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6,498 results for “Physicality”
New Physics Mining at the Large Hadron Collider: W -> l nu
<p><span class="math-tex">\(W \to \ell \nu\)</span> background events reconstructed by inclusive single-muon selection.</p> <p>Events are represented as an array of physics-motivated high-level features.</p> <p>Details are given in https://arxiv.org/abs/1811.10276</p>
New Physics Mining at the Large Hadron Collider: QCD multijet production
<p>QCD multijet background events reconstructed by inclusive single-muon selection.</p> <p>Events are represented as an array of physics-motivated high-level features.</p> <p>Details are given in https://arxiv.org/abs/1811.10276</p>
Fig. 4 in Physical habitat simulation for small-sized characid fish species from tropical rivers in Brazil
Fig. 4. The velocities and depth comparison between the field measurement and the numerical model for points in 50% cros- s-section width of the Velhas reach (a-b) and in 50% width of the Curimataí reach (c-d).
Chemico-physical soil properties in Lilium pomponium populations
<p>Chemico-physical soil properties in <em>Lilium pomponium</em> populations. Soil data includes silt (g/kg), clay (g/kg), sand (g/kg), total nitrogen (g/kg), active carbonate (g/kg), total carbonate (g/kg), available extractable Ca (meq/100g), cation exchange capacity (meq/100g), electrical conductivity (1:5, dS.m-1), available extractable P (Olsen method, mg/kg), available extractable Mg (meq/100g), pH, available extractable K (meq/100g), carbon/nitrogen ratio, available extractable Na (meq/100g), organic matter (g/kg). Data of populations are reported in relevé locality data table</p>
Geographical distribution of co-authors of Nobel laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine
<p>Geographical distribution of co-authors of Nobel laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine. Appendix to the article «Quantitative analysis of the co-publications of Ukrainian scientists with the Nobel laureates 1994-2018 in Science».</p>
Physical Fitness in Subacute Stroke (Phys-Stroke)
<p>Raw data of efficacy endpoint data and analyses scripts</p> <p> </p> <p>In this version a '.csv' File with the baseline data is added to enable more open access to the provided data.</p>
Test Scene Dataset for Physically Based Rendering
<p>A test scene data set for physically based rendering.<br> The scenes include different geometries, materials and illumination setups of varying complexity to facilitate the evaluation and testing of Monte-Carlo rendering algorithms.<br> The source files are provided in the Blender file format for easy editing and additional exports to the Mitsuba XML format are included.</p>
Knocking on giants' doors I: The associated modelled physical parameters
<p>The dataset contains the modelled gas-phase metallicities and gas masses based on SED-derived dust and stellar properties for 300 ALMA detected dusty star-forming galaxies analysed in Donevski et al. A&A accepted, arXiv:2008.09995. </p>
Poster for the Physics Divisional Library of the Göttingen State and University Library
<p>This poster has been created to celebrate the Physics Divisional Library of the Göttingen State and University Library.<br> https://www.sub.uni-goettingen.de/en/locations-facilities/locations-and-opening-hours/physics-divisional-library/</p>
Intrinsische Motivation von Schülerinnen und Schülern beim Physical Computing im Informatikunterricht
<p>Der KIM-Fragebogen wurde genutzt, um die intrinsische Motivation von Schülerinnen und Schülern bezüglich des Physical-Computing-Unterrichts zu erheben. Anschließend wurde der Fragebogen verwendet um zu untersuchen, welche Physical-Computing-Tätigkeiten besonders positiv auf die intrinsische Motivation wirken können. Zusätzlich wurde in offenen Fragen erhoben, welche Tätigkeiten die Schülerinnen und Schüler im Unterricht besonders mochten. </p>
Children's learning new word-picture pairs during physical exercise in the classroom
<p>This dataset includes memory performance and questionnaire data. In three experiments school aged children performed physical exercises in the classroom while learning new word-picture associations during motion or sitting conditions (repeated measures). The words were in an artificial language. <em>Free recall </em>memory performance was collected from a group of children (aged 8-9 years) in <em>running</em> and sitting conditions (experiment 1). <em>Recognition memory</em> performance was collected from a different group of children (aged 8-9 years) in <em>running</em> and sitting conditions (experiment 2). <em>Recognition memory</em> performance was collected from a different group of children (aged 8-9 years) in <em>stepping</em> and sitting conditions (experiment 3). Experiment 2 showed higher recognition memory performance during the sitting compared to running condition. Gender, English as an additional language (EAL), and whether the children received extra support for language work was recorded before the experiment. A survey after the experiment asked children which condition they preferred. All data were collected by Naomi Kefford in one school in Surrey (UK) during the MSc thesis at the School of Psychology at the University of Surrey. Naomi Kefford was supervised by Peter Klaver. </p>
Maternal and genetic correlations between morphology and physical performance traits in a small captive primate, Microcebus murinus
<p>Physical performance traits are key components of fitness and direct targets of selection. Maternal effects are important components of integrated phenotypes in a variety of species. Yet their contribution to variation in performance, and phenotypes closely associated with performance, remains poorly understood. We used an animal model approach to quantify the contribution of maternal effects to performance trait variation (in bite force and pull strength) and the relationships between performance and the relevant underlying morphology in <i>Microcebus murinus</i>. We show that bite force is heritable (h<sup>2</sup>~0.23), and that maternal effects are also important source of variation, resulting in a medium inclusive heritability (IH<sup>2</sup>~0.47). Grip strength presented a rather low and non-significant narrow-sense heritability suggesting a higher selective pressure on this trait. Genetic correlations between performance traits and their associated morphometric traits were significant and high (0.47 bite force-head width; 0.48 grip strength-radius length), as was the maternal correlation for bite force-head width (0.75). Further studies evaluating the heritability of performance for other taxa and the role of maternal effects are badly needed to better understand the drivers of variation in performance ultimately allowing for a better understanding of the importance of these types of traits in an evolutionary context.</p>
Physical activities data
<p>This upload contains anonymized open data for physical activities.</p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.
