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4,325 results for “real”
Real-time solution of coupled Ehrenfest-Maxwell-Pauli-Kohn-Sham equations for a nanoplasmonic dimer, distance d = 0.1 nm
<p>Here we provide the movie mentioned in https://arxiv.org/abs/1812.05049 that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.1 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 6.89 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Data from: Familiar size affects perception differently in virtual reality and the real world
<p>The promise of virtual reality as a tool for perceptual and cognitive research rests on the assumption that perception in virtual environments generalizes to the real world. Here, we conducted two experiments to compare size and distance perception between virtual reality and physical reality (Maltz et al., 2021). In Experiment 1, we used virtual reality (VR) to present dice and Rubik's cubes at their typical sizes or reversed sizes at distances that maintained a constant visual angle. After viewing the stimuli binocularly (to provide vergence and disparity information) or monocularly, participants manually estimated perceived size and distance. Unlike physical reality, where participants relied less on familiar size and more on presented size during binocular vs. monocular viewing, in VR participants relied heavily on familiar size regardless of the availability of binocular cues. In Experiment 2, we demonstrated that the effects in VR generalized to other stimuli and to a higher-quality VR headset. These results suggest that the utility of binocular cues and familiar size differs substantially between virtual and physical reality. A deeper understanding of perceptual differences is necessary before assuming that research outcomes from VR will generalize to the real world.</p>
Efficient Real-Time Selective Genome Sequencing on Resource-Constrained Devices
<p>This dataset contains the curated nanopore raw signal data in <a href="https://www.nature.com/articles/s41587-021-01147-4">BLOW5 format </a>used to benchmark <a href="https://github.com/beebdev/HARU/">Hardware Accelerated Read Until (HARU)</a>. This dataset was created by using the publicly available datasets: <a href="https://community.artic.network/t/links-to-raw-fast5-fastq-data-for-artic-protocol/17">SARS-CoV-2 SP1</a> (1.382M reads) and <a href="https://ncbi.nlm.nih.gov/sra/SRX11368475">NA12878 PromethION subset</a> (500,000 reads). The tarball when extracted will have the following directory structure:</p> <p>haru-data<br> ├── na12878-rfc1<br> │ ├── blow5-rawsignal<br> │ │ ├── na12878_dna_0.blow5<br> │ │ ├── na12878_dna_100.blow5<br> │ │ ├── na12878_dna_101.blow5<br> │ │ ├── na12878_dna_102.blow5<br> │ │ ├── ...<br> │ └── reference<br> │ └── rfc1.fa<br> └── SARS-CoV-2-sp1<br> ├── blow5-rawsignal<br> │ ├── readgroup0<br> │ │ ├── reads_0_0.blow5<br> │ │ ├── reads_0_10.blow5<br> │ │ ├── reads_0_11.blow5<br> │ │ ├── reads_0_12.blow5<br> │ │ ├── ...<br> │ └── readgroup1<br> │ ├── reads_1_0.blow5<br> │ ├── reads_1_10.blow5<br> │ ├── reads_1_11.blow5<br> │ ├── ....<br> └── reference<br> └── nCoV-2019.reference.fasta</p> <p>nCoV-2019.reference.fasta is the SARS-CoV-2 MN908947.3 reference genome. rfc1.fa is the genomic region hr4:39262456-39391375 extracted from hg38 human genome. </p> <p> </p> <p> </p>
Galaxy clustering in real space
<p>Galaxy clustering in real space from BAM galaxy catalogs. Marked and two point correlation function0</p>
Halo power spectrum computed in real and redshift space
<p>Halo power spectrum computed from the halo catalogs in-<br> terpolated on a 4003 mesh, in real (P(k)) and redshift space, the lat-<br> ter represented through the monopole P0(k), the quadrupole P2(k)<br> and the hexadecapole P4(k). The main panels show the mean from<br> 80 SLICS realizations (green dashed line) and the mean from the<br> same number of BAM mocks (solid gray lines). The bottom panels<br> show the ratio between the mean spectra from the BAM mocks to<br> the mean from the SLICS. The gray area in the ratios show the 1σ<br> region computed from the means and their respective errors.</p>
Gauge data used in 'An improved near-real-time precipitation retrieval for Brazil' by Pfreundschuh et al.
