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    DataONE Module 01: Why Data Management?
    Trends in data collection, storage and loss, the importance and benefits of data management, and an introduction to the data life cycle.
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    Data Ethics
    Data ethics and the use of digital data
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    Introduction to Python for Data Science Course
    Free Introduction to Python for Data Science Course at DataCamp
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    Phenology publications
    Collection of modifications to TIEE phenology module from Data Discovery FMN

    Keywords: phenology

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    Remote sensing data processing – NEON airborne data
    Concepts of remote sensing and data logistics, and NEON remote sensing data are introduced. Students learn how to apply spatial data processing and visualization skills using R coding program to...
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    Yeast Cell Growth
    Numbers Count resource using Excel for data analysis
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    Data from: Agent-based and Individual-based Modeling: A Practical Introduction, Chapter 5, Section 5.5 [Elevation]
    Elevation data input file for Section 5.5 (tab-separated plain text).
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    A Laboratory Module That Explores RNA Interference and Codon Optimization Through Fluorescence Microscopy Using Caenorhabditis elegans
    Scientific research experiences are beneficial to students allowing them to gain laboratory and problem-solving skills, as well as foundational research skills in a team-based setting. We designed...
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    Data Visualization
    This is an introduction to the idea of data visualization and the importance of presenting data for understanding.
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    Earth Analytics in Python Course
    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...
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    Summer 2019 Biobytes
    This series includes the four Biobytes that were piloted at the 2019 BioQUEST / QUBES Summer Workshop titled, “Evolution of Data in the Classroom: From Data to Data Science”.
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    Statistical Exploration of Climate Data
    In this module the students will learn some basic concepts in statistical thinking about data, with emphasis on exploratory data analysis.
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    Morphological and Molecular Analysis of Plant Phylogeny and Diversity
    In this lab, students perform phylogenetic analyses on morphological and molecular data, and compare.
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    Introductory Data Science Pipeline Activity – Yellow Fever and Global Precipitation
    Students follow the steps of a tiny data science project from start to finish. They are given a research question "Are the number of cases of yellow fever associated with global average...
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    Cell Signaling Pathways - a Case Study Approach
    Signaling and gene expression are fundamental to cell biology and developmental biology. Although these topics are highly interrelated, they typically appear in separate units in a course. We use a...
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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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    3-017-StackedCoffeeFiltersFalling-ModelingScenario
    Data on free falling 2, 4, 6, and 8 stacked coffee filters is offered. Students form a model using a resistance term proportional to velocity, velocity squared, or velocity to some general power....
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    Serenity now! Keep calm and do science with real data in the classroom
    Session on using real data in the classroom with Serenity, a platform-independent browser-based interface to data science tools for the classroom.
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    Dynamic Daphnia: An inquiry-based research experience in ecology that teaches the scientific process to first-year biologists
    This authentic research experience lesson teaches the core concept of systems and the competencies of quantitative reasoning, communication, and the ability to apply science. The research is...
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    A Beginner's Guide To Sampling Rockpools
    In this guide, we'll teach you how to sample rockpools, collect data, make visuals from data, and interpret data in order to see if any correlations exist.
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