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52 results for “real measurements”
D^2EPC BIM-based Digital Twin data model example and real-time building measurements
<p>An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six Case Studies (CERTH nZEB Smart House DIH). The following files are provided:</p><p>i) The BIM-based data model of the building parameters (.json file)</p><p>ii) Building real-time collected measurements within the project (in separate .json files):</p><ul><li>Living room: CO2, temperature, humidity, luminance, presence, PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting & appliances)</li><li>Building PV installation: electrical energy production</li></ul><p> </p>
Hybrid Solar Panel Real Measurements in Switzerland
<h3>Measurements of a hybrid solar panel</h3> <p>Datasheet of the hybrid PV panel (<a title="Datasheet" href="https://cdn.enfsolar.com/Product/pdf/Crystalline/55adc587c2506.pdf" target="_blank" rel="noopener">Here</a>)</p> <p>These measurements have taken place in two places in Valais, Switzerland.</p> <p>One is "Granges" in longitude: 7.4649965° and latitude: 46.2647793° and the other is "Sion" in longitude: 7.364832° and latitude: 46.227372°.</p> <p> </p>
Dataset of vehicle emission measurements in real-world subfreezing winter conditions
<p>Dataset of vehicle emission measurements in real-world subfreezing winter conditions. Measured by chasing the measured vehicle. See Info.txt for description of the data.</p>
Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement", published in <em>Journal of Micromechanics and Microengineering </em>on 05 Nov 2019.</p>
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Real-time measurement and source apportionment of elements in Delhi's atmosphere
<p>Here we present semi-continuous and real-time measurements of elemental composition for PM2.5 and PM10 aerosols in Delhi, India, at a time resolution of 30 min to 1 h during two consecutive winters in 2018 and 2019, to identify the prevailing sources. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, brake wear, a S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-30 Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the pollutants enter Delhi via three distinct air corridors during winters including Nepal and Uttar Pradesh (India) in the east, and Pakistan, Punjab (India) and Haryana (India) in the north-west, during winter, when the national capital’s air quality is at its worst.</p> <p>This repository has excel file corresponding to each figures presented in main text published version.</p>
A versatile microfluidic platform measures hyphal interactions between Fusarium graminearum and Clonostachys rosea in real-time
<p>Routinely, fungal-fungal interactions (FFIs) are studied on agar surfaces. However, this format restricts high-resolution dynamic imaging. To gain experimental access to FFIs at the hyphal level in real-time, we developed a microfluidic platform, a FFI device. This device utilises microchannel geometry to enhance the visibility of hyphal growth and provides control channels to allow comparisons between localised and systemic effects. We demonstrate its function by investigating the FFI between the biological control agent (BCA) <em>Clonostachys rosea </em>and the plant pathogen <em>Fusarium graminearum. </em>Microscope image analyses confirm the inhibitory effect of the necrotrophic BCA and we show that a loss of fluorescence in parasitised hyphae of GFP-tagged <em>F. graminearum </em>coincides with the detection of GFP in mycelium of <em>C. rosea</em>. The versatility of our device to operate under both water-saturated and nutrient-rich as well as dry and nutrient-deficient conditions, coupled with its spatio-temporal output, opens new opportunities to study relationships between fungi.</p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Real measurement of Coulomb diamonds
<p>Data used in the paper "Efficiently measuring a quantum device using machine learning".</p> <p>https://arxiv.org/abs/1810.10042</p> <p>It can be loaded directly from Numpy:</p> <p>import numpy as np</p> <p>data = np.load('data_real.npy')</p> <p> </p>
Simulated Real Measure Data In a Birth Cohort
<pre><span>These data mimic what we can find when observing fetal growth using ultrasounds. </span></pre> <pre><span>They are not observed at the same moments in time for each mother-fetus binomial or in the same number of times. </span></pre> <pre><span>They are data simulated through a transformation of the Wiener process to the non-homogeneous lognormal diffusion process called Gompertz-lognormal.</span></pre>
Measurements of type 3 non-uniqueness between 660 °C and 960 °C for 18SIB02 EMPIR project "Real-K"
