The WiDS Datathon 2020 included 951 teams analyzing 130,000 hospital patient health records.
To help encourage deeper exploration and the development of collaborative data science innovations, the WiDS Datathon Committee and the National Science Foundation Big Data Innovation Hubs are hosting a second phase of the WiDS Datathon this year—with a new WiDS Datathon Excellence in Research Award.
As you work with the WiDS Datathon 2020 data, are you…
considering how your predictive models perform across gender, ethnicity, and age?
diving into the implications of missing values and the “messiness” of real-world Intensive Care Unit data?
wondering how you can contribute to the broader health data community beyond the WiDS Datathon 2020 leaderboard?
We invite you to share more details about your work with WiDS Datathon 2020 data through a one-page research paper!
In light of COVID-19, participants may continue to submit research papers through April 30, 2020.
These research paper templates are provided for convenience: Word format and PDF. Many other examples are available, e.g. https://www.overleaf.com/latex/templates/tagged/academic-journal.
Submissions will be reviewed by subject matter experts from the WiDS Datathon Committee, the National Science Foundation Big Data Innovation Hubs, and the MIT GOSSIS community. Papers will be evaluated on their potential for real-world impact, rigor in scientific methodology, and clarity of communication.
We request submission content in English to facilitate the review process, but welcome additional translations and encourage participants to share their papers broadly. Papers will also be eligible to be published on the WiDS Datathon website.
Papers should address questions including but not limited to:
- What assumptions did you make in preparing your models and analyzing the data?
- What are the potential implications of your assumptions?
- What are your main insights and contributions?
- What are some of your ‘lessons learned’ and meaningful or surprising results?
- What unanswered questions do you have about the data, your analysis, and the analysis from the research community?
- How might you and others address these unanswered questions and/or advance related research?
- Are there open problems in mathematics, statistics, computer science, applied medical research or related fields that could benefit from deeper exploration of this data set?
Submissions should include an abstract (4-5 sentence summary) and a list of references (e.g., cited research papers, books, tutorials, or other resources used).
How to Participate
- Register for the WiDS Datathon
- Access the data on Kaggle, accepting the terms for the first phase of the datathon by February 21. Note that the Kaggle leaderboard closes February 24.
- Submit your paper by March 31.
The individual or team will receive:
- Up to 4 VIP tickets (1 per team member) for WiDS 2021 or complimentary registrations for the Stanford ICME Summer Data Science workshops held Aug 17-22, 2020
- Up to $2000 USD per team in domestic airfare for the team to travel to WiDS 2021, the Summer Data Science workshops, or an equivalent professional development opportunity
- Special invitation to participate in the MIT GOSSIS Datathon in Rio de Janeiro, Brazil, June 2020
- A WiDS Datathon Excellence in Research Award Certificate
To be eligible for the award, entrants must participate in the first phase of the WiDS Datathon 2020 on Kaggle. Individuals and teams should submit their papers with the same names and contact emails used for WiDS Datathon 2020 registration.
The award will be announced in May 2020 online and at the first-ever All Hubs Summit with the four Regional Big Data Innovation Hubs and the National Science Foundation.
The WiDS Datathon Excellence in Research Award 2020 competition is hosted by the National Science Foundation West Big Data Innovation Hub, Midwest Big Data Innovation Hub, Northeast Big Data Innovation Hub, and South Big Data Innovation Hub, in collaboration with the Women in Data Science (WiDS) Datathon. This material is based upon work supported by the National Science Foundation under Grants 1916573, 1916481, 1915774, 1916613, 1916585, 1916589, and 1916454 as part of a national network of Regional Big Data Innovation Hubs.