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1,481 results for “data processing”
Processed data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the processed data from the Car scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Processed data from Mobile scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the processed data from the Mobile scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Fig. 6. Data processing from a in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 6. Data processing from a stack of 2D images to a 3D model. Image by MNHN.
MPM-CFD entrainment: Processed data and visualization
<p>Data supporting "The role of dilatancy and permeability of wet bed sediments eroded by a granular flow on erosion and runout: Two-phase MPM–CFD simulations". Content:</p> <p>- The <strong>full simulation results</strong> cannot be included as a supplementary material as the files are too large. However, we include the input files, which can be run with the MPM–CFD code (https://github.com/QuocAnh90/Uintah_NTNU/tree/1.0). We also include the processed data from the simulations, which can be used for visualization. </p> <p>- <strong>Processed data</strong> from the MPM–CFD simulations (particles positions and displacements, pore pressure).</p> <p> Each simulation is named with a code. For instance:</p> <ul> <li>dry / wes: dry or wet simulation</li> <li>30: indicates the effective friction angle</li> <li>k-3: -3 is the Log of the D_p parameter</li> <li>Name: data type of the particles: <ul> <li>particlesX: position</li> <li>particlesDispl: displacement</li> <li>particlespwp: pore pressure (absolute value, including "atmospheric pressure"), corrected with a default value of p_atm=101325 Pa</li> <li>particleStress: effective stress tensor</li> <li>pressCC: pore pressure (absolute value, including "atmospheric pressure"</li> </ul> </li> <li>m: material: 1=flow; 2=bed; 0=any material</li> <li>t= (time(s)+1) * 10</li> </ul> <p>- <strong>Matlab files</strong> to process the data and create the figures.</p> <ul> <li>The code Erosion_runout.m serves to plot flow and particle displacements and to calculate the flow runout and erosion volume</li> <li>The code pwp.m serves to plot pore pressure ratio and calculate the median pore pressure ratio</li> <li>The code pwp_evolution.m served to plot the time evolution of the median value of the pore pressure ratio</li> </ul> <p> </p> <p> </p>
Example for USA metrological years 2020 and 2022 results data sets and post processing python code
<p>The files contain LCF model output data for 2020 and 2022 metrological years in the US as well as a python post-processing code to aggregate the reults of these files.</p>
Interview data for 'Risks in the offshore wind supply chain and tendering process impacts: Insights from industry expert elicitations'
<p>Updated 3 category labels to reduce potential for confusion. (v3)</p> <p>Interview data with restored functionality of some unused data analysis methods. (v2)</p> <p>Original upload. (v1)</p>
CORDIS H2020 PROJECT DATA PROCESSED
<p>Dataset about research projects funded under the programme Horizon 2020, from 2014 to 2020. Each row corresponds to the participation of a specific organisation in a specific research project.</p>
CORDIS H2020 PROJECT DATA PROCESSED TEXT
<p>Dataset about research projects funded under the programme Horizon 2020, from 2014 to 2020. Each text file corresponds to a specific research project (grant agreement).</p>
Pre-processed data and trained model weight in ADAF
<h1>Pre-proccesd data</h1> <p>The pre-proccesd data consists of input-target pairs. The inputs include surface weather observations within a 3-hour window, GOES-16 satellite imagery within a 3-hour window, HRRR forecast, and topography. The target is a combination of RTMA and surface weather observations. The table below summarizes the input and target datasets utilized in this study. All data were regularized to grids of size 512 $\times$ 1280 with a spatial resolution of 0.05 $\times$ 0.05 $^\circ$. </p> <table> <tbody> <tr> <td> </td> <td><strong>Dataset</strong></td> <td><strong>Source</strong></td> <td><strong>Time window</strong></td> <td><strong>Variables/Bands</strong></td> </tr> <tr> <td><strong>Input</strong></td> <td>Surface weather observations</td> <td>WeatherReal-Synoptic (Jin et al., 2024)</td> <td>3 hours</td> <td>Q, T2M, U10, V10</td> </tr> <tr> <td><strong>Input</strong></td> <td>Satellite imagery</td> <td>GOES-16 (Tan et al., 2019)</td> <td>3 hours</td> <td>0.64, 3.9, 7.3, 11.2 $\mu m$</td> </tr> <tr> <td><strong>Input</strong></td> <td>Background</td> <td>HRRR forecast (Dowell et al., 2022)</td> <td>N/A</td> <td>Q, T2M, U10, V10</td> </tr> <tr> <td><strong>Input</strong></td> <td>Topography</td> <td>ERA5 (Hersbach et al., 2019)</td> <td>N/A</td> <td>Geopotential</td> </tr> <tr> <td><strong>Target</strong></td> <td>Analysis</td> <td>RTMA (Pondeca et al., 2011)</td> <td>N/A</td> <td>Q, T2M, U10, V10</td> </tr> <tr> <td><strong>Target</strong></td> <td>Surface weather observations</td> <td>WeatherReal-Synoptic (Jin et al., 2024)</td> <td>N/A</td> <td>Q, T2M, U10, V10</td> </tr> </tbody> </table> <p><a href="https://zenodo.org/api/records/14020879/draft/files/2022-10-01_06.nc/content" target="_blank" rel="noopener noreferrer">2022-10-01_06.nc</a> is a sample of pre-proccesd data. The vairables in this file contain the input-target pairs mentioned above.