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Found 47 result(s)
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The version 1.0 of the open database contains 1,151,268 brain signals of 2 seconds each, captured with the stimulus of seeing a digit (from 0 to 9) and thinking about it, over the course of almost 2 years between 2014 & 2015, from a single Test Subject David Vivancos. All the signals have been captured using commercial EEGs (not medical grade), NeuroSky MindWave, Emotiv EPOC, Interaxon Muse & Emotiv Insight, covering a total of 19 Brain (10/20) locations. In 2014 started capturing brain signals and released the first versions of the "MNIST" of brain digits, and in 2018 released another open dataset with a subset of the "IMAGENET" of The Brain. Version 0.05 (last update 09/28/2021) of the open database contains 24,000 brain signals of 2 seconds each, captured with the stimulus of seeing a real MNIST digit (from 0 to 9) 6,000 so far and thinking about it, + the same amout of signals with another 2 seconds of seeing a black screen, shown in between the digits, from a single Test Subject David Vivancos in a controlled still experiment to reduce noise from EMG & avoiding blinks.
The DOE Data Explorer (DDE) is an information tool to help you locate DOE's collections of data and non-text information and, at the same time, retrieve individual datasets within some of those collections. It includes collection citations prepared by the Office of Scientific and Technical Information, as well as citations for individual datasets submitted from DOE Data Centers and other organizations.
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DAIS - Digital Archive of the Serbian Academy of Sciences and Arts is a joint digital repository of the Serbian Academy of Sciences and Arts (SASA) and the research institutes under the auspices of SASA. The aim of the repository is to provide open access to publications and other research outputs resulting from the projects implemented by the SASA and its institutes. The repository uses a DSpace-based software platform developed and maintained by the Belgrade University Computer Centre (RCUB).
The Harvard Dataverse Repository is a free data repository open to all researchers from any discipline, both inside and outside of the Harvard community, where you can share, archive, cite, access, and explore research data. Each individual Dataverse collection is a customizable collection of datasets (or a virtual repository) for organizing, managing, and showcasing datasets.
Academic Torrents is a distributed data repository. The academic torrents network is built for researchers, by researchers. Its distributed peer-to-peer library system automatically replicates your datasets on many servers, so you don't have to worry about managing your own servers or file availability. Everyone who has data becomes a mirror for those data so the system is fault-tolerant.
Discovery is the digital repository of research, and related activities, undertaken at the University of Dundee. The content held in Discovery is varied and ranges from traditional research outputs such as peer-reviewed articles and conference papers, books, chapters and post-graduate research theses and data to records for artefacts, exhibitions, multimedia and software. Where possible Discovery provides full-text access to a version of the research. Discovery is the data catalogue for datasets resulting from research undertaken at the University of Dundee and in some instances the publisher of research data.
The Research Collection is ETH Zurich's publication platform. It unites the functions of a university bibliography, an open access repository and a research data repository within one platform. Researchers who are affiliated with ETH Zurich, the Swiss Federal Institute of Technology, may deposit research data from all domains. They can publish data as a standalone publication, publish it as supplementary material for an article, dissertation or another text, share it with colleagues or a research group, or deposit it for archiving purposes. Research-data-specific features include flexible access rights settings, DOI registration and a DOI preview workflow, content previews for zip- and tar-containers, as well as download statistics and altmetrics for published data. All data uploaded to the Research Collection are also transferred to the ETH Data Archive, ETH Zurich’s long-term archive.
Content type(s)
A machine learning data repository with interactive visual analytic techniques. This project is the first to combine the notion of a data repository with real-time visual analytics for interactive data mining and exploratory analysis on the web. State-of-the-art statistical techniques are combined with real-time data visualization giving the ability for researchers to seamlessly find, explore, understand, and discover key insights in a large number of public donated data sets. This large comprehensive collection of data is useful for making significant research findings as well as benchmark data sets for a wide variety of applications and domains and includes relational, attributed, heterogeneous, streaming, spatial, and time series data as well as non-relational machine learning data. All data sets are easily downloaded into a standard consistent format. We also have built a multi-level interactive visual analytics engine that allows users to visualize and interactively explore the data in a free-flowing manner.
MetaCyc is a curated database of experimentally elucidated metabolic pathways from all domains of life. MetaCyc contains pathways involved in both primary and secondary metabolism, as well as associated metabolites, reactions, enzymes, and genes. The goal of MetaCyc is to catalog the universe of metabolism by storing a representative sample of each experimentally elucidated pathway. MetaCyc applications include: Online encyclopedia of metabolism, Prediction of metabolic pathways in sequenced genomes, Support metabolic engineering via enzyme database, Metabolite database aids. metabolomics research.
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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)
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.
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sciencedata.dk is a research data store provided by DTU, the Danish Technical University, specifically aimed at researchers and scientists at Danish academic institutions. The service is intended for working with and sharing active research data as well as for safekeeping of large datasets. The data can be accessed and manipulated via a web interface, synchronization clients, file transfer clients or the command line. The service is built on and with open-source software from the ground up: FreeBSD, ZFS, Apache, PHP, ownCloud/Nextcloud. DTU is actively engaged in community efforts on developing research-specific functionality for data stores. Our servers are attached directly to the 10-Gigabit backbone of "Forskningsnettet" (the National Research and Education Network of Denmark) - implying that up and download speed from Danish academic institutions is in principle comparable to those of an external USB hard drive. Data store for research data allowing private sharing and sharing via links / persistent URLs.
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Base of Knowledge Wrocław University of Environmental and Life Sciences / Research Data Repository is an institutional open research data repository, offering the possibility to deposit datasets (as well as publications) created by researchers, PhD candidates and students of Wrocław University of Environmental and Life Sciences. It is intended for scientific data from the disciplines related to the University’s profile. It is a platform where research data can be safely collected, stored and openly shared with others, obtaining a permanent Digital Object Identifier (DOI) for each dataset and choosing a data usage license. Research Data Repository applies the FAIR Principles (data is findable, accessible, interoperable and reusable).