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Large Twitter network data. Node features, circles, and ego networks. 1,768,149 Text Clustering, graph analysis 2012 [51] [52] J. McAuley et al. Twitter Dataset for Arabic Sentiment Analysis Arabic tweets. Samples hand-labeled as positive or negative. 2000 Text Classification 2014 [53] [54] N. Abdulla Buzz in Social Media Dataset
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.
Social statistics data (3 C, 30 P) ... Statistical databases (1 C, 4 P) Surveys (human research) (4 C, 55 P) Pages in category "Statistical data sets"
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The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files. [2] In the open data discipline, data set is the unit to measure the information released in a public open data repository. The European data ...
The availability of non-open scientific data decays rapidly: in 2014 a retrospective study of biological datasets showed that "the odds of a data set being reported as extant fell by 17% per year" [122] Consequently, the "proportion of data sets that still existed dropped from 100% in 2011 to 33% in 1991". [65]
Descriptive research; Descriptive statistics; Design effect; Design matrix; Design of experiments. The Design of Experiments (book by Fisher) Detailed balance; Detection theory; Determining the number of clusters in a data set; Detrended correspondence analysis; Detrended fluctuation analysis; Deviance (statistics) Deviance information ...
The Overhead Imagery Research Data Set (OIRDS) is a collection of an open-source, annotated, overhead images that computer vision researchers can use to aid in the development of algorithms. [1] Most computer vision and machine learning algorithms function by training on a large set of example data. [ 2 ]