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2,353 results for “channel”
IODP Expedition 350 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
IODP Expedition 376 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
Higgs to diphoton channel at least 2 jet datasets
<p>Datasets of Higgs decay to diphoton with the requirement of at least two jets contained in the events. Events are generated with either Powheg or MadGraph at particle level, then processed with Pythia for parton showering and Delphes for detector simulation.</p> <p>All samples contain ggF Higgs events with at least 2 jets. <a href="/api/files/a6021c00-f37a-423d-87fd-2a617f5845de/processed_madgraph_2j_1.0_1000000.npz?versionId=ea79229c-7665-4b4f-949c-6463471fb051">processed_madgraph_2j_1.0_1000000.npz</a> is the MadGraph sample with the nominal value of photon detector resolution. <a href="/api/files/a6021c00-f37a-423d-87fd-2a617f5845de/processed_madgraph_2j_1.2_1000000.npz?versionId=c2cbe56e-412c-49f2-8926-32cc323707df">processed_madgraph_2j_1.2_1000000.npz</a> is the MadGraph sample with the photon detector resolution scaled by a factor of 1.2 (worse resolution). <a href="/api/files/a6021c00-f37a-423d-87fd-2a617f5845de/processed_powheg_2j_1.2_200000.npz?versionId=8b1e772a-ed9f-4257-8e7f-8c60ee904d56">processed_powheg_2j_1.2_200000.npz</a> is the Powheg sample with the photon detector resolution scaled by a factor of 1.2. <a href="/api/files/a6021c00-f37a-423d-87fd-2a617f5845de/processed_madgraph_2j_syst_1000000.npz?versionId=1a490fc5-3b8e-474c-bd12-66227e868ab6">processed_madgraph_2j_syst_1000000.npz</a> is the MadGraph sample with the photon detector resolution uniformly varied from the nominal value by the factor in the range (0.5, 1.5).</p>
X-ray scattering datasets associated with the publication "Side chain length dependent dynamics and conductivity in self assembled ion channels"
<p>X-ray scattering datasets for samples described in the 2022 publication "Side chain length dependent dynamics and conductivity in self assembled ion channels". This dataset includes both raw and processed X-ray scattering data for samples ILC8, ILC10, ILC12, ILC14 and ILC16 alongside background measurement files (BKG).</p>
dataset relate to article: "Functional Characterization of Two Variants at the Intron 6-Exon 7 Boundary of the KCNQ2 Potassium Channel Gene Causing Distinct Epileptic Phenotypes"
<p><strong>Sequencing Analysis performed at Fondazione Besta and carried out as part of the study mentioned at title</strong></p>
Global Channel Belt (GCB)
<p>The Global Channel Belt (GCB) map show the global extent of river channel belts and the morphology of the river channel as either single- or multi-threaded. The data are derived based on a VGG-19 modified CNN classifying 2020 Landsat-8 imagery at a 30 m global resolution.</p>
IODP Expedition 385 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
Results from a National Survey of Canadian Perspectives on Predicting River Channel Migration and River Bank Erosion
<p>This dataset contains findings from a national survey of Canadian perspectives on predicting river channel migration and river bank erosion. The online survey was conducted by researchers from the University of Guelph from November 2, 2021 through January 31, 2022 and received more than 40 responses from regions across Canada.</p>
A 5 m vertical distance to channel network index (VDCNI) across France
<p>The vertical distance to channel network index (VDCNI) expresses the vertical height (in meter) between the elevation of a pixel and the nearest channel. It was derived from the national airborne DTM (RGE ALTI ®) at 5 m spatial resolution, which is available from the website of the French National Geographic Institute (IGN) (<a href="https://geoservices.ign.fr/">https://geoservices.ign.fr/</a>), and the GIS layer of the channel network in the national hydrological database <a href="https://www.sandre.eaufrance.fr/atlas/srv/fre/catalog.search#/metadata/3b3d3c56-d9b6-4625-a57e-ba054e798274">https://www.sandre.eaufrance.fr/atlas/srv/fre/catalog.search#/metadata/3b3d3c56-d9b6-4625-a57e-ba054e798274</a>.</p> <p>Dataset includes:</p> <ul> <li>280 GeoTIFF raster files (VDCNI_000.tif) projected in the French Lambert-93 system (EPSG code 2154), each file corresponding to a 50 x 50 km tile. The number indicates the tile of interest ;</li> <li>1 vector tile index at Google Earth format (tile_index.kmz) showing the location of each tile. This file has been created to facilitate download layer only on area of interest.</li> </ul> <p>To reduce storage space and download time, each raster file has been packed at 7-Zip freeware format.</p> <p>A complete description of the dataset can be found in Panhelleux, L., Rapinel, S., Lemercier, B., Gayet, G., Hubert-Moy, L., 2023. A 5 m dataset of digital terrain model derivatives across mainland France. Data in Brief 109369. https://doi.org/10.1016/j.dib.2023.109369</p>
IODP Expedition 396 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
FIG. 4 in Populations of a new morphotype of corrugate Lessonia Bory in the Beagle Channel, sub-Antarctic Magellanic ecoregion: a possible case of on-going speciation
FIG. 4. — Phylogenetic tree based on concatenated ITS1 and atp8-trnS partial sequences (418 bp). Note that corrugated Lessonia Bory specimens are grouped with Lessonia flavicans Bory specimens which have smooth blade surface. First number on the branches refers to the bootstrap value determined from the ML phylogeny and the second is the posterior probability from the BI analysis. Scale bar: 0.05 substitution per site.
