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4,325 results for “real”
Supplementary Data: Real-time optimal flood control decision making under uncertainty
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Pubugou Reservoir data;</li> <li>Source code and results of the Martingale Model of Forecast Evolution (MMFE);</li> <li>Source code and results of the SMAA-2 model;</li> <li>Source code and results of SMAA-TOPSIS model;</li> <li>Source code and results of the stochastic programming with recourse model.</li> </ul>
Data and example reconstruction code for "Investigating the missing wedge problem in small-angle x-ray scattering tensor tomography across real and reciprocal space"
<p>Data sets and example code for the paper "Investigating the missing wedge problem in small-angle x-ray scattering tensor tomography across real and reciprocal space".</p> <p> </p> <p>Requires the software Mumott, see: https://doi.org/10.5281/zenodo.7798530</p>
Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor
<p>Dataset and code to replicate the paper:</p> <p>Orlandi et al., "Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor"</p>
Data set for the figures in the manuscript "Real-Time Identification of Aerosol-Phase Carboxylic Acid Production Using Extractive Electrospray Ionization Mass Spectrometry"
Open the record for dataset details and reuse information.
DMP: Real estate evaluations_Melbourne vs Allegheny County
<p>This is the DMP in PDF format for the Real Estate Evaluation project. The input and output files can be found in the connected GitHub repository. The files can be created by running the Juypter Notebooks that are available in the linked repository. Details on the DMP can be found in the PDF document (this also includes the DOI of the repository connected to the project). </p>
Closed-loop microstimulations of the orbitofrontal cortex during real-life gaze interaction enhance dynamic social attention
Open the record for dataset details and reuse information.
Data for GRL paper "On the move: 2023 observations on real time graben formation, Grindavík, Iceland"
<div><strong>This repository contains following data:</strong></div> <div> </div> <div><strong>1. Seismicity data </strong></div> <div> • file: <em>Grindavik_Relocations.txt </em></div> <div>Earthquake relative locations </div> <div> </div> <div><strong>2. InSAR data </strong></div> <div> • file: <em>Grindavik_nearU_m.tif</em></div> <div>Near-Up vertical displacements during 10-11 November 2023 derived from pixel offset tracking of SAR imagery from ICEYE, COSMO-SkyMed and Sentinel-1 satellites used in Fig. 2.</div> <div>The values are in meters and relative (the 0 is arbitrary).</div> <div>The coordinate system is WGS84 (geographic, EPSG:4326), the resolution is about 30 m East and 20 m North.</div> <div> </div> <div><strong>3. Lidar-derived DEM data </strong></div> <div> • file: <em>2023-11-17_Grindavik_east-west_ground_epsg8088_50cm.tif</em><br> • file: <em>2023-11-23_Grindavik_east-west_ground_epsg8088_50cm.tif</em><br> • file: <em>2023-12-11_Grindavik_east-west_ground_epsg8088_50cm.tif</em></div> <div>Reference coordinate system - ISN2016, EPSG:8088, resolution is 50 cm, used in Fig. 3</div> <div>Time of individual campaigns:</div> <div>2023-11-17 started 13:25 and ended 16:48</div> <div>2023-11-23 started 11:25 and ended 13:41</div> <div>2023-12-11 started 11:08 and ended 13:23<br><br><strong>4. GNSS data</strong><br> • file: <em>de_Pascale_etal_GPS.zip </em></div> <div>24-hour GPS solutions made using Gipsy/Oasis v 6.4.<br>Format: decimal_year east north up (in m) uncertainty_east uncertainty_north uncertainty_up covariance_en covariance_eu covariance_nu time_seconds time_YYYYMMDDhhmmss<br><br> • file: <em>SUDV_rel_VMOS_rt.txt</em><br>Radial and transverse horizontal displacement of SUDV relative to VMOS (5-minute solutions used in Fig. 4)<br>Format: date (YYYY MM DD hh mm ss), along-baseline displacement, across-baseline displacement (in mm)<br> <br> • file: <em>VMOS_a.txt </em><br> • file: <em>GRIC_a.txt </em><br> • file: <em>SUDV_a.txt </em><br>5-minute GPS solutions (in mm)<br>Format: YYYY MM DD hh mm ss north east up<br> <br> • file: <em>calcdisps_5min_10Nov1600_to_12nov0000.dat </em><br>Displacements from 16:00 on Nov 10 to 00:00 on 12 Nov, 2023 <br>Format: station latitude longitude east north up (in mm) uncertainties (in east north up)</div> <p> </p>
