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Found 22 result(s)
SUNScholarData is an institutional research data repository which can be used for the registration, archival storage, sharing and dissemination of research data produced or collected in relation to research conducted under the auspices of Stellenbosch University. The repository has a public interface which can be used for finding content. It also has private user accounts which can be used by Stellenbosch University users in order to upload, share or publish their research data. In addition to this Stellenbosch University researchers can also use SUNScholarData in order to collaborate with researchers from other institutions whilst working on their research projects. The repository creates a medium through which Stellenbosch University’s research data can be made findable and accessible. It also facilitates the interoperability and re-usability of the university’s research data.
The University of Cape Town (UCT) uses Figshare for institutions for their data repository, which was launched in 2017 and is called ZivaHub: Open Data UCT. ZivaHub serves principal investigators at the University of Cape Town who are in need of a repository to store and openly disseminate the data that support their published research findings. The repository service is provided in terms of the UCT Research Data Management Policy. It provides open access to supplementary research data files and links to their respective scholarly publications (e.g. theses, dissertations, papers et al) hosted on other platforms, such as OpenUCT.
The National Sleep Research Resource (NSRR) is an NHLBI-supported repository for sharing large amounts of sleep data (polysomnography, actigraphy and questionnaire-based) from multiple cohorts, clinical trials, and other data sources. Launched in April 2014, the mission of the NSRR is to advance sleep and circadian science by supporting secondary data analysis, algorithmic development, and signal processing through the sharing of high-quality data sets.
Brainlife promotes engagement and education in reproducible neuroscience. We do this by providing an online platform where users can publish code (Apps), Data, and make it "alive" by integragrate various HPC and cloud computing resources to run those Apps. Brainlife also provide mechanisms to publish all research assets associated with a scientific project (data and analyses) embedded in a cloud computing environment and referenced by a single digital-object-identifier (DOI). The platform is unique because of its focus on supporting scientific reproducibility beyond open code and open data, by providing fundamental smart mechanisms for what we refer to as “Open Services.”
The Comparative Agendas Project (CAP) assembles and codes information on the policy processes of governments from around the world. CAP enables scholars, students, policy-makers and the media to investigate trends in policy-making across time and between countries. It classifies policy activities into a single, universal and consistent coding scheme.
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It is a statistical system developed for collection, computerization, analysis and use of educational and allied data for planning, management, monitoring and feedback. So, DISE is an initiative of the Department of Educational Management Information System (EMIS) of NUEPA for developing and strengthening the educational management information system in India. The initiative is coordinated from district level to state and extended up to national level are being constantly collected and disseminated. It provides information on vital parameters relating to students, teachers and infrastructure at all levels of education in India. Presently DISE has three modules U-DISE, DISE, and SEMIS. DISE also provides several other derivative statistical products, such as, District Report Cards, State Report Cards, School Report Cards, Flash Statistics, Analytical Reports, Rural/Urban Statistics, etc.
Knoema is a knowledge platform. The basic idea is to connect data with analytical and presentation tools. As a result, we end with one uniformed platform for users to access, present and share data-driven content. Within Knoema, we capture most aspects of a typical data use cycle: accessing data from multiple sources, bringing relevant indicators into a common space, visualizing figures, applying analytical functions, creating a set of dashboards, and presenting the outcome.
OpenML is an open ecosystem for machine learning. By organizing all resources and results online, research becomes more efficient, useful and fun. OpenML is a platform to share detailed experimental results with the community at large and organize them for future reuse. Moreover, it will be directly integrated in today’s most popular data mining tools (for now: R, KNIME, RapidMiner and WEKA). Such an easy and free exchange of experiments has tremendous potential to speed up machine learning research, to engender larger, more detailed studies and to offer accurate advice to practitioners. Finally, it will also be a valuable resource for education in machine learning and data mining.
The United States Census Bureau (officially the Bureau of the Census, as defined in Title 13 U.S.C. § 11) is the government agency that is responsible for the United States Census. It also gathers other national demographic and economic data. As a part of the United States Department of Commerce, the Census Bureau serves as a leading source of data about America's people and economy. The most visible role of the Census Bureau is to perform the official decennial (every 10 years) count of people living in the U.S. The most important result is the reallocation of the number of seats each state is allowed in the House of Representatives, but the results also affect a range of government programs received by each state. The agency director is a political appointee selected by the President of the United States.
Data.gov increases the ability of the public to easily find, download, and use datasets that are generated and held by the Federal Government. Data.gov provides descriptions of the Federal datasets (metadata), information about how to access the datasets, and tools that leverage government datasets
This is the KONECT project, a project in the area of network science with the goal to collect network datasets, analyse them, and make available all analyses online. KONECT stands for Koblenz Network Collection, as the project has roots at the University of Koblenz–Landau in Germany. All source code is made available as Free Software, and includes a network analysis toolbox for GNU Octave, a network extraction library, as well as code to generate these web pages, including all statistics and plots. 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.