This guide walks viewers through how to access data through the NEON data portal and a few related resources for working with NEON data.  The NEON data portal remains in dynamic development. This photographic guide will be regularly updated. If the data portal you are viewing does not match this guide, please check for an updated version on the NEONScience.org website and let Megan know so she can update it here.

The guide is laid out to be followed sequentially from starting on the NEONScience.org website to downloading data to your computer. After this sequence additional slides are provided that details on other pages or topics of interest to data users.

This description is taken from: https://bio-link.org/blog/need-math 

Math--how to teach it, how to use it, who needs it, and how to keep it from being a barrier between students and jobs.  These are all topics of discussion at the Needed Math, a National Science Foundation Advanced Technological Education Funded Conference.  This conference, held from January 12-15, 2018 brought together employers in three STEM fields (biotechnology, manufacturing technology, and information and communication technology), post-secondary instructors of technical subjects related to those fields and mathematics educators.  Employers surveyed for the Manufacturing Institute's 2014 Skills Gap study report a "sizeable gap" between the talent they need and that available on the job market.  

The basic recommendation from the conference?   The mathematics standards, assessments, and curriculum need to be revisited and revised so as to place greater emphasis on the skills needed to solve the kinds of problems that arise in the real world.  This will also help to avoid the trap of "bad at math" when often the student is not bad at math, but just does not fit well into a curriculum that studies math without a clear application.  

The report is available and very interesting, with Needed Math examples that show quite clearly what employers are looking for. Take a look yourself!

Learning outcomes for the first year of biology compiled and consented to at the NWBC 2018 Winter Workshop.

The workshops will provide hands-on laboratory and computational experience in molecular biology and computational biology. Each workshop site will focus on waterways affected by contaminants (industrial or mining) and associated microbial communities. We have expanded the GSLI workshop by 2 days to add more data science methods. We recognize the cultural perspective of learning and incorporate this in our curriculum as genomics is a culturally sensitive area. We will give priority to freshmen or sophomore American Indian and Alaskan Native (AIAN), early exposure to the area of study and introduction to being in a research lab. We welcome applications from non-AIAN and upper-level students. The application deadline is February 8^th.

Learn more about the previous workshop: http://bit.ly/gsli2018-overview

Two Workshops Application Links to Register:

Fort Lewis College, Durango, CO : May 19 – 25, 2019 (http://bit.ly/GSLI-FLC)

CU Denver, Denver, CO : June 2 – 8, 2019 (http://bit.ly/GSLI-CUDenver)

Reference sheet from the workshop. Adapted from 2011 Vision and Change report by Stasinos Stavrianeas.

Scroll down to find links to webinars on Dynamic Governance, Curriculum Mapping, and more.

Powerpoint slides used during the SABER West 2019 "Scaffolding Core Competency Learning Outcomes across the Undergraduate Biology Curriculum" workshop.

QUBES site for the NWBC group. To learn more about NWBC.

The 2011 report. See pages 14-15 for core competencies.

Abstract: In this paper, we offer a brief review of six pedagogical and theoretical approaches used in education and science education that we grouped as inclusive pedagogies. Though not an exhaustive list, these pedagogies are more commonly used in educational research and have commonalities, yet are distinctive in some ways. They collectively contribute to making science teaching and learning more inclusive to a broader population of learners, such as students from diverse cultural, linguistic, and social backgrounds and students with physical and learning differences who have traditionally been marginalized in learning science. Furthermore, these inclusive pedagogies aim to decrease educational inequities and raise the level of academic rigor and access for all students. Finally, we discuss ways these inclusive pedagogies can be extended to address reform efforts in science education.

http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_189501.pdf

The goal of this effort is to produce an ESA-sanctioned framework that can be useful to ecology educators, the ESA Board of Professional Certification process, environmental professionals, decision makers, and others.

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So far, you’ve learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand your data through transformation, visualisation and modelling. However, it doesn’t matter how great your analysis is unless you can explain it to others: you need to communicate your results.

Now that you are equipped with powerful programming tools we can finally return to modelling. You’ll use your new tools of data wrangling and programming, to fit many models and understand how they work. The focus of this book is on exploration, not confirmation or formal inference. But you’ll learn a few basic tools that help you understand the variation within your models.

In this part of the book, you’ll improve your programming skills. Programming is a cross-cutting skill needed for all data science work: you must use a computer to do data science; you cannot do it in your head, or with pencil and paper.

In this part of the book, you’ll learn about data wrangling, the art of getting your data into R in a useful form for visualisation and modelling. Data wrangling is very important: without it you can’t work with your own data!

The goal of the first part of this book is to get you up to speed with the basic tools of data exploration as quickly as possible. Data exploration is the art of looking at your data, rapidly generating hypotheses, quickly testing them, then repeating again and again and again. The goal of data exploration is to generate many promising leads that you can later explore in more depth.

Course Description

Visualization is an essential component of interactive data analysis in R. Traditional (base) graphics is powerful, but limited in its ability to deal with multivariate data. Trellis graphics is the natural successor to traditional graphics, extending its simple philosophy to gracefully handle common multivariable data visualization tasks. This course introduces the lattice package, which implements Trellis graphics for R, and illustrates its basic use.

Course Description

This course provides a comprehensive introduction on how to plot data with R’s default graphics system, base graphics.

After an introduction to base graphics, we look at a number of R plotting examples, from simple graphs such as scatterplots to plotting correlation matrices. The course finishes with exercises in plot customization. This includes using R plot colors effectively and creating and saving complex plots in R.

Base Graphics Background
R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset.

Course Description

This course will help you take your data visualization skills beyond the basics and hone them into a powerful member of your data science toolkit. Over the lessons we will use two interesting open datasets to cover different types of data (proportions, point-data, single distributions, and multiple distributions) and discuss the pros and cons of the most common visualizations. In addition, we will cover some less common alternatives visualizations for the data types and how to tweak default ggplot settings to most efficiently and effectively get your message across.