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Data available in the project's website. Data is also available here. [367] Zampieri et al. Cyber reports from the National Cyber Security Centre This data is not pre-processed. Threat reports, reports and advisory, news, blog-posts, speeches. Alternate list of reports. [368] APT reports by Kaspersky This data is not pre-processed. [369] The ...
Overhead Imagery Research Data Set: Annotated overhead imagery. Images with multiple objects. Over 30 annotations and over 60 statistics that describe the target within the context of the image. 1000 Images, text Classification 2009 [166] [167] F. Tanner et al. SpaceNet SpaceNet is a corpus of commercial satellite imagery and labeled training data.
Download as PDF; Printable version; ... Social statistics data (3 C, 30 P) Sports records and statistics ... Category: Statistical data sets.
SOFA Statistics is an open-source statistical package. The name stands for S tatistics O pen F or A ll. It has a graphical user interface and can connect directly to MySQL , PostgreSQL , SQLite , MS Access (map), and Microsoft SQL Server .
Dumps from any Wikimedia Foundation project: dumps.wikimedia.org and the Internet Archive; English Wikipedia dumps in SQL and XML: dumps.wikimedia.org /enwiki / and the Internet Archive. Download the data dump using a BitTorrent client (torrenting has many benefits and reduces server load, saving bandwidth costs).
Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.
The four datasets composing Anscombe's quartet. All four sets have identical statistical parameters, but the graphs show them to be considerably different. Anscombe's quartet comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]