Announcing the 2024 DataWorks! Prize Winners

Friday, May 1, 2026

Diagram comparing two patient pathways (female and male) from AKI to CKD progression and death, informed by MIMIC EHR data including demographics, comorbidities, medications/procedures, labs, vitals, and fluids.

In early 2024, ODSS partnered with the Federation of American Societies for Experimental Biology (FASEB) to launch the second annual DataWorks! Prize to highlight examples of innovative data sharing and reuse.

This year, 82 teams registered for the challenge to demonstrate their accomplishments. The 311 team members came from a wide variety of disciplines, including biochemistry, clinical research, genomics, immunology, molecular biology, neuroscience, and more.

Representatives from the grand prize-winning team will have the opportunity to share at an NIH forum.

Grand Prize $100,000

Diagram comparing two patient pathways (female and male) from AKI to CKD progression and death, informed by MIMIC EHR data including demographics, comorbidities, medications/procedures, labs, vitals, and fluids.

Divergent Equilibria

Sex Differences in Acute Kidney Injury Risk 

Divergent Equilibria investigates sex differences in Acute Kidney Injury and its progression to Chronic Kidney Disease.

Distinguished Achievement Awards $75,000

Diagram of a machine learning workflow: data extraction and processing, meta-learning across tasks to build a meta-learner, and transfer learning to fine-tune a model on a small new dataset for prediction.

EarlyByrd Medical 

MEGATRON Meta-Learning for Technology Acceleration 

EarlyByrd is a medical device startup that is seeking to design, implement and evaluat a meta-learning model trained on medical imaging data and tested on limited Electrical Impedance Tomography (EIT) data to validate the deployment on EIT diagnostic tools.

Composite brain imaging workflow showing MRI scans and a stroke case with patient details (age, sex, diagnosis, medical history), followed by extraction of brain regions for functional prediction and biomarker identification, and a feature analysis plot with SHAP values.

Andreia Vaconcellos Faria's team

Functional Stroke Prediction Through Open Science

Andreia Vaconcellos Faria’s team aims to develop models to predict functional domains that can be used in assessing the clinical impact of acute ischemic strokes.

This page last reviewed on May 1, 2026