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80 results for “Opera”
On the PhonE Respiratory rAte Study (OPERA)
ClinicalTrials.gov study NCT04077593. IPD Sharing: NO. Countries: 1. Publications: 0.
OPERA Dynamic Surface Water Extent from Sentinel-1 CalVal Database (Version 1)
This dataset contains the calibration/validation (CalVal) database for the OPERA DSWx-S1 product. The CalVal database is a zip file of an Amazon Web Services S3 bucket containing classification items that enable algorithm calibration and validation of OPERA products. The CalVal database contains a Reference Document that further describes the structure and usage of the database, as well as a Validation Results document. Example notebooks demonstrating how to read the database tables, query for specific items, and download corresponding data files are available through the CalVal GitHub repository here: https://github.com/OPERA-Cal-Val/calval-database
OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1)
The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product consists of Single Look Complex (SLC) images which contain both amplitude and phase information of the complex radar return. The amplitude is primarily determined by ground surface properties (e.g., terrain slope, surface roughness, and physical properties), and phase primarily represents the distance between the radar and ground targets corrected for the geometrical distance between the two based on the knowledge from Digital Elevation Model and platform’s position, i.e., the CSLC phase represents residual geometrical distance between the sensor and target, the atmospheric propagation delay and the target movements. The CSLC-S1 product is derived from Copernicus Sentinel-1A and Sentinel-1B Interferometric Wide (IW) SLC data. The CSLC images are precisely aligned or “coregistered” to a pre-defined UTM/Polar stereographic map projection systems and posted at 5x10 m spacing in east and north direction, respectively. Each CSLC-S1 product corresponds to a single S1 burst and is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers (e.g., geocoded complex backscatter, low-resolution correction look-up tables) and product metadata. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The OPERA CSLC-S1 product contains modified Copernicus Sentinel data (2016-2025).Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CLSLC-S1 product, as they are produced only once or a limited number of times. The static layers are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1).
OPERA Surface Displacement from Sentinel-1 validated product (Version 1)
The Level-3 OPERA Sentinel-1 Surface Displacement (DISP) product is generated through interferometric time-series analysis of Level-2 Coregistered Sentinel-1 Single Look Complex (CSLC) datasets. Using a hybrid Persistent Scatterer (PS) and Distributed Scatterer (DS) approach, this product quantifies Earth's surface displacement in the radar line-of-sight. The DISP products enable the detection of anthropogenic and natural surface changes, including subsidence, tectonic deformation, and landslides. The OPERA DISP suite comprises complementary datasets derived from Sentinel-1 and NISAR inputs, designated as DISP-S1 and DISP-NI, respectively. Each product, created per acquisition, adheres to a consistent structure, HDF5 file format, file-naming convention, and a 30 m spatial posting. This collection specifically includes DISP-S1 products, derived from Sentinel-1 data. DISP-S1 products provide spatial coverage across North America, encompassing the United States, U.S. territories within 200 km of the U.S. border, Canada, and mainland countries from the southern U.S. border to Panama. These products are generated from Sentinel-1 Interferometric Wide (IW) swath mode acquisitions starting in mid-2016.The OPERA DISP-S1 product contains modified Copernicus Sentinel data (2016-2025).
OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 provisional product (Version 1)
This dataset contains Level-3 Dynamic OPERA provisional surface water extent product version 1. The data are provisional surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.
OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 provisional product (Version 0)
The OPERA_L3_DIST-ALERT-HLS Version 0 data product was decommissioned on April 25, 2025. Users are encouraged to use the [OPERA_L3_DIST-ALERT-HLS V1](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product which was released on March 14, 2024, and has achieved stage 1 validation.The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) provisional data product Version 0 maps vegetation disturbance alerts from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). Vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes to provide information about more general disturbance trends. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration, or small magnitude/long duration. The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within in the DIST-ALERT product: vegetation disturbance status, current vegetation cover indicator, current vegetation anomaly value, historical vegetation cover indicator, max vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, and vegetation disturbance duration. See the Product Specification for a more detailed description of the individual layers provided in the DIST-ALERT product. Known Issues* Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).
