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Found 10 result(s)

IPB MassBank

IPB MS Database

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MassBank is the first public repository of mass spectral data for sharing them among scientific research community. MassBank data are useful for the chemical identification and structure elucidation of chemical comounds detected by mass spectrometry.MassBank system is originally designed for public sharing of reference mass spectra for metabolite identification. It is also useful for their in-house or local sharing. Recently it finds another application; sharing mass spectra of unknown metabolites for metabolite profiling. The IPB is operating the first european MassBank site, that is part of the consortial MassBank Project. You can access both the set of IPB Tandem-MS and Ion Trap spectra, as well as the other massbank sites.

MassBank Europe

MassBank

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Database of mass spectra of known, unknown and provisionally identified substances. MassBank is the first public repository of mass spectral data for sharing them among scientific research community. MassBank data are useful for the chemical identification and structure elucidation of chemical compounds detected by mass spectrometry.

Top Down Proteomics Repository

TDPR

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This database will provide a central location for scientists to browse uniquely observed proteoforms and to contribute their own datasets. Top-down proteomics is a method of protein identification that uses an ion trapping mass spectrometer to store an isolated protein ion for mass measurement and tandem mass spectrometry analysis.

Human protein-protein interaction network database search

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<<<!!!<<< This repository is no longer available. >>>!!!>>> A human interactome map. The sequencing of the human genome has provided a surprisingly small number of genes, indicating that the complex organization of life is not reflected in the gene number but, rather, in the gene products – that is, in the proteins. These macromolecules regulate the vast majority of cellular processes by their ability to communicate with each other and to assemble into larger functional units. Therefore, the systematic analysis of protein-protein interactions is fundamental for the understanding of protein function, cellular processes and, ultimately, the complexity of life. Moreover, interactome maps are particularly needed to link new proteins to disease pathways and the identification of novel drug targets.

LIAS

A Global Information System for Lichenized and Non-Lichenized Ascomycetes

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LIAS is a global information system for Lichenized and Non-Lichenized Ascomycetes. It includes several interoperable data repositories. In recent years, the two core components ‘LIAS names’ and ‘LIAS light’ have been much enlarged. LIAS light is storing phenotypic trait data. They includes > 10,700 descriptions (about 2/3 of all known lichen species), each with up to 75 descriptors comprising 2,000 traits (descriptor states and values), including 800 secondary metabolites. 500 traits may have biological functions and more than 1,000 may have phylogenetic relevance. LIAS is thus one of the most comprehensive trait databases in organismal biology. The online interactive identification key for more than 10,700 lichens is powered by the Java applet NaviKey and has been translated into 19 languages (besides English) in cooperation with lichenologists worldwide. The component ‘LIAS names’ is a platform for managing taxonomic names and classifications with currently >50,000 names, including the c. 12,000 accepted species and recognized synonyms. The LIAS portal contents, interfaces, and databases run on servers of the IT Center of the Bavarian Natural History Collections and are maintained there. 'LIAS names' and ‘LIAS light’ also deliver content data to the Catalogue of Life, acting as the Global Species Database (GSD) for lichens. LIAS gtm is a database for visualising the geographic distribution of lichen traits. LIAS is powered by the Diversity Workbench database framework with several interfaces for data management and publication. The LIAS long-term project was initiated in the early 1990s and has since been continued with funding from the DFG, the BMBF, and the EU.

COEMS Open Data

Continuous Observation of Embedded Multicore Systems Data

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<<<!!!<<< This repository is no longer available. >>>!!!>>>

hPSCreg

human Pluripotent Stem Cell registry

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The human pluripotent stem cell registry (hPSCreg) is a public registry and data portal for human embryonic and induced pluripotent stem cell lines (hESC and hiPSC). The Registry provides comprehensive and standardized biological and legal information as well as tools to search and compare information from multiple hPSC sources and hence addresses a translational research need. To facilitate unambiguous identification over different resources, hPSCreg automatically creates a unique standardized name (identifier) for each cell line registered. In addition to biological information, hPSCreg stores extensive data about ethical standards regarding cell sourcing and conditions for application and privacy protection. hPSCreg is the first global registry that holds both, manually validated scientific and ethical information on hPSC lines, and provides access by means of a user-friendly, mobile-ready web application.

Data Center - SAFE

Data Center - Sustainable Architecture for Finance in Europe

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Research on German and European financial markets suffers from a lack of pan-European data sets. Also, existing sets do not provide a standard identification of, for example, companies. Therefore, researchers often utilize data from the United States where the integration of different databases is more advanced. As a consequence, empirical analyses are mostly based on non-European data. Because of the institutional differences, political recommendations that result from these analyses cannot – or only in a limited scope – be transferred to Europe. Against this background, the SAFE Research Data Center not only draws on the usual international data sources but also creates new European data sets, brings existing data together and processes them. The aim is to place the central research areas of SAFE on a common European data footing. Data access is provided by 'SAFE data sources' https://datacenter.safefrankfurt.de/datacenter/_databases/ and 'FiF - Repositorium für Forschungsdaten aus dem Finanzbereich (Preview version)' https://fif.safe-frankfurt.de/xmlui/

DEEMY

Determination of Ectomycorrhizae

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DEEMY is collecting descriptive data on ectomycorrhizae, including extant character descriptions and definitions. Ectomycorrhizae are mutualistic structures formed by fungi and the roots of forest trees. They are predominantly found in the temperate and boreal climate zones but occur also in humid tropic regions, as well as in soils of poor nutrition. Without mycorrhizae, trees would not be able to take up water and minerals. Ectomycorrhizae show a wide range of anatomical diversity which represents their possible function in tree nutrition and ecology. Their anatomical data, in general, allow a quick determination and provide at the same time ecologically important information about possible functions for tree nutrition.

MUSE-Wide

MUSE-Wide archive

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We present the MUSE-Wide survey, a blind, 3D spectroscopic survey in the CANDELS/GOODS-S and CANDELS/COSMOS regions. Each MUSE-Wide pointing has a depth of 1 hour and hence targets more extreme and more luminous objects over 10 times the area of the MUSE-Deep fields (Bacon et al. 2017). The legacy value of MUSE-Wide lies in providing "spectroscopy of everything" without photometric pre-selection. We describe the data reduction, post-processing and PSF characterization of the first 44 CANDELS/GOODS-S MUSE-Wide pointings released with this publication. Using a 3D matched filtering approach we detected 1,602 emission line sources, including 479 Lyman-α (Lya) emitting galaxies with redshifts 2.9≲z≲6.3. We cross-match the emission line sources to existing photometric catalogs, finding almost complete agreement in redshifts and stellar masses for our low redshift (z < 1.5) emitters. At high redshift, we only find ~55% matches to photometric catalogs. We encounter a higher outlier rate and a systematic offset of Δz≃0.2 when comparing our MUSE redshifts with photometric redshifts. Cross-matching the emission line sources with X-ray catalogs from the Chandra Deep Field South, we find 127 matches, including 10 objects with no prior spectroscopic identification. Stacking X-ray images centered on our Lya emitters yielded no signal; the Lya population is not dominated by even low luminosity AGN. A total of 9,205 photometrically selected objects from the CANDELS survey lie in the MUSE-Wide footprint, which we provide optimally extracted 1D spectra of. We are able to determine the spectroscopic redshift of 98% of 772 photometrically selected galaxies brighter than 24th F775W magnitude. All the data in the first data release - datacubes, catalogs, extracted spectra, maps - are available at the website.