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ResBaz AZ '23 - Track A

Welcome to ResBaz AZ 2023!
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Use this doc to get info on sessions you want to attend, see notes on sessions you can't attend, collaborate, help archive our conference for future learners, and much more!

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HackMD - Track B

Monday

Workshop: Intro to machine learning in R (HPC)

Apr 17, 1:00-2:30 PM (PDT)
Chris Reidy
Location: Zoom

Get a brief overview of machine learning, see the implementation of RStudio on HPC, and participate in hands-on exercises

tags: beginner, high performance computing, R, machine learning
Session Notes

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Getting Started

There will be hands-on exercises that do not assume more than basic knowledge of RStudio. It would be helpful to already ‘install.packages(“VIM”)’

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Learning Objectives

Gain some take way knowledge of concepts of machine learning and reusable exercises.

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Introductions

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    Instructor: Chris Reidy; UArizona University Information Technology Services; chrisreidy@arizona.edu
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    Helper: Viviana Zapata

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Session Notes

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Session Questions

Speaker Session: Distributed Computing (DISCOVER Platform)

Apr 18, 3-3.30 PM (PDT)
Morgan Vigil-Hayes
Location: Zoom

Learn about research infrastructure for distributed sensing and computing over sparse dnvironments through the DISCOVER Research Infrastructure project hosted at NAU.

The DISCOVER cyberinfrastructure, funded through the NSF CISE Community Research Infrastructure Program, allows researchers to study how Internet of things (IoT) devices and networks can be designed to work in technically challenging rural or remote areas.

tags: speaker, infrastructure
Session Notes

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Speaker Bio

Morgan Vigil-Hayes is an assistant professor of computer science in the School of Informatics, Computing, and Cyber Systems at NAU.

Her research focuses on human-centered aspects of computer networking with the goal of innovating networked architectures, services, and policies for underserved communities.

She is a Co-PI on the DISCOVER CCRI Project.

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DISCOVER Website

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DISCOVER Experimenter Portal

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Session Notes

  1. What is the DISCOVER Platform?
  2. DISCOVER Sites
  3. DISCOVER Infrastructure
  4. DISCOVER Experimenter Portal

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Session Questions

Workshop: Intro to text mining for humanists and social scientists

Apr 17, 3:30-5:00 PM (PDT)
Anuj Gupta
Location: Zoom

Text mining is a valuable tool for researchers in the humanities and social sciences. By using computer languages, like Python, to analyze large textual datasets, researchers can discover new patterns and insights that were previously hidden.

This virtual workshop is designed for advanced undergraduate and graduate students who want to learn how to use text mining tools for their research. The workshop will cover a range of techniques, including distant reading, close reading, and data visualization.

tags: beginner, python, text
Session Notes

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Getting Started

No prior experience with programming or mining is necessary. Participants will need a computer, good internet connection and familiarity with using Zoom.

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Learning Objectives

By completing this workshop, you will:

Goal 1: EXPLORE iconic studies in Humanities and Social Sciences that have used text mining techniques

Goal 2: PRACTICE how to use computational methods using Python that can help in anlaysing textual data using distant reading (frequency distributions, dispersions) and close-reading (collocations, concordances) methods.

Goal 3: BRAINSTORM how some of those techniques could be applied to your own work

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Introductions

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    Instructor: Anuj Gupta; UArizona English department; anujgupta@arizona.edu
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    Helper: Tina L. Johnson
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    Helper: Chen Chen

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Session Notes

https://crow.corporaproject.org/

https://elicorpora.info/main

https://dataverse.harvard.edu/dataverse/gwu-libraries

https://catalog.docnow.io/

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Session Questions

Tuesday

Workshop: Intro to text analysis

Apr 18, 9:00-10:00 AM (PDT)
Rongbo Jin
Location: Zoom

This workshop will cover basic comcepts and steps in text analysis for social scientists in R.

tags: beginner, R, text
Session Notes

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Getting Started

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Learning Objectives

Learners will leave with an understanding of:

  • Procedures of text analyses in R
  • Basic models

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Introductions

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    Instructor: Rongbo Jin; UArizona School of Government and Public Policy; rongbojin@arizona.edu
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    Helper:

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Session Notes

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Session Questions

Workshop: Making your first R package

Apr 18, 10:00 AM -12:00 PM (PDT)
Eric Scott
Location: Zoom

tags: intermediate, R, packages

In this workshop, build a functional, installable R package and learn the fundamentals of what is involved in taking your package to the next level by submitting it to a repository like CRAN.

Session Notes

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Getting Started

For this workshop you'll need:

  • Some familiarity with writing code in R and using RStudio
  • recent versions of R and RStudio
  • a (free) GitHub account
  • Some familiarity with git and GitHub is useful (check out Heidi's workshop on Monday)

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Learning Objectives

Learners will:

  • Understand the basic components of an R package
  • Understand what a unit test is and why it's important
  • Learn to write a simple function in R
  • Learn to write documentation for functions with roxygen2
  • Learn to install a development package from GitHub
  • Gain familiarity to submitting to package repositories, like CRAN

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Introductions

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    Instructor: Eric Scott; UArizona Communications & Cyber Technologies Data Science; ericrscott@arizona.edu
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    Helper:
  • Viviana Freire Zapata, 3rd year PhD student at Environmental Science. Working with omics data.

