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Covertype Dataset Data for predicting forest cover type strictly from cartographic variables. Many geographical features given. 581,012 Text Classification 1998 [310] [311] J. Blackard et al. Abscisic Acid Signaling Network Dataset Data for a plant signaling network. Goal is to determine set of rules that governs the network. None. 300 Text
The dataset is labeled with semantic labels for 32 semantic classes. over 700 images Images Object recognition and classification 2008 [56] [57] [58] Gabriel J. Brostow, Jamie Shotton, Julien Fauqueur, Roberto Cipolla RailSem19 RailSem19 is a dataset for understanding scenes for vision systems on railways. The dataset is labeled semanticly and ...
The datasets are tagged and can be easily filtered. A number of download formats, including GIS files, are supported: CSV, XLS, XLSX, ArcGIS, Esri, GeoJSON, KML, and SHP. Some datasets are also offered as HTML. Again, as of January 2017, four apps are available. Some are web-based and run from a browser.
Before starting a download of a large file, check the storage device to ensure its file system can support files of such a large size, check the amount of free space to ensure that it can hold the downloaded file, and make sure the device(s) you'll use the storage with are able to read your chosen file system.
Magnetoencephalography, structural MRI datasets, and demographics Human Macroscopic MEG, T1 MRI datasets, demographic data Healthy, ADHD, Traumatic brain injury: Yes [40] The PAIN Repository Structural, Diffusion and Functional MRI datasets Human Human Macroscopic MRI datasets and Metadata Healthy and Pain Conditions Yes [41] Pig Brain Atlas
The USGS Gap Analysis Program maintains four primary data sets: land cover, protected areas, species and aquatic. The GAP Land Cover Data Set is the most complete map ever produced of vegetative associations for the US. Classified into 551 ecological systems, and 32 modified ecological systems (where human impacts have had an effect).
Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2). They were released on two CD-ROMs. They were released on two CD-ROMs. SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers.
The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. [1] [2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. [3]