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The NIH Common Fund's Gabriella Miller Kids First Pediatric Research Program’s (“Kids First”) vision is to “alleviate suffering from childhood cancer and structural birth defects by fostering collaborative research to uncover the etiology of these diseases and by supporting data sharing within the pediatric research community.” The program continues to generate and share whole genome sequence data from thousands of children affected by these conditions, ranging from rare pediatric cancers, such as osteosarcoma, to more prevalent diagnoses, such as congenital heart defects. In 2018, Kids First launched the Gabriella Miller Kids First Data Resource Center, charged with building a large-scale data platform supporting clinical and genetic data from these patients and their families in order to accelerate discovery and ultimately clinical impact. Researchers can search, access, aggregate, and analyze these data through the Kids First Data Resource Portal. Additionally, by using cloud-b[...]

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RarePlanes is a unique open-source machine learning dataset from CosmiQ Works and AI.Reverie that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset specifically focuses on the value of AI.Reverie synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very high resolution dataset built to test the value of synthetic data from an overhead perspective. The real portion of the dataset consists of 253 Maxar WorldView-3 satellite scenes spanning 112 locations and 2,142 km^2 with 14,700 hand-annotated aircraft. The accompanying synthetic dataset is generated via AI.Reverie’s novel simulation platform and features 50,000 synthetic satellite images with ~630,000 aircraft annotations.

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These data correspond to the International LOFAR Telescope observations of the sky field ELAIS-N1 (16:10:01 +54:30:36) during the cycle 2 of observations. There are 11 runs of about 8 hours each plus the corresponding observation of the calibration targets before and after the target field. The data are measurement sets (MS) containing the cross-correlated data and metadata divided in 371 frequency sub-bands per target centred at ~150 MHz.

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Height Above Nearest Drainage (HAND) is a terrain model that normalizes topography to the relative heights along the drainage network and is used to describe the relative soil gravitational potentials or the local drainage potentials. Each pixel value represents the vertical distance to the nearest drainage. The HAND data provides near-worldwide land coverage at 30 meters and was produced from the 2021 release of the Copernicus GLO-30 Public DEM as distributed in the Registry of Open Data on AWS.

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The Harvard EEG Database will encompass data gathered from four hospitals affiliated with Harvard University:Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), Beth Israel Deaconess Medical Center (BIDMC), and Boston Children's Hospital (BCH).

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The Allen Institute for Neural Dynamics (AIND) is committed to FAIR, Open, and Reproducible science. We therefore share all of the raw and derived data we collect publicly with rich metadata, including preliminary data collected during methods development, as near to the time of collection as possible.

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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."