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86 results for “Laboratory measurement”
The laboratory data of the measurements carried out on an n-decane saturated limestone sample
<p>This supporting information provides the numerical results of the laboratory experiments conducted on an n-decane saturated limestone sample with varying dead fluid volume, which correspond to the data presented in manuscript "The Effect of Boundary Conditions on the Elastic Moduli Measurements at Low Frequencies" submitted to Journal of Geophysical Research: Solid Earth.</p>
Results of statistical analyses of the comparison of phosphorous measurement laboratory methods
<p>The study in the related article compares the phosphorus (P) analysis methods of ammonium lactate (AL), Mehlich 3 (M3); water extraction (P-WA(P)&P-WA(PO<sub>4</sub>)), cobalt hexamine (CoHex) and X-ray fluorescence (XRF, as an estimate of total soil P). The ratio of the P-content/XRF was calculated and compared with the whole dataset first. Based on the comparison of all the data there were significant differences between the results of P-WA(P) and P-WA(PO<sub>4</sub>) vs M3 and AL, CoHex vs M3 and CoHex vs AL methods (p<0.001). The influencing factors were also analysed for a more in-depth understanding of their role (CaCO<sub>3</sub>-content, pH, soil texture and clay content). The file contains the results of the statistical analyses.</p>
Dataset for "Characterization of a modified printed optical particle spectrometer for high-frequency and high-precision laboratory and field measurements."
<p><strong>The folder includes scripts and data to create figures in the manuscript titled "Characterization of a modified printed optical particle spectrometer for high-frequency and high-precision laboratory and field measurements."</strong></p>
Muon Scattering Radiography (MSR) measurements on blocks of ice in laboratory, and on simulated snowpack
<p>Experimental setup (scenario 5):</p> <p>Muon data used in this work has been collected with our muon detection system. This muon monitoring system is currently in use for both scientific and industrial purposes <a href="https://www.zotero.org/google-docs/?broken=RG5rWA">(Martínez-Ruiz del Árbol et al., 2022)</a>. The particle detectors are composed of four Multi-Wire Proportional Chambers (MWPC) and each chamber has two layers with 224 detection wires, all of them separated by 4 mm. The two layers form a two-dimensional grid of wires which covers an area of 89.6 x 89.6 cm and detects the positions where muons cross it.</p> <p>When a muon event is identified, our system detects four points located in the horizontal two-dimensional grids, two points before the particle goes through the target and another two points after the particle traverses it. With this data, way-in and way-out trajectories can be reconstructed, and muon deviations calculated. Specifically, in the numerical analysis of this work, we utilised the projection of muon deviations in two planes perpendicular to the detection wires.</p> <p>Simulation setup (scenarios 1 to 4):</p> <p>The snowpack was simulated using a one-dimensional snow model forced by surface meteorological data. We have used the SNOWPACK model <a href="https://www.zotero.org/google-docs/?EqYNAT">(Bartelt & Lehning, 2002</a><a href="https://www.zotero.org/google-docs/?LUKxAu">)</a> to realistically simulate the behaviour of the snowpack along two seasons, 2015/2016 (1_Modelling) and 2016/2017 (2_Testing). SNOWPACK was forced by the ERA5-Land surface reanalysis <a href="https://www.zotero.org/google-docs/?QCLBxK">(Muñoz-Sabater et al., 2021)</a>. The simulations were performed in the Pyrenees, using the ERA5-Land cell whose centroid falls closer to the Monte Perdido massif (42.7°N, -0.1°E), at an elevation of 2041m asl.