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Found 7 result(s)
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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.
Network Repository is the first interactive data repository for graph and network data. It hosts graph and network datasets, containing hundreds of real-world networks and benchmark datasets. Unlike other data repositories, Network Repository provides interactive analysis and visualization capabilities to allow researchers to explore, compare, and investigate graph data in real-time on the web.
The Registry of Open Data on AWS provides a centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge to their users. Anyone can access these data sets from their Amazon Elastic Compute Cloud (Amazon EC2) instances and start computing on the data within minutes. Users can also leverage the entire AWS ecosystem and easily collaborate with other AWS users.
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Technology has allowed Concordia to collect large amounts of data and information on a variety of topics, which the university feels should be accessible to all. Concordia’s open data makes machine-readable data easy to access from a single point and free to reuse without copyright, patents or other restrictions.
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AusGeochem is an easy-to-use platform for uploading, visualising, analysing and discovering georeferenced sample information and data produced by various geoscience research institutions such as universities, geological survey agencies and museums. With respect to analytical research laboratories, AusGeochem provides a centralised repository allowing laboratories to upload, archive, disseminate and publish their datasets. The intuitive user interface (UI) allows users to access national publicly funded data quickly through the ability to view an area of interest, synthesise a variety of geochemical data in real-time, and extract the required data, gaining novel scientific insights through multi-method data collation. Lithodat Pty Ltd has integrated built-in data synthesis functions into the platform, such as cumulative age histograms, age vs elevation plots, and step-heating diagrams, allowing for rapid inter-study comparisons. Data can be extracted in multiple formats for re-use in a variety of software systems, allowing for the integration of regional datasets into machine learning and AI systems.
PSnpBind is a large database of protein–ligand complexes covering a wide range of binding pocket mutations and small molecules’ landscape. This database can be used as a source of data for different types of studies, for example, developing machine learning algorithms to predict protein–ligand affinity or mutation's effect on it which requires an extensive amount of data with a wide coverage of mutation types and small molecules. Also, studies of protein-ligand interactions and conformer orientation changes across different mutated versions of a protein can be established using data from PSnpBind.
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.