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Universal Design for Learning - Faculty Mentoring Network Introduction
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2020-03-04 18:39:22 | Contributor(s): Andrew Osborne Hasley, Hayley Orndorf | doi:10.25334/JG5W-E031
Overview of Universal Design for Learning, presented to a faculty mentoring network during a virtual meeting
Investigating Evidence for Climate Change (Project EDDIE)
2020-02-18 20:23:14 | Contributor(s): Melissa Hage | doi:10.25334/FPA6-AS71
This multi-part activity allows students to discover the relationships between CO2 and temperature and how these variables have changed over time using real-world data.
Water Quality Investigation (Project EDDIE)
2020-02-18 13:57:23 | Contributor(s): Melissa Hage | doi:10.25334/C4C3-3S91
This multi-part module aims to help you learn about water quality implications by understanding the variability of concentrations of nitrate in stream water through the evaluation of real-time data and identifying the reasons for this variability.
Chi-squared test of independence between two categorical variables
2020-02-13 17:07:38 | Contributor(s): Matthew Aiello-Lammens | doi:10.25334/EM4Y-X154
In this lesson, students will have the opportunity to work through a chi-squared test of independence between two categorical variables.
Population Demography in Swirl
2020-02-12 20:27:04 | Contributor(s): Emily Weigel | doi:10.25334/NANC-M515
The students will learn to generate, test, and graphically represent basic hypotheses on data distributions using large datasets.
Population Ecology in Swirl: Estimating Population Sizes
2020-02-12 20:18:35 | Contributor(s): Mary E McWhirt, Emily Weigel | doi:10.25334/ABP7-GA97
The students will learn to estimate population sizes and consider assumptions of mathematical models and their applicability to the ecology of an organism/population
Responses to Climate Change in California Chipmunks: Move, Adapt, or Die
2020-02-10 22:18:37 | Contributor(s): Talisin Hammond, Rachel E. Walsh, Eileen Lacey | doi:10.25334/BEY3-QH48
A series of three modules using data from natural history museum collections to examine responses to climate change in multiple chipmunk species. Elevation and morphological data, beginner skills in R, ecological niche modeling in Maxent, and more.
2020-01-27 21:11:02 | Contributor(s): Emily Weigel, Alycia Lackey | doi:10.25334/N0T7-DD80
The students will practice identifying the appropriate basic statistical tests when given a scenario and learn how to run and interpret those statistical tests in R.
Exploring the evolution of caffeine biosynthesis enzymes
2020-01-23 01:18:35 | Contributor(s): Shuchismita Dutta | doi:10.25334/EE3E-S080
Caffeine is a very common stimulant consume worldwide as tea, coffee, mate, cocoa, and chocolate. Ever wondered why plants make caffeine? Do all caffeine producing plants have the same enzymes for caffeine biosynthesis?
Introduction to UNIX command line
2020-01-22 13:53:21 | Contributor(s): Serghei Mangul, Kristen Butela, Michael Sierk, Lihua Stefan | doi:10.25334/H7TG-G648
A laboratory module for an upper-level undergraduate biology course in which students will be able to carry out simple tasks using the Unix command-line environment.
Graphing grouped continuous data in R with swirl
2020-01-13 20:13:06 | Contributor(s): Marney Pratt | doi:10.25334/FM8B-PM89
This lesson helps students know some of the options for how to graph grouped continuous data (such as those involved in doing a t-test or ANOVA) and how to choose the best option.
Conducting Analysis of Variance (ANOVA) in R with swirl
2020-01-13 20:00:00 | Contributor(s): Kevin Geyer | doi:10.25334/92KV-EH29
This lesson offers an introduction to ANOVA, including 1) how this statistical test can be differentiated from others and 2) a step-by-step guide to conducting and interpreting ANOVA results in R, including assumption testing and post-hoc analysis.
Interpreting one-factor ANOVA in R with swirl
2020-01-13 19:37:01 | Contributor(s): Jeremy Claisse | doi:10.25334/SA4D-PF95
Interpreting One-Factor Analysis of Variance (ANOVA)
Checking Normality in R with swirl
2020-01-13 19:30:34 | Contributor(s): Bill Morgan | doi:10.25334/PQ79-N018
How to use the "three-prong" approach to check for normality
One-way ANOVA in R with swirl
2020-01-13 19:18:39 | Contributor(s): Bengt Allen | doi:10.25334/2MRV-SA62
One-way analysis of variance (ANOVA), following the text in Chapter 5 of Beckerman et al. (2017) Getting started with R: an introduction for biologists, 2nd edition.
Following the Data - PowerPoint Addition to Module
2020-01-10 21:29:24 | Contributor(s): Darlene Panvini | doi:10.25334/731M-8788
This PowerPoint presentation guides instructors and students through the key background information for the module.
Data is the New Science - Modified and combined with Following the Data Module
2020-01-10 20:52:41 | Contributor(s): Darlene Panvini | doi:10.25334/RN01-VX30
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 a question.
BLAST Exercises for Analyzing Cloning Results
2020-01-10 01:47:33 | Contributor(s): Erin R. Morris | doi:10.25334/YY9D-4W75
Genome Solver began as a way to teach undergraduate faculty some basic skills in bioinformatics; no coding or scripting is required. These activities have modified Lesson 1: Introduction to Genome Solver.
Databases: A Study of Influenza
2020-01-09 21:35:48 | Contributor(s): Selene Nikaido | doi:10.25334/Z8VP-G378
This lesson introduces the student to the Influenza Research Database. Students will select Influenza genome sequences to see how they are related. Students tests hypotheses about evolutionary ideas involving reassortment in Influenza.
Introduction to Genome Annotation
2020-01-09 21:32:41 | Contributor(s): Selene Nikaido | doi:10.25334/2ANZ-SV60
This exercise is an adaptation of the Annotation Lesson by Rosenwald et al. It introduces the use of bioinformatics tools to extract information from genome databases. It is a basic lesson on genome annotation databases.
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