</p> <p>We coupled the SNOWPACK simulations with a full MSR simulation setup that uses the Cosmic RaY generator <a href="https://www.zotero.org/google-docs/?oSIPTu">(Hagmann et al., 2012)</a> to reproduce the atmosphere muon flux and GEANT4 <a href="https://www.zotero.org/google-docs/?3ZDRDN">(Agostinelli et al., 2003)</a> to simulate the muon scattering caused by the snowpack. GEANT4 is a state-of-the-art software designed and maintained at CERN to simulate the interactions of particles and matter in high-energy and nuclear physics. Our simulation framework contains a model of our experimental setup including the muon detectors and their response. This framework has been successfully applied to multiple industrial problems, for instance, to steel-made pipe wear <a href="https://www.zotero.org/google-docs/?F8nYbS">(Martínez-Ruiz del Árbol et al., 2018)</a>. Similar simulation frameworks are typically used to research applications of muography <a href="https://www.zotero.org/google-docs/?115QeU">(Mori et al., 2017)</a>.</p> <p>We expanded the one-dimensional snowpack geometry to a 1m² snow column, assuming homogeneous snow layers in the longitude and latitude dimensions. Then, we propagated and measured muons penetrating the whole snow column, virtually reproducing the detection process using GEANT4. We collected muon deviations and their Root-mean-square (RMS) value for different accumulations of snow during the two simulated seasons.</p>
Flow velocity measurements over a migrating train of dunes in a flume in the laboratory
<p>Acoustic Doppler Velocimeter (ADV) measurements conducted over a migrating train of dunes in a flume in the laboratory. The files with an .ntk extension are the raw data as measured and recorded by the instrument (Nortek Vectrino Profiler) and those with an extension .mat are the raw data as exported from the original software into a MatLab readable file format. <br> The data was used to create a streamwise flow velocity profile in the publication associated with this dataset. <br> File names have a nominal distance to the bed in mm expressed by the numbers at the end of the name. For example, 00_10 indicates measurements from 0 to 10 mm. However, as the measurements were conducted over a migrating train of dunes, those numbers are not as precise. However, the instrument records the distance to the bed and it is available inside the files. The distance inside the files is the one used to create the figure for the publication. <br> <br> In the upcoming publication the data was used to plot figure 4(d)<br> <br> The figure is available as 4D in the preprint found in this link: https://www.researchsquare.com/article/rs-1370465/v1</p> <p> </p>
Absolute Gravity Measurements in the Mass Laboratory of ILNAS in Capellen, Luxembourg
<p>This report contains the results of absolute gravity measurements carried out in the Mass Laboratory of ILNAS in Capellen, Luxembourg. The measurements took place on the floor tile next to the pillar where the mass comparators are installed. The absolute gravimeter FG5X#216 was operated by Olivier Francis from the Geophysics Laboratory of the University of Luxembourg which is also a Designated Institute (DI) for gravity by the Bureau Luxembourgeois de Métrologie (BLM).</p>
Movies, temperature and pressure measurements associated with the study "Volumetric changes of mud on Mars: evidence from laboratory simulations"
<p>Movies, temperature and pressure measurements logs associated with the study "Volumetric changes of mud on Mars: evidence from laboratory simulations" that are showing behavior of muds with different viscosities.</p>
Data accompanying the publication: Measurements and modelling of pore-pressure gradients in the swash zone under large-scale laboratory bichromatic waves
<p>Data accompanying the publication: Measurements and modelling of pore-pressure gradients in the swash zone under large-scale laboratory bichromatic waves. See the README.txt file for more detail.</p>
Stability Measurement of a 340 nm UV Source at the Optical Laboratory of BFKH.
<p>Dataset archive of the stability Measurement of a 340 nm UV Source at the Optical Laboratory of BFKH.</p><p>The project 19ENV02 RemoteALPHA has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. - https://remotealpha.drmr.nipne.ro/</p>
Data S1. Laboratory behavioral data from: Measuring the fitness advantage conferred by autotomy in the wild
Open the record for dataset details and reuse information.
