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4,107 results for “pointing”
Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned~Dataset
<p>This refers to dataset of the paper Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned to appear in IEEE WoWMoM 2023. Those files labeled with "beamsnr" correspond to 60 GHz while others with "data_processed" correspond to CSI at 5GHz. "ENV1" and "ENV2" represent two environments. "orient90" means user performs gestures 90 degree with respect to LoS.</p> <p> </p> <p>Abstract:</p> <p>In recent years, channel state information (CSI) at sub-6 GHz has been widely exploited for Wi-Fi sensing, particularly for activity and gesture recognition. In this work, we instead explore mmWave (60 GHz) Wi-Fi signals for gesture recognition/pose estimation. Our focus is on the mmWave WiFi signals so that they can be used not only for high data rate communication but also for improved sensing e.g., for extended reality (XR) applications. For this reason, we extract spatial beam signal-to-noise ratios (SNRs) from the periodic beam training employed by IEEE 802.11ad devices. We consider a set of 10 gestures/poses motivated by XR applications. We conduct experiments in two environments and with three people. As a comparison, we also collect CSI from IEEE 802.11ac devices. To extract features from the CSI and the beam SNR, we leverage a deep neural network (DNN). The DNN classifier achieves promising results on the beam SNR task with stateof-the-art 96.7% accuracy in a single environment, even with a limited dataset. We also investigate the robustness of the beam SNR against CSI across different environments. Our experiments reveal that features from the CSI generalize without additional re-training, while those from beam SNRs do not. Therefore, retraining is required in the latter case.</p>
Apache Point Observatory Lunar Laser-ranging Operation (APOLLO) normal point data: 2006 through 2020
<p>Normal point data from the Apache Point Observatory Lunar Laser-ranging Operation (APOLLO) covering the 15-year span from April 2006 through the end of 2020. APOLLO measures the earth-moon separation by recording the round-trip travel time of photons from the Apache Point Observatory to five retro-reflector arrays on the moon. The APOLLO data set, combined with the 50-year archive of measurements from other lunar laser ranging (LLR) stations, can be used to probe fundamental physics such as gravity and Lorentz symmetry, as well as properties of the moon itself. These normal points have a median nightly accuracy of 1.7mm, which is an order of magnitude better than other LLR stations. Data is provided in both the "JPL" format (.np) and the Consolidated Laser Ranging Data Format (.crd)</p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event TEST_EVENT
Attached in a .json file is the completed pointing information for 14 observation(s) for the EM counterpart search associated with the gravitational wave event TEST_EVENT. These observations were taken on the DLT40 instrument.
Water retention curve measurement (evaporation + dew point method)
<p>Experimental video series: measurement of the water retention curve with evaporation method (HYPROP), and dew point (WP4C). Explanation of laboratory methodology for measuring soil hydraulic properties. English and Spanish subtitles.</p> <p>Video serie experimental: medición de la curva de retención de agua con método de evaporación (HYPROP), y punto de rocío (WP4C). Explicación metodología en laboratorio para medición de propiedades hidráulicas del suelo. Subtítulos en Inglés y en Español</p>
Comparing thermal regime stages along a small Yakutian fluvial valley with point scale measurements, thermal modeling and near surface geophysics
