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Found 4 result(s)

Chempound

a Web2.0-inspired repository for physical science data

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Chempound is a new generation repository architecture based on RDF, semantic dictionaries and linked data. It has been developed to hold any type of chemical object expressible in CML and is exemplified by crystallographic experiments and computational chemistry calculations. In both examples, the repository can hold >50k entries which can be searched by SPARQL endpoints and pre-indexing of key fields. The Chempound architecture is general and adaptable to other fields of data-rich science. The Chempound software is hosted at http://bitbucket.org/chempound and is available under the Apache License, Version 2.0

European Environment Agency, Datasets

EEA

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The European Environment Agency (EEA) is an agency of the European Union. Our task is to provide sound, independent information on the environment. We are a major information source for those involved in developing, adopting, implementing and evaluating environmental policy, and also the general public. Currently, the EEA has 33 member countries. EEA's mandate is: To help the Community and member countries make informed decisions about improving the environment, integrating environmental considerations into economic policies and moving towards sustainability To coordinate the European environment information and observation network (Eionet)

Neuroscience Information Framework

NIF

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The Neuroscience Information Framework is a dynamic index of data, materials, and tools. Please note, we do not accept direct data deposits, but if you wish to make your data repository or database available through our search, please contact us. An initiative of the NIH Blueprint for Neuroscience Research, NIF advances neuroscience research by enabling discovery and access to public research data and tools worldwide through an open source, networked environment.

PSnpBind

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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.