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Found 52 result(s)
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.
Country
The UWA Research Repository contains research publications, research datasets and theses created by researchers and postgraduates affiliated with UWA. It is managed by the University Library and provides access to research datasets held at the University of Western Australia. The information about each dataset has been provided by UWA research groups. Dataset metadata is harvested into Research Data Australia (RDA: https://researchdata.ands.org.au/). Language: The user interface language of the research data repository.
The Comprehensive Epidemiologic Data Resource (CEDR) is the Department of Energy's (DOE) electronic database comprised of health studies of DOE contract workers and environmental studies of areas surrounding DOE facilities. DOE recognizes the benefits of data sharing and supports the public's right to know about worker and community health risks. CEDR provides independent researchers and the public with access to de-identified data collected since the Department's early production years. Current CEDR holdings include more than 80 studies of over 1 million workers at 31 DOE sites. Access to these data is at no cost to the user. Most of CEDR's holdings are derived from epidemiologic studies of DOE workers at many large nuclear weapons plants, such as Hanford, Los Alamos, the Oak Ridge reservation, Savannah River Site, and Rocky Flats. These studies primarily use death certificate information to identify excess deaths and patterns of disease among workers to determine what factors contribute to the risk of developing cancer and other illnesses. In addition, many of these studies have radiation exposure measurements on individual workers. CEDR is supported by the Oak Ridge Institute for Science and Education (ORISE) in Oak Ridge, Tennessee. Now a mature system in routine operational use, CEDR's modern internet-based systems respond to thousands of requests to its web server daily. With about 1,500 Internet sites pointing to CEDR's web site, CEDR is a national user facility, with a large audience for data that are not available elsewhere.
4TU.ResearchData, previously known as 3TU.Datacentrum, is an archive for research data. It offers the knowledge, experience and the tools to share and safely store scientific research data in a standardized, secure and well-documented manner. 4TU.Centre for Research Data provides the research community with: Advice and support on data management; A long-term archive for scientific research data; Support for current research projects; Tools for reusing research data.
The National Science Digital Library provides high quality online educational resources for teaching and learning, with current emphasis on the sciences, technology, engineering, and mathematics (STEM) disciplines—both formal and informal, institutional and individual, in local, state, national, and international educational settings. The NSDL collection contains structured descriptive information (metadata) about web-based educational resources held on other sites by their providers. These providers have contribute this metadata to NSDL for organized search and open access to educational resources via this website and its services.
ETH Data Archive is ETH Zurich's long-term preservation solution for digital information such as research data, documents or images. It serves as the backbone of data curation and for most of its content, it is a “dark archive” without public access. In this capacity, the ETH Data Archive also archives the content of ETH Zurich’s Research Collection which is the primary repository for members of the university and the first point of contact for publication of data at ETH Zurich. All data that was produced in the context of research at the ETH Zurich, can be published and archived in the Research Collection. In the following cases, a direct data upload into the ETH Data Archive though, has to be considered: - Upload and registration of software code according to ETH transfer’s requirements for Software Disclosure. - A substantial number of files, have to be regularly submitted for long-term archiving and/or publishing and browser-based upload is not an option: the ETH Data Archive may offer automated data and metadata transfers from source applications (e.g. from a LIMS) via API. - Files for a project on a local computer have to be collected and metadata has to be added before uploading the data to the ETH Data Archive: -- we provide you with the local file editor docuteam packer. Docuteam packer allows to structure, describe, and organise data for an upload into the ETH Data Archive and the depositor decides when submission is due.
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 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.
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.
Country
Curtin University has 222 data records in Research Data Australia, which cover 199 subjects areas including Applied research, EARTH SCIENCES and GEOLOGY and involve 32 group(s)
The UA Campus Repository is an institutional repository that facilitates access to the research, creative works, publications and teaching materials of the University by collecting, sharing and archiving content selected and deposited by faculty, researchers, staff and affiliated contributors.
FLOSSmole is a collaborative collection of free, libre, and open source software (FLOSS) data. FLOSSmole contains nearly 1 TB of data covering the period 2004 until now, about more than 500,000 different open source projects.
Country
Ocean Networks Canada maintains several observatories installed in three different regions in the world's oceans. All three observatories are cabled systems that can provide power and high bandwidth communiction paths to sensors in the ocean. The infrastructure supports near real-time observations from multiple instruments and locations distributed across the Arctic, NEPTUNE and VENUS observatory networks. These observatories collect data on physical, chemical, biological, and geological aspects of the ocean over long time periods, supporting research on complex Earth processes in ways not previously possible.
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.
The Northwest Knowledge Network (NKN) supports researchers with data management services and is focused on building and maintaining a catalog of research data and standards-compliant metadata. Quality data and metadata developed in research projects and curated by NKN is a valuable, long-term asset for a public land-grant University and provides a foundation and source upon which to develop and build new research and science.
SeedMe is a result of a decade of onerous experience in preparing and sharing visualization results from supercomputing simulations with many researchers at different geographic locations using different operating systems. It’s been a labor–intensive process, unsupported by useful tools and procedures for sharing information. SeedMe provides a secure and easy-to-use functionality for efficiently and conveniently sharing results that aims to create transformative impact across many scientific domains.