Laboratory mesocosm data measuring the impact of bioturbation frequency on greenhouse gas emissions from reservoir sediments
Inland aquatic systems are major global contributors to the atmospheric carbon budget through greenhouse gas (GHG) emissions, although the amount and form of carbon released varies widely across and within systems. Bioturbation of aquatic sediments can impact biogeochemical conditions and physically release sediment-bound bubbles containing GHGs, but variation in the frequency of such disturbance may modify the rate and composition of resulting GHG emissions. We hypothesized that an intermediate bioturbation frequency would result in the greatest methane (CH4) releases due to mechanical release of trapped bubbles, while frequent disturbance would result in greater diffusive carbon dioxide (CO2) releases relative to CH4, due to increased aeration of the sediment. We tested this bioturbation frequency hypothesis using laboratory mesocosms containing homogenized reservoir sediment. We used mechanical disturbance to simulate bioturbation at 3, 7, 14, or 21-day intervals; a control treatment was undisturbed for the duration of the experiment. We measured GHG emission (ebullition and diffusion) rates. An intermediate frequency of disturbance (7 days) produced the highest total GHG emission rate, while the most frequent disturbance interval (3 days) and least frequent interval (0 days) reduced overall GHG emissions relative to weekly disturbance by 24% and 15%, respectively. These patterns were primarily driven by differences in CH4 ebullition. Contrary to our hypothesis, there was no relationship between disturbance frequency and diffusive CO2 emissions. For all disturbance treatments, the majority of ebullition occurred during disturbance events, suggesting mechanical release of entrapped bubbles is an important emission mechanism. The frequency of disturbance has variable effects on GHG emissions and may explain conflicting results in prior studies of bioturbation. Our study provides insight into bioturbation as a driver of within-system variation in GHG emissions and h
Laboratory spectral reflectance measurements of pine/spruce twigs and snow
<p>The dataset contains laboratory spectral reflectance (350-2500 nm) measurements of natural snow samples and pine and spruce twigs measured with an ASD Field Spec Pro JR spectroradiometer. For snow types sampled, in situ snow measurements are accompanied with the reflectance data. The measurements have been conducted at the premises of the Arctic Space Centre of the Finnish Meteorological Institute, Sodankylä, Finland (N67.361833, E26.634154, WGS84).</p> <p>The data are structured as CSV-files. Snow specific surface area (SSA) information and photographs of the snow samples are available for part of the measured snow types and are organized into separate folders.</p> <p>For this version a data example plot (Data_example_laboratory.png) was added to have a quick visualisation of the sort of the data available.</p> <p>For further information contact Henna-Reetta Hannula (henna-reetta.hannula@fmi.fi) or Kirsikka Heinilä (kirsikka.heinila@ymparisto.fi)</p>
Experimental measurements of the effects of surface roughness on large-scale downburst-like impinging jets at the WindEEE Dome laboratory
<p>Thunderstorm downbursts originate as negatively buoyant currents of cold air descending from cumulonimbus clouds. Upon impacting the ground, a strong radial outflow develops with maximum wind velocities occurring at the near-ground level. These types of flows pose serious hazard to the natural and built environment. Their restricted time and spatial extent as well as their intermittent and non-Gaussian fluctuating nature make them extremely challenging to be recorded and analyzed through classic full-scale measurements in nature. Alongside synoptic-scale extra tropical cyclones, downbursts govern the wind climate at the mid-latitude areas around the globe. Recent trends in climate change studies suggest both an increased intensity as well as frequency of occurrence of these events. Therefore, their scientific comprehension urges serious consideration.