Data & Donuts

This workshop will guide attendees through open source GIS workflows using QGIS's approachable user interface and demonstrate how to replicate those processes programmatically in Python for enhanced reproducibility and efficiency. GIS beginners can expect a well rounded introduction to geospatial data and software, while seasoned users will benefit from techniques for automating their workflows with Python.

When you start working with large datasets or need to develop reproducible workflows that can openly be shared with others, the ability to develop scripted processes for data processing and analysis can be essential. Tune in for this workshop to learn the basics of using Python, a popular free and open source scripting language, to process and manage research data.

In this workshop you will have a chance to go beyond the basic of Python to gain experience using scripts to retrieve data from different online sources that you can utilize in your research. After retrieving data using APIs, you will learn how to process and clean the data for further analysis and visualization using pandas and matplotlib.

Never have plot envy again! This hands-on workshop will demystify the fundamentals of using the ggplot2 package in R and will supercharge your plots with statistical data. You will get the most out of this workshop if you already know R basics (e.g., how to define a variable and how to specify input parameters to a function), but otherwise assumes no knowledge of coding in R or building plots in ggplot2.

There are a wide range of platforms available to researchers to share their data, and it can be overwhelming trying to find the best home for your data (including which platforms to avoid). This workshop will provide an overview on how to pick a repository for publishing your data, with an emphasis on generalist (non-discipline-specific) platforms, and provide best practices for sharing high-quality metadata and data that maximizes their accessibility and reuse potential and that complies with federal agencies' policies.

Sharing research data is a requirement of many funding agencies and journals. When the data includes information which, when made public, could be used to harm research participants, researchers must maintain a duty of care to keep the data secure and to protect participants’ identities. In this workshop, we’ll discuss how to meet requirements for data sharing while ensuring ethical treatment and protection of sensitive data. We will address consent issues and IRB, secure storage, anonymization, and de-identification, as well as data (re)use agreements. We will also point out which data can be shared.

In this workshop, participants will learn best practices for working with spreadsheet data. The session will showcase how researchers can use tools like OpenRefine and Python scripts to manage tabular data effectively. Please download and install OpenRefine – http://openrefine.org/download.html prior to the workshop. OpenRefine runs in a browser. It works best with Chrome, Firefox, or Safari.

This workshop will go over helpful strategies and techniques for effective research data management in all stages of the research lifecycle, from the drafting of comprehensive data management plans to successful publication of research data. Join this session to learn how to overcome data management challenges and stay in compliance with research data management regulations.

When you utilize custom research software to manage your data collection, processing, analysis, or visualization it is important to ensure that your code is backed up and versioned. Join this session to learn how you can utilize Git and GitHub to effectively manage the research code you develop and share it with other researchers as open source software.

R is a powerful statistical computing and graphics software that is open source and widely popular—particularly in academia. In this workshop we will cover how to use the R scripting language for working with research data.