brian avery

brian avery

genetics, neuro, & data nerd. professor at Westminster College.

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

housing wage data by district

Collate Utah data to make visible the proportion of households in each Utah legislative district that make below what is required to rent a 2 bedroom apartment in their county.

publications

Retrospective study of rFVIIa, 4-factor PCC, and a rFVIIa and 3-factor PCC combination in improving bleeding outcomes in the warfarin and non-warfarin patienty.

Published in American Journal of Hematology, 2016

Retrospective study of rFVIIa, 4-factor PCC, and a rFVIIa and 3-factor PCC combination in improving bleeding outcomes in the warfarin and non-warfarin patient.

Recommended citation: DeLoughery E, Avery B, DeLoughery T. Retrospective study of rFVIIa, 4-factor PCC, and a rFVIIa and 3-factor PCC combination in improving bleeding outcomes in the warfarin and non-warfarin patient. Am J Hematol, 91(7):705-708, July 2016. http://dx.doi.org/10.1002/ajh.24384

talks

Teaching quantitative and computational skills to undergraduates using Jupyter Notebooks.

Published:

As the data that we collect dramatically increases in both quantity and complexity, all college graduates will need more quantitative and computational skills to be productive and successful members of society. I will present my experience using the open source Jupyter Notebook system as an undergraduate instructor and mentor. My colleagues and I have developed inquiry based, active learning materials in Jupyter Notebooks to teach coding and quantitative skills to undergraduate students at several different levels. Our materials address basic calculation and graphing skills, reproducible research, and include a semester long scientific computing course using Python. Jupyter Notebooks have several advantages for teaching and learning over traditional coding in the shell or an IDE. The system is relatively easy to install, combines text, code, and output all in one place that is easily exportable. It also makes it easy to guide students through solving problems with code and to see students’ thought process as they work through everything from simple exercises to complex data analysis projects. We see Jupyter Notebooks as an easily accessible tool to get students at various levels engaged in doing data science.
More information on csv,conf

Teaching foundational quantitative and computational skills to early undergraduates using Jupyter Notebooks.

Published:

As the data that we collect dramatically increases in both quantity and complexity, all college graduates will need more quantitative and computational skills to be productive and successful members of society. Neuroscience students are no exception. Instructors are often looking for efficient and exciting (or at least non-off-putting) ways to introduce undergraduates early in their careers to the powerful data analysis tools that they will learn and use later, such as the programming languages, R and Python, without overwhelming introductory students. My question was: what is the most accessible and effective framework for teaching quantitative and computational skills in introductory neuroscience and genetics classes that will meet our learning goals and prepare students for continued development in data analysis and advanced courses? I have developed inquiry based, active learning materials in the Jupyter Notebook system to teach coding and quantitative skills to undergraduate students at several different levels. I will present our materials and discuss our experience using this open source system as an effective and accessible tool to teach quantitative and computational skills in neuroscience classes. My materials address basic calculation and graphing skills, reproducible data analysis, and basic statistics using R or Python. I will discuss the advantages of Jupyter Notebooks for teaching and learning over using more basic tools such as Excel or systems with a relatively steep learning curve such as Rstudio. The Jupyter Notebook system is relatively easy to install or use in the cloud, and combines text, code, and output all in one place that is easily exportable to HTML and PDF. It is also easy to guide students through solving scaffolded problems with code that they can adapt and modify, and to record and evaluate students’ thought processes as they work through everything from simple exercises to complex data analysis projects. I see Jupyter Notebooks as an easily accessible tool to get students at various levels engaged in doing data science related to neuroscience.
More information on CSAIL

Teaching foundational quantitative and computational skills to early undergraduates using Jupyter Notebooks.

Published:

As the data that we collect dramatically increases in both quantity and complexity, all college graduates will need more quantitative and computational skills to be productive and successful members of society. Ecology students are no exception. Instructors are often looking for efficient and exciting (or at least non-off-putting) ways to introduce undergraduates early in their careers to the powerful data analysis tools that they will learn and use later, such as the statistical programming language, R, without overwhelming introductory students. Our question was: what is the most accessible and effective framework for teaching quantitative and computational skills in introductory ecology classes that will meet our learning goals and prepare students for continued development in statistics and advanced courses?
Co-presented with Christy Clay, Ph.D., also of Westminster College

teaching

Stem Cells and Development Fall 2017

undergraduate, Westminster College, 2017

Advanced undergraduate course on the biology of stem cells, development, and the applications of stem cells.

Neurogenetics Spring 2018

undergraduate, Westminster College, 2018

An advanced genetics class focusing on the methods used in human population and family studies at the genome wide level and how they are applied to complex human behavioral phenotypes.

Behavioral Genetics Fall 2018

undergraduate, Westminster College, 2018

This course is an exploration of the role of genetic inheritance on human behavior. We focus on modern genetic analysis and the molecular techniques used to study both complex normal human behaviors and diseases. Taught as a learning community in conjunction with Probability, Risk, and Reward (Bill Bynum).

Cellular Neuroscience Fall 2018

undergraduate, Westminster College, 2018

Neuroscience at the cellular level including the specific cell biology and development of neurons, electrophysiology, synapses, wiring a nervous system, sensory receptor systems, and learning and memory at the cellular level.