Repositories of datasets

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Trust network datasets
( Datasets categoryRepositories of datasetsNetwork file formats • .. )

This is an overview of repositories of datasets. The page "Trust network datasets" contains a list of released social network datasets.

TODO: analyze the single datasets contained in the following repositories and add them to the list of datasets.
  • (visualizations at ) University of Florida Sparse Matrix Collection: Maintained by Tim Davis. The University of Florida Sparse Matrix Collection is a large, widely available, and actively growing set of sparse matrices that arise in real applications. Its matrices cover a wide spectrum of problem domains, both those arising from problems with underlying 2D or 3D geometry: structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations; and those that typically do not have such geometry: optimization, circuit simulation, networks and graphs (including web connectivity matrices), economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, and power networks. The collection meets a vital need that artificially-generated matrices cannot meet, and is widely used by the sparse matrix algorithms community for the development and performance evaluation of sparse matrix algorithms. The collection includes software for accessing and managing the collection, from MATLAB, Fortran, and C. As of May 2008, it contains 1890 problems (some of which are sequences of dozens of matrices). The smallest is 5-by-5 with 19 nonzero entries. The largest has dimension 9.8 million, and the matrix with the most nonzeros has 99.2 million entries. The matrices are available in three formats: MATLAB mat-file, Rutherford-Boeing, and Matrix Market. The size of the collection in each format is about 9 GB. Note that the MATLAB mat-files can only be read by MATLAB 7.0 or later.
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