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Found 47 result(s)
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Risklayer Explorer is a collaboration between Risklayer GmbH and the Karlsruhe Institute of Technology's Center for Disaster Risk Management and Risk Reduction Technology (CEDIM). This website is still under development, but we are going live with it already, because we want to present data on the Novel Coronavirus (COVID-19) to help inform the public of the current situation. You will be able to track disaster events and read about our analysis here. Our work is a continuation of a new style of disaster research started by CEDIM in 2011 to analyze disasters immediately after their occurrence, assess the impacts, and retrace the temporal development of disaster events. We are already analyzing damaging earthquakes globally, providing you with event characteristics, earthquake's intensity footprints, as well as the population affected by earthquakes. In addition to earthquake events, we expect to be tracking and analyzing tropical cyclone, volcano and extreme weather events in 2020.
For datasets big and small; Store your research data online. Quickly and easily upload files of any type and we will host your research data for you. Your experimental research data will have a permanent home on the web that you can refer to.
The figshare service for the University of Sheffield 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 Wolfram Data Repository is a public resource that hosts an expanding collection of computable datasets, curated and structured to be suitable for immediate use in computation, visualization, analysis and more. Building on the Wolfram Data Framework and the Wolfram Language, the Wolfram Data Repository provides a uniform system for storing data and making it immediately computable and useful. With datasets of many types and from many sources, the Wolfram Data Repository is built to be a global resource for public data and data-backed publication.
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Datatang is a professional data pre-processing company. We are engaged in data collecting, annotating, and customizing to meet our clients’ various needs. We assist our clients from university research labs and company R&D departments to waive trivial yet necessary data processing procedure and make their approach to the highest-value data in a more efficient way.
>>>!!!<<< This site is going away on April 1, 2021. General access to the site has been disabled and community users will see an error upon login. >>>!!!<<< Socrata’s cloud-based solution allows government organizations to put their data online, make data-driven decisions, operate more efficiently, and share insights with citizens.
Government of Yukon open data provides an easy way to find, access and reuse the government's public datasets. This service brings all of the government's data together in one searchable website. Our datasets are created and managed by different government departments. We cannot guarantee the quality or timeliness of all data. If you have any feedback you can get in touch with the department that produced the dataset. This is a pilot project. We are in the process of adding a quality framework to make it easier for you to access high quality, reliable data.
Teesside University Research Data Repository links to the University's Research Portal and enables your datasets to be linked to your staff profile. It helps prevent data loss by storing it in a safe secure environment and enables your research data to be open access. https://researchdata.tees.ac.uk/about.
Kaggle is a platform for predictive modelling and analytics competitions in which statisticians and data miners compete to produce the best models for predicting and describing the datasets uploaded by companies and users. This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modelling task and it is impossible to know beforehand which technique or analyst will be most effective.
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DBIE is a data warehouse of the Department of Statistics and Information Management (DSIM), under the Reserve Bank of India. It disseminates data on various aspects of the Indian Economy through several of its publications, by means of it’s three parts – namely has mainly three parts Home, Statistics and Time-series Publications. Again, Home is divided into two parts viz. Important Economic Indicators &amp; Economy at a Glance through dashboards. The entire statistics have been presented in seven subject areas - Real Sector, Corporate Sector, Financial Sector, Financial Market, External Sector, Public Finance, Socio-Economic Indicators. Sectors have different sub-sectors and reports under the sub-sectors have been organized on periodicity wise. Downloading of data can be possible into Excel, CSV, PDF formats. User can use the data for their research work with courtesy to the Database on Indian Economy, Reserve Bank of India.
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The Institutional Repository of the Universidad Santo Tomás manages, preserves, stores, disseminates and provides access to digital objects, the product of all academic and administrative production.
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<<<!!!<<< The pages were merged. Please use "Forschungsdaten- und Servicezentrum der Bundesbank" https://www.re3data.org/repository/r3d100012252 >>>!!!<<<
Additionally to the institutional repository, current St. Edward's faculty have the option of uploading their work directly to their own SEU accounts on stedwards.figshare.com. Projects created on Figshare will automatically be published on this website as well. For more information, please see documentation
As 3D and reality capture strategies for heritage documentation become more widespread and available, there has emerged a growing need to assist with guiding and facilitating accessibility to data, while maintaining scientific rigor, cultural and ethical sensitivity, discoverability, and archival standards. In response to these areas of need, The Open Heritage 3D Alliance (OHA) has developed as an advisory group governing the Open Heritage 3D initiative. This collaborative advisory group are among some of the earliest adopters of 3D heritage documentation technologies, and offer first-hand guidance for best practices in data management, sharing, and dissemination approaches for 3D cultural heritage projects. The founding members of the OHA, consist of experts and organizational leaders from CyArk, Historic Environment Scotland, and the University of South Florida Libraries, who together have significant repositories of legacy and on-going 3D research and documentation projects. These groups offer unique insight into not only the best practices for 3D data capture and sharing, but also have come together around concerns dealing with standards, formats, approach, ethics, and archive commitment. Together, the OHA has begun the journey to provide open access to cultural heritage 3D data, while maintaining integrity, security, and standards relating to discoverable dissemination. Together, the OHA will work to provide democratized access to primary heritage 3D data submitted from donors and organizations, and will help to facilitate an operation platform, archive, and organization of resources into the future.
The FigShare service for University of Auckland, New Zealand was launched in January 2015 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 recieves a Digital Object identifier (DOI), which allows the data to be cited. 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.
Provided by the University Libraries, KiltHub is the comprehensive institutional repository and research collaboration platform for research data and scholarly outputs produced by members of Carnegie Mellon University and their collaborators. KiltHub collects, preserves, and provides stable, long-term global open access to a wide range of research data and scholarly outputs created by faculty, staff, and student members of Carnegie Mellon University in the course of their research and teaching.
The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. The core of the dataset is the feature analysis and metadata for one million songs, provided by The Echo Nest. The dataset does not include any audio, only the derived features. Note, however, that sample audio can be fetched from services like 7digital, using code we provide.
the Data Hub is a community-run catalogue of useful sets of data on the Internet. You can collect links here to data from around the web for yourself and others to use, or search for data that others have collected. Depending on the type of data (and its conditions of use), the Data Hub may also be able to store a copy of the data or host it in a database, and provide some basic visualisation tools.
myExperiment is a collaborative environment where scientists can safely publish their workflows and in silico experiments, share them with groups and find those of others. Workflows, other digital objects and bundles (called Packs) can now be swapped, sorted and searched like photos and videos on the Web. Unlike Facebook or MySpace, myExperiment fully understands the needs of the researcher and makes it really easy for the next generation of scientists to contribute to a pool of scientific methods, build communities and form relationships — reducing time-to-experiment, sharing expertise and avoiding reinvention. myExperiment is now the largest public repository of scientific workflows.