Allen Institute for Brain Science - Synaptic Physiology Public Data Set

The Allen Mouse Brain Atlas is a genome-scale collection of cellular resolution gene expression profiles using in situ hybridization (ISH). Highly methodical data production methods and comprehensive anatomical coverage via dense, uniformly spaced sampling facilitate data consistency and comparability across >20,000 genes. The use of an inbred mouse strain with minimal animal-to-animal variance allows one to treat the brain essentially as a complex but highly reproducible three-dimensional tissue array. The entire Allen Mouse Brain Atlas dataset and associated tools are available through an unrestricted web-based viewing application (http://mouse.brain-map.org). The collection of > 650,000 images have been made available in this Open Data bucket to enable efficient access and analysis of the this dataset.

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This bucket contains multiple datasets (as Quilt packages) created by the Allen Institute for Cell Science. The types of data included in this bucket are listed below: 1) Field of view or cropped images of cells 2) Segmentations of structures in the images (e.g., boundaries of cells, DNA, other intracellular structures, etc.) 3) Processed versions of the above images and segmentations 4) Machine learning predictions and labels of the data listed above 5) Models trained on the previously listed data 6) Additional supporting non-image data related to the above listed data types (e.g., gene expression data, whole genome sequencing data, features derived from the images or model predictions, metadata) 7) Simulation, analysis, and visualization data of in silico cell structures, cells, and cell populations External funding: The generation of some datasets was supported by the National Human Genome Research Institute of the National Institutes under Award Number UM1HG011593. The cont[...]

Extracellular electrophysiology data is growing at a remarkable pace. This data, collected neuropixels probes by the Allen Institute and the International Brain Lab can be used to benchmark throughput rates and storage ratios of various data compression algorithms.

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.