FIG. 5 in Populations of a new morphotype of corrugate Lessonia Bory in the Beagle Channel, sub-Antarctic Magellanic ecoregion: a possible case of on-going speciation
FIG. 5. — Phylogenetic tree based on concatenated cox1 and cox3 partial sequences (1176 bp). Note that corrugated Lessonia Bory specimens are grouped with Lessonia flavicans Bory specimens which have smooth blade surface. First number on the branches refers to the bootstrap value determined from the ML phylogeny and the second is the posterior probability from the BI analysis. Scale bar: 0.01 substitution per site.
FIG. 3 in Populations of a new morphotype of corrugate Lessonia Bory in the Beagle Channel, sub-Antarctic Magellanic ecoregion: a possible case of on-going speciation
FIG. 3. — Internal morphology of the corrugate Lessonia Bory: A, small angular meristodermal cells on the surface of blade; B, transverse section through a sterile portion of the blade; C, detail of meristoderm and cortex showing cells with full (right arrow) and reduced (left arrow) protoplast and golden bodies in outer cortex and mid cortex; and rounded light refracting bodies (arrowheads); D, detail of medulla showing cylindrical cells (arrow) and some elongated filaments (arrowhead) immersed in a dense intercellular matrix; E, surface view of a sorus; F, transverse section through a sorus; G, detail of sorus. Abbreviations: mer, meristoderm; oc, outer cortex; mc, mid cortex; ic, inner cortex; m, medulla; p, paraphyses; l, part of the lacuna; s, sporangia. Scale bars: A, 15 µm; B, 50 µm; C, G, 20 µm; D, E, 40 µm; F, 30 µm.
FIG. 1 in Populations of a new morphotype of corrugate Lessonia Bory in the Beagle Channel, sub-Antarctic Magellanic ecoregion: a possible case of on-going speciation
FIG. 1. — Map of the sub-Antarctic ecoregion of Magellan showing the collection sites of the corrugate morphotype of Lessonia Bory, Lessonia flavicans Bory, and Lessonia searlesiana Asensi & Reviers. Strait of Magellan-Cockburn channel: a, Fuerte Bulnes; b, Carlos III Island. Beagle channel-Orange Bay: c, London Island; d, Puerto Aguirre; e, London Island; f, Cormoran Bay; g, Paula Cove; h, Puerto Toro; i, Tekenika Bay; j, Orange Bay. Cape Horn-Diego Ramirez Island: k, Diego Ramirez Island.
FIG. 2. — A in Populations of a new morphotype of corrugate Lessonia Bory in the Beagle Channel, sub-Antarctic Magellanic ecoregion: a possible case of on-going speciation
FIG. 2. — A, External morphology of the corrugate Lessonia Bory showing the brownish terete stipe; B, corrugated blades; C, that arise from the base of the blade and from dichotomously divided branches; D, the holdfast is rhizoidal in shape and composed mainly of fused haptera. Scale bars: holotype, 30 cm; A, C, 2 cm; B, 5 cm; D, 8 cm.