Multi-Camera and Multi-View Immersive Recording of "Roméo et Juliette" Opera Performance at the Gran Teatre del Liceu (Barcelona)
<p>360º, Multi-Camera and Multi-View Immersive Recording of "Roméo et Juliette" Opera Performance at the Gran Teatre del Liceu (Barcelona) recorded in February 2018.</p> <p>The opera was recorded during the general essay Gounod’s opera with the objective of creating an interactive product that allows the viewer to consume opera in a multiscreen format. The viewer will be able to see a traditional opera video production in a web browser in a main screen (PC or smart TV) while tablet and HMDs (Head Mounted Displays) will be used to choose between the classic television production and alternative views in 360º and 180º. All contents will be synchronized across screens which will mean that one single user or group of users, in a home environment, can watch in parallel the same content from different points of view. Furthermore, opera being one of the most demanding scenarios when it comes to audio quality, more than 80 different audio tracks were recorded, which will now allow the audio production crew to provide detailed sound landscapes given a certain user’s (camera) position, so viewers can have also an independent immersive audio experience depending on the chosen camera and alternate this sound experience with a regular sound experience.</p> <p>This pilot activity is used to test already defined accessibility requirements for subtitling and audio description in immersive environments, and a number of focus groups and validation activities will be run in order to elicit the perception of these new services by different communities being addressed such as persons with disabilities, and those who use subtitles when attending opera performances.</p> <p> </p>
International opera singer
<u>Source</u>: Europeana <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1653128196.7663.jpg">https://4dcity.org/imgupload/1653128196.7663.jpg</a> <br><u>Original Image URL</u>: <a href="https://api.europeana.eu/thumbnail/v2/url.json?uri=http%3A%2F%2Fcontribute.europeana.eu%2Fmedia%2Fcd58c250-c340-0137-3824-6eee0af7162b&type=IMAGE">https://api.europeana.eu/thumbnail/v2/url.json?uri=http%3A%2F%2Fcontribute.europeana.eu%2Fmedia%2Fcd58c250-c340-0137-3824-6eee0af7162b&type=IMAGE</a> <br><br><u>Image-Metadata:</u><br>Filename: 1653128196.7663.jpg<br>Image Dimensions: 400x600<br>Megapixels: 0.24 MP<br>Filesize: 36.57 KB<br>
Semper Opera In Fog
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1661456313.4382.jpg">https://4dcity.org/imgupload/1661456313.4382.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/4478/26192988199_0ff1da0dd6_m.jpg">https://live.staticflickr.com/4478/26192988199_0ff1da0dd6_m.jpg</a> <br><br><u>Image-Metadata:</u><br>Filename: 1661456313.4382.jpg<br>Image Dimensions: 240x160<br>Megapixels: 0.04 MP<br>Filesize: 4.51 KB<br>
Yellow boat in front of National Opera building
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1665336659.0262.jpg">https://4dcity.org/imgupload/1665336659.0262.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/52404569867_6c3b17a31a_m.jpg">https://live.staticflickr.com/65535/52404569867_6c3b17a31a_m.jpg</a> <br><br><u>Image-Metadata:</u><br>Filename: 1665336659.0262.jpg<br>Image Dimensions: 240x96<br>Megapixels: 0.02 MP<br>Filesize: 15.84 KB<br><br>ExifOffset: 38
Mr Arbitrium - Opera di Emanuele Giannelli a Palazzo Mediceo di Seravezza (LU) in Toscana
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677514914.5579.jpg">https://4dcity.org/imgupload/1677514914.5579.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/51164949900_e091afdb87_m.jpg">https://live.staticflickr.com/65535/51164949900_e091afdb87_m.jpg</a>
Mr Arbitrium - Opera di Emanuele Giannelli a Palazzo Mediceo di Seravezza (LU) in Toscana
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677514065.5692.jpg">https://4dcity.org/imgupload/1677514065.5692.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/51164949900_e091afdb87_m.jpg">https://live.staticflickr.com/65535/51164949900_e091afdb87_m.jpg</a>
Opera di Mitoraj e nuvole sulla spiaggia di Viareggio in Versilia
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677512322.6436.jpg">https://4dcity.org/imgupload/1677512322.6436.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/51852585463_e74edd5a0f_m.jpg">https://live.staticflickr.com/65535/51852585463_e74edd5a0f_m.jpg</a>
Semperoper - Saxon State Opera
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677501960.1065.jpg">https://4dcity.org/imgupload/1677501960.1065.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/52642189461_8c50dc68dd_m.jpg">https://live.staticflickr.com/65535/52642189461_8c50dc68dd_m.jpg</a>
Sample test - opera phenix
<p>Sample test</p>
The OPERa Study: Evaluating QoL After Rectal Cancer Surgery
ClinicalTrials.gov study NCT04893876. IPD Sharing: NO. Countries: 0. Publications: 0.
