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Found 24 result(s)
Network Repository is the first interactive data repository for graph and network data. It hosts graph and network datasets, containing hundreds of real-world networks and benchmark datasets. Unlike other data repositories, Network Repository provides interactive analysis and visualization capabilities to allow researchers to explore, compare, and investigate graph data in real-time on the web.
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.
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.
<<<!!!<<< This repository is no longer available. >>>!!!>>> TeachingWithData.org is a portal where faculty can find resources and ideas to reduce the challenges of bringing real data into post-secondary classes. It allows faculty to introduce and build students' quantitative reasoning abilities with readily available, user-friendly, data-driven teaching materials.
<<<!!!<<< the repository is offline, data can be found here: https://osf.io/gjp53/ >>>!!!>>> Our lab investigates how cognition manifests in, and is influenced by, the social contexts in which it occurs. We focus: 1) on how conversational interactions can reshape memory, by promoting shared remembering and shared forgetting, and 2) on how socio-cognitive processes affect the formation of collective memories and beliefs, and the dynamics of collective decisions. In exploring these issues, while maintaining high ecological validity, our lab integrates a wide range of methodologies, including laboratory experiments, field studies, social network analysis, and agent-based simulations.
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The Johanna Mestorf Academy provides data from several archaeology related projects. JMA supports open access/open data and open formats. The JMA promotes research and education pertaining to the field of ‘Societal, Environmental, Cultural Change’ (Kiel SECC), which is one of the four research foci of CAU.
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AURIN is a collaborative national network of leading researchers and data providers across the academic, government, and private sectors. We provide a one-stop online workbench with access to thousands of multidisciplinary datasets, from over 100 different data sources.
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SAGE is a data and research platform that enables the secondary use of data related to child and youth development, health and well-being. It currently contains research data, and at a later stage we aim to also house administrative and community service delivery data. Technical infrastructure and governance processes are in place to ensure ethical use and the privacy of participants. This dataverse provides metadata for the various data holdings available in SAGE (Secondary Analysis to Generate Evidence), a research data repository based in Edmonton Alberta and an intiative of PolicyWise for Children & Families. In general, SAGE contains data holdings too sensitive for open access. Each study lists a security level which indicates the procedure required to access the data.
ICRISAT performs crop improvement research, using conventional as well as methods derived from biotechnology, on the following crops: Chickpea, Pigeonpea, Groundnut, Pearl millet,Sorghum and Small millets. ICRISAT's data repository collects, preserves and facilitates access to the datasets produced by ICRISAT researchers to all users who are interested in. Data includes Phenotypic, Genotypic, Social Science, and Spatial data, Soil and Weather.
DataON is Korea's National Research Data Platform. It provides integrated search of metadata for KISTI's research data and domestic and international research data and links to raw data. DataON allows users (researchers, policy makers, etc.) to perform the following tasks: Easily search for various types of research data in all scientific fields. By registering research results, research data can be posted and cited. Build a community among researchers and enable collaborative research. It provides a data analysis environment that allows one-stop analysis of discovered research data.
ORTOLANG is an EQUIPEX project accepted in February 2012 in the framework of investissements d’avenir. Its aim is to construct a network infrastructure including a repository of language data (corpora, lexicons, dictionaries etc.) and readily available, well-documented tools for its processing. Expected outcomes comprize: promoting research on analysis, modelling and automatic processing of our language to their highest international levels thanks to effective resource pooling; facilitating the use and transfer of resources and tools set up within public laboratories to industrial partners, notably SMEs which often cannot develop such resources and tools for language processing given the cost of investment; promoting French language and the regional languages of France by sharing expertise acquired by public laboratories. ORTOLANG is a service for the language, which is complementary to the service offered by Huma-Num (très grande infrastructure de recherche). Ortolang gives access to SLDR for speech, and CNRTL for text resources.
The Scholarly Database (SDB) at Indiana University aims to serve researchers and practitioners interested in the analysis, modeling, and visualization of large-scale scholarly datasets. The online interface provides access to six datasets: MEDLINE papers, registered Clinical Trials, U.S. Patent and Trademark Office patents (USPTO), National Science Foundation (NSF) funding, National Institutes of Health (NIH) funding, and National Endowment for the Humanities funding – over 26 million records in total.
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Created in 2005 by the CNRS, CNRTL unites in a single portal, a set of linguistic resources and tools for language processing. The CNRTL includes the identification, documentation (metadata), standardization, storage, enhancement and dissemination of resources. The sustainability of the service and the data is guaranteed by the backing of the UMR ATILF (CNRS - Université Nancy), support of the CNRS and its integration in the excellence equipment project ORTOLANG .
Repository for New Mexico Experimental Program to Stimulate Competitive Research Data Collection. Provides access to data generated by the Energize New Mexico project as well as data gathered in our previous project that focused on Climate Change Impacts (RII 3). NM EPSCoR contributes its data to the DataONE network as a member node: https://search.dataone.org/#profile/NMEPSCOR Digital Repository NM EPSCoR is part of UNM Digital Repository https://digitalrepository.unm.edu/ see also: https://data.nmepscor.org/
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.
