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1,140 results for “TOPS”
NPTP top
Realized within the project of the sourced-based reconstruction of the New Synagogue in Wroclaw/Breslau (Poland). Link to the 3D model documentation: http://www.vfu-oppler.hs-mainz.de/en/wisski/navigate/5667/view. Supported by »The German Federal Government Commissioner for Culture and the Media « (02/2018-11/2019) and »The Foundation for Polish-German Cooperation« (05/2018). Magnified by https://architekturinstitut.hs-mainz.de/. Published under CC-BY-NC-SA 4.0. Source: Objaverse 1.0 / Sketchfab
Top end of pump tube
On a day with exceptionally low tide in the mouth of Køge Å, Zealand, Denmark, a pedestrian discovered this pump tube (2887x1) on the exposed riverbed. Barnacles on the piece suggest that is was brought to the find spot from more saline waters. A likely scenario is that it was caught in a fishing net or discovered by divers and subsequently brought to the harbor and deposited in the harbor; perhaps awaiting reporting to the museum? The manual force pump is broken above the spout, but the iron handle bearing is preserved, its dimensions suggesting that the handle too was made of iron. The handle was connected to the pump rod, moving the piston up and down the tube. In maritime use, a pump like this would have lifted the ship's bilge water to a level above the waterline, where it could be diverted outboard, either plainly over the deck or via a hollow deck beam. The pump is perhaps a ship's most important piece of equipment. Without it, the ship may sink. H: 64cm (+handle 20cm), Ø: 22cm (outer)/14cm (inner). Source: Objaverse 1.0 / Sketchfab
Input data files for simulation: top_hat_cg_supg
Input data files for simulation: top_hat_cg_supg
Amatrice Earthquake - Sentinel-1 TOPS Ascending - Coherence Map (S1A_20160815-S1A_20160827)
<p>Coherence levels of the ascending S1 TOPSAR interferogram (S1A_20160815-S1A_20160827).</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p> <p> </p>
Amatrice Earthquake - Sentinel-1 TOPS - Vertical Motion
<p>Vertical motion component of the Amatrice earthquake from Sentinel-1.</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Amatrice Earthquake - Sentinel-1 TOPS - E-W Motion
<p>E-W motion component of the Amatrice earthquake from Sentinel-1.</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Amatrice Earthquake - Sentinel-1 TOPS Descending - Differential Interferogram (S1A_20160821-S1B_20160827)
<p>Differential S1 TOPS interferogram (S1A_20160821-S1B_20160827) from descending orbit 22.</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/) including TOPS InSAR processing, removal of topographic phase, phase filtering and orthorectification.</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Amatrice Earthquake - Sentinel-1 TOPS Descending - Coherence Map (S1A_20160821-S1B_20160827)
<p>Coherence levels of the descending S1 TOPSAR interferogram (S1A_20160821-S1B_20160827).</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Amatrice Earthquake - Sentinel-1 TOPS Ascending - Differential Interferogram (S1A_20160815-S1A_20160827)
<p>Differential S1 TOPS interferogram (S1A_20160815-S1A_20160827) from ascending orbit 117.</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/) including TOPS InSAR processing, removal of topographic phase, phase filtering and orthorectification.</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Most popular songs on 11/11/2023 from the SoundCloud platform: Top 50
<p>This dataset contains the top 50 songs of all genres from the SoundCloud website and stores them in a CSV. This project corresponds to a practice assignment for 'Tipologia i Cicle de Vida de les Dades' (UOC)</p>
Albums top de CloundSound
<p>This dataset provides a detailed view of the "SoundCloud Top Albums", offering a fairly rich perspective with information about album titles, artists, track count or popularity. With a diversity of records covering varied musical genres. This compilation captures the richness and diversity of emerging music available on the web.</p>
Finanzas, que muestra la evolución, seguimiento, y todo tipo de indicadores y avisos para seguir el top 100 de los valores de mayor intradía a nivel mundial
<p>Este conjunto de datos presenta una recopilación de información sobre la evolución y seguimiento de valores financieros, abarcando una amplia variedad de indicadores y avisos. </p><p>La información incluida abarca datos clave como los precios de apertura, máximos, mínimos, cierre, cierre ajustado, volumen y dividendo de acciones de empresas notables a lo largo de los últimos 5 años.</p>
Survey of storage systems topics covered at top universities
<p>The CSV file contains our survey results with links to the specific courses.<br>The jupyter notebook contains the scripts used to generate the plot and table we use in the paper.<br>To run the jupyter notebook:<br>1. Install required dependencies using `pip install -r requirements.txt`<br>2. Start `jupyter lab`<br>3. Run the cells in order</p>
Top quark pair events for heavy flavour tagging and vertexing at the LHC
