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10,554 results for “measurements”
Turbulent flux measurements of the near-surface and residual-layer small particle events
<p>According to recent field studies, almost half of the New Particle Formation (NPF) events occur aloft, in a residual layer, near the top of the boundary layer. Therefore, measurements of the meteorological parameters, precursor gas concentrations, and aerosol loadings conducted at the ground level are often not representative of the conditions where the NPFs take place. This paper presents new measurements obtained during the Turbulent Flux Measurements of the Residual Layer Nucleation Particles, conducted at the Southern Great Plains research site. Vertical turbulent fluxes of 3–10 nm-sized particles were measured using a sonic anemometer and two condensation particle counters with nominal cutoff diameters of 3 nm and10 nm mounted at the top of the 10-m telescoping tower. Aerosol number size distribution (5 to 300 nm) was determined through the ground-based Scanning Mobility Particle Sizers. The size selected (15 to 50 nm) particle hygroscopicity was derived with the Humidified Tandem Differential Mobility Analyzer. The ground-level observations were supplemented by vertically-resolved measurements of horizontal and vertical wind speed and aerosol backscatter. The data analysis suggests that 1) turbulent flux measurements of 3-10 nm particles can distinguish between near-surface and residual-layer small particle events; 2) sub-50 nm particles had a hygroscopicity value of 0.2, suggesting that organic compounds dominate atmospheric nanoparticle chemical composition at the site; and 3) current methodologies are inadequate for estimating dry deposition velocity of sub-10 nm particles because it is not feasible to measure particle concentration very near the surface, in the diffusion sublayer.</p>
Materials used in "Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality"
<p>The materials in this record are the audio and video files, together with various configuration files, used by the "SEAT" software in the study described in</p> <p>Moore, Green, Brookes & Naylor (2022) "Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality"</p> <p>They are shared in this form so that the experiment may be reproduced. For any other use please contact the authors to obtain the original database(s) from which these materials are derived.</p> <p>The materials were created to be compatible with v0.3 of SEAT, which is available from <a href="https://github.com/ImperialCollegeLondon/sap-elospheres-audiovisual-test/releases/tag/v0.3">GitHub</a>. Note that the materials must be placed at <code>C:\seat_experiments\cafe_AV.</code></p>
Measurement of bacterial concentration with FLUIDION ALERT System - Milan site
<p>The dataset includes data collected during the monitoring campaign in Milan in 2019 using ALERT and in 2021 campaign, using ALERT V2, and laboratory determinations, performed in the laboratories of CAP. </p> <p><br> From September 2019 to January 2020 the ALERT LAB was tested, and its outcomes were compared with laboratory determinations. Bacteriological analyses of wastewater samples were performed by the Fluidion ALERT Lab device and in the microbiological laboratory of the CAP Group. The monitoring campaign was implemented in Line 2 of the WWTP, where three different sampling points were selected:<br> • IN-BIO: Before biological treatment, performed with BIOFOR system;<br> • IN-UV: Before the UV disinfection treatment;<br> • OUT-UV: After the UV disinfection treatment (i.e., the final wastewater effluent).</p> <p>A lower number of samples was analysed also from the Line 1 of the WWTP. Sampling points were:<br> • IN-OXI: Before biological oxidation;<br> • IN-PAA: Before the disinfection treatment with peracetic acid;<br> • OUT-PAA: After the disinfection treatment with peracetic acid, corresponding to WWTP effluent.</p> <p> </p> <p>The ALERT V2 SYSTEM has been installed in Peschiera Borromeo WWTP from July 2021 to September 2021, and its outcomes were compared with laboratory determinations.</p> <p> </p>
Supplementary Material to: A secondary zone of uplift measured after megathrust earthquakes: caused by early downdip afterslip?
