ABOUT THE ANNUAL DATA CHALLENGE EXPO
The Annual Data Challenge Expo is jointly sponsored by three ASA Sections – Statistical Computing, Statistical Graphics, and Government Statistics. It is open to anyone who is interested in participating— including government, industry, academia, retirees, and students. Each year, this contest challenges participants to analyze a core data set using statistical and visualization tools and methods.
There will be two award categories:
- Professional (one level with a $500 award)
- Student (three levels with awards at $1,500, $1,000, and $500)
ABOUT THE 2021 CHALLENGE
The theme for the 2021 challenge is “Helping Families, Businesses, and Communities Respond to COVID-19” (view problem statement). This year’s data was selected in collaboration with the The Opportunity Project — a technology accelerator out of the Census Open Innovation Labs, the innovation arm of the U.S. Census Bureau— that facilitates virtual technology development sprints to catalyze the creation of digital products to address national challenges utilizing federal open data.
The 2021 Data Challenge Expo’s core data set is the U.S. Census Bureau’s 2019 American Community Survey (ACS) 1-year Estimates, a survey that provides vital information on a yearly basis about occupations, work locations, educational attainment, housing, and other topics in geographies with populations of 65,000+. Contestants must use some portion of the ACS data but are encouraged to combine other data sources— including the U.S. Census Bureau’s COVID Pulse Surveys of Small Businesses and Households. Supplemental datasets, along with data points of contact, can be found on The Opportunity Project Data Curation Hub. Contestants can also develop digital products as part of their participation in the challenge. For a guidebook to transforming federal open data into digital tools for the American people, visit The Opportunity Project Product Development Toolkit.
Note: While the challenge’s theme and associated problem statement offer a guide, contestants may address other problems of interest to them, as long as they incorporate the core data set.
HOW TO PARTICIPATE
- To enter, contestants must do the following by April 14, 2021 (Note: group submissions are acceptable):
- Submit a SPEED e-poster contributed abstract to the JSM 2021 website (https://ww2.amstat.org/meetings/jsm/2021/index.cfm ). Specify the Statistical Computing Section as the main sponsor. You may include the Government Statistics Section and the Statistical Graphics Section as additional sponsors. Abstract submission starts March 16, 2021.
- Forward the abstract submission email to Wendy Martinez (firstname.lastname@example.org)
- Contestants will then present the results of their analysis (and digital product if applicable) in a SPEED e-poster session at the JSM.
- Note: Presenters are responsible for their own JSM registration and travel costs, and any other costs associated with JSM attendance.
- September 2020: The challenge launches on September 17th, 2020.
- March 2021: Abstract submission period opens on March 16, 2021.
- April 2021: Abstract submission period closes on April 14, 2021. (Deadline to enter the challenge.)
- August 7 - 12, 2021: E-posters/digital products presented at the JSM taking place in Seattle, WA. The data challenge will still take place even if JSM should go virtual.
The entries will be judged based on presentations, results, and discussions at JSM 2021. The following criteria will be used:
- Topic: Is it relevant, important, justified, clear, innovative?
- Presentation: Is it logical, interesting, clear, on-topic?
- Methods: Are the methods used well-designed, accurate, appropriate?
- Visuals: Are any graphics or visuals effective, informative, correct?
- Interpretation of study results and conclusion: Are results well-articulated, appropriate, accurate?
For questions on the ASA Data Challenge Expo please reach out to Wendy Martinez (email@example.com)
For questions regarding The Opportunity Project Data Hub please reach out to firstname.lastname@example.org