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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.
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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.
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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.
Academic Commons is a freely accessible digital collection of research and scholarship produced at Columbia University or one of its affiliate institutions (Barnard College, Teachers College, Union Theological Seminary, and Jewish Theological Seminary). The mission of Academic Commons is to collect and preserve the digital outputs of research and scholarship produced at Columbia and its affiliate institutions and present them to a global audience. Academic Commons accepts articles, dissertations, research data, presentations, working papers, videos, and more.