<p>Dataset coming from type 3 non uniqueness investigation at INRiM, for project Real-K.</p> <p>The dataset has been collected by 5 test HT-SPRTs, inserted into a sodium heat pipe, calibrated at the fixed points of water, tin, zinc, silver and aluminium. The measurements have been performed at 760, 780, 800, 820, 840 and 860 °C.</p> <p>RPTW_Polito.xlsx are measurements taken on 6 probes at the triple point of water. </p> <p>MAP-HP-SODIO.txt contains measurements performed with 2 reference sensors in order to evaluate the uniformity among the HP wells.</p> <p>note-durante.xlsx contains notes taken during the "MAP-HP-SODIO" measurements</p> <p>760-SODI-2.txt contains the actual measurements of the 5 probes + 1 control probe (in channel 4).</p> <p>calibrations_2023.xlsx contains the calibration measurements and calculations of the test HT-SPRTs</p> <p> </p>
Dataset of KEMAR-BRIRs measured at several positions and head orientations in a real room
<p>Measurement of BRIRs at 25 positions (1-25) in a reverberant room for two sound sources (A,B). The dummy head is rotated by 360° counterclockwise in 5° steps, respectively, at every position. The measuring points are arranged at a distance of 0.5m in x and y axis, respectively. The sound sources are arranged at a distance of 1.0m from the measuring grid.</p> <p>A whole description of the measurement can be found in:<br> http://opac.lbs-ilmenau.gbv.de/DB=1/SET=1/TTL=2/SHW?FRST=1<br> or contact stephan.werner@tu-ilmenau.de</p> <p>Sketch of the measuring grid:</p> <p> A<br> |<br> |<br> 5 - 4 - 3 - 2 - 1<br> | | | | |<br> 10 - 9 - 8 - 7 - 6<br> | | | | |<br> B - -15 - 14 - 13 - 12 - 11<br> | | | | |<br> 20 - 19 - 18 - 17 - 16<br> | | | | |<br> 25 - 24 - 23 - 22 - 21</p>
Real-Time Dataset of Metheorological Measurements Collected During the Project
<p>This dataset contains a collection of real-time meteorological data during the Resisto project. This information has been recorded and is structured into different fields, each representing a specific meteorological variable of the deployed sensors in the Doñana National Park. </p>
Real-Time Dataset of Fire Sensor Measurements Collected During the Resisto Project
<p>This dataset contains real-time environmental measurements from fire detection sensors across multiple locations. These sensors has been deployed on diferent locations, principally on the Doñana National Park. </p>
Video Supplement for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive contains the video supplement 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. The file contains an animation of the V2.1 and NRT average retrieved extinction coefficient between 19.5--21.5 km at 997 nm for the 2024 Ruang eruptions.</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>
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>
Real Operatiοn Data from PV, WT, Battery (measurements from pilot site)
<p>Real Operatiοn Data from PV, WT, Battery (measurements from pilot site)</p>
Remote sensing of the light-obscuring smoke properties in real-scale fires using a photometric measurement method
<p>This data set complements the article <a href="https://doi.org/10.1007/s10694-023-01470-z">https://doi.org/10.1007/s10694-023-01470-z</a>.</p> <p>The data set contains the raw image data of three representative experiments and the corresponding input configuration data files for the LEDSA simulations.</p> <p>The data set contains:</p> <p>- The processed LEDSA simulation data of all experiments, mainly consisting of the measured and normalized intensities of all LEDs and the computed extinction coefficients according to the applied layer discretization (ledsa_simulations.zip).</p> <p>- The recorded measurement data of all experiments from three MIREX devices (MIREX.zip).</p> <p>- An exemplary data set consisting of the LEDSA raw data as RAW image files from three representative experiments in shortened form, containing every third image (image_data_{...}.zip).</p> <p>- LEDSA Input and Config files for analysis and simulation of the provided exemplary image data (ledsa_simulations_short.zip).</p> <p>- Python scripts for reading and post-processing the LESDA simulation data (postprocessing_tools.zip)</p> <p>- Juptyter Lab Python scripts that have been used to create the plots in the associated paper (data_analysis.zip).</p> <p>- The figures from the associated paper (figures.zip).</p> <p>Notes</p> <p>In order to run the Python scripts in Juptyer Labs properly, the given directory structure must be maintained, resulting when all archives are unpacked on the same hierarchical level.</p> <p> </p> <p>The authors gratefully acknowledge the financial support of the German Federal Ministry of Education and Research. The extensive LEDSA computations were performed largely on the high-performance computer system funded as part of the CoBra project with the grant number 13N15497.</p>
Real time temperature measurements during aplication of PFA to rat ventricular tissue
<p>Dataset containing the temperature recordings performed during PFA in ventricular tissue as described in publication : doi: 10.1161/CIRCEP.122.010992.</p> <p> </p> <p> </p>
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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.