Simulations dataset and pre-trained models of "Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory" Ph.D. project
<p>Ph.D. project datasets and models release, <br><em>Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory.</em></p>
Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing - Real and mock datasets
<p>This repository contains the reads, assemblies, and references required to replicate the <strong>real and mock</strong> results presented in the paper: https://doi.org/10.1101/2023.01.31.526521</p>
Experimental datasets on "Real-time Observation of sub-100-fs Charge and Energy Transfer Processes in DNA Dinucleotides"
<p>Experimental datasets referring to the manuscript entitled "Real-time Observation of sub-100-fs Charge and Energy Transfer Processes in DNA Dinucleotides"</p>
Analysis of thermal and dielectric loss features of lunar regolith considering real-time effect solar irradiance
<p>ESI data for "Analysis of thermal and dielectric loss features of lunar regolith considering real-time effect solar irradiance".</p>
Data supporting "A real-time, scalable, fast and resource-efficient decoder for a quantum computer"
<p>Data includes the circuits (stim_circuits.zip) used to create samples to benchmark CC decoder across different noise rates and code sizes. The resulting accuracy and cycle data is in fpga_accuracy_data.csv. The memory footprint (in KB) of the algorithm for different code sizes is in fpga_memory_data.csv.</p> <p>Weights of syndromes for different noise rates for both phenomenological and circuit-level noise at distance d=23 and d=21 are in noise_rate_sampling_full_d23.csv and noise_rate_sampling_full_d21.csv respectively.</p>
Research Compendium for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive is the Reproducible Research Compendium for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques.</p> <p>This compendium includes all files related to MARGE associated with the manuscript.</p> <p>NN model files are split into smaller files for convenience, given their sizes. To recombine the files, do, e.g., <br> cat cnn_weights_NH-LW.h5* > cnn_weights_NH-LW.h5</p> <p>User interested in running MARGE will need to clone the GitHub repo (https://github.com/exosports/MARGE), apply the patch file to checksum fc95b3c, organize the relevant files into directories as listed in the configuration files (Zenodo does not support organizing files into directory structures) and calculate the number of training, validation, and test cases to be stored in the relevant input file specified in the configuration file. MARGE is under the Reproducible Research Software License (https://planets.ucf.edu/resources/reproducible-research/software-license/). For more details on MARGE, see the User Manual on GitHub.</p>
Impact of Ambient Temperature on Light-duty Gasoline Vehicle Fuel Consumption under Real-World Driving Conditions
<p>This dataset includes the vehicle operating (speed, acceleration), fuel consumption rate, and weather condition (temperature, pressure, humidity, heat index) data of Beijing, China.</p> <p>See our published paper for more information: </p> <div> <div>Fan, P., Song, G., Lu, H., Yin, H., Zhai, Z., Wu, Y., Yu, L., 2024. Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions. International Journal of Sustainable Transportation 1–16. <a href="https://doi.org/10.1080/15568318.2024.2385635">https://doi.org/10.1080/15568318.2024.2385635</a></div> </div>
Dataset of paper "Real Driving Emissions—Event Detection for Efficient Emission Calibration"