</p> <p>In the context of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC) Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa), an extensive experimental campaign was recently conducted at the WindEEE Dome wind chamber. This campaign focused on measuring downburst-like flows (DLFs) generated by large-scale impinging jets. The dataset presented here encompasses a portion of the measurements collected during this comprehensive experimental initiative. Specifically, this series of tests aimed to unravel the role of surface roughness in potentially altering the configuration and dynamics of the overall radial outflow. The term "surface roughness" pertains to the ground patch under examination, encompassing both the natural features of the terrain and any obstacles, such as buildings, present on the ground. While surface roughness plays a decisive role in changing the shape and magnitudes of the wind speed vertical profiles for extra-tropical cyclones, the scientific literature has not yet thoroughly addressed its impact on downburst winds, which are different being dominated by intense vortex dynamics.</p> <p>Impinging jets, considered representative for the simulation of downburst like flow (DLF), are here simulated as transient phenomena through the opening and closing of the bell-mouth that connects the test chamber and the upper plenum of the dome, the latter being pressurized before releasing the jet. As a result, the velocity records exhibit a distinct pattern, featuring a sudden ramp-up of velocity, followed by a velocity peak, a statistically-stationary phase, and ultimately, a gradual velocity deceleration—mirroring the behavior observed in real-world scenarios.</p> <p>The database consists of six ASCII tab-delimited text files, denoted as ‘windspeedDB89z0eq007.txt’, ‘windspeedDB89z0eq020.txt’, ‘windspeedDB89z0eq320.txt’, ‘windspeedDB124z0eq007.txt’, ‘windspeedDB124z0eq020.txt’, and ‘windspeedDB124z0eq320.txt’, aligning with the two jet intensities and three rough surfaces employed in the experiments. These filenames correspond to: (i) centerline jet velocities at the nozzle outlet section, with values of <em>Wjet</em> = 8.9 and 12.4 m/s (indicated as “<em>Wjet</em>” in the database files); (ii) equivalent full-scale roughness lengths <em>z0eq</em> = 0.007, 0.020, 0.32 m (“<em>z0eq</em>” in the database files, see details below). Each file encompasses wind speed timeseries, detailed as follows:</p> <p>The three-component velocity measurements were recorded by means of 11 Cobra probes (sampling frequency 2,500 Hz) mounted on a stiff mast. The heights (<em>z</em>) of the probes were <em>z</em> = 0.040, 0.070, 0.100, 0.125, 0.150, 0.200, 0.300, 0.400, 0.500, 0.700, 1.000 m above the surface. Within the database files, the wind speed linked to various heights is labeled as “<em>v_zXXXXmm</em>”. In this notation, '<em>v</em>' designates the velocity component: longitudinal ‘<em>U</em>’ (along the horizontal axis of the probe), corresponding to the radial outflow of the downburst, with a positive value when the flow is directed toward the probe. Transversal, ‘<em>V</em>’, represents the velocity component transverse to the probe's centerline axis, having a positive value when the flow is directed right-to-left concerning an observer facing the probe's head. The vertical component is denoted as ‘<em>W</em>’ with a positive value indicating an upward direction. The term “XXXX” signifies the height of the probe, specified in millimeters (mm). The mast with the Cobra probes was subsequently positioned at ten radial <em>r</em> distances with respect to the jet impingement position in the range <em>r/D</em> (<em>D</em> = 3.2 m is the jet diameter) between 0.2–2.0 with an increment of 0.2. Note that the position <em>r/D</em> = 0.8 was adjusted to <em>r/D</em> = 0.75. This modification was necessary due to irregularities on the chamber floor at <em>r/D</em> = 0.8, which could have otherwise introduced bias into the measurements. The radial distance is identified with “<em>r/D_distance</em>” in the dataset files. The ceiling height of the testing chamber is <em>H</em> = 3.75 m, which leads to <em>H/D</em> > 1 allowing for a full vertical development of the downburst radial outflow. For every <em>r/D</em> position, each experiment with the same initial condition (i.e., <em>Wjet</em>) was repeated 10 times (“<em>repetition#</em>” in the database files) to inspect the repeatability of the tests and their variance. Each velocity record lasted 12 s (12 × 2,500 = 30,000 samples) and the duration of the downburst-like part of the record varied between 3–5 s. Overall, 6,600 total time series (2 <em>Wjet</em> × 3 rough surface × 10 repetitions × 10 <em>r/D</em> positions × 11 heights <em>z</em>) of downburst-like outflows were recorded during this set of experimental tests.