Excellence in Research Award
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 hosted a second phase of the WiDS Datathon with an Excellence in Research Award. We invited participants to share more details about their work with WiDS Datathon 2021 data through a research paper of up to 3 pages.
Congratulations to the WiDS Datathon Excellence in Research Award Winners!
Maya Tadmor-Saghiv and Pavel Vodolazov, team ScientificMind, from Israel
Research Paper: Bridge Over Troubled Data – Practices for Handling Missing Data in Intensive Care Unit Predictive Modeling
Congratulations to the WiDS Datathon Excellence in Research Award Winners!
Maya Tadmor-Saghiv and Pavel Vodolazov, team ScientificMind, from Israel
Research Paper: Bridge Over Troubled Data – Practices for Handling Missing Data in Intensive Care Unit Predictive Modeling
Maya Tadmor-Saghiv and Pavel Vodolazov
Excellence in Research Award Details
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Submissions were reviewed by subject matter experts from the WiDS Datathon Committee, the National Science Foundation Big Data Innovation Hubs, and the MIT GOSSIS community. Papers were evaluated on their potential for real-world impact, rigor in scientific methodology, and clarity of communication.
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Papers address questions including but not limited to:
Submissions also include an abstract (4-5 sentence summary) and a list of references (e.g., cited research papers, books, tutorials, or other resources used).
- 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 also include an abstract (4-5 sentence summary) and a list of references (e.g., cited research papers, books, tutorials, or other resources used).
Prizes
For the WiDS Datathon 2021, the winning individual or team will receive:
In addition to the Excellence in Research Award, up to $2,500 USD will be distributed for honorable mentions across submitted research papers. Phase 2 winners will be recognized via WiDS and the Big Data Innovation Hubs' social media channels. Special thanks to Kaggle for providing the cash prizes for the winners.
- $3,000 USD cash prize for the team
- 1 complimentary VIP registration per team member for WiDS 2022
- A WiDS Datathon Excellence in Research Award Certificate for each team member
- An invitation to be featured in a #WiDStory article
In addition to the Excellence in Research Award, up to $2,500 USD will be distributed for honorable mentions across submitted research papers. Phase 2 winners will be recognized via WiDS and the Big Data Innovation Hubs' social media channels. Special thanks to Kaggle for providing the cash prizes for the winners.
Eligibility
To be eligible for the award, all entrants must have participated in the first phase of the WiDS Datathon 2021 on Kaggle. Individuals and teams should submit their papers with the same names and contact emails used for WiDS Datathon 2021 registration. The Submittable website opened on March 9th, and closed on April 30, 2021.
Questions?
Refer to the WiDS Datathon FAQ on Kaggle. If you still have questions, please send an email to widsdatathon@stanford.edu.
The WiDS Datathon Excellence in Research Award 2021 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 WiDS Datathon Committee. 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.