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    Earth Analytics in R Course
    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...
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    Earth Analytics Bootcamp Course
    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...
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    Spatial Justice, Marginal Populations, and Data Integrity
    Spatial justice refers to the consequential geography of a place where spaces are socially produced. Understanding how spatial injustice is created at different spatial scales is an essential part...
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    Changes in Lake Ice: Ecosystem Response to Global Change
    Data Set included in included in Teaching Issues and Experiments in Ecology (TIEE) Volume 3
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    Data Management in Excel and R using National Ecological Observatory Network's (NEON) Small Mammal Data
    Students use small mammal data from the National Ecological Observatory Network to understand necessary steps of data management from data collection to data analysis by re-organising excel sheets...
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    Ontology for UDL and Related Content
    This is a description of tagging ontology that can be used for UDL resources
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    Quantitative Reasoning: Peer Review 16(3), 2014
    In this issue of the journal Peer Review, the authors discuss the issues surrounding quantitative literacy.
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    Introduction to Earth Data Science Textbook
    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...
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    Natural history collections data and Biodiversity Literacy in Undergraduate Education (BLUE)
    Presentation made by Elizabeth Ellwood et al. as part of the "Bringing Research Data to the Ecology Classroom: Opportunities, Barriers, and Next Steps” Session at the Ecological Society of...
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    Resource list for inclusive teaching in STEM (HHMI)
    A resource collection created for HHMI BioInteractive to guide the development of teaching resources aligned with principles of equity and inclusion.
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    Serenity now! Keep calm and do science with real data in the classroom
    Session presented at NABT 2018 in San Diego, CA
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    Sustainability Metrics (Project EDDIE)
    In this module, students will use an analytical framework with publicly available data to formulate questions, analyze data, and report metrics of sustainability.

    Keywords: World Geography

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    Sustainability Metrics (Project EDDIE)
    In this module, students will use an analytical framework with publicly available data to formulate questions, analyze data, and report metrics of sustainability.
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    Sustainability Metrics (Project EDDIE)
    In this module, students will use an analytical framework with publicly available data to formulate questions, analyze data, and report metrics of sustainability.
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    Changes in Lake Ice: Ecosystem Response to Global Change
    Data Set included in included in Teaching Issues and Experiments in Ecology (TIEE) Volume 3
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    Water Quality Module (Project EDDIE)
    Students utilize real-time nitrate data from the US Geological Survey to statistically evaluate water quality impacts and to identify their causes
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    REMNet Tutorial, Excel Part 3: Organizing Your Microbiome Data 5.3.19
    Video on organizing microbiome using Excel from the Research Experiences in Microbiomes Network
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    Developing Quantitative Skills Using HHMI BioInteractive Resources
    A workshop on using HHMI BioInteractive data activities to teach science content and quantitative skills
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    Best Practices and Recommendations for Designing Activities for Highlighting Diverse Scientists Through Quantitative Skill Development
    Poster on best practices to guide the design of quantitative skill-based learning activities that help create an inclusive environment. Presented at the 2021 BIOME Institute.
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    Communicating with Data: Infographic with Digital Data
    Working collaboratively in teams of 2-4 researchers, students engage in targeted projects to address critical information and research needs using digital biodiversity data.
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