<p>This dataset includes signal traces and resulting data used in the publication "Real Driving Emissions—Event Detection for Efficient Emission Calibration", MDPI Gases, 2024 (doi: 10.3390/gases4030010).</p> <p>The time trace data is available in a frequency of 1 Hz.</p> <p>The general vehicle specifications are listed below. For further information please refer to the paper.</p> <table> <tbody> <tr> <td> <p><strong>Criteria</strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Criteria</strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p>Vehicle mass</p> </td> <td> <p>> 2000 kg</p> </td> <td> <p>Drivetrain</p> </td> <td> <p>All-wheel drive (AWD)</p> </td> </tr> <tr> <td> <p>Engine</p> </td> <td> <p>Inline 4 cylinder</p> </td> <td> <p>Gearbox</p> </td> <td> <p>Automatic Transmission (AT)</p> </td> </tr> <tr> <td> <p>Max. power</p> </td> <td> <p>> 180 kW</p> </td> <td> <p>EATS</p> </td> <td> <p>Three-way catalytic converter (TWC) and Gasoline particulate filter (GPF)</p> </td> </tr> <tr> <td> <p>Max. torque</p> </td> <td> <p>> 300 Nm</p> </td> <td> <p>Fuel</p> </td> <td> <p>Gasoline</p> </td> </tr> </tbody> </table> <p> </p> <p>Description of files:</p> <ul> <li><strong>Data_Measurement_NOx_Events_HistoryInfluence</strong>: Profile of examplary cycle in 1 Hz with time [s], speed [km/h], NOx mass flow [g/s], downstream lambda sensor voltage (U_HEGO) [V], flag fuel cut-off (b_fco) [-], distance specific NOx intensity for window layout (20 %, 50 % and 80 % History) [g/km], flag for critical window intensity (b_SE for 20 %, 50 % and 80 % history) [-]. (Refers to figure 9 and 10)</li> <li><strong>Data_NOx_RelativePositionOfEmissionIntensityPeak</strong>: Map of relative position of NOx intensity peak within the detected event based on the window layout. The position is displayed over the Window Duration [s] in rows and the History Share [%] in columns. (Refers to figure 8)</li> <li><strong>Data_PN_RelativePositionOfEmissionIntensityPeak</strong>: Map of relative position of PN intensity peak within the detected event based on the window layout. The position is displayed over the Window Duration [s] in rows and the History Share [%] in columns. (Refers to figure 7)</li> <li><strong>Data_NOx_WindowDuration_NumberEvents_AverageDuration_20percHistory</strong>: Overview of statistics for number of detected events in an examplary cycle and average duration of these events for different window durations and a window layout that considers 20 % history for NOx emissions. (Refers to figure 6)</li> <li><strong>Data_PN_WindowDuration_NumberEvents_AverageDuration_20percHistory</strong>: Overview of statistics for number of detected events in an examplary cycle and average duration of these events for different window durations and a window layout that considers 20 % history for PN emissions. (Refers to figure 5)</li> <li><strong>Data_Threshold_Statistics_NOx</strong>: Data for statistical evaluation of the threshold impact for event detection for the NOx emissions. The data are binned at a step size of 0.0001 g/km from 0 to 0.06 g/km (Threshold). For these bins, the detectable share of samples (relative) is listed for only urban samples, rural samples, motorway samples and total number of samples. (Refers to figure 4)</li> <li><strong>Data_Threshold_Window_Speed_Distance_Intensity_NOx</strong>: Data for threshold analysis. Contains the information of the analysis windows for each measurement sample that show a distance of > 0.002 km. For each window, the average window speed [km/h], the travelled window distance [km] the distance specific NOx emissions [g/km] and the integrated NOx emissions within this window [g] are displayed. (Refers to figure 3)</li> <li><strong>Overview_FinalEvent_Duration</strong>: Contains a list with the number of events and the duration of the events for the final event detection results with the named threshold values</li> <li><strong>Overview_FinalEvent_Profiles</strong>: Contains a list with the explicit data of the final event set. The time based data are displayed in a 1 Hz frequency over rows. The first column names the ID of the event, then the time in the cycle [s], the vehicle speed [km/h], the flag fuel cut-off (b_fco) [-], the downstream lambda sensor voltage (U_HEGO) [V], the Lambda value [-] and the NOx mass flow [g/s] are displayed.</li> </ul>
Supplementary Material to "Evaluation of Active Occlusion Effect Cancellation in Earphones by Subjective, Real-Ear and Coupler Measurements"
