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Found 7 result(s)
Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library. It is written in C++ and easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. SNAP is also available through the NodeXL which is a graphical front-end that integrates network analysis into Microsoft Office and Excel. The SNAP library is being actively developed since 2004 and is organically growing as a result of our research pursuits in analysis of large social and information networks. Largest network we analyzed so far using the library was the Microsoft Instant Messenger network from 2006 with 240 million nodes and 1.3 billion edges. The datasets available on the website were mostly collected (scraped) for the purposes of our research. The website was launched in July 2009.
KONECT (the Koblenz Network Collection) is a project to collect large network datasets of all types in order to perform research in network science and related fields, collected by the Institute of Web Science and Technologies at the University of Koblenz–Landau. KONECT contains over a hundred network datasets of various types, including directed, undirected, bipartite, weighted, unweighted, signed and rating networks. The networks of KONECT are collected from many diverse areas such as social networks, hyperlink networks, authorship networks, physical networks, interaction networks and communication networks. The KONECT project has developed network analysis tools which are used to compute network statistics, to draw plots and to implement various link prediction algorithms. The result of these analyses are presented on these pages. Whenever we are allowed to do so, we provide a download of the networks.
The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. It is used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times.
Cell phones have become an important platform for the understanding of social dynamics and influence, because of their pervasiveness, sensing capabilities, and computational power. Many applications have emerged in recent years in mobile health, mobile banking, location based services, media democracy, and social movements. With these new capabilities, we can potentially be able to identify exact points and times of infection for diseases, determine who most influences us to gain weight or become healthier, know exactly how information flows among employees and productivity emerges in our work spaces, and understand how rumors spread. In an attempt to address these challenges, we release several mobile data sets here in "Reality Commons" that contain the dynamics of several communities of about 100 people each. We invite researchers to propose and submit their own applications of the data to demonstrate the scientific and business values of these data sets, suggest how to meaningfully extend these experiments to larger populations, and develop the math that fits agent-based models or systems dynamics models to larger populations. These data sets were collected with tools developed in the MIT Human Dynamics Lab and are now available as open source projects or at cost.
Social Computing Data Repository hosts data from a collection of many different social media sites, most of which have blogging capacity. Some of the prominent social media sites included in this repository are BlogCatalog, Twitter, MyBlogLog, Digg, StumbleUpon, del.icio.us, MySpace, LiveJournal, The Unofficial Apple Weblog (TUAW), Reddit, etc. The repository contains various facets of blog data including blog site metadata like, user defined tags, predefined categories, blog site description; blog post level metadata like, user defined tags, date and time of posting; blog posts; blog post mood (which is defined as the blogger's emotions when (s)he wrote the blog post); blogger name; blog post comments; and blogger social network.
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The Informatics Research Data Repository is a Japanese data repository that collects data on disciplines within informatics. Such sub-categories are things like consumerism and information diffusion. The primary data within these data sets is from experiments run by IDR on how one group is linked to another.