workshop format) or statistics knowledge required.
Project EDDIE (Environmental Data-Driven Inquiry and Exploration) is a suite of education projects composed of STEM disciplinary and educational researchers. We develop flexible classroom teaching modules using large, publicly available datasets to engage students in STEM and improve their quantitative reasoning. Teaching modules span topics such as ecology, limnology, geology, hydrology, and environmental sciences. EDDIE also helps build the associated professional development needed to ensure effective use of the teaching modules.
This module guides students in an examination of how surface ocean productivity relates to global climate on glacial-interglacial timescales and how the availability of ocean nutrients can be correlated with changes in productivity.
In this module, students will practice answering a specific question about how climate change has affected flowering date in American elm trees. Students can then practice on a species of their own interest.
This activity explores the question: which species will be most affected by temperature changes, and how will changes in the phenology of one species affect its interaction with others as the climate warms.
Christopher Berg, Julie Elliott, Beth Pratt-Sitaula
Version: 1.0
Students work with high precision GPS data to explore how motion near a plate boundary is distributed over a larger region and hypothesize the area over which boundary-related earthquake hazards might exist. Primary emphasis is strike-slip regions.
Students will explore different possible abiotic drivers of plant growth, defined as greenness and height. In the final step, students will analyze data from around the United States and consider macroscale patterns of vegetation controls.
In this module, students will use an analytical framework with publicly available data to formulate questions, analyze data, and report metrics of sustainability.
This module introduces students to the concepts of Ekman transport, eastern boundary currents, and upwelling, while learning how to find a location on a map using latitude and longitude, how to build and interpret a wind rose plot in Excel.
N E Bader, T. Meixner, C. A. Gibson, Catherine O'Reilly, D. N. Castendyk
Version: 1.0
Stream discharge is a fundamental measure of water supply in stream systems. Low discharge may cause problems with water supply and fish passage, while high discharge may mean flooding. In this module, students explore real-time stream discharge...
Students explore new modeling and computing tools while learning fundamental concepts about how lake phytoplankton blooms occur through cross-scale interactions.
Genome Solver began in 2012 with the goal to create a community of undergraduate educators interested in the Human Microbiome Project (HMP) as a means to teach the fundamentals of bioinformatics. Since then, Genome Solver instructors have provided training to more than 250 faculty members from approximately 125 institutions in a 1- or 2-day workshop. All Genome Solver curriculum materials are available on QUBES.
We reviewed the materials available on Genome Solver (other lessons here) and described our new python pipeline for examining horizontal gene transfer and a new synteny lesson in detail.
Bioinformatics Education, introductory, Lab, Online course, Teaching material, Lecture, High School, Undergraduate, Majors, Non-majors, More than 1 hour, Fostering a Sense of Belonging, Promoting Engagement and Self-Efficacy
Genome Solver began as a way to teach undergraduate faculty some basic skills in bioinformatics; no coding or scripting is required. This pre-lesson introduces the BLAST tool.
The lesson teaches about synteny or the order of genes along a chromosome, which is useful for looking at orthologous genes between two species or strains.
Genome Solver began as a way to teach undergraduate faculty some basic skills in bioinformatics; no coding or scripting is required. Lesson III introduces Annotation, or assigning meaning to all the A's, C's, T's, and G's.
Genome Solver's Community Science Project was developed as a way to use the skills taught in the previous lessons. We're asking members of the community to provide instances of potential phage genes embedded in bacterial genomes.
The Genome Solver Project began as a way to teach faculty some basic skills in bioinformatics - no coding or scripting. These Lessons also work well in the undergraduate classroom, culminating with an authentic community research project.
Genome Solver began as a way to teach undergraduate faculty some basic skills in bioinformatics; no coding or scripting is required. Lesson III introduces Annotation, or assigning meaning to all the A's, C's, T's, and G's.
Genome Solver began as a way to teach undergraduate faculty some basic skills in bioinformatics; no coding or scripting is required. Lesson III introduces Annotation, or assigning meaning to all the A's, C's, T's, and G's.
Genome Solver began as a way to teach undergraduate faculty some basic skills in bioinformatics; no coding or scripting is required. Lesson V is an introduction to Phylogenetics.
Genome Solver began as a way to teach undergraduate faculty some basic skills in bioinformatics; no coding or scripting is required. Lesson IV, Comparative Genomics, discusses some of the tools available for looking at genes across different genomes.