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Session Notes

Notes

  1. Intro Slides
  2. Coding notes
  3. Suggested book
  4. Project Setup
  5. Git/GitHub Setup
  6. Convert to a Package
  7. Write a Function
  8. Add Some Data
  9. Write Tests and Automate
  10. Share Your Package

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Session Questions

Workshop: Using geospatial tools for big data processing in R + GDAL

Apr 18, 1:00-3:00 PM (PDT)
Ivan Gonzalez
Location: NAU - Du Bois South Union, Juniper room

tags: intermediate, R

Hi all! We need to install the programs and libraries in the following order: QGIS (OSGeo4W), R, Rtools, Rstudio and R libraries.
This will take a long, so please consider to install them a day before
Here the folder with the executables: https://drive.google.com/drive/folders/1CLhh_Quiwt78hDk2b5AUW7ezkZhgp3Nk?usp=share_link

  • QGIS: 01_QGIS-OSGeo4W-3.22.6-1.msi
  • R:02_R-4.2.0-win.exe
  • RTools: 03_rtools42-5253-5107.exe
  • RStudio: 04_RStudio-2022.02.2-485.exe
  • R libraries: 05_Install_libs_R.R
Session Notes

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Getting Started

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Learning Objectives

  • Undesrtand GDAL/OGR work
  • Compare R and GDAL/OGR performance
  • Run practical cases for improving speed and file sizes
  • Run GDAL external programs from R

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Introductions

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    Instructor: Ivan Gonzalez
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    Helper:

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Session Notes

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Session Questions

Workshop: GPU acceleration: General approaches to implementing GPUs in computation

Apr 18, 3:00-4:00 PM (PDT)
Gil Speyer
Location: ASU - Goldwater Center for Science and Engineering, room 487

tags: intermediate, graphics processing

GPUs can potentially offer order-of-magnitude scaling, but it is important to understand the kinds of computations that lend themselves to this.

This session will survey the various methods of exploiting GPU in computing.

Session Notes

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Getting Started

It will be helpful for learners to have an understanding of programming concepts (no specific language)

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Learning Objectives

Learners will improve their general understanding of GPU.

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Introductions

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    Instructor: Gil Speyer; Arizona State University Research Technology Office; speyer@asu.edu
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    Helper:

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Session Notes

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Session Questions

Workshop: HPC and GPU approaches with Matlab

Apr 18, 4:00-5:00 PM (PDT)
Gil Speyer
location: ASU - Goldwater Center for Science and Engineering, room 487

tags: intermediate, graphics processing, high performance computing

This workshop will focus on approaches to porting MATLAB applications to a larger scale or accelerated compute resource such as a supercomputer or a GPU.

This is not an introduction to MATLAB course - learners should have a general understanding of Matlab programming

jfdkls

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Session Notes

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Getting Started

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Learning Objectives

Learners will gain a better understanding of computational possibilities with Matlab.

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Introductions

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    Instructor: Gil Speyer; Arizona State University Research Technology Office; speyer@asu.edu
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    Helper:

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Session Notes

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Session Questions

Social: Tucson Python Meetup
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Apr 18, 6:00 PM -8:00 PM (PDT)
Tucson Python Meetup community
Location: UArizona Libraries CATalyst Studio, room 254
Zoom link: https://roche.zoom.us/j/92517258720
Passcode: 632128

tags: python, social

Tucson Python Meetup @ ResBaz AZ: Poppy Argus talks about "Web in Python with Flask"

Wednesday

Speaker Session: Data Publication for Reproducible Research

Apr 19, 9:00-10:00 AM (PDT)
Fernando Rios, PhD
Location: Zoom

tags: beginner, data publishing

How do you publish data to make it findable and reusable?

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Learn how to curate and prepare your data for publication in data repositories like ReDATA.

Session Notes

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Speaker Bio

Fernando Rios is situated within the Libraries and provides data management and curation services for UA researchers.

His academic background is in physics, geography, and computational hydrogeology.

Slides: https://osf.io/d8kfh

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Session Notes

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Session Questions

Workshop: Basic probability theory for statistics

Apr 19, 10:00 AM -12:00 PM (PDT)
MD Nafis Ul Alam
Location: UArizona Libraries CATalyst Studio, Zoom

tags: beginner, intermediate, statistics, R

We will begin with basic concepts in probability theory such as random variables, expectation values and probability distributions.
We will then look at some properties of commonly used distributions like the Binomial, Poisson, Normal and Gamma. We will inspect the central limit theorem and observe how statistical tests compute p-values.
We will end with conditional probabilities and the basics of Bayesian estimation. We will practice some intuitive methods for parameter estimation using maximum likelihood and Markov Chain Monte Carlo.

