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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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    Population Ecology of the Northern Spotted Owl
    The mathematical modeling of populations utilizing field-collected demographic data is an important component of lab curricula in a variety of undergraduate biology lab courses. During the global...
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    The Avocado Lab: An Inquiry-Driven Exploration of an Enzymatic Browning Reaction
    Typical biochemistry labs exploring basic enzyme activity rely on costly, time-consuming protein purification and rarely explore enzyme function in situ. Further, complex purification procedures...
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    Snail Races: An Inquiry-Based Approach to Learn Invasive Species Ecology
    Inquiry-based lessons allow learning that is hands-on and student-driven, fostering engagement and retention of knowledge in any discipline. Here, we use this learning framework to engage students...
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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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    An Inquiry-Based Project Added to Traditional Labs: Hands-On Fish Awareness Research in Freshwater Systems Conducted by First-Year Biology Students
    This project involves students in an inquiry-based project added to the introductory biology laboratory course for majors in fall 2019. Research has shown that student engagement in authentic...
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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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    Data Visualization
    This is an introduction to the idea of data visualization and the importance of presenting data for understanding.
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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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    Furry with a chance of evolution: Exploring genetic drift with tuco-tucos
    Genetic drift is an important mechanism of evolution, yet undergraduates often fail to understand how it leads to evolutionary change due in part to its random nature. This lesson plan describes a...
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    Isolation and Functional Analysis of a Pancreatic Enzyme in an Introductory Student Lab
    Structure and function are correlated at all levels of biology. This topic is typically addressed early in an undergraduate class in general or molecular biology before students have gained much...
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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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    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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    Backward Design with Digital Data
    Best practices for designing educational modules using biodiversity data
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