<p>This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms </p> <p>We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references). </p> <p>This repository contains 3 different files:</p> <p>TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions</p> <p>TableS2_physical_priors.xlsx - contains three tabs:<br> ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) + ChIP-seq datasets (GSE28350, GSE81521) from (Hochbaum et. al, 2011, Li et. al, 2016).</p> <p>eY1HATAC- contains 3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016).</p> <p>motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1</p> <p>TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources)</p> <p>---</p> <p>Description of methodology to obtain regulatory interactions in TableS2:</p> <p>Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations.</p> <p>For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands.</p> <p>An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs.</p> <p>For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair.</p> <p>For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).</p>
r-process abundances in neutron-rich merger ejecta given different theoretical nuclear physics inputs
<p>This data release contains nucleosynthesis predictions for the r-process abundances presented in Côté, Eichler, Yagüe, Vassh et al. (2021) for compact object merger ejecta based on the publicly available simulation trajectories of Rosswog et al. (2013). All ejecta for the merger scenarios considered here are very neutron-rich (Ye ~ 0.016-0.11). Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating (here with a reheating efficiency of 50%). Results are reported for several different theoretical nuclear physics inputs but all calculations make use of the GEF fission yield prescription (see Vassh et al. 2019). All abundances are given at 1 Myr (10^6 years) post-merger. Please see the README file for more details and references.</p> <p>When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh et al. 2021), and refer to Vassh et al. (2019) and Côté, Eichler, Yagüe, Vassh et al. (2021) for further details on the nuclear data applied as well as Rosswog et al. (2013), Piran et al. (2013), and Korobkin et al. (2012) for further details on the merger ejecta trajectories.</p>
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
<p>Data set for the paper entitled “<strong>Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review</strong>”</p> <p>In this repo, we have some pictures and Excel files.</p> <ul> <li>Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.</li> <li>Excel files are as follows:</li> </ul> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 21.8789%;"><col style="width: 78.1211%;"></colgroup> <tbody> <tr> <td><strong>Excel’s file name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Keyword_analysis </td> <td>In this file, you can see the evolution of our keyword selection.</td> </tr> <tr> <td>Articles_InclusionExclusion_QA </td> <td>In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column.</td> </tr> <tr> <td>Extracted_data </td> <td>In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important.</td> </tr> </tbody> </table> <p> <br>If you have any questions, you can read the corresponding paper and contact the authors.</p>
Dataset of physical, biological and chemical soil properties from 10 European long-term experiments
<p>This dataset contains all measurements that were conducted within the Workpackage #2 of the SoilX Project (2022-2024). The data contains physical, chemical and biological soil parameters that were measured in ten European long-term field experiments, as well as soil management indicators calculated with the SoilManageR packager for R. Each data table contains different parameters, and they can be linked by the identifying columns (LTE, treatment, depth, block, replicate). Further information can be found in the ReadMe.txt.</p> <pre> </pre>
LSD4WSD : An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling
<p><strong>LSD4WSD V2.0</strong></p><p><strong>L</strong>earning <strong>S</strong>AR <strong>D</strong>ataset for <strong>W</strong>et <strong>S</strong>now <strong>D</strong>etection - Full Analysis Version. </p><p>The aim of this dataset is to provide a basis for automatic learning to detect wet snow. It is based on Sentinel-1 SAR GRD satellite images acquired between August 2020 and August 2021 over the French Alps. The new version of this dataset is no longer simply restricted to a classification task, and provides a set of metadata for each sample.</p><p>Modification and improvements of the version 2.0.0 :</p><ul><li><i>Number of massif:</i> add 7 new massif to cover the all Sentinel-1 images (cf `info.pdf`).</li><li><i>Acquisition:</i> add images of the descending pass in addition to those originally used in the ascending pass.</li><li><i>Sample: </i>reduction in the size of the samples considered to 15 by 15 to facilitate evaluation at the central pixel.