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10,554 results for “measurements”
Supporting Information: Measuring Functional Redundancy Using Generalized Hill Numbers
<p>A number of metrics for quantifying the amount of functional redundancy in a community have been proposed over the years. Two of the most popular metrics are based on comparing a taxonomic diversity measure with a generalized form of the same measure that accounts for functional dissimilarities between taxa. These two metrics express redundancy as either an absolute or relative difference between the taxonomic diversity measure and its generalized form. Because they express the amount of redundancy in a community in terms of raw diversity values, both redundancy metrics are susceptible to the same issues that complicate the interpretation of most commonly used diversity indices.</p> <p>It is possible to overcome these issues by restating these two indices using a Hill numbers framework. As a growing number of authors have noted, these modified metrics provide a more intuitive quantitative definition of functional redundancy when used to rank communities. Beyond this intuitive definition, measuring redundancy in terms of Hill numbers allows researchers to control the influence of rare taxa on the output value, enabling ecologists to better predict how a community is expected to respond when exposed to an external perturbation that selectively eliminates rare or common taxa.</p> <p>Here I show that, of the two possible Hill number-based redundancy metrics, the form based on a popular absolute redundancy metric is extremely sensitive to differences in taxonomic diversity and can provide a misleading picture of how much redundancy is present in a community. For this reason, I argue that Hill number-based functional redundancy should be quantified using a relative metric that explicitly accounts for differences in effective taxonomic diversity. The proposed Hill number-based relative redundancy measure is shown to provide a much more complete picture of the distribution of redundant taxa within a community, highlighting subtle patterns that are completely missed by the Hill number-based absolute redundancy metric.</p>
3D-Printed Encapsulation of Thin-Film Transducers for Reliable Force Measurement in Biomedical Applications
<p>Data csv</p>
Experimentally measuring weak fracture toughness anisotropy in graphene
<p>Extended finite