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The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the OMOP Common Data Model format. SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and government (although a citation would be appreciated). You can read our first academic paper here: https://doi.org/10.1093/jamia/ocx079

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This bucket contains multiple neuroimaging datasets that are part of the International Neuroimaging Data-Sharing Initiative. Raw human and non-human primate neuroimaging data include 1) Structural MRI; 2) Functional MRI; 3) Diffusion Tensor Imaging; 4) Electroencephalogram (EEG) In addition to the raw data, preprocessed data is also included for some datasets. A complete list of the available datasets can be seen in the documentation lonk provided below.

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COCO is a large-scale object detection, segmentation, and captioning dataset. This is part of the fast.ai datasets collection hosted by AWS for convenience of fast.ai students. If you use this dataset in your research please cite arXiv:1405.0312 [cs.CV].

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"The DARPA Invisible Headlights Dataset is a large-scale multi-sensor dataset annotated for autonomous, off-road navigation in challenging off-road environments. It features simultaneously collected off-road imagery from multispectral, hyperspectral, polarimetric, and broadband sensors spanning wave-lengths from the visible spectrum to long-wave infrared and provides aligned LIDAR data for ground-truth shape. Camera calibrations, LiDAR registrations, and traversability annotations for a subset of the data are available."

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This dataset includes the sequencing and assembly of a reference standard human genome (GM12878) using the MinION nanopore sequencing instrument with the R9.4 1D chemistry.

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The Aurora Multi-Sensor Dataset is an open, large-scale multi-sensor dataset with highly accurate localization ground truth, captured between January 2017 and February 2018 in the metropolitan area of Pittsburgh, PA, USA by Aurora (via Uber ATG) in collaboration with the University of Toronto. The de-identified dataset contains rich metadata, such as weather and semantic segmentation, and spans all four seasons, rain, snow, overcast and sunny days, different times of day, and a variety of traffic conditions. The Aurora Multi-Sensor Dataset contains data from a 64-beam Velodyne HDL-64E LiDAR sensor and seven 1920x1200-pixel resolution cameras including a forward-facing stereo pair and five wide-angle lenses covering a 360-degree view around the vehicle. This data can be used to develop and evaluate large-scale long-term approaches to autonomous vehicle localization. Its size and diversity make it suitable for a wide range of research areas such as 3D reconstruction, vir[...]

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The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The AllWISE Data Release combines data from all cryogenic and post-cryogenic survey phases and provides a comprehensive view of the mid-infrared sky. The Images Atlas includes 18,240 FITS image sets at 3.4, 4.6, 12 and 22 microns. The Source Catalog contains position, apparent motion, and flux information for over 747 million objects detected on the Atlas Images.

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The OpenCell project is a proteome-scale effort to measure the localization and interactions of human proteins using high-throughput genome engineering to endogenously tag thousands of proteins in the human proteome. This dataset consists of the raw confocal fluorescence microscopy images for all tagged cell lines in the OpenCell library. These images can be interpreted both individually, to determine the localization of particular proteins of interest, and in aggregate, by training machine learning models to classify or quantify subcellular localization patterns.

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The Cancer Genome Characterization Initiatives (CGCI) program supports cutting-edge genomics research of adult and pediatric cancers. CGCI investigators develop and apply advanced sequencing methods that examine genomes, exomes, and transcriptomes within various types of tumors. The program includes Burkitt Lymphoma Genome Sequencing Project (BLGSP) project and HIV+ Tumor Molecular Characterization Project - Cervical Cancer (HTMCP-CC) project. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, and miRNA Expression Quantification data. This dataset also contains controlled WGS/Targeted Sequencing/RNA-Seq/miRNA-Seq Aligned Reads, and RNA-Seq Splice Junction Quantification

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OpenNeuro is a database of openly-available brain imaging data. The data are shared according to a Creative Commons CC0 license, providing a broad range of brain imaging data to researchers and citizen scientists alike. The database primarily focuses on functional magnetic resonance imaging (fMRI) data, but also includes other imaging modalities including structural and diffusion MRI, electroencephalography (EEG), and magnetoencephalograpy (MEG). OpenfMRI is a project of the Center for Reproducible Neuroscience at Stanford University. Development of the OpenNeuro resource has been funded by the National Science Foundation, National Institute of Mental Health, National Institute on Drug Abuse, and the Laura and John Arnold Foundation.