<ul> <li>Dataset associated with the paper :</li> </ul> <p>"<em>Comparing thermal regime stages along a small Yakutian fluvial valley with point scale measurements, thermal modeling and near surface geophysics</em>"</p> <p>Emmanuel Léger 1 , Albane Saintenoy 1 ,Christophe Grenier 2,† , Antoine Séjourné1 , Eric<br> Pohl2,3 , Frédéric Bouchard4, Marc Pessel1 , Kirill Bazhin5 , Kencheeri Danilov5, François<br> Costard1 , Claude Mugler2 , Alexander Fedorov5 , and Ivan Khristoforov5 and Pavel<br> Konstantinov5<br> 1 Laboratoire Geosciences Paris-Saclay, Université Paris-Saclay, CNRS, GEOPS, 91405, Orsay, France.<br> 2 Laboratoire des Sciences du Climat et de L’Environnement (LSCE), CEA CNRS UVSQ, Université<br> Paris-Saclay, Gif-sur-Yvette, France<br> 3 Department of Geosciences, University of Fribourg, Fribourg, Switzerland<br> 4 Universite de Sherbrooke Département de géomatique appliquée, Sherbrooke, QC, CA<br> 5 Melnikov Permafrost Institute, Yakutsk, Yakutia.</p>
Figure 2 in First data on water mite (Acari, Hydrachnidia) assemblages of Point Rosa Marsh, Harrison Township, Michigan, USA, and their use as environmental bioindicators of aquatic health
Figure 2 Point Rosa Marsh along Lake St. Clair, Harrison Township, Michigan, USA. (A) Taken at
Figure 1 in First data on water mite (Acari, Hydrachnidia) assemblages of Point Rosa Marsh, Harrison Township, Michigan, USA, and their use as environmental bioindicators of aquatic health
Figure 1 Map of Lake St. Clair Metropark with inset showing placement in the Lake St. Clair
Indoor point cloud dataset for BIM related applications
<p>Annotated point cloud of the CRAS labs@FEUP. The point cloud is in ASCII format. Variables: Point X coordinate (m); Point Y coordinate (m); Point Z coordinate (m); Point colour (R); Point colour (G); Point colour (B); Intensity; Label. A total of 21 scans were made, producing 584,701,977 points. The point clouds from the 21 scans were registered with Leica Cyclone Register 360 (3 mm average error). The points were labeled according to 33 classes: 0-unassigned; 1-ceiling; 2-floor; 3-wall; 4-door; 5-window; 6-desk; 7-chair; 8-cabinet; 9-mobile cabinet; 10-shelf; 11-vents; 12-water tank; 13-bin; 14-box; 15-board; 16-computer; 17-screen; 18-printer; 19-vest; 20-switch; 21-paper dispenser; 22-alcohol dispenser; 23-cable; 24-phone; 25-robot; 26-water kettle; 27-stairs; 28-ladder; 29-oil heater; 30-divider; 31-hanger; 32-fan; 33-water dispenser.</p> <p>Additionally, the as-built IFC model of the space is provided in order to test Scan-to-BIM and Scan-vs-BIM algorithms.</p>
Dataset of stereo and multi-channel IRs for a 50-point Lebedev quadrature.
<p>We present a free dataset of Impulse Response measurements for all positions on a 50-point Lebedev arranged loudspeaker array for a variety of stereo microphone configurations, 32 Eigenmic capsules, and up to 4th Order Ambisonics. This dataset has particular relevance for those interested in training novel stereo to ambisonic upmix algorithms. </p>
Points cloud of 3D CAD models of artificial reefs designed by E.Riera from "Unleashing the Potential of Artificial Reefs Design" (txt files)
<p>Here you will find points cloud of 3D CAD models in txt format.</p> <p>These are 3D CAD models of artificial reefs designed by E.Riera from the paper "Unleashing the Potential of Artificial Reefs Design" (<a href="https://doi.org/10.32942/X2G300">https://doi.org/10.32942/X2G300</a>).</p> <p>These txt files (with others not available on open access) have been used to run a script on R "Computation of fractal dimension" to compute the fractal dimension from the point clouds using the Minkowski-Bouligand method (or "box-counting") using the R statistical framework (version 4.0.3) and “est.boxcount” function of the package “Rdimtools” (You and Shung, 2022). </p>
A calibration point for stellar evolution from massive star asteroseismology: supplementary dataset
<p>Dataset related to the article "A calibration point for stellar evolution from massive star asteroseismology" by Burssens et al. 2022 published in Nature Astronomy on the 22nd of June 2023. </p> <p>This dataset contains:</p> <p>1) The frequency list derived from the cycle 2 TESS data set of HD192575 in machine-readable format. Units are provided in the article. </p> <p>2) The MESA/GYRE inlists needed to reproduce models and figures. </p> <p>See the article for more detailed information.</p> <p> </p> <p> </p> <p> </p> <div> </div>
NPM3D dataset with instance label. Dataset used in paper "A Review of Panoptic Segmentation for Mobile Mapping Point Clouds"
