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  1. The Ames Test

    The Ames Test

    2021-01-08 14:40:35 | Contributor(s): Nathan Goodson-Gregg, Elizabeth A. De Stasio | doi:10.25334/P18R-F263

    Introduction to the Ames Test, published as GSA Learning Resource

  2. Investigating human impacts on Southeastern US stream ecology using R

    Investigating human impacts on Southeastern US stream ecology using R

    2021-01-01 23:40:32 | Contributor(s): Alicia Caughman, Emily Weigel | doi:10.25334/P995-7G91

    Adaptation of the "Investigating human impacts on stream ecology: Scaling up from Local to National with a focus on the Southeast" specifically to focus on self-paced R code instruction

  3. Yeti or not: Do they exist?

    Yeti or not: Do they exist?

    2020-12-31 22:20:57 | Contributor(s): Keith Johnson, Adam Kleinschmit, Jill Rulfs, William (Bill) Morgan | doi:10.25334/GDSW-P773

    Through this 4-part bioinformatics case study, students will be led through the forensic analysis of putative Yeti artifacts based on published findings.

  4. Closing the Gap in the Open Educational Resources (OER) Life Cycle for Using Research Data in the Ecology Classroom

    Closing the Gap in the Open Educational Resources (OER) Life Cycle for Using Research Data in the Ecology Classroom

    2020-12-30 17:56:21 | Contributor(s): Kristine Grayson, Kaitlin Bonner, Arietta Fleming-Davies, Ben Wu, Raisa Hernández-Pacheco | doi:10.25334/YCJ8-W154

    Poster presented at the 2018 meeting of the Ecological Society of America on gaps in the curriculum cycle for open educational resources (OER) and our work supporting sharing and adaptation of data-centric teaching resources for ecology classrooms

  5. Reflective Writing Tools: Building Skills and Habits of Thinking in Becoming a Scientist

    Reflective Writing Tools: Building Skills and Habits of Thinking in Becoming a Scientist

    2020-12-25 05:06:18 | Contributor(s): Sally Molloy, William Davis, Elizabeth Moy, GC Jernstedt, Melinda Harrison, Vinayak Mathur, Matthew David Mastropaolo | doi:10.25334/2PD1-NT03

    Reflective writing tools are intended to help students better connect current learning experiences to prior learning, engage the role of emotion in current and future learning, and assess learning experiences to improve future learning.

  6. Investigating Evidence for Climate Change (Project EDDIE) with CO2 and 13CO2 data: adapted for R

    Investigating Evidence for Climate Change (Project EDDIE) with CO2 and 13CO2 data: adapted for R

    2020-12-23 23:08:55 | Contributor(s): Marguerite Mauritz | doi:10.25334/ZTBT-CF45

    This is an adaptation to work in R of Investigating Evidence for Climate Change (Project) by Hage, M. 2020. Students will investigate geologic and modern evidence for global temperature and atmospheric CO2 change using ice-core data and Mauna Loa records.

  7. Biostatistics using R: A Laboratory Manual

    Biostatistics using R: A Laboratory Manual

    2020-12-23 20:35:44 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/EWZM-NS95

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language to biology majors.

  8. Chapter 11: Correlation and regression analyses

    Chapter 11: Correlation and regression analyses

    2020-12-23 19:28:18 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/GWFH-C377

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 11 introduces correlation and regression analyses.

  9. Chapter 10: Two-way analysis of variance

    Chapter 10: Two-way analysis of variance

    2020-12-23 19:25:08 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/HYHZ-GX44

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 10 introduces the two-way analysis of variance.

  10. Chapter 9: One-way analysis of variance

    Chapter 9: One-way analysis of variance

    2020-12-23 19:23:15 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/5892-ZK23

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 9 introduces the one-way analysis of variance.

  11. Chapter 8: Comparing two means: the t-test

    Chapter 8: Comparing two means: the t-test

    2020-12-23 19:21:27 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/TZXB-HY86

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 8 introduces the Student's t-test.

  12. Chapter 7: The normal distribution

    Chapter 7: The normal distribution

    2020-12-23 19:18:47 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/V6P0-A283

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 7 introduces the normal distribution.

  13. Chapter 6: Population proportions and the binomial distribution

    Chapter 6: Population proportions and the binomial distribution

    2020-12-23 19:16:57 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/XK24-NT12

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 6 introduces the binomial distribution.

  14. Chapter 5: Hypothesis testing

    Chapter 5: Hypothesis testing

    2020-12-23 19:15:10 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/7NZK-QZ34

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 5 introduces hypothesis testing.

  15. Chapter 4: Probability distributions

    Chapter 4: Probability distributions

    2020-12-23 19:13:17 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/TNR2-SM37

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 4 introduces probability distributions.

  16. Chapter 3: Visualizing data

    Chapter 3: Visualizing data

    2020-12-23 19:10:18 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/KXYH-F608

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 3 introduces data visualization.

  17. Chapter 2: Data sampling, accuracy, and precision

    Chapter 2: Data sampling, accuracy, and precision

    2020-12-23 19:08:18 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/66G4-8G08

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 2 introduces sampling, accuracy, and precision.

  18. Chapter 1: Introduction to R and RStudio

    Chapter 1: Introduction to R and RStudio

    2020-12-23 19:03:53 | Contributor(s): Raisa Hernández-Pacheco, Alexis A Diaz | doi:10.25334/FRPR-2J11

    Biostatistics Using R: A Laboratory Manual was created with the goals of providing biological content to lab sessions by using authentic research data and introducing R programming language. Chapter 1 introduces R and RStudio.

  19. Introduction to R with Biodiversity Data

    Introduction to R with Biodiversity Data

    2020-12-16 23:00:13 | Contributor(s): Shelly Gaynor | doi:10.25334/84FC-TE88

    Students will learn R basics while downloading biodiversity data from multiple data repositories. This module will walk students through installing R, navigating R,writing reproducible scripts in R, and using R to download biodiversity data.

  20. Cross-Cultural Virtual Classroom for High School Calculus

    Cross-Cultural Virtual Classroom for High School Calculus

    2020-12-14 18:25:15 | Contributor(s): Chonilo Sales Saldon | doi:10.25334/2WBV-9S28

    The Covid-19 pandemic has forced institutions to conduct distance learning. Though challenging, this has also created pedagogical possibilities. Students can now experience cultural exchange though virtual, and be taught by experts.