<p>This data contains jets from top anti-top decays. The event and parton shower are simulated in Pythia 8 with a centre of mass energy of 13 TeV, with detector response modelled in the Delphes framework. The detector response is modelled on the ATLAS detector, and a mean pileup of 50 was used.</p> <p>The dataset consists of jets, jet constituents, and truth heavy-flavour hadrons. For each jet, up to 50 charged constituents and 5 truth hadrons are included. Each constituent includes a link to the truth hadron associated, if such a link exists. </p> <p>Provided are 5 files, which are detailed below</p> <ul> <li>class_dict.yaml - Details the relative weights for classification labels, based on the frequency of occurrence for a given entry. Labels are detailed below.</li> <li>norm_dict.yaml - Contains the means and standard deviations of variables that can be used for training, allowing for scaling.</li> <li>pp_output_train.h5 - 13.5 million training jets, consisting of 4.5 million b-jets, c-jets, and light-flavoured jets. Resampling is applied over the jet pT and eta, to ensure equivalent kinematic distributions</li> <li>pp_output_val.h5 - 1.35 million jets for validation, consisting of 450,000 of each jet flavour. Kinematics are resampled in the same way as the training file.</li> <li>pp_output_test_ttbar.h5 - 1.35 million jets for evaluation, consisting of 450,000 of each jet flavour, with no kinematic resampling applied.</li> </ul> <p>Each h5 file contains the following groups:</p> <ul> <li>Jets - (N,) - N jets, including variables such as jet kinematics, flavours, and summary statistics on the number of hadrons and constituents in the jet.</li> <li>Consts (N,50) - Up to 50 charged constituents per jet. Includes details on constituent kinematics and identification. A variable 'valid' is True for tracks in the jet, and False for all other tracks. The additional variable 'truth_hadron_idx' details which hadron (if any) a constituent is associated to.</li> <li>Hadrons (N, 5) - Up to 5 truth heavy-flavour hadrons per jet. Each hadron includes details on kinematics. The variable 'hadron_idx' represents an ID for the hadron, and matches to the constituent variable 'truth_hadron_idx'.</li> </ul> <p>Each group contains both variables that can be used in training, and truth labels, which are as follows:</p> <ul> <li>Jets <ul> <li>flavour - Flavour ID of the jet, 5 for b-jets (containing at least 1 b-hadron within a 0.4 dR(jet, hadron) match), 4 for c-jets (no b-hadrons, and contains at least 1 c-hadron), 0 for light-flavoured jets (contains no b- or c-hadrons)</li> </ul> </li> <li>Consts <ul> <li>truth_hadron_idx - integer that refers to hadron that produced the constituent. '-1' for padded tracks, or tracks with no truth heavy flavour hadron (e.g, pileup, hadronisation).</li> <li>truth_vertex_idx - integer that refers to the vertex a constituent came from - if two tracks have an equivalent truth_vertex_idx, they originate from the same vertex.</li> </ul> </li> <li>Hadrons <ul> <li> <div>hadron_idx - The idx of the hadron, value is '-1' for padded hadrons, 0-4 for remaining hadrons. If a constituent 'truth_hadron_idx' matches with this value, then the constituent came from this heavy flavour hadron decay.</div> </li> <li>flavour - The flavour of the hadron. '5' if the hadron contains at least 1 b-quark, '4' if there is no b-quark but a c-quark is present, '-1' for padded hadrons</li> <li>pt, lxy, dr, mass - The transverse momentum (pt) [GeV], transverse distance between hadronic decay vertex and the primary vertex (lxy) [mm], dR(Jet, Hadron), and the hadron truth mass [GeV]</li> </ul> </li> </ul> <p><span>This dataset allows for studies into algorithms that aim to perform vertex reconstruction.</span></p>
380-400R640-650 Block, Top
**380-400R640-650 block at top of subsoil** Location: Wall site (31Or11), Orange County, North Carolina. Period: Late Woodland (AD 1500-1600). Dimensions: length, 20.00 ft (6.10 m) north-south; width, 10.00 ft (3.05 m) east-west; maximum depth, 1.03 ft (0.31 m). Notes: An archaeological excavation at the northeast edge of the Wall site, undertaken by the Research Laboratories of Archaeology, University of North Carolina at Chapel Hill, in 2015. This excavation revealed the tops of six refuse-filled pits and postholes representing parts of two defensive palisades. Model by Steve Davis. Source: Objaverse 1.0 / Sketchfab
SPTP top
Realized within the project of the sourced-based reconstruction of the New Synagogue in Wroclaw/Breslau (Poland). Link to the 3D model documentation: http://www.vfu-oppler.hs-mainz.de/en/wisski/navigate/5667/view. Supported by »The German Federal Government Commissioner for Culture and the Media « (02/2018-11/2019) and »The Foundation for Polish-German Cooperation« (05/2018). Magnified by https://architekturinstitut.hs-mainz.de/. Published under CC-BY-NC-SA 4.0. Source: Objaverse 1.0 / Sketchfab
Roof top survey
The model is focused around the roof and thus side detail is not required.. Mavic Pro 2, Leica GNSS etc.... Source: Objaverse 1.0 / Sketchfab
Tenor Ukulele Moldable - Top (Part 1 of 2)
Injection Moldable design of a Tenor scaled ukulele. Part 1 of 2. See http://www.mind2form.com for more details. Source: Objaverse 1.0 / Sketchfab
Old column top
An Roman column top fragment found at the site of Castra ad Montanesium, Montana, Bulgaria. Composed from 77 images using [Meshroom](https://alicevision.github.io/#meshroom) Location of the artifact: [Montana](https://goo.gl/maps/4WukqWHrCxHCXWuj9) Source: Objaverse 1.0 / Sketchfab
Window Top
Photo realistic Window top 100K triangles Reality Capture. Higher reslolution textures could be possible along with higher density triangle mesh. Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.