<p>NPM3D is a public benchmark for point cloud semantic segmentation, with 10 classes including: ground, building, pole (road sign and traffic light), bollard, trash can, barrier, pedestrian, car, natural (vegetation) and unclassified. Results are evaluated only w.r.t. 9 classes, disregarding the "unclassified" label. The data has been captured with a mapping-grade mobile laser scanning system in different cities in France. There are 4 regions designated for training, all captured in Paris and Lille; and 3 regions for testing, captured in Dijon and Ajaccio. The standard 10-class version described above has actually been derived from a more fine-grained version of the dataset by keeping only the most frequent labels. The original annotations feature 50 different semantic classes (most of which are very rare), and also individual object instance labels for the training regions. For panoptic segmentation, a new version has been generated that still uses the 10 semantic category labels listed above, but also includes instance labels. The classes ground, building and barrier are considered "stuff" and are not separated into instances. As no instance labels are available for the 3 test regions, our version for panoptic (or pure instance) segmentation only contains 4 different regions from Paris and Lille. Instead of a fixed training/test split all experiments therefore use 4-fold cross-validation.</p>
Dataset for Paper "An Event Model for Trace-Based Performance Analysis of MPI Partitioned Point-to-Point Communication"
<p>Test cases, measurement and processing scripts, and measurement data for the referenced paper. </p>
Balancing Accuracy and Evaluation Overhead in Simulation Point Selection
<p>This is the dataset collected for the paper "Balancing Accuracy and Evaluation Overhead in Simulation Point Selection" published at IISWC 2023. It contains 710850 SimPoint configurations and their statistics collected over 22 SPEC2017 benchmarks.</p>
Electric vehicle occupancy of charging points in the city of Paris
<p>Dataset collected by EDF R&D using the <em>Paris Data</em> open data platform, providing real-time occupancy of public charging points for electric vehicles in the city of Paris. This <strong>data.zip</strong> archive should be used at the root of the following gitlab repository (it replaces the empty data folder): <a href="https://gitlab.com/smarter-mobility-data-challenge/additional_materials">smarter-mobility-data-challenge/additional_materials</a>. V1 corresponds to the data provided for the Smarter Mobility Challenge, augmented with exogenous features such as weather and traffic. V2 corresponds to additional raw observations of occupancy data collected and accompanied by initial data processing.</p>
One-point Integral and Turbulent wind Characteristics at the FINO1 Platform (2007-2008)
<p><strong>Dataset</strong>: One-point Integral and Turbulent wind Characteristics at the FINO1 Platform (2007-2008)</p> <p><strong>Authors:</strong> Cheynet, E.</p> <p><strong>Description:</strong></p> <p>This dataset offers a comprehensive collection of wind measurements derived from continuous sonic anemometer recordings at the FINO1 offshore platform during the years 2007 and 2008. The measurements were conducted to investigate the properties and characteristics of wind turbulence in the marine atmospheric boundary layer, especially in relation to atmospheric stability. The data provide insights that have potential implications for the design and safety of offshore structures, particularly wind turbines, as they relate to wind loads. The database includes aggregated mean flow data alongside one-point turbulent characteristics.</p> <p><strong>Tutorial:</strong></p> <p><a href="https://se.mathworks.com/matlabcentral/fileexchange/134446-offshore-wind-turbulence-characteristics-at-fino1">https://se.mathworks.com/matlabcentral/fileexchange/134446-offshore-wind-turbulence-characteristics-at-fino1</a></p> <p><strong>Content Overview:</strong></p> <ul> <li><strong>Format:</strong> Matlab (.mat) files.</li> <li><strong>Data Duration:</strong> The dataset spans two years, from 2007 to 2008.</li> <li><strong>Volume:</strong> 24 separate .mat files, approximately one file for each month of the two-year period.