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Found 24 result(s)
GroupLens is a research lab in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities specializing in recommender systems, online communities, mobile and ubiquitous technologies, digital libraries, and local geographic information systems.
The National Science Digital Library provides high quality online educational resources for teaching and learning, with current emphasis on the sciences, technology, engineering, and mathematics (STEM) disciplines—both formal and informal, institutional and individual, in local, state, national, and international educational settings. The NSDL collection contains structured descriptive information (metadata) about web-based educational resources held on other sites by their providers. These providers have contribute this metadata to NSDL for organized search and open access to educational resources via this website and its services.
MTSA is a Metropolitan Travel Survey Archive to store, preserve, and make publicly available, via the internet, travel surveys conducted by metropolitan areas, states and localities. As a result of cooperation from several agencies, we now have been able to post databases along with relevant documentation for many regions in the archive . The databases and the documentation can be obtained from this website. In addition to making these databases publicly available, we are also in the process of converting all the databases to a common format to enhance the readability and usability of each survey, so many surveys can be used online, see analyze The results from the first year of the project, along with issues related to archiving travel survey data are provided in our reports page . Papers written by Yacov Zahavi, an instrumental figure in the development of travel surveys, are also provided here.
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.
Statistics Greenland collects, processes, and publicizes statistical material concerning social issues in Greenland. Information is published in English, Greenlandic, and Danish, although not all information has been translated.
SEDAC, the Socioeconomic Data and Applications Center, is one of the Distributed Active Archive Centers (DAACs) in the Earth Observing System Data and Information System (EOSDIS) of the U.S. National Aeronautics and Space Administration. SEDAC is a regular member of the World Data System and focuses on human interactions in the environment. Its mission is to develop and operate applications that support the integration of socioeconomic and Earth science data and to serve as an "Information Gateway" between the Earth and social sciences.
DR-NTU (Data) is the institutional open access research data repository for Nanyang Technological University (NTU). NTU researchers are encouraged to use DR-NTU (Data) to deposit, publish and archive their final research data in order to make their research data discoverable, accessible and reusable.
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.
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.
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.
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.
Data.uel is the new research data repository for UEL. Researchers at UEL can deposit their research data here, and link it to their publications in ROAR. This is still a new service and we are very excited about the possibilities for the future. There may be further changes to data.uel going forwards.
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,, 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.
Academic Commons is a freely accessible digital collection of research and scholarship produced at Columbia University or one of its affiliate institutions (Barnard College, Teachers College, Union Theological Seminary, and Jewish Theological Seminary). The mission of Academic Commons is to collect and preserve the digital outputs of research and scholarship produced at Columbia and its affiliate institutions and present them to a global audience. Academic Commons accepts articles, dissertations, research data, presentations, working papers, videos, and more. increases the ability of the public to easily find, download, and use datasets that are generated and held by the Federal Government. provides descriptions of the Federal datasets (metadata), information about how to access the datasets, and tools that leverage government datasets