</p> <p>The reported accuracy of Cobra probes from the manufacturer is +/- 0.5 m/s and +/- 1° for velocity and yaw/pitch angles respectively, up to approximately 30% of turbulence intensity. All velocity magnitudes below 1 m/s were removed and converted to NaN (Not a Number) in the database due to the poor accuracy of Cobra probes for velocities below this threshold. In addition, some velocity values were reported as null in the instrument readings due to the incoming flow being outside the probe's spatial cone of measurement (+/- 45° in respect to the probe horizontal axis). These values are flagged as NULL values in the database. This notation aligns with that utilized in a preceding database of measurements collected within the same experimental campaign at the WindEEE Dome (Canepa et al., 2021; <a href="https://doi.org/10.1594/PANGAEA.931205">https://doi.org/10.1594/PANGAEA.931205</a>).</p> <p>DLFs were tested on three different surfaces: (i) WindEEE Dome bare floor; (ii) Carpet; (iii) Artificial grass. A 1 m × 8 m rectangular section was selected from each of the three surfaces for testing purposes. Each surface was positioned with a 1 m offset relative to the geometric location of the jet impingement. It was identified by an equivalent full-scale roughness length, “<em>z0eq</em>”based on matching atmospheric boundary layer profiles measured in WindEEE in boundary layer mode with standard ESDU (Engineering Science Data Unit) profiles. A total of 15 different Atmospheric Boundary Layer (ABL)-like profiles were tested inside the chamber by varying the rotation-per-minute (rpm) of the fans across the 4 rows of the 60-fan wall—a peripheral wall of the hexagonal WindEEE Dome chamber comprising a matrix of 4 × 15 (rows × columns) fans that is used to produce ABL -like flows. A specific configuration of the 60-fan wall and a length scale of 1:200 were chosen based on correlation analysis between physically reproduced ABL profiles and curve fitting through Eq. A1.8 of the ESDU 82026. This scale is deemed suitable for both ABL and downburst winds produced at the laboratory. Through a linear fitting of the measured data on the <em>U – ln(z)</em> chart, employing the logarithmic law-of-the-wall (dependent on roughness length <em>z0</em> and friction velocity <em>u*</em>), the equivalent roughness lengths for the three surfaces were determined: <em>z0eq</em> = 0.007, 0.020, 0.320 m for the WindEEE Dome bare floor, carpet, and artificial grass, respectively.</p> <p>In summary, each experimental velocity time series consists of 30,000 rows, with 33 columns detailing the three velocity components (<em>U</em>, <em>V</em>, <em>W</em>) across the 11 Cobra probe heights. Columns 34 to 37 provide information on the repetition number, radial position of measurement, equivalent full-scale roughness length, and jet intensity. The subsequent timeseries within the dataset refer to the parameters in columns 34 to 37, each one spanning its entire range in the specified order.</p> <p>Researchers can leverage this database to validate and calibrate numerical and analytical models of thunderstorm winds, in addition to interpreting full-scale measurements of the phenomenon. It also serves as a valuable resource for the fluid dynamics community, particularly those interested in the physical comprehension of downscaled flows or the surface flow dynamics of large Reynolds number impinging jets.</p>
GPR Dataset of Moisture Measurements on Building Floors in Laboratory and On-Site
<h2>GPR Moisture Measurements on Building Floors in Laboratory and On-Site</h2> <p><strong>Related work</strong></p> <p>Laboratory Study: <br>Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors<br>https://doi.org/10.3390/app11198820</p> <p>On-Site Study:<br>Classification of Practical Floor Moisture Damage Using GPR - Limits and Opportunities <br>https://doi.org/10.1007/s10921-024-01111-7</p> <p>Doctoral Thesis:<br>Non-destructive classification of moisture deterioration in layered building floors using ground penetrating radar<br>https://doi.org/10.14279/depositonce-19306</p> <p><strong>Measurement Parameters</strong></p> <p>The GPR measurements were carried out with the SIR 20 from GSSI and a 2 GHz antenna pair (bandwidth 1 GHz to 3 GHz) in common-offset configuration. Each B-Scan consists of N A-Scans, each including 512 samples of a 11 ns time window. Survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan No Gains were applied. </p> <p><strong>Folder Description:</strong></p> <p><em>Lab_dry, Lab_insulDamage, Lab_screedDamage</em><br>- each contain 168 Measurements (B-Scans) in .csv on 84 dry floors, floors with insulation damage and screed damage.