{"id":13,"date":"2022-08-18T23:13:37","date_gmt":"2022-08-18T23:13:37","guid":{"rendered":"https:\/\/libraries.mit.edu\/opendata\/?page_id=13"},"modified":"2026-10-08T15:25:40","modified_gmt":"2026-10-08T15:25:40","slug":"mit-prize","status":"publish","type":"page","link":"https:\/\/libraries.mit.edu\/opendata\/open-data-mit-home\/mit-prize\/","title":{"rendered":"MIT Prize for Open Data"},"content":{"rendered":"

To highlight the value of open data at MIT, and to encourage the next generation of researchers, the MIT School of Science and the MIT Libraries present the MIT Prize for Open Data.<\/span><\/p>\n

Congratulations to the winners of the 2026 MIT Prize for Open Data! We hope you’ll join us for Open Data @ MIT<\/a> on Tuesday, October 20, to celebrate this year’s honorees.<\/strong><\/p>\n

<\/h1>\n

 <\/p>\n

2026 Winners<\/h1>\n

Kaveh Alim<\/b>, graduate student, Institute for Data, Systems, and Society (IDSS); Hao Wang<\/strong>, senior research scientist, MIT-IBM Watson AI Lab; Ojas Gulati,<\/strong> undergraduate student, computer science and mechanical engineering; Akash Srivastava<\/strong>, principal research scientist, MIT-IBM Watson AI Lab; Navid Azizan<\/strong>, associate professor, mechanical engineering and IDSS
\nDifferentially Private Synthetic Data Generation
\n<\/b>This groundbreaking method addresses a fundamental barrier to open science: sharing the scientific value of sensitive, interconnected data while rigorously protecting individual privacy. By providing publicly available, reproducible tools, backed by foundational theoretical advances, this work has the potential to unlock new open-data resources across disciplines.
\nPaper<\/a> | Github<\/a><\/p>\n

 <\/p>\n

Matteo Di Bernardo<\/b>, graduate student, Computational and Systems Biology; Roshan Kern,<\/strong> graduate student, Biological Engineering
\nBrieflow
\n<\/b>The inability to process and openly share large-scale imaging data has become a substantial limitation for a growing number of research efforts. To address this, the team created Brieflow, an open-source pipeline that turns raw images from large-scale CRISPR screens into standardized per-cell data and browsable maps of gene function, built from modular components that any lab can adapt to its own microscopes and assays.
\nPaper<\/a> | Github<\/a><\/p>\n

 <\/p>\n

Kexin Dong<\/b>, graduate student, Biology; Samuel I. Gould<\/b>, postdoctoral associate, Whitehead Institute; Francisco J. S\u00e1nchez Rivera<\/b>, Eisen and Chang Career Development Professor, biology, MIT
\nComputational prediction of human genetic variants in the mouse genome
\n<\/b>H2M<\/a> (human-to-mouse) is an open-source computational pipeline that automates the prediction of human variants in the mouse genome and generated a first-of-this-kind open dataset of a dictionary of 3,171,709 cross-species variant mappings.
\nPaper<\/a> | Github<\/a><\/p>\n

 <\/p>\n

\u00c5se H\u00e5tveit, <\/b>research fellow, MIT Senseable City Lab; Orlando Closs<\/strong>, former research fellow, MIT Senseable City Lab; Titus Venverloo<\/strong>, lab lead, MIT Senseable City Amsterdam
\nSensing Garden<\/b><\/a>
\nAddressing critical baseline data scarcity in global insect ecology, Flik is an open-source, low-cost monitoring system that leverages open Global Biodiversity Information Facility (GBIF) data via the bplusplus tool to automatically curate and train hierarchical deep-learning models. By combining real-time edge computer vision with synchronous microclimate and air quality sensing, Flik delivers biological and abiotic datasets required to untangle the drivers of urban insect decline.
\nGithub<\/a><\/p>\n

 <\/p>\n

Lennart Justen, <\/b>graduate student, Media Arts and Sciences, on behalf of the CASPER consortium
\nDeep untargeted wastewater metagenomic sequencing from sewersheds across the United States
\n<\/b>This project publishes data from an active U.S. network using deep, untargeted wastewater metagenomic sequencing to bolster national and global pandemic early warning capabilities while opening a broad new window into the microbial ecology of our cities. To date the network has deposited roughly 665 trillion nucleotides from over 2,200 samples across more than 30 sites on the NCBI Sequence Read Archive for unrestricted use, making it the largest publicly available wastewater sequencing dataset in the world by nearly fourteenfold.
\nPreprint<\/a> | Dataset<\/a><\/p>\n

 <\/p>\n

Eric Anton Moreno<\/b>, graduate student, Laboratory for Nuclear Science
\nCOLLIDE-2V: an open, dual-view dataset of one billion simulated LHC collisions for machine learning
\n<\/b>A 50TB public dataset of roughly one billion simulated High-Luminosity LHC collision events, provided both as a full detector reconstruction and as the low-resolution view, which is what is available to the real-time trigger, giving collider physics its first shared, general-purpose training corpus for machine learning.
\nDataset<\/a><\/p>\n

 <\/p>\n

Ghadi Nehme<\/b>, graduate student, Mechanical Engineering; Brandon Man<\/b>, SM \u201825; Md Ferdous Alam<\/b>, assistant professor, Georgia Institute of Technology, former postdoctoral associate, MIT; and Faez Ahmed<\/b>, associate professor of Mechanical Engineering, MIT
\nVideoCAD: A Dataset and Model for Learning Long-Horizon 3D CAD UI Interactions from Video
\n<\/b> VideoCAD<\/a> is an open dataset of more than 41,000 annotated CAD interaction videos that enables AI systems to learn how to operate professional design software and reason about 3D geometry. Downloaded more than 27,000 times from Harvard Dataverse, it provides a widely used open foundation for research in AI-driven engineering and software-operating agents.
\nArticle<\/a> | Github<\/a><\/p>\n