<p>The rain gauge measurements were compiled by the National Institute of Meteorology of Brazil 40 and consist of hourly gauge measurements covering the time range May 2000 until May 2020. They are used as reference data 'An improved near-real-time precipitation retrieval for Brazil' by Pfreundschuh et al. and published here in order to ensure reproducibility.</p>
dhaw/fluCodeImperial: Using real-time data to guide decision-making during an influenza pandemic: a modelling analysis
<p><strong>All codes and data used for "Using real-time data to guide decision-making during an influenza pandemic: a modelling analysis" are included in this folder. The file "runExamples.m" contains a step-by-step method for reproducing figures and running model fits. Ensure that all data and code files are in the same directory, then a single execution of “runExamples” on the command line will generate all main and supplementary figures in the manuscript. There is one line of code per figure, clearly marked, that can be commented out as desired. In order to run the MCMC adaptive algorithm, a single line of code, also clearly marked, must be commented back in. Instructions to change the single-state example are given at the top of the file “runExamples.m”. The saved state selection of California (“state=1”) is consistent with all results presented in the manuscript. </strong></p> <p><strong> </strong></p> <p><strong>Plots make use of files from the following sources, with some modifications:</strong></p> <p><strong>Holger Hoffmann (2022). Violin Plot (https://www.mathworks.com/matlabcentral/fileexchange/45134-violin-plot);</strong></p> <p><strong>Evan (2022). Plot Groups of Stacked Bars (https://www.mathworks.com/matlabcentral/fileexchange/32884-plot-groups-of-stacked-bars);</strong></p> <p><strong>John Onofrey (2022). Shaded Plots and Statistical Distribution Visualizations (https://www.mathworks.com/matlabcentral/fileexchange/69203-shaded-plots-and-statistical-distribution-visualizations)</strong></p>
Real-time Alignments for Predicting Decision-Making Time for Diagnostics over NGS Cycles
<p>Input datasets for an interpretable learning approach to predict decision-making time for diagnostics over NGS cycles. The dataset contains alignment files of two clinical sputum samples sequenced with an Illumina MiSeq sequencing device, and following a real-time sequencing protocol.</p> <p>The datasets consist of real-time alignment files for specific sequencing cycle intervals. The alignment has been performed to a database of respiratory microbes. Data preprocessing, including the removal of human host DNA, is integrated into the real-time alignment approach that has been applied to generate the alignments.</p>
Data from: A real data-driven simulation strategy to select an imputation method for mixed-type trait data
<p>Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for selecting a suitable imputation method is advantageous. We invoked a real data-driven simulation strategy to select an imputation method for a given mixed-type (categorical, count, continuous) target dataset. Candidate methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Using a trait dataset of squamates (lizards and amphisbaenians; order: Squamata) as a target dataset, a complete-case dataset consisting of species with nearly completed information was formed for the imputation method selection. Missing data were induced by removing values from this dataset under different missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). For each method, combinations with and without phylogenetic information from single gene (nuclear and mitochondrial) or multigene trees were used to impute the missing values for five numerical and two categorical traits. The performances of the methods were evaluated under each missing mechanism by determining the mean squared error and proportion falsely classified rates for numerical and categorical traits, respectively. A random forest method supplemented with a nuclear-derived phylogeny resulted in the lowest error rates for the majority of traits, and this method was used to impute missing values in the original dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to complete-case data. However, caution should be taken when imputing trait data as phylogeny did not always improve performance for every trait and in every scenario. Ultimately, these results support the use of a real data-driven simulation strategy for selecting a suitable imputation method for a given mixed-type trait dataset.</p>