<p>This archive contains supplementary material to the publication "A secondary zone of uplift measured after megathrust earthquakes: caused by early downdip afterslip?"</p> <p>This archive is divided into two folders. One contains scripts and parameterization used for our subduction zone toy models, input files for use with the Pylith software, and slip optimization utilities. A second folder contains scripts and parameterization for our study of the 2010 Mw8.8 Maule (Chile) earthquake. Note that slip optimization utilities rely on the use of the Classic Slip Inversion python library (https://github.com/jolivetr/csi).</p>
Measurement of several quality/flow parameters at crop field
<p>DWC_Probe(1:6).csv<br> Sentek soil moisture, temperature and salinity data.<br> -WC[depth cm] = volumetric water content at specified depth.<br> -S[depth cm] = volumetric ion content.<br> -T[depth cm] = temperature</p> <p>DWC_GWL(Border,Drip).csv :<br> Groundwater depth measured in the piezometric wells (cm from the soil surface)</p>
Data from: Clustering Deviation Index (CDI): A robust and accurate internal measure for evaluating scRNA-seq data clustering
<div> <div> <p>The clustering of cells has been widely used to explore the heterogeneity of cell populations in single-cell RNA-sequencing (scRNA-seq). We proposed a parametric model for monoclonal and polyclonal scRNA-seq data to evaluate clustering results. Based on the parametric model, we proposed a metric (CDI) to quantify the goodness-of-fit of cell clustering to the data. Here we presented CT26.WT and T-CELL as two datasets to examine the performance of our model and metric. CT26.WT contains wild-type CT26 cells from the murine colorectal carcinoma cell line, and cells in CT26.WT are highly homogeneous. T-CELL contains T-cells from tumor tissue of mice three weeks after 4T1 tumor injection. From these datasets and public datasets, we validated our model and benchmarked our metric.</p> </div> </div>
GLORIA 3-D temperature and ALIMA temperature measurements from flight 12 of the SouthTRAC measurement campaign
<p>This dataset consists of temperature measurements acquired from the German HALO research aircraft during a research flight over Southern Andes on 20-21 September, 2019, as part of the SouthTRAC measurement campaign. </p> <p>ALIMA lidar instrument is developed and operated by the German Aerospace center (Deutsches Zentrum für Luft- und Raumfahrt, DLR). The data included here covers the whole research flight.</p> <p>GLORIA infrared limb imaging spectrometer is jointly developed and operated by the Jülich Research Center (Forschungszentrum Jülich). The data included here is the 3-D temperature retrieval from the hexagonal flight pattern, which was flown from 02:50 UTC till 06:10 UTC on 21 Septermber, 2019.</p>
Viscoelastic properties of suspended cells measured with shear flow deformation cytometry
<p>Numerous cell functions are accompanied by phenotypic changes in viscoelastic properties, and measuring them can help elucidate higher-level cellular functions in health and disease. We present a high-throughput, simple and low-cost microfluidic method for quantitatively measuring the elastic (storage) and viscous (loss) modulus of individual cells. Cells are suspended in a high-viscosity fluid and are pumped with high pressure through a 5.8 cm long and 200 μm wide microfluidic channel. The fluid shear stress induces large, near ellipsoidal cell deformations. In addition, the flow profile in the channel causes the cells to rotate in a tank-treading manner. From the cell deformation and tank treading frequency, we extract the frequency-dependent viscoelastic cell properties based on a theoretical framework developed by R. Roscoe that describes the deformation of a viscoelastic sphere in a viscous fluid under steady laminar flow. We confirm the accuracy of the method using atomic force microscopy-calibrated polyacrylamide beads and cells. Our measurements demonstrate that suspended cells exhibit power-law, soft glassy rheological behavior that is cell cycle-dependent and mediated by the physical interplay between the actin filament and intermediate filament networks.</p>
Data for "Kinetic limitations affect cloud condensation nuclei activity measurements under low supersaturation"
<p>Data for the manuscript "Kinetic limitations affect cloud condensation nuclei activity measurements under low supersaturation" by Tao et al.</p>