</li><li><i>Sample: </i>increased density of extracted windows, with a distance of approximately 500 meters between the centers of the windows.</li><li><i>Sample:</i> removal of the pre-processing involving the use of logarithms.</li><li><i>Sample:</i> removal of the pre-processing involving the normalisation.</li><li><i>Labels:</i> new structure for the labels part: dictionary with keys: `topography`, `metadata` and `physics`.</li><li><i>Labels:</i> `physics`: addition of direct information from the CROCUS model for 3 simulations: Liquid Water Content, snow height and minimum snowpack temperature.</li><li><i>Labels:</i> `topography`: information on the slope, altitude and average orientation of the sample.</li><li><i>Labels:</i> `metadata` : information on the date of the sample, the mountain massif and the run (ascending or descending).</li><li><i>Dataset</i>: removal of the train/test split*</li></ul><p>*We leave it up to the user to use the Group Kfold method to validate the models using the alpine massif information.</p><p>Finally, it consists of 2467516 samples of size 15 by 15 by 9. For each sample, the 9 metadata are provided, using in particular the <a href="https://www.umr-cnrm.fr/spip.php?article265&lang=en">Crocus</a> physical model:</p><ul><li>topography:<ul><li>elevation (meters) (average),</li><li>orientation (degrees) (average),</li><li>slope (degrees) (average),</li></ul></li><li>metadata:<ul><li>name of the alpine massif,</li><li>date of acquisition,</li><li>type of acquisition (ascending/descending),</li></ul></li><li>physics<ul><li>Liquid Water Content (km/m2),</li><li>snow height (m),</li><li>minimum snowpack temperature (Celsius degree).</li></ul></li></ul><p>The 9 channels are in the following order:</p><ul><li>Sentinel-1 polarimetric channels: VV, VH and the combination C: VV/VH in linear,</li><li>Topographical features: altitude, orientation, slope</li><li>Polarimetric ratio with a reference summer image: VV/VVref, VH/VHref, C/Cref**</li></ul><p>** The reference image selected is that of August 9th 2020, as a reference image without snow (cf. <a href="https://ieeexplore.ieee.org/document/842004">Nagler&al</a>)</p><p>An overview of the distribution and a summary of the sample statistics can be found in the file info.pdf.</p><p>The data is stored in .hdf5 format with gzip compression. We provide a python script to read and request the data. The script is dataset_load.py. It is based on the h5py, numpy and pandas libraries. It allows to select a part or the whole dataset using requests on the metadata. The script is documented and can be used as described in the README.md file</p><p>The processing chain is available at the following <a href="https://github.com/Matthieu-Gallet/LSD4WSD-dataset"><strong>Github</strong></a> address.</p><p>The authors would like to acknowledge the support from the National Centre for Space Studies (CNES) in providing computing facilities and access to SAR images via the PEPS platform.</p><p>The authors would like to deeply thank Mathieu Fructus for running the Crocus simulations.</p><p><strong>Erratum :</strong></p><p>In the dataloader file, the name of the "aquisition" column must be added twice, see the correction below.:</p><blockquote><p>dtst_ld = Dataset_loader(path_dataset,shuffle=False,descrp=["date","massif","aquisition","aquisition","elevation","slope","orientation","tmin","hsnow","tel",],) </p></blockquote><p>If you have any comments, questions or suggestions, please contact the authors: </p><ul><li>matthieu.gallet@univ-smb.fr</li><li>fatima.karbou@meteo.fr</li><li>abdourrahmane.atto@univ-smb.fr</li><li>emmanuel.trouve@univ-smb.fr</li></ul>
Data package for paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events
<p>This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events".</p>
Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
<div> <div> <p>Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.</p> <p>In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on <strong>two experimental datasets</strong> based on silicon photomultipliers (SiPM) as main transducers:</p> <ol> <li>In the toy experiment, we collect data from a pair of SiPM sensors from a common laser source. The neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. </li> <li>In the electromagnetic calorimeter experiment, we collect data from an eight-channel calorimeter module. Several neural network models (Fully-Connected, Convolutional Neural Network and Long Short Term Memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. </li> </ol> <p>In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely. <strong>The dataset in this repository serves as a basis for similar researches on timing performance of SiPM-based nuclear detectors, and on application of neural networks to typical signals of nuclear radiation detectors.</strong></p> </div> </div>
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.