<br>- each floor setup was measured twice on two orthogonal survey lines, indicated by _Line1_ and _Line2_ in the file name.<br>- the file names encode the building floor setup e.g. CT50XP100 describes a 50 mm cement screed with 100 mm extruded polystyrene below<br>- the material codes are<br> CT: cement screed, CA: anhydrite screed, EP: expanded polystyrene, XP: extruded polystyrene, GW: glass wool, PS: perlites</p> <p>further information can be found in the publication https://doi.org/10.3390/app11198820<br><br><em>OnSite_</em><br>- 5 folders containing B-Scans on 5 different practical moisture damages<br>- the building floor setup is encoded according to the lab with an additional measurement point numbering at the start and a damage case annotation at the end of the file name with _dry, _insulationDamage and_screedDamage<br><br><strong>File Description:</strong></p> <p><em>B-Scans, Measurement files - no header</em><br>- dimension: 512 x N data point with N beeing the number of A-Scans including 512 samples of a 11 ns time window. <br>- survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan</p> <p><em>Moisture References</em><br>- Moist_Reference of On-Site Locations include the columns MeasPoint: Measurement point, wt%Screed: moisture content of screed layer in mass percent; wt%Insul: moisture content of insulation layer in mass percent. References were obtained by drilling cores with 68 mm diameter in the center of each survey line.<br>- Moist_Reference_Screed of Lab data include the columns Screed: Screed material and thickness in mm, wt%Screed moisture content of screed layer in mass percent<br>- Moist Reference_Insul of Lab data include the columns Insulation: Insulation material and thickness in mm, water addition in l: water added to the insulation layer in liters, V%Insulation: water added to the insulation layer in volume percent, RH%: resulting relative humidy in the insulation layer during measurement. These References are only avaible for Lab measurements on insulation damages.</p> <p> </p> <p> </p>
Laboratory and Clinical Measurements - Influence of anode/filtration setup on X-ray multimeter energy response in mammography applications
<p>The data contains measurement results for selected types of X-ray multimeters in mammography applications. In the Laboratory conditions absolute and relative response were determined for available software settings of the tested multimeters. In the clinical mammography units the multimeters' response was evaluated in different anode/filtration setups.</p>
AMT-2021-217 (Berg, Chiodo, Georgin) Laboratory measurements of gas temperature, pressure, and flow rate..
<p>These data are laboratory measurements of gas temperature, pressure, and flow rate made in the development of a humidity generator.</p>
Data in "The uncertainties in the laboratory-measured short-wave refractive indices of mineral dust aerosols and the derived optical properties: A theoretical assessment"
<p>This is the data for publication "The uncertainties in the laboratory-measured short-wave refractive indices of mineral dust aerosols and the derived optical properties: A theoretical assessment"</p> <p>Version 1: data</p> <p>Version 2: rename the data files and add a readme file</p>
Dataset for 'Mix and measure II – joint high-energy laboratory powder diffraction and microtomography for cement hydration studies'
<p>Dataset for 'Mix and measure II – joint high-energy laboratory powder diffraction and microtomography for cement hydration studies'. Includes data for: calorimetry, PSD, thermal analysis, LXRPD and μCT.</p>
Longitudinal Recovery of Laboratory, Clinical, and Community-Based Measures of Head and Trunk Control in People With Acquired Vestibulopathy
ClinicalTrials.gov study NCT04594057. IPD Sharing: NO. Countries: 1. Publications: 0.
Hemoglobin Measured by "Orsense NBM-200MP" Device and Laboratory Measurement
ClinicalTrials.gov study NCT01321593. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.