ReSyRIS: Real-Synthetic Rock Instance Segmentation dataset
<pre># ReSyRIS The Real-Synthetic Rock Instance Segmentation dataset (ReSyRIS) is created for training and evaluation of rock segmentation, detection and instance segmentation in (quasi-)extra-terrestrial environments. It consists of a set of annotated, real images of rocks on a lunar-like surface, a precisely mimicked synthetic version thereof, and respective synthetic assets for training data generation. In the folders, you find the following structure: - `stone_models`: all 36 .obj files of the 3d reconstructed stones - `test_data_realworld`: the real world recordings with accompanying ground truth - `test_data_synthetic`: the synthetic renderings matching approximately the real world recordings, with accompanying ground truth - `oaisys`: config files for rendering synthetic training data with oaisys If you find this dataset useful for your work please consider citing our paper: https://elib.dlr.de/194113/. </pre>
High-rate GNSS Raw Doppler Positive Impact on Cascading Filter-based Approach for Improving Real-time Transient Coseismic Velocities Modeling
<p>The high-rate GNSS average and instantaneous coseismic velocity waveforms for the 2016 Mw 6.6 Norcia earthquake and the 2011 Mw 9.1 Tohoku earthquake are included in this repository.</p>
Location Location Location: Survival of Antarctic biota requires the best real-estate
<p class="Teaser"><span>The origin of terrestrial biota in Antarctica has been debated since the discovery of springtails on the first historic voyages to the southern continent more than 120 years ago. A plausible explanation for the long-term persistence of life requiring ice-free land on continental Antarctica has, however, remained elusive. The default <em>glacial eradication</em> scenario has dominated because hypotheses to date have failed to provide a mechanism for their widespread survival on the continent, particularly through the Last Glacial Maximum when geological evidence demonstrates that the ice sheet was more extensive than present. Here, we provide support for the alternative <em>nunatak</em> <em>refuge</em> hypothesis – that ice-free terrain with sufficient relief above the ice sheet provided refuges and was a source for terrestrial biota found today. This hypothesis is supported here by an increased understanding from the combination of biological and geological evidence, and we outline a mechanism for these refuges during successive glacial maxima that also provides a source for coastal species. Our cross-disciplinary approach provides future directions to further test this hypothesis that will lead to new insights into the evolution of Antarctic landscapes and how they have shaped the biota through a changing climate.</span></p>
Real-time frequency estimation of a qubit without single-shot-readout
<p>Dataset for figures 3a, 3b and 4b in our manuscript.</p>
Effect of magnetic configuration on real-time wall conditioning in DIII-D
<p>Using EMC3-EIRENE modeling, the impact of parallel impurity forces on the edge transport of injected material and ionized impurities, including scrape-off layer (SOL) main ion flows, has been investigated. The study involved comparing impurity powder injections in different divertor configurations, namely DIII-D lower single null, upper single null, and double null configurations, with plasma edge transport and dust migration and ablation modeling. The injections, which were in powder and granular form, were conducted for real-time wall conditioning, ELM control, and divertor power exhaust at DIII-D [1]. Divertor configuration changes resulted in a redirection of SOL flows, which affected the conditioning of plasma-facing components on either the low field side or the high field side. Moreover, the injection location's poloidal shifts could modify the injected materials' penetration depths and trajectories in the plasma boundary. These changes had an impact on the local deposition of materials on plasma-facing components, which is essential for active conditioning and replenishment of functional coatings in future long-pulse scenarios.</p> <p>[1] F. Effenberg <em>et al</em> 2022 <em>Nucl. Fusion</em> <strong>62</strong> 106015 <strong>DOI</strong> 10.1088/1741-4326/ac899d</p>