</p> <div> <p>A sample file contains the following variables:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Decription</strong></td> <td><strong>Dimension</strong></td> </tr> <tr> <td>z</td> <td>Topography, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_t</td> <td>T2M from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_q</td> <td>Q from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_u10</td> <td>U10 from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_v10</td> <td>V10 from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>sta_t</td> <td>T2M from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>sta_q</td> <td>Q from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>sta_u10</td> <td>U10 from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>sta_v10</td> <td>V10 from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI02</td> <td>ABI Band 2: visible (red), normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI07</td> <td>ABI Band 7: shortwave infrared, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI10</td> <td>ABI Band 10: low-level water vapor, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI14</td> <td>ABI Bands 14: longwave infrared, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>hrrr_t</td> <td>T2M from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> <tr> <td>hrrr_q</td> <td>Q from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> <tr> <td>hrrr_u_10</td> <td>U10 from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> <tr> <td>hrrr_v_10</td> <td>V10 from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> </tbody> </table> <p> </p> </div> <h1>Pre-comuted normalization statistics</h1> <p><a href="https://zenodo.org/api/records/14020879/draft/files/stats.csv/content" target="_blank" rel="noopener noreferrer">stats.csv</a> is pre-comuted normalization statistics.</p> <h1>Pre-trained model weights</h1> <p><a href="https://zenodo.org/api/records/14020879/draft/files/best_ckpt.tar/content" target="_blank" rel="noopener noreferrer">best_ckpt.tar</a> is the pre-trained model weights.</p> <h1>References</h1> <ol> <li>Jin, W. et al. WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models. (2024).</li> <li>Dowell, D. et al. The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part I: Motivation and System Description. Weather and Forecasting 37, (2022).</li> <li>Tan, B., Dellomo, J., Wolfe, R. & Reth, A. GOES-16 and GOES-17 ABI INR assessment. in Earth Observing Systems XXIV vol. 11127 290–301 (SPIE, 2019).</li> <li>Hersbach, H. et al. ERA5 monthly averaged data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) 10, 252–266 (2019).</li> <li>Pondeca, M. S. F. V. D. et al. The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development. Weather and Forecasting 26, 593–612 (2011).</li> </ol>
Data from: Concurrent temporal channels for auditory processing: oscillatory neural entrainment reveals segregation of function at different scales
Natural sounds convey perceptually relevant information over multiple timescales, and the necessary extraction of multi-timescale information requires the auditory system to work over distinct ranges. The simplest hypothesis suggests that temporal modulations are encoded in an equivalent manner within a reasonable intermediate range. We show that the human auditory system selectively and preferentially tracks acoustic dynamics concurrently at 2 timescales corresponding to the neurophysiological theta band (4–7 Hz) and gamma band ranges (31–45 Hz) but, contrary to expectation, not at the timescale corresponding to alpha (8–12 Hz), which has also been found to be related to auditory perception. Listeners heard synthetic acoustic stimuli with temporally modulated structures at 3 timescales (approximately 190-, approximately 100-, and approximately 30-ms modulation periods) and identified the stimuli while undergoing magnetoencephalography recording. There was strong intertrial phase coherence in the theta band for stimuli of all modulation rates and in the gamma band for stimuli with corresponding modulation rates. The alpha band did not respond in a similar manner. Classification analyses also revealed that oscillatory phase reliably tracked temporal dynamics but not equivalently across rates. Finally, mutual information analyses quantifying the relation between phase and cochlear-scaled correlations also showed preferential processing in 2 distinct regimes, with the alpha range again yielding different patterns. The results support the hypothesis that the human auditory system employs (at least) a 2-timescale processing mode, in which lower and higher perceptual sampling scales are segregated by an intermediate temporal regime in the alpha band that likely reflects different underlying computations.
Combining process-based and data-driven approaches to forecast dune and beach change Data
<p>Data and code associate with "Combining process-based and data-driven approaches to forecast dune and beach change Data" manuscript</p>
Figure 5 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 5 - Example of a scientific name entry.
Figure 1 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 1 - A typical species record from the checklist.
Figure 4 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 4 - Data correctly aligned with columns.
Figure 6 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 6 - Synonym records (highlighted) added and linked.
Figure 2 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 2 - Taxon records imported into a database.
Video Process Mining Evaluation Data
<p>Evaluation data for the case study conducted using <a href="https://github.com/arvidle/video-process-mining-public">https://github.com/arvidle/video-process-mining-public</a></p> <p>Contains object detection, multiple object tracking and spatio-temporal action detection results.<br> Additionally, the training data and weights of the finetuned models are included.</p>
scRNA-seq processed data and code for Losa, Barozzi et al.
<p>scRNA-seq processed data and code for Losa, Barozzi et al. Data from limbs of mouse embryos (E11.5).</p>
Processed data for neurodegeneration vs tumorigenesis analyses
<p>Processed data for neurodegeneration vs tumorigenesis analyses. </p>
Example data for "nctoolkit: A Python package for netCDF analysis and post-processing"
<p>This is data for the coding example in the paper "nctoolkit: A Python package for netCDF analysis and post-processing".</p> <p>The files are projected surface temperature from the the MPI-ESM-2-LR model covering the years 1850-2099, under the SSP5 8.5 scenario and the r1i1p1f1 variant. </p> <p>The files were downloaded from the Earth System Grid Federation: https://esgf-node.llnl.gov/projects/esgf-llnl/</p> <p>Data covers the historical period 1850-2014 and the SSP5 8.5 period 2015-2099.</p> <p>Files are for illustrative purposes only and are unchanged from the originals.</p> <p> </p> <p> </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.