</li> <li><strong>Measurement Tool:</strong> Sonic anemometer positioned at the FINO1 offshore platform.</li> <li><strong>Primary Features:</strong> Aggregated mean flow and one-point turbulent characteristics.</li> </ul> <p> </p> <p><strong>Relevance and Applications:</strong></p> <ol> <li>Exploration of the validity of various turbulence models (e.g., Kaimal or Mann spectral models) in offshore environments.</li> <li>Study of the single-point auto-spectral and cross-spectral densities of wind turbulence.</li> <li>Assessment of the applicability of certain standards, such as IEC 61400-1 and IEC 61400-3, especially in the context of the North Sea wind turbines.</li> </ol> <p><strong>Literature Related to the Dataset:</strong></p> <ul> <li>Cheynet, E., Jakobsen, J. B., & Obhrai, C. (2017). Spectral characteristics of surface-layer turbulence in the North Sea. Energy Procedia, 137, 414-427. Elsevier.</li> <li>Cheynet, E., Jakobsen, J. B., & Reuder, J. (2018). Velocity spectra and coherence estimates in the marine atmospheric boundary layer. Boundary-layer meteorology, 169(3), 429-460. Springer Netherlands.</li> <li>Cheynet, E. (2019). Influence of the Measurement Height on the Vertical Coherence of Natural Wind. Lecture Notes in Civil Engineering, 27, 207-221. Springer.</li> </ul>
PRESCOTT/ESCOTT/iGEMME mutational effect predictions of all single point mutations for about 3000 proteins
<p>PRESCOTT/ESCOTT/iGEMME mutational effect predictions of all single point mutations for about 3000 proteins</p> <p>This dataset contains four tgz files:</p> <p> </p>
Fig.3. P. p. relicta – L in Vantage point surveys of cetaceans (Mammalia, Cetacea) and their interactions with marine birds
Fig.3. P. p. relicta – L. michahellis feeding duo (photo by V. Delov).
Data from: Historical biogeography supports Point Conception as the site of turnover between temperate East Pacific ichthyofaunas
<p>The cold temperate and subtropical marine faunas of the Northeastern Pacific meet within California as part of one of the few eastern boundary upwelling ecosystems in the world. Traditionally, it is believed that Point Conception is the precise site of turnover between these two faunas due to sharp changes in oceanographic conditions. However, evidence from intraspecific phylogeography and species range terminals do not support this view, finding stronger biogeographic breaks elsewhere along the coast. Here I develop a new application of historical biogeographic approaches to uncover sites of transition between faunas without needing an <em>a priori</em> hypothesis of where these occur. I used this approach to determine whether the point of transition between northern and southern temperate faunas occurs at Point Conception or elsewhere within California. I also examined expert-vetted latitudinal range data of California fish species from the 1970s and the 2020s to assess how biogeography could change with the backdrop of climate change. The site of turnover was found to occur near Point Conception, in concordance with the traditional view. I suggest that recent species- and population-level processes could be expected to give signals of different events from historical biogeography, possibly explaining the discrepancy across studies. Species richness of California has increased since the 1970s, mostly due to species's ranges expanding northward from Baja California (Mexico). Range shifts under warming conditions seem to be increasing the disparity between northern and southern faunas of California, creating a more divergent biogeography.</p>
Activaiton of regions at peaks of global signal and random time points
<p>Seven datasets were collected in the study, including: Sleep dataset, Anesthesia dataset, eyes-open/eyes-closed (EO/EC) dataset, two unresponsive wakefulness syndrome (UWS) dataset, REM sleep dataset, and N3 sleep/UWS dataset. The activation of brain regions during the peaks of global signal and random time points were extracted under different arousal conditions.</p>
ScienceDex guides
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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.