Description of previous DataFest Challenges and PSU DataFest finalists and winners:
- 2022 – Yale School of Medicine – Play2Prevent Lab
- Goal: Help researchers who created Elm City Stories to see if their game might be useful in understanding real-life behavior. To do this, they’re asking you to try to characterize, measure, observe, and display patterns of play within the game. One goal for future games is to design them so that the games produce real-time data that is useful to psychology researchers. Your answers will help the Play2Prevent Lab researchers better understand what types of data this might be
- 2021 – Rocky Mountain Poison and Drug Safety Center
- Goal: discovering and identifying patterns of drug use, with particular attention paid to identifying misuse. For example, patterns might describe demographic profiles within a given category of drug, or combinations of drugs frequently used across groups of people, or combinations of drugs that frequently appear together. One goal of these data is to predict future drug misuse cases.
- 2019 – Canadian National Women’s Rugby Team
- Goal: How do we quantify the role of fatigue and workload in a team’s performance in Rugby 7s? How reliable are the subjective wellness Data? Should the quality of the opponent or the outcome of the game be considered when examining fatigue during a game? Can widely used measurements of training load and fatigue be improved? How reliable are GPS data in quantifying fatigue?
- 2018 – Indeed
- Goal: What advice would you give a new high school about what major to choose in college? How does Indeed’s data compare to official government data on the labor market? Can it be used to provide good economic indicators?
- 2017 – Expedia.com
- Goal: How do visitors’ searches relate to the choices of hotels booked or not booked? What role do external factors play in hotel choice?
- Expedia provided DataFesters with data from search results from millions of visitors around the world who were interested in traveling to destinations all over the world. The data were in two files, one of which included data collected on search results from visitors’ sessions, and another which contained detailed information about the destinations that visitors searched for.
- 2016 – TicketMaster
- Goal: How can site visits be converted to ticket sales, and how can TicketMaster identify “true fans” of an artist or band?
- Data consisted of three sets. One included events from the last 12 months that tracked customer travel through the website. Another provided information about advertising campaigns on Google, and the third included data on the events themselves.
- 2015 – Edmunds.com
- Goal: Detect insights into the process of car shopping that can help make the process easier for customers.
- Data consist of visitor ‘pathways’ through a website that helps customers configure car features and shop for cars. Five data files were linked by a customer key, and including data about the customer, about his or her visits to the webpage, and, when applicable, about the car purchased and the dealership where the car was purchased.