 <\/p>\n

Ben Workman, <\/b>graduate student, Economics
\nInside the Black Box: Using Machine Learning to Predict and Understand Effective Teaching
\n<\/b>This project reanalyzes an existing dataset of elementary school math lessons, linked to information about student achievement, to predict and better understand teachers’ effects on student learning.
\nPaper<\/a><\/p>\n

 <\/p>\n

Honorable Mentions<\/h1>\n

Madeline Loui Anderson<\/strong>
\nSkyScraper<\/a> (paper<\/a> | dataset<\/a>)<\/p>\n

Elijah Appelson<\/strong>
\nTracking 287(g)<\/a><\/p>\n

Fabio Duarte and Stefania Dimitrov<\/strong>
\nData Clouds<\/a> (paper<\/a> | dataset A<\/a>, dataset B<\/a>)<\/p>\n

Jovana Kondic<\/strong>
\nChartNet: A Million-Scale Open Multimodal Dataset for Robust Chart Understanding<\/a> (paper<\/a> | dataset<\/a>)<\/p>\n

Rupa Kurinchi-Vendhan and Julia Chae<\/strong>
\nINQUIRE-Search: Interactive Discovery in Large-Scale Biodiversity Databases<\/a> (paper<\/a> | code<\/a>)<\/p>\n

Sean Hardesty Lewis<\/strong>
\nSearchable.City: An Open-Vocabulary Semantic Atlas<\/a> (paper<\/a> | Github<\/a>)<\/p>\n

Diane Tchuindjo<\/strong>
\nOBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries (paper<\/a> | dataset<\/a>)<\/p>\n

Nils Wolff<\/strong>
\nPedestrian Trajectory Dataset of Public European Squares (paper<\/a> | Github<\/a> | dataset<\/a>)<\/p>\n

 <\/p>\n

2026 Committee<\/b>
\n<\/b><\/h1>\n

Committee Co-Chairs<\/b><\/p>\n

    \n
  • Chris Bourg, Director, MIT Libraries<\/li>\n
  • Rebecca Saxe, Associate Dean of Science, School of Science<\/li>\n<\/ul>\n

    Committee Members<\/b><\/p>\n

      \n
    • Paul Berube, Research Scientist, Civil and Environmental Engineering<\/li>\n
    • Timur Cinay, PhD candidate, Department of Earth, Atmospheric and Planetary Sciences.<\/li>\n
    • Steve Flavell, Associate Professor, Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences<\/li>\n
    • Satrajit Ghosh, Director of the Open Data in Neuroscience Initiative, McGovern Institute, and Director of Data Models and Integration, ReproNim<\/li>\n
    • Rafael Jaramillo, Thomas Lord Career Development Professor, Associate Professor of Materials Science and Engineering<\/li>\n
    • Stuart Levine, BioMicro Center Director<\/li>\n
    • Peace Ossom, Director of Research Data Services, MIT Libraries<\/li>\n
    • Tom Pollard, research scientist, Laboratory for Computational Physiology<\/li>\n
    • Justin Reich, Associate Professor, Comparative Media Studies\/Writing, and Director, Teaching Systems Lab<\/li>\n
    • Sadie Roosa, Collections Strategist for Repository Services, MIT Libraries<\/li>\n
    • Virginia Spanoudaki, Scientific Director, Preclinical Imaging and Testing, Koch Institute<\/li>\n
    • Nada Tarkhan, research collaborator, Sustainable Design Lab<\/li>\n<\/ul>\n

      Co-sponsored by the MIT School of Science and MIT Libraries<\/em><\/p>\n

       <\/p>\n

      \"MIT\u00a0 \u00a0 \"MIT<\/p>\n","protected":false},"excerpt":{"rendered":"

      To highlight the value of open data at MIT, and to encourage the next generation of researchers, the MIT School of Science and the MIT Libraries present the MIT Prize for Open Data. Congratulations to the winners of the 2026 MIT Prize for Open Data! We hope you’ll join us for Open Data @ MIT on Tuesday, October 20, to celebrate this year’s honorees.   2026 Winners Kaveh Alim, graduate student, Institute for Data, Systems, and Society (IDSS); Hao Wang, senior research scientist, MIT-IBM Watson AI Lab; Ojas Gulati, undergraduate student, computer science and mechanical engineering; Akash Srivastava, principal research […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":4,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"templates\/page.php","meta":{"footnotes":""},"class_list":["post-13","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/libraries.mit.edu\/opendata\/wp-json\/wp\/v2\/pages\/13","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/libraries.mit.edu\/opendata\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/libraries.mit.edu\/opendata\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/libraries.mit.edu\/opendata\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/libraries.mit.edu\/opendata\/wp-json\/wp\/v2\/comments?post=13"}],"version-history":[{"count":146,"href":"https:\/\/libraries.mit.edu\/opendata\/wp-json\/wp\/v2\/pages\/13\/revisions"}],"predecessor-version":[{"id":354,"href":"https:\/\/libraries.mit.edu\/opendata\/wp-json\/wp\/v2\/pages\/13\/revisions\/354"}],"up":[{"embeddable":true,"href":"https:\/\/libraries.mit.edu\/opendata\/wp-json\/wp\/v2\/pages\/4"}],"wp:attachment":[{"href":"https:\/\/libraries.mit.edu\/opendata\/wp-json\/wp\/v2\/media?parent=13"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}