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Found 7 result(s)
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The Animal Sound Archive at the Museum für Naturkunde in Berlin is one of the oldest and largest collections of animal sounds. Presently, the collection consists of about 120,000 bioacoustical recordings comprising almost all groups of animals: 1.800 bird species 580 mammalian species more then150 species of invertebrates; some fishes, amphibians and reptiles
PhysioBank is a large and growing archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community.
The Bavarian Archive for Speech Signals (BAS) is a public institution hosted by the University of Munich. This institution was founded with the aim of making corpora of current spoken German available to both the basic research and the speech technology communities via a maximally comprehensive digital speech-signal database. The speech material will be structured in a manner allowing flexible and precise access, with acoustic-phonetic and linguistic-phonetic evaluation forming an integral part of it.
Modern signal processing and machine learning methods have exciting potential to generate new knowledge that will impact both physiological understanding and clinical care. Access to data - particularly detailed clinical data - is often a bottleneck to progress. The overarching goal of PhysioNet is to accelerate research progress by freely providing rich archives of clinical and physiological data for analysis. The PhysioNet resource has three closely interdependent components: An extensive archive ("PhysioBank"), a large and growing library of software ("PhysioToolkit"), and a collection of popular tutorials and educational materials
The National Sleep Research Resource (NSRR) is an NHLBI-supported repository for sharing large amounts of sleep data (polysomnography, actigraphy and questionnaire-based) from multiple cohorts, clinical trials, and other data sources. Launched in April 2014, the mission of the NSRR is to advance sleep and circadian science by supporting secondary data analysis, algorithmic development, and signal processing through the sharing of high-quality data sets.
IEEE DataPort™ is a universally accessible online data repository created, owned, and supported by IEEE, the world’s largest technical professional organization. It enables all researchers and data owners to upload their dataset without cost. IEEE DataPort makes data available in three ways: standard datasets, open access datasets, and data competition datasets. By default, all "standard" datasets that are uploaded are accessible to paid IEEE DataPort subscribers. Data owners have an option to pay a fee to make their dataset “open access”, so it is available to all IEEE DataPort users (no subscription required). The third option is to host a "data competition" and make a dataset accessible for free for a specific duration with instructions for the data competition and how to participate. IEEE DataPort provides workflows for uploading data, searching, and accessing data, and initiating or participating in data competitions. All datasets are stored on Amazon AWS S3, and each dataset uploaded by an individual can be up to 2TB in size. Institutional subscriptions are available to the platform to make it easy for all members of a given institution to utilize the platform and upload datasets.
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.