<p>Supplementary figures for the Publication</p> <p> </p> <p>F. Denk, L. Jürgensen, H. Husstedt (2024) <a href="https://doi.org/10.17743/jaes.2022.0124." target="_blank" rel="noopener"><strong>Evaluation of Active Occlusion Effect Cancellation in Earphones by Subjective, Real-Ear and Coupler Measurements</strong></a>, <em>Journal of the Audio Engineering Society</em> 72(3), pp. 145-160</p> <p> </p> <p>This supplementary material contains 3 types of figures:</p> <ul> <li>Overview_REMall_Levels_T: Overview of 1/6 octave long-term average levels of Real-Ear Measured (REM) sound pressure levels at eardrum for each subject (panel columns), Device (panel rows, rows 1-4 correspond to devices A-D, respectively) and condition (see line color). For better readability, two separate figures 1 and 2 are given for subjects 1-6 and 7-12.</li> <li>Overview_REMall_R: As Overview_REMall_Levels_T, but showing the Occlusion Effect OE, i.e., the difference to the Open-Ear condition.</li> <li>REM_Retest: Test and Retest results for three subjects (individual panels) for which the experiment was repeated. The results for the individual devices are given in separate figures.</li> </ul>
Mg(OH)2 Recovery from Real Bitterns: a Proof of Concept at Pilot Scale
<p>Water, energy, and minerals are fundamental pillars for the future of humankind. Sustainable and renewable productive processes and resources are the only possibility to face the continuously growing global population and high living standards requirements. The present work introduces a proof of concept for the pilot scale production of magnesium hydroxide, Mg(OH)<sub>2</sub>, from waste saltworks bitterns located in the district of Trapani, Italy. Mg(OH)<sub>2</sub> was produced by adopting a proprietary “Magnesium Crystals Granulometry Controlled Reactor”, Mg-CGCR. The influence of several parameters was investigated on Mg(OH)<sub>2</sub> suspensions and powders characteristics: (i) the bittern flow rate, (ii) alkaline sodium hydroxide, NaOH, concentrations solutions (adopted as the precipitant agent) and (iii) the final suspension pH value (stoichiometric or OH<sup>-</sup> excess amounts). A Mg<sup>2+</sup> recovery >99 % can be achieved thanks to the adoption of a product recycling strategy in the reactor. Furthermore, highly pure Mg(OH)<sub>2</sub> powders, addressed by cationic purity, were synthesized. Results demonstrate the possibility of producing highly pure Mg(OH)<sub>2</sub> products from waste-concentrated saline solutions, thus turning waste into valuable compounds.</p>
Eletronica Criativa: Propriedade de LEDS e suas cores, com o Tinkercad e no experimento real
<p>Esta atividade permite comparar dados obtidos por meio do simulador Tinkercad (ciruitos) e o resultado real utilizando os mesmos componentes do simulador. No simulador tinkercad não ha nenhuma diferença no valor da resistencia de leds de cores diferentes submetidos a uma mesma ddp total e associados em paralelo com dois resistores de 10 Kohms. Se houver qualquer diferença na condutividade destes componentes a corrente que atravessara o ramo onde o led se encontra deve ser alterada, produzindo alteraçoes na corrente total do circuito. Temos um video produzido por um kit de baixo custo e que mostra a ddp fornecida em cada parte do circuito e possibilita o calculo da resistencia eletrica de cada um dos leds adicionados ao circuito. Um led vermelho e um led azul. </p> <p>A proposta é mostrar exatamente os limites de uso destes simuladores e a realidade física</p> <p> </p> <p>Video youtube -> Exercicio 1 led vermelho <a href="https://youtu.be/I0GYJcMNa2Q?si=f4d4snklTEcrmkuZ">https://youtu.be/I0GYJcMNa2Q?si=f4d4snklTEcrmkuZ </a></p> <p>Video Youtube -> Exerccio 2 led azul <a href="https://youtu.be/9q3c-bENr6w?si=da0UvzRP4yQlVCur">https://youtu.be/9q3c-bENr6w?si=da0UvzRP4yQlVCur</a> </p> <p> </p>
Fig. 3 in Alta densidad de Lestes macrostigma (Eversmann, 1836) (Odonata: Lestidae) en un humedal de La Mancha Ḣmeda (Ciudad Real, España).
Fig. 3.- Tándem de Lestes macrostigma en la Junta de los Ríos el día 5 de junio de 2019.
Fig. 2 in Alta densidad de Lestes macrostigma (Eversmann, 1836) (Odonata: Lestidae) en un humedal de La Mancha Ḣmeda (Ciudad Real, España).
Fig. 2.- Aspecto de la Junta de los Ríos en la zona de castañuela que se visitó en junio de 2019.
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.