Session Notes

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Getting Started

Slides: https://tinyurl.com/RezBaz23slides
Code: https://tinyurl.com/RezBaz23code

As we cover theory and concepts, we will follow along with examples and exercises in RStudio. Prior knowledge of R is not a prerequisite, but it will be helpful to have RStudio installed on your computer so you can execute the provided scripts, play around with the code and try solving practice problems.

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Introductions

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    Instructor: MD Nafis Ul Alam; UArizona Plant Sciences, Arizona Genomics Institute; mdalam@arizona.edu
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    Helper: Qiuyu Jiang; UArizona Ecology and Evolutionary Biology; qiuyujiang@arizona.edu

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Learning Objectives

Learners will:

  • Build familiarity with commonly used probability distributions
  • Learn the theory behind statistical tests and p-values
  • Understand the idea behind sophisticated parameter estimation methods such as maximum likelihood and MCMC

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Session Notes

Content headers

Section 1: Fundamental concepts

  • Prelude to probability: the birthday problem
  • Probability space and events
  • Random variables, expectation values and linearity of expectation
    numerical vaules -
    expectaion value - average value of the random variable weighted by probabilty
    #even random variables are dependent on each other, linearity always holds;
    https://www.youtube.com/@rmcelreath(statistical rethinking)
  • Probability distributions
    Discrete random variables
    Normal distribution: mean and standard deviation
    Continuous distribution (PX=x) = 0; probability is calculated across a range

Section 2: Applications of probability distributions

  • Distribution functions in R
    dfunc; pfunc; qfunc(qfunc); rfunc(simulating random variables)

  • Discrete distributions: Binomial and Poisson

  • Discrete distribution
    Binomial
    Poisson: expectation = variance =
    Bimomial converges to poisson when rates goes below 1, we can treat it as probability; For poisson there is no upper/lower bounds.

  • Countinuous distribution
    Normal distribution

  • The central limit theorem and standard error of the mean

  • Continuous distributions: Normal, Gamma, Chi2, T, F
    Gamma: a factorial for a fractions;
    Sum of squares: how spread the data are; model comparison
    T test: whether a particular mean is siginificant from the population mean
    F test:
    Section 3: Parameter estimation

  • Prelude to simulations: the three-door problem

  • Conditional probabilities and Bayes’ theorem

  • Maximum likelihood estimation

  • Monte Carlo estimations and MCMC sampling

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Session Questions

  • fill in quetsion here

Workshop: Reproducibility in Python: intro to testing

Apr 19, 1:00-2:00 PM (PDT)
Ken Youens-Clark
Location: Zoom

tags: beginner, python

TBA

Session Notes

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Getting Started

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Introductions

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    Instructor: Ken Youens-Clark, MS; DNAnexus; kyclark@gmail.com
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    Helper:

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Learning Objectives

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Session Notes

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Session Questions

Workshop: Blender for cinematic visualization

Apr 19, 2:00-3:00 PM (PDT)
Devin Bayly
Location: Zoom

tags: beginner, 3D animation

This workshop will be an introduction to using blender in the domain of scientific visualization.

We will cover bringing in csv data and how we can associate it with geometry that we render for high quality visualizations.

Open to all learners!

–> Please Download Blender ahead of time https://www.blender.org/download/
–> Then go get the release files here https://github.com/DevinBayly/blender_CSV_resbaz

Session Notes

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Getting Started

Please make sure to install the latest blender program (3.5) @ https://www.blender.org/download/

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Learning Objectives

Participants will become familiar with blender in the following ways:

  • navigating the user interface and 3d viewport
  • importing csv data
  • exporting rendered images
  • basic tasks in the geometry nodes editor
  • positioning cameras for still frame and animated renders

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Introductions

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    Instructor: Devin Bayly; UArizona Libraries; baylyd@arizona.edu
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    Helper: Ben Kruse; Undergraduate CS/Math/Allstar

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Session Notes

  • step 1 placing points
  • step 2 instancing
  • step 3 adding color
  • step 4 creating source and target
  • step 5 making curve lines
  • step 7 full lines
  • step 8 sticks not lines
  • step 9 applying to B
  • step 10 adding camera control

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Session Questions

Thursday

[Event - separate registration] Women in Data Science - Tucson

Apr 20, 8:30 AM - 5:00 PM (PDT)
UArizona Student Union Kachina Room

tags: data science, speaker sessions

Women in Data Science (WiDS) Tucson is an independent event organized by the University of Arizona to coincide with the Global WiDS Conference held at Stanford University and an estimated 200+ locations worldwide annually.

Social: Hacky Hour

Apr 20, 4:00 PM -7:00 PM (PDT)
Snakes and Lattes Tempe, 20 West 6th St, Tempe, AZ
Mother Road Brewing Company, 7 S Mikes Pike St, Flagstaff, AZ
Snakes & Lattes, 988 E University Blvd, Tucson, AZ

tags: social

Celebrate the end of ResBaz AZ 2023!

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