I/Q measurements with 5G SRS signals and receiver 4-port 3D Vector Antenna for positioning studies
<p>This dataset contains the I\Q data of four received signals from a 4-port 3D Vector Antenna (3D VA) as well as *fig and *png examples of the angle of arrival (AoA)/azimuth angle estimation using the MUSIC algorithm based on the raw data. The data was collected from four ports (p5, p6, p7, p8) of a 3D VA provided by ENAC. A single Yagi antenna has been used as a transmitter at 2.1GHz carrier frequency and horizontal polarization.</p>
COVID-19: data and indicators to measure the return to work in Area Science Park after the emergency phase
<p>Data to measure the impact of SARS-CoV-2 virus on work, activities and services of Area Science Park and the return to work after the emergency epidemiological phase.</p> <p>Files available:</p> <ul> <li>Report Restart Area</li> <li>Stima_Presenze_Area_Science_Park</li> </ul> <p> </p> <p> </p>
D-TECT: SolPol measurements
<p><strong>National Observatory of Athens, ReACT & University of Hertfordshire - SolPol Solar Polarimeter </strong></p> <p>PIs: Dr. Vassilis Amiridis (vamoir@noa.gr), Prof. William Martin (w.e.martin@herts.ac.uk)<br> co-PI: Vasiliki Daskalopoulou (vdaskalop@noa.gr), Alexandra Tsekeri (atsekeri@noa.gr)</p> <p>Database distribution under CC-BY-SA (CC) rights<br> GitHub: @NOA-ReACT/SolPol</p> <p>Each data folder includes the following for the individual locations of operation:</p> <p>- `Data_Antikythera`:<br> folder containing all the raw .txt data acquired by SolPol when installed in the PANhellenic Geophysical Observatory of Antikythera - PANGEA, Greece<br> measurement period: September 2018 to March 2022<br> location: lat = 35.86099, long = 23.30982,alt = 193</p> <p>\txt: data acquired with default instrument aperture size (@5.5mm) labeled as pol_DDMMYYYY_HHMM(in UTC)_antik_duration.txt</p> <p>\txt_iris: data acquired with different aperture sizes (@4.5mm, 5.5mm and 7mm) labeled as polirisXX_DDMMYYYY_HHMM(in UTC)_antik_duration</p> <p>\txt_dark: closed aperture dark measurements labeled as poldark_DDMMYYYY_HHMM(in UTC)_antik_duration, either with instrument tracker not tracking (NT index) or standard tracking (T index)</p> <p><br> - `Data_Athens`:<br> folder containing all the raw .txt data acquired by SolPol when installed in the National Observatory of Athens (NOA), Athens, Greece<br> measurement period: 18/04/2020 to 14/05/2020<br> location: lat = 37.966295, long = 23.710624,alt = 60</p> <p>\txt: data acquired with default instrument aperture size (@5.5mm) labeled as pol_DDMMYYYY_HHMM(in UTC)_ath_duration.txt</p> <p><br> - `Data_Cyprus`:<br> folder containing all the raw .txt data acquired by SolPol for the duration of the preliminary ASKOS 2019 campaign, in the Cyprus Institute - Nicosia, Cyprus<br> (lat = 35.14063, long = 33.38135,alt = 181)</p> <p>\txt: data acquired with default instrument aperture size (@5.5mm) labeled as pol_DDMMYYYY_HHMM(in UTC)_cyp1_duration.txt</p> <p><br> - `Data_Mindelo`:<br> folder containing all the raw .txt data acquired by SolPol for the duration of the ASKOS 2022 campaign, in the Ocean Science Centre Mindelo (OSCM) - Mindelo, Cape Verde<br> (lat = 16.87775, long = -24.994889,alt = 20)</p> <p>\txt: data acquired with default instrument aperture size (@5.5mm) labeled as poliris55_DDMMYYYY_HHMM(in UTC)_mndl_duration.txt</p> <p>\txt_dark: closed aperture dark measurements labeled as poldark_DDMMYYYY_HHMM(in UTC)_mndl_duration</p> <p><br> ## Text file Header</p> <p>The raw data `header` is:</p> <p>SOLAR POLARIMETER</p> <p>1. `Polarimeter Position [deg]`:<br> Instrument rotational position at 0 and 45 degrees from reference</p> <p>2. `Rotator Position [deg]`:<br> Polarizer position in sets of [0, 40, 130, 220, 310] in degrees</p> <p>3. `PEM Setting [nm]`:<br> Photoelastic Modulator Head operating wavelength, default 550nm</p> <p>4. `Retardation [waves]`:<br> PEM induced retardation</p> <p>5. `Wavelength Filter (Wavelength-Bandwidth)`:<br> Filter wheel filter selection, default 550nm</p> <p>6. `ND-Filter`:<br> Neutral density 0.3 filter (pre-defined)</p> <p>7. `Time (UTC)`:<br> Measurement starting time in HH:MM:SS (UTC)</p> <p>8. `Bias Voltage on Diode`<br> Should be zero if instrument working properly, forward bias</p> <p>9. `LabJack, mean DC (AIN0)`:<br> DC output voltage from the DAQ in Volts</p> <p>10. `LabJack, other`:<br> State zero if instrument works properly</p> <p>11. `Lock-in, 1w`:<br> RMS Voltage output from channel 1 of the Lock-in amplifier, signal phase on resonant frequency (ω) in degrees</p> <p>12. `Lock-in, 2w`:<br> RMS Voltage output from channel 2 of the Lock-in amplifier, signal phase on twice the resonant frequency (2ω) in degrees</p> <p>## Data validity</p> <p>! Data file to be valid needs to contain **5** sets of measurements [1-12 above] for each `Rotator Position [deg]` per `Polarimeter Position [deg]`. <br /></p> <p><br> ! Every valid file stops at a `Polarimeter Position [deg]` == 45 degs & `Rotator Position [deg]` == 310.</p> <p> </p>