Towards Efficient Training in Deep Learning Side-Channel Attacks
<p>Datasets used to develop my Master's Thesis <em>Towards Efficient Training in Deep Learning Side-Channel Attacks</em> at Politecnico di Milano. </p> <p>The datasets contain power consumption measurements taken from multiple <em>Riscure Piñata </em>(STM32F4) boards (3) considering multiple keys (11) while executing AES-128.</p> <p>unprotected-AES.zip contains the traces related to the execution of a software unprotected implementation of AES-128.</p> <p>masked-AES.zip contains the traces related to the execution of a software masked implementation of AES-128.</p>
IODP Expedition 354 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
Dataset used in "Comment on "Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam" by Kim-Anh Nguyen, Yuei-An Liou, Ha-Phuong Tran, Phi-Phung Hoang and Thanh-Hung Nguyen"
<p>The Excel file provides all the data included in Tab.4 of Nguyen et al. 2020 plus reflectances extracted from the Landsat 8 OLI image acquired on 14 February 2017 and downloaded from the USGS Earth Explorer website. Observations on the number of pixels falling of water, land and mixed water/land surfaces are provided as well as water percentage cover estimated using regular spaced points. </p> <p>The dataset includes vector files (kml format) of the grids corresponding to the selected L8 pixels as well as the regularly spaced points generated within the selected pixels. These files can be imported in QGIS, Google Earth Pro and other free GIS software.</p>
Datasets for Deep Learning Based Radio Frequency Side-Channel Attack on Quantum Key Distribution
<p>The dataset contains measurements of radio-frequency electromagnetic emissions from a home-built sender module for BB84 quantum key distribution. The goal of these measurements was to evaluate information leakage through this side-channel. This dataset supplements our <a href="https://link.aps.org/doi/10.1103/PhysRevApplied.20.054040">publication</a> and allows to reproduce our results together with the source code hosted at <a href="https://github.com/XQP-Munich/EmissionSecurityQKD">GitHub</a> (and also on <a href="https://doi.org/10.5281/zenodo.7965628">Zenodo</a> via integration with GitHub).<br><br>The measurements are performed using a magnetic near-field probe, an amplifier and an oscilloscope. The dataset contains raw measured data in the file format output by the oscilloscope. Use our source code to make use of it. Detailed descriptions of measurement procedure can be found in our paper and in the metadata JSON files found within the dataset.</p> <p><strong>Commented list of datasets</strong></p> <p>This file lists the datasets that were analyzed and reported on in the paper. The datasets in the list refer to directories here. Note that most of the datasets contain additional files with metadata, which detail where and how the measurements were performed. The mentioned Jupyter notebooks refer to the source code repository https://github.com/XQP-Munich/EmissionSecurityQKD (not included in this dataset). Most of those notebooks output JSON files storing results. The processed JSON files are also included in the source code repository.</p> <p>In naming of datasets,</p> <ul> <li><em>Antenna</em> refers to the log-periodic dipole antenna. All datasets that do not contain `Antenna` in their name are recorded with the magnetic near-field probe.</li> <li><em>Rev1</em> refers to the initial electronics design, while `rev2` refers to the revised electronics design which contains countermeasures aiming to reduce emissions.</li> <li><em>Shielding</em> refers to measurements where the device is enclosed in a metallic shielding and the measurement takes place outside the shielding.</li> <li><em>Rotation</em> refers to orientation of the magnetic near-field probe at the same spacial location</li> </ul> <p><strong>Datasets collected with near-field probe for Rev1 electronics</strong></p> <ul> <li><strong>Rev1Distance</strong>: contains measurements at different distances from the Rev1 electronics performed above the FPGA. The deep learning attack is analyzed in `TEMPEST_ATTACK.ipynb`. The amplitude is analyzed in `get_raw_data_RMS_amplitude.ipynb`.</li> <li><strong>Rev12D</strong>: different locations on a 2d grid at a constant distance from the electronics. The deep learning attack is analyzed in `TEMPEST_ATTACK.ipynb`.</li> <li><strong>Rev130meas2.5cm</strong>: 30 measurements above the FPGA at a hight of 2.5cm. Used to evaluate how much amount of training data affects neural network performance. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`. In particular, `TEMPEST_ATTACK_VARY_TRAINING_DATA.ipynb` is used on this dataset.</li> <li><strong>Rev1Rotation10deg</strong> contains a measurement for varying orientation of the probe at the same location. This is not mentioned in the paper and is only included for completeness. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`.</li> <li><strong>Rev1TEMPESTShieldingFPGA</strong> Measurements with and without shielding at 4cm above the FPGA.</li> <li>- <strong>Rev1TEMPESTShieldingUSBHole</strong> Measurements with shielding in front of a hole of size about 2cm x 2cm. The deep learning attack is analyzed in `TEMPEST_ATTACK*.ipynb`.</li> </ul> <p><strong>Datasets collected with near-field probe for Rev2 electronics</strong></p> <ul> <li><strong>Rev2Distance</strong> contains measurements at different distances from the Rev2 electronics performed above the FPGA.</li> <li><strong>Rev22D</strong> and <strong>Rev22Dstart_7_0</strong> contain measurements on a 2d grid performed on the revised electronics. The dataset is split in two directories because the measurement procedure crashed in the middle. This split structure was kept in order to maintain consistency with the automatic metadata.</li> <li><strong>Rev230meas2.5cm</strong> 30 measurements above the FPGA at a hight of 2.5cm. Used to evaluate how much amount of training data affects neural network performance. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`. In particular, `TEMPEST_ATTACK_VARY_TRAINING_DATA.ipynb` is used on this dataset.</li> </ul> <p><strong>Other datasets</strong></p> <ul> <li><strong>BackgroundTuesday</strong> background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 21st.</li> <li><strong>BackgroundSaturday</strong> background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 11th.</li> <li><strong>AntennaSpectra</strong> Dataset of spectra directly recorded by the oscilloscope. Used to demonstrate ability of telling apart the situation of sending QKD key (standard operation) and having the device turned on but not sending any key at a distance. Analyzed in notebook `Comparing_KeyNokey_Measurements.ipynb`.</li> <li><strong>Rev2ShieldingAntenna</strong> Raw amplitude measurements with log-periodic dipole antenna on Rev2 electronics including shielding enclosure, collected at various distances. None of our attacks against this scenario were successful. The dataset represents a challenge to test more advanced attacks using improved data processing.</li> </ul> <p> </p>
IODP Expedition 369 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
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.