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Found 22 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.
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bonndata is the institutional, FAIR-aligned and curated, cross-disciplinary research data repository for the publication of research data for all researchers at the University of Bonn. The repository is fully embedded into the University IT and Data Center and curated by the Research Data Service Center (https://www.forschungsdaten.uni-bonn.de/en). The software that bonndata is based on is the open source software Dataverse (https://dataverse.org)
The Information Marketplace for Policy and Analysis of Cyber-risk & Trust (IMPACT) program supports global cyber risk research & development by coordinating, enhancing and developing real world data, analytics and information sharing capabilities, tools, models, and methodologies. In order to accelerate solutions around cyber risk issues and infrastructure security, IMPACT makes these data sharing components broadly available as national and international resources to support the three-way partnership among cyber security researchers, technology developers and policymakers in academia, industry and the government.
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 figshare service for The Open University was launched in 2016 and allows researchers to store, share and publish research data. It helps the research data to be accessible by storing metadata alongside datasets. Additionally, every uploaded item receives a Digital Object Identifier (DOI), which allows the data to be citable and sustainable. If there are any ethical or copyright concerns about publishing a certain dataset, it is possible to publish the metadata associated with the dataset to help discoverability while sharing the data itself via a private channel through manual approval.
The CONP portal is a web interface for the Canadian Open Neuroscience Platform (CONP) to facilitate open science in the neuroscience community. CONP simplifies global researcher access and sharing of datasets and tools. The portal internalizes the cycle of a typical research project: starting with data acquisition, followed by processing using already existing/published tools, and ultimately publication of the obtained results including a link to the original dataset. From more information on CONP, please visit https://conp.ca
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This data archive of experiments studying the dynamics of pedestrians is build up by the Institute for Advanced Simulation 7: Civil Safety Research of Forschungszentrum Jülich. The landing page provides our own data of experiments. Data of research colleagues are listed within the data archive at https://ped.fz-juelich.de/extda For most of the experiments, the video recordings, as well as the resulting trajectories of single pedestrians, are available. The experiments were performed under laboratory conditions to focus on the influence of a single variable. You are very welcome to use our data for further research, as long as you name the source of the data. If you have further questions feel free to contact Maik Boltes.
GigaDB primarily serves as a repository to host data and tools associated with articles published by GigaScience Press; GigaScience and GigaByte (both are online, open-access journals). GigaDB defines a dataset as a group of files (e.g., sequencing data, analyses, imaging files, software programs) that are related to and support a unit-of-work (article or study). GigaDB allows the integration of manuscript publication with supporting data and tools.
The ColabFit Exchange is an online resource for the discovery, exploration and submission of datasets for data-driven interatomic potential (DDIP) development for materials science and chemistry applications. ColabFit's goal is to increase the Findability, Accessibility, Interoperability, and Reusability (FAIR) of DDIP data by providing convenient access to well-curated and standardized first-principles and experimental datasets. Content on the ColabFit Exchange is open source and freely available.
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TUL Open Research Data Repository (RDB.open) is a service addressed to the scientific and research community of the Lodz University of Technology. The main purpose of RDB.open is to collect, share and store the open research data, both during the research and after its completion, at least for the minimum period indicated by the funder or the scientists. The RDB.open is a place where research data can be openly shared, accessed and then reused by others.
OpenKIM is an online suite of open source tools for molecular simulation of materials. These tools help to make molecular simulation more accessible and more reliable. Within OpenKIM, you will find an online resource for standardized testing and long-term warehousing of interatomic models and data, and an application programming interface (API) standard for coupling atomistic simulation codes and interatomic potential subroutines.
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Yale-NUS Dataverse is the institutional research data repository of Yale-NUS College. The goals of Yale-NUS Dataverse are to collect, preserve and showcase the research output of Yale-NUS researchers and through this, increase the research visibility of Yale-NUS researchers and demonstrate the research excellence of Yale-NUS College to the world.
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The INESC TEC data repository showcases datasets produced or used by INESC TEC researchers and their partners. The repository is organized in four groups (institutional clusters). Computer Science, Power and Energy, Network and Intelligent Systems and Power and Energy.
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Discover the data on entrepreneurship projects, innovation plans, digital transformation proposals, consumers, and financial markets. Also, explore research on business, management, and entrepreneurship research development at our Business school.