Dataset: Ultra-Wideband Ranging Measurements Acquired With Three Different Platforms (Qorvo, TDSR, 3db Access)
<p>This dataset contains distance measurements acquired with three different ultra-wideband (UWB) platforms developed by Qorvo (DW3000), TDSR (P452A), and 3db Access (3DB6380C) at the same locations.</p> <p>The dataset accompanies the paper: "Challenges in Platform-Independent UWB Ranging and Localization Systems" by Laura Flueratoru, Elena Simona Lohan, Dragoș Niculescu, published in the 16th ACM Workshop on Wireless Network Testbeds, Experimental evaluation and Characterization (WiNTECH) 2022. If you find this dataset useful, please consider citing our paper.</p> <p>The dataset (<strong>uwb_multiple_platforms.zip</strong>) contains the following directories:</p> <ul> <li><strong>parallel_measurements</strong> -- The actual dataset, containing all the measurements acquired with the three UWB platforms at the same locations. This directory contains three subdirectories, one for each device. The structure of the subdirectory of each platform is the following: <ul> <li><strong>[location_name] </strong> <ul> <li><strong>[LOSi/NLOSi]</strong> -- where i is the index of the recording and LOS/NLOS indicates whether that recording was acquired in LOS or NLOS <ul> <li><strong>info.csv</strong> -- CSV file which contains information about the recording, such as: the device it was acquired with, the LOS/NLOS condition, the type of obstruction (if any), etc.</li> <li><strong>unaligned_processed_data.csv</strong> -- CSV file which contains the data. Each row has the following fields: timestamp, true distance, measured distance, time of arrival index, channel impulse response (stored as a list), and the LOS/NLOS label.</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>split_train_test_val</strong> -- Datasets that were used to train and test the models from Section 4 from the paper. The datasets contain the same information as the directory <strong>parallel_measurements</strong>, only aligned to the TOA and randomized according to the procedure described in the paper. We include the generated sets to ensure the repeatability of our results.</li> <li><strong>trained_models_error_prediction</strong> -- Models trained for error prediction that were used to obtain the results from Section 4 from the paper.</li> </ul> <p>We also provide code examples for reading the data, training and testing the models, and analyzing the data at the following repository:</p> <p><a href="https://github.com/lauraflu/uwb-multiple-platforms">https://github.com/lauraflu/uwb-multiple-platforms</a></p> <p>The accompanying code is subject to change in the case of bugs/errors.</p> <p>For more information about how the measurements were acquired, please refer to the file <strong>documentation_dataset.pdf</strong>, which includes detailed information about each of the rooms, the device setup, the structure of the directories, etc.</p> <p>For any questions, do not hesitate to contact the authors of the paper.</p> <p><strong>Note</strong>: The dataset (in the <strong>parallel_measurements</strong> directory) contains measurements acquired with the devices at fixed locations and also "free movement" measurements, during which one of the devices was moved freely around a certain area. Therefore, free-movement recordings with the same name but from different devices were <em>not</em> acquired at exactly the same locations, only in the same rooms. The free-movement recordings were not used in the paper (because they do not contain ground truth distances), but we nevertheless include them in this dataset, as they can be useful to test future algorithms.</p>
Benchmarking single-photon sources from an auto-correlation measurement
<p>Dataset of measurements for "Benchmarking single-photon sources from an auto-correlation measurement".</p>