Local Opera Viewing Combined Medical Gymnastics for Elderly PSCI
ClinicalTrials.gov study NCT06458348. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Mr Arbitrium - Opera di Emanuele Giannelli a Palazzo Mediceo di Seravezza (LU) in Toscana
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1656262176.3609.jpg">https://4dcity.org/imgupload/1656262176.3609.jpg</a> <br>
Beijing Opera Percussion Pattern Dataset
<p>The Beijing Opera Percussion Pattern (BOPP) dataset is a collection of audio examples of percussion patterns played by the percussion ensemble in Beijing Opera (Jingju, 京剧). The percussion ensemble in Jingju plays a set of pre-defined and labeled percussion patterns, which serve many functions. The percussion patterns can be defined as sequences of strokes played by different combinations of the percussion instruments, and the resulting variety of timbres are transmitted using oral syllables as mnemonics. More information on the percussion instruments used in Beijing Opera can be found at <a href="http://compmusic.upf.edu/examples-percussion-bo">http://compmusic.upf.edu/examples-percussion-bo</a>.</p> <p>The dataset presented here was used as the training dataset in the referenced paper. A detailed description of percussion patterns in Jingju can also be found in it.</p> <p><strong>DATASET</strong></p> <p>The dataset is a collection of 133 audio percussion patterns spanning five different pattern classes as described below. The scores for the patterns and additional details about the patterns are at: <a href="http://compmusic.upf.edu/bo-perc-patterns">http://compmusic.upf.edu/bo-perc-patterns</a></p> <p><strong>Audio Content</strong></p> <p>The audio files are short segments containing one of the above mentioned patterns. The audio is stereo, sampled at 44.1 kHz, and stored as wav files. The segments were chosen from the introductory parts of arias. The recordings of arias are from commercially available releases spanning various artists. The audio and segments were chosen carefully by a musicologist to be representative of the percussion patterns that occur in Jingju. The audio segments contain diverse instrument timbres of percussion instruments (though the same set of instruments are played, there can be slight variations in the individual instruments across different ensembles), recording quality and period of the recording. Though these recordings were chosen from introductions of arias where only percussion ensemble is playing, there are some examples in the dataset where the melodic accompaniment starts before the percussion pattern ends. </p> <p><strong>Annotations</strong></p> <p>Each of the audio patterns has an associated syllable level transcription of the audio pattern. The transcription is obtained from the score for the pattern and is not time aligned to the audio. The transcription is done using a reduced set of five syllables and is sufficient to computationally model the timbres of all the syllables. The annotations are stored as Hidden Markov Model Toolkit (HTK) label files. There is also a single master label file provided for batch processing using HTK (<a href="http://htk.eng.cam.ac.uk/">http://htk.eng.cam.ac.uk/</a>). </p> <p><strong>Dataset organization</strong></p> <p>The dataset has wav files and label files. The files are named as</p> <pre><code><pID><InstID>.<extension></code></pre> <p>The pID is as in Table 1, instID is a three digit identifier for the specific instance of the pattern, and extension can be .wav for the audio file or .lab for the label file. pID ϵ {10, 11, 12, 13, 14}, InstID ϵ {1, 2, ..., N<sub>pID</sub>}. e.g. The audio file and the label file for the fifth instance of the pattern duotuo is named 12005.wav and 12005.lab, respectively. The master label file is called masterLabels.lab</p> <p><strong>Using this dataset</strong></p> <p>If you use the dataset in your work, please cite the following publication:</p> <blockquote> <p>Ajay Srinivasamurthy, Rafael Caro Repetto, Harshavardhan Sundar, Xavier Serra, "Transcription and Recognition of Syllable based Percussion Patterns: The Case of Beijing Opera," in Proceedings of the 15th International Society for Music Information Retrieval (ISMIR) Conference, Taipei, Taiwan, Oct 2014.</p> </blockquote> <p><a href="http://hdl.handle.net/10230/25677">http://hdl.handle.net/10230/25677</a></p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p><strong>CONTACT</strong></p> <p>If you have any questions or comments about the dataset, please feel free to write to us.</p> <p>Ajay Srinivasamurthy (ajays.murthy@upf.edu)</p> <p>Rafael Caro Repetto (rafael.caro@upf.edu)</p> <p> </p> <p><a href="http://compmusic.upf.edu/bopp-dataset">http://compmusic.upf.edu/bopp-dataset</a></p>
Note Entomologiche e sul Disseppellimento dei Cadaveri a Opera della Fauna Selvatica nel Procedimento penale 104/11 R.G.N.R., 2142/11 R.G. G.I.P. "Caso Goffo"
<p>Perizia relativa a procedimento penale e conseguente bozza in inglese di pubblicazione pronta alla sottomissione</p>
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