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The Research Data Centre (Forschungsdatenzentrum, FDZ) at the Institute for Educational Quality Improvement (Institut zur Qualitätsentwicklung im Bildungswesen, IQB) archives and documents data sets resulting from national and international assessment studies (such as DESI, PIRLS, PISA, IQB-Bildungstrends). Moreover, the FDZ makes these data sets available for re- and secondary analysis. Members of the scientific community can apply for access to the data sets archived at the FDZ.
The Open Science Framework (OSF) is part network of research materials, part version control system, and part collaboration software. The purpose of the software is to support the scientist's workflow and help increase the alignment between scientific values and scientific practices. Document and archive studies. Move the organization and management of study materials from the desktop into the cloud. Labs can organize, share, and archive study materials among team members. Web-based project management reduces the likelihood of losing study materials due to computer malfunction, changing personnel, or just forgetting where you put the damn thing. Share and find materials. With a click, make study materials public so that other researchers can find, use and cite them. Find materials by other researchers to avoid reinventing something that already exists. Detail individual contribution. Assign citable, contributor credit to any research material - tools, analysis scripts, methods, measures, data. Increase transparency. Make as much of the scientific workflow public as desired - as it is developed or after publication of reports. Find public projects here. Registration. Registering materials can certify what was done in advance of data analysis, or confirm the exact state of the project at important points of the lifecycle such as manuscript submission or at the onset of data collection. Discover public registrations here. Manage scientific workflow. A structured, flexible system can provide efficiency gain to workflow and clarity to project objectives, as pictured.
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The National Biodiversity Information System (SNIB) of Mexico by the National Commission for the Knowledge and Use of Biodiversity (CONABIO). The SNIB is of strategic importance in a megadiversity country like Mexico, making it clear to CONABIO from the beginning that the SNIB should rely on the work of the multiplicity of institutions and national and foreign experts that for years have been dedicated to the study of biodiversity of Mexico. The creation of this system was expressed as a mandate for CONABIO in the General Law of Ecological Balance and Environmental Protection (LGEEPA Art. 80 fraction V). The participation of specialists in the generation of data and information for the SNIB is one of the various ways in which they collaborate with this system, since having an information system that allows the country to make informed decisions regarding its biodiversity requires that it be made up of data and information supported by a broad network of experts.
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The National Microbial Data Center (NMDC) is jointly constructed by the Institute of Microbiology of the Chinese Academy of Sciences (IMS), the Institute of Oceanography of the Chinese Academy of Sciences, the Institute of Infectious Diseases of the Chinese Center for Disease Control and Prevention, the Institute of Plant Physiology and Ecology of the Chinese Academy of Sciences, and the Computer Network Information Centre of the Chinese Academy of Sciences. The General Office of the Chinese Academy of Sciences is the parent department. The data resources covering the whole life cycle of microbiological research, including microbiological resources, microbiological and cross-technological methods, research processes and engineering, microbiomics, microbiological technologies, as well as microbiological literature, patents, experts and results. The Centre focuses on promoting the convergence and integration of scientific and technological resources in the field of microbiology to the national platform, strengthening the development, application and analysis of microbiological resources, enhancing the effective use of microbiological resources and the ability to support scientific and technological innovation, and providing high-quality scientific and technological resource sharing services for scientific research, technological progress and social development.
The International Food Policy Research Institute (IFPRI) seeks sustainable solutions for ending hunger and poverty. In collaboration with institutions throughout the world, IFPRI is often involved in the collection of primary data and the compilation and processing of secondary data. The resulting datasets provide a wealth of information at the local (household and community), national, and global levels. IFPRI freely distributes as many of these datasets as possible and encourages their use in research and policy analysis. IFPRI Dataverse contains following dataverses: Agricultural Science and Knowledge Indicators - ASTI, HarvestChoice, Statistics on Public Expenditures for Economic Development - SPEED, International Model for Policy Analysis of Agricultural Commodities and Trade - IMPACT, Africa RISING Dataverse and Food Security Portal Dataverse.
The Arctic Data Center is the primary data and software repository for the Arctic section of NSF Polar Programs. The Center helps the research community to reproducibly preserve and discover all products of NSF-funded research in the Arctic, including data, metadata, software, documents, and provenance that links these together. The repository is open to contributions from NSF Arctic investigators, and data are released under an open license (CC-BY, CC0, depending on the choice of the contributor). All science, engineering, and education research supported by the NSF Arctic research program are included, such as Natural Sciences (Geoscience, Earth Science, Oceanography, Ecology, Atmospheric Science, Biology, etc.) and Social Sciences (Archeology, Anthropology, Social Science, etc.). Key to the initiative is the partnership between NCEAS at UC Santa Barbara, DataONE, and NOAA’s NCEI, each of which bring critical capabilities to the Center. Infrastructure from the successful NSF-sponsored DataONE federation of data repositories enables data replication to NCEI, providing both offsite and institutional diversity that are critical to long term preservation.