Simulated and real datacubes for developing and testing changepoint algorithms for spatially correlated, short and noisy time-series
<p>A set of datacubes where z-dimension is time, thus each (x,y,.) is a timeseries. The idea is to detect sudden changes in each series, assuming 1) the series can be quite short and noisy 2) the change occurs in spatial patches. The set has synthetic examples with known change-events, and a real-world dataset with unknown change-events. The data files are related to the pape</p> <p>T Rajala, P Packalen, M Myllymäki, A Kangas (2023): Improving detection of changepoints in short and noisy time-series with local correlations: Connecting the events in pixel neighbourhoods, "Journal of Agricultural, Biological and Environmental Statistics", https://doi.org/10.1007/s13253-023-00546-1</p> <p>More of the NFI data is available from Natural Resources Institute Finland, https://kartta.luke.fi/index-en.html</p> <p>See `data/00data_readme.txt` for further details.</p> <p> </p>
Parcel2D Real - A real-world image dataset of cuboid-shaped parcels with 2D and 3D annotations
<p>Real-world dataset of ~400 images of cuboid-shaped parcels with full 2D and 3D annotations in the <a href="https://cocodataset.org/#format-data">COCO format</a>.</p> <p>Relevant computer vision tasks:</p> <ul> <li>bounding box detection</li> <li>instance segmentation</li> <li>keypoint estimation</li> <li>3D bounding box estimation</li> <li>3D voxel reconstruction (.binvox files)</li> <li>3D reconstruction (.obj files)</li> </ul> <p>For details, see our <a href="https://ieeexplore.ieee.org/abstract/document/10069342">paper</a> and <a href="https://a-nau.github.io/parcel2d/">project page</a>.</p> <p> </p> <p>If you use this resource for scientific research, please consider citing</p> <pre><code>@inproceedings{naumannScrapeCutPasteLearn2022, title = {Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics}, author = {Naumann, Alexander and Hertlein, Felix and Zhou, Benchun and Dörr, Laura and Furmans, Kai}, booktitle = {{{IEEE Conference}} on {{Machine Learning}} and Applications ({{ICMLA}})}, date = 2022 }</code></pre> <p> </p>
Leveraging IoT Data Stream for Near-Real-Time Calibration of City-Scale Microscopic Traffic Simulation
<p>This repository includes input and output data of the methodology presented in the <a href="https://arxiv.org/abs/2210.17315">paper</a> for generating a calibrated dynamic microscopic traffic simulation.</p> <ul> <li>The input data includes the network, initial normalized origin-destination matrix, and hourly traffic counts from stationary city sensors.</li> <li>The output is a 24-hour calibrated microscopic traffic simulation for the city of Tartu, Estonia.</li> </ul> <p>All source codes are available at <a href="https://github.com/Khoshkhah/NRTCalib">https://github.com/Khoshkhah/NRTCalib</a>.<br> </p>
Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: low- and high-altitude slackline walking
<p>It has been indicated that extreme sport activities, despite providing fear, stress and anxiety, also result in a highly rewarding experience. Studies have related this experience to the concept of flow, a positive feeling that individuals undergo when they are completely immersed in an activity. However, little is known about the exact nature of these experiences, and, there are still no empirical results to characterize the brain dynamics during extreme sport practice. This work aimed at investigating changes in psychological responses while recording physiological (heart rate – HR, and breathing rate – BR) and neural (electroencephalographic – EEG) data of eight volunteers, during outdoors slackline walking in a mountainous environment at two different altitude conditions (1 m – low-walk – and 45 m – high-walk – from the ground). Low-walk showed a higher score on flow scale, while high-walk displayed a higher score in the negative affect aspects, which together point to some level of flow restriction during high-walk. The order of task performance was shown to be relevant for the physiological and neural variables. The brain behavior during flow, mainly considering attention networks, displayed the stimulus-driven ventral attention network – VAN, regionally prevailing (mainly at the frontal lobe), over the goal-directed dorsal attention network – DAN. Therefore, we suggest an interpretation of flow experiences as an opened attention to more changing details in the surroundings, i.e., configured as a ‘task-constantly-opened-to-subtle-information experience’, rather than a ‘task-focused experience’.</p>
Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: low- and high-altitude slackline walking
<p>It has been indicated that extreme sport activities, despite providing fear, stress and anxiety, also result in a highly rewarding experience. Studies have related this experience to the concept of flow, a positive feeling that individuals undergo when they are completely immersed in an activity. However, little is known about the exact nature of these experiences, and, there are still no empirical results to characterize the brain dynamics during extreme sport practice. This work aimed at investigating changes in psychological responses while recording physiological (heart rate – HR, and breathing rate – BR) and neural (electroencephalographic – EEG) data of eight volunteers, during outdoors slackline walking in a mountainous environment at two different altitude conditions (1 m – low-walk – and 45 m – high-walk – from the ground). Low-walk showed a higher score on flow scale, while high-walk displayed a higher score in the negative affect aspects, which together point to some level of flow restriction during high-walk. The order of task performance was shown to be relevant for the physiological and neural variables. The brain behavior during flow, mainly considering attention networks, displayed the stimulus-driven ventral attention network – VAN, regionally prevailing (mainly at the frontal lobe), over the goal-directed dorsal attention network – DAN. Therefore, we suggest an interpretation of flow experiences as an opened attention to more changing details in the surroundings, i.e., configured as a ‘task-constantly-opened-to-subtle-information experience’, rather than a ‘task-focused experience’.</p>
Real-space correlation functions of nearest-neighbour spin ice
<p>Real-space correlation functions <span class="math-tex">\(\langle \sigma_i\sigma_j\rangle\)</span> of the nearest-neighbour spin ice Hamiltonian <span class="math-tex">\(H = J \sum_{\langle ij\rangle} \sigma_i\sigma_j\)</span> on the pyrochlore lattice, from Monte Carlo simulations on 128×128×128 cubic unit cells.</p> <p><strong>File formats</strong></p> <p>The file name indicates the temperature (in units of J) where the measurement was taken.</p> <p>Files with extension .dat contain the correlation functions as double-precision real floats.<br> Files with extension .err contain the standard errors as single-precision real floats.</p> <p>Both file types contain an array of shape (4, 4, 4, 128, 128, 128). The meaning of the indices (from major to minor):</p> <ul> <li>sublattice index μ</li> <li>sublattice index ν</li> <li>index λ of FCC lattice point in the cubic unit cell</li> <li>offset of cubic lattice cells</li> </ul> <p>Entry [μ, ν, λ, x, y, z] corresponds to the correlator <span class="math-tex">\(\langle \sigma(\vec R + \vec r^\mathrm{FCC}_\lambda + \vec r_\mu)\sigma(\vec r_\nu)\rangle\)</span>, where <span class="math-tex">\(\vec R = [xyz] \)</span>, the FCC index λ corresponds to the lattice points <span class="math-tex">\(\vec r^\mathrm{FCC}_0 = [000], \vec r^\mathrm{FCC}_1=[011]/2, \vec r^\mathrm{FCC}_2=[101]/2,\vec r^\mathrm{FCC}_3 = [110]/2\)</span>, and the sublattice indices correspond to the site offsets <span class="math-tex">\(r_0 = [111]/8, r_1=[1\bar1\bar1]/8, r_2=[\bar11\bar1]/8,r_3=[\bar1\bar1 1]/8\)</span> from the nearest FCC lattice point. Coordinates x,y,z run between 0 and 127 in periodic boundary conditions.</p> <p>The utility loader.py loads the files, shapes them in the correct array format, and extracts individual correlators.</p> <p><strong>Details of data generation</strong></p> <p>We performed Monte Carlo simulations of nearest-neighbour spin ice using the efficient loop-string algorithm introduced in <a href="https://doi.org/10.1103/PhysRevB.90.220406">Phys. Rev. B 90, 220406(R)</a>. For nonzero temperatures, correlations between different strings were excluded, which effectively averages all spin configurations compatible with a given loop graph. We ran 32 independent Markov chains at each temperature point and obtained 4096 Monte Carlo samples in each. The reported standard errors are the error on the mean of the 32 Markov chains.</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.