Data for continuous and discrete measurements of carbonate parameters in a productive coastal region in the Northwestern Pacific (36°09'13.5''N, 129°24'04.9''E) from January to September in 2019 and from March to December in 2020
<p><span>Photosynthetic organisms shift the dynamics of surface pCO<sub>2</sub> driven by the sea surface temperature change (thermodynamic driver) by assimilating C from seawater. Here we measured net C uptake </span><span>in a macroalgal habitat</span> <span>of coastal Korea for two years (2019–2020) and found that the macroalgal habitat</span> <span>contributed </span><span>5.8 g</span><span> C m</span><sup><span>-</span><span>2</span></sup><span> month</span><sup><span>-</span><span>1</span></sup><span> of </span><span>the net C uptake during the growing period (the cooling period, September</span><span>-</span><span>May). This massive C uptake changed the thermodynamics-driven seasonal dynamics such that the air</span><span>-</span><span>sea equilibrium of </span><span>pCO<sub>2</sub></span><span> was pushed into disequilibrium. T</span><span>he </span><span>surface </span><span>pCO<sub>2</sub></span><span> dynamics during the cooling period were </span><span>mostly influenced by the seasonal decrease in temperature and the proliferation of macroalgae, while the dynamics </span><span>during the warming period </span><span>(the stagnant period, </span><span>June</span><span>-</span><span>August) </span><span>closely followed that predicted based solely on the change in sea surface temperature only </span><span>(thermodynamic driver)</span><span>.</span><span> In contrast to the phytoplankton-dominated offshore waters (where phytoplankton populations are large in spring and summer), the impact of coastal water macroalgae on surface </span><span>pCO<sub>2</sub></span><span> dynamics was most pronounced during the cooling period, when the magnitude of </span><span>pCO<sub>2</sub></span><span> change was as much as twice that resulting from temperature change. Our study shows that</span><span> t</span><span>he distinctive features of the macroalgal habitat—in particular the seasonal temperature extremes (~18°C difference), the </span><span>active macroalgal metabolism,</span><span> and anthropogenic </span><span>nutrient</span><span> inputs—collectively influenced</span><span> the seasonal decoupling of seawater and air </span><span>pCO<sub>2</sub></span><span> dynamics</span><span>.</span></p>
Houghton House - 360 Measurements (I,B)
Houghton House, Bedford, UK "Houghton House is a ruined mansion house in the parish of Houghton Conquest, Bedfordshire. It is a Grade I listed building, positioned above the surrounding countryside, and commands excellent views. It is said that the house was the model for House Beautiful in John Bunyan's The Pilgrim's Progress." 360 Measurements - Preview (Internal, Black & White) SIAD REF: OX107 Source: Objaverse 1.0 / Sketchfab
Houghton House - 360 Measurements (I,C)
Houghton House, Bedford, UK "Houghton House is a ruined mansion house in the parish of Houghton Conquest, Bedfordshire. It is a Grade I listed building, positioned above the surrounding countryside, and commands excellent views. It is said that the house was the model for House Beautiful in John Bunyan's The Pilgrim's Progress." 360 Measurements - Preview (Internal, Coloured) SIAD REF: OX107 Source: Objaverse 1.0 / Sketchfab
Trinity Buoy Wharf - 360 Measurements (E,C)
Trinity Buoy Wharf, London, UK "Docklands site providing artists' studios and gallery space, rehearsal rooms, cafe and diner" 360 Measurements - Preview (External, Coloured) SIAD REF : OX112 Source: Objaverse 1.0 / Sketchfab
Automated Performance Regression Detection in Microservice Architectures - Raw Measurements
<p>These measurements contain a ZIP folder in which influxdb dumps of the several load testing runs of the corresponding bachelor's thesis "Automated Performance Regression Detection in Microservice Architectures" can be found. The dumps are named by date. The two dumps which are additionally named, contain the data sets which were used for the final evaluation.</p>
Cognitive Styles Measurements
<p>Data sets used for my study (Unkept promises of cognitive styles).</p> <p>You can try to predict CSI scores with personality inventory</p>
ScienceDex guides
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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.