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This is an archived site for Love Data Week 2022. For recent events, please visit our Love Data Week website.
Love Data Week is an international celebration of data that aims to promote good data practices, while building and engaging a community around topics related to research data management, sharing, preservation, and reuse. During the 2022 Love Data Week (February 14 - 18), the Center for Research Data & Digital Scholarship (CRDDS) will host a series of workshops and activities to introduce more people to the center, its resources and services and show the campus community a path to better use and management of data in their work.
This page provides a brief synopsis of the various events over the course of CU Boulder's Love Data Week, and links to relevant materials (such as slide decks and lesson plans).
Presenter: Jordan Wrigley
Time: 10 am to 11:15 AM
Synopsis: Want to make sure you get cited (love) for your data work as well as your manuscripts? Getting cited for both can increase impact as well as illustrate transparency and reproducibility in science and research. This session will show easy steps to make a research data set findable and citable to future researchers, including re-using and adding language for metadata, using README templates, filetypes, and finding a repository that will reach your audience.
Link to workshop materials:
Presenter: Tim Dunn
Time: 1 PM to 3:00 PM
Synopsis: Data is the current gold rush in today’s world! But how do you find, obtain, load, view, work with, analyze, visualize, and package all your data to share with colleagues and the world at-large? That’s where Python comes in. Python is well suited and the number one language for digging deep into your data to manipulate, analyze, visualize, and share it with the world. In this workshop, we will look at some of the foundation Python building blocks to aid you in all your data adventures and storytelling. Along the way, we will also discuss where do you go from here for several of the main features we will cover. By the end of the session, you will have a fundamental knowledge of how to use Python to obtain data, how to load it and view it and manipulate it. This will lead us into how to perform analytics on your data and how to visualize your data. Open to anybody knowing no Python to intermedia Pythoneers who want to learn about how to leverage their data needs using Python. No knowledge of Python is required but we will be skipping over some of the fundamental coding mechanics for any/all languages. Bring a computer with Jupyter and Python installed.
Presenter: Tim Dunn
Time: 2:00 PM to 3:00 PM
Synopsis: Plotly is a modern, rich graphics package providing presentation and publication-quality graphics along with Jupyter and webpage based interactive controls for data exploration, and which can be extended out to rich interactive dashboards. Learn how to develop various kinds of Plotly charts and visualizations, how to customize them and venture into some more advanced features including other plotting extensions, animation, and interactive visualizations. By the end of the session you will have a fundamental knowledge of what Plotly has to offer, how its organized and utilized. How to create and save static plots, charts, and other images as well as how to make them interactive with a tease at building rich, deployable dashboards.
Presenter: Phil White
Time: 12:00 PM to 1:30 PM
Synopsis: Ever see an interesting map in the news and wonder how it was created? In this workshop, we'll pluck a map from the news and discuss approaches to breaking down the methods and technology used to make it. We will then walk through the process of reverse engineering a previously published map.
Presenter: Adi Ranganath
Time: 2:00 PM to 3:30 PM
Synopsis: This workshop introduces students and applied researchers to R Studio as a data analysis and visualization platform. Topics covered include installing packages and loading libraries; reading in external datasets into the R Studio environment; basic data wrangling and manipulation; calculating summary statistics; visualizing data; and implementing regression analysis. All are welcome, but the workshop would be especially beneficial to social scientists who are already familiar with other data analysis platforms (such as SPSS or Stata), and are interested in adding R Studio to their research toolkit.
Presenter: Jordan Wrigley
Time: 4:00 to 5:00 PM
Synopsis: Play trivia with Data Librarian Jordan Wrigley in Kahoots for research data management. Trivia questions will cover data sources, terminology, grants sponsor requirements, and tools.
Link to workshop materials:
Presenter: Adi Ranganath
Time: 10:00 AM to 12:00 PM
Synopsis: This hands-on workshop will introduce the United States census through R's tidycensus package. No prior experience with the census, or data analysis in R, is presumed, so all are welcome!
Presenter: Adi Ranganath
Time: 3:00 PM to 5:00 PM
Synopsis: This workshop rovides participants with a hands-on introduction to the process of extracting, processing, and exploring Twitter data in R Studio using the rtweet package.
Presenter: Nickoal Eichmann-Kalwara
Time: 10:00 AM to 11:30 AM
Synopsis: Oral history interviews are forms of qualitative data that offer unique insights into the communities we engage with and study. This workshop will offer an introduction to organizing and thinking about your oral history interviews as data, in order to enable computational readings and render visualizations for analysis and engagement. We’ll then demonstrate the digital humanities/minimal computing tool that can facilitate your processes and data publishing, Oral History (as) Data, and explore sample digital projects that utilize this minimal computing approach. Participants are encouraged to bring their own data for discussion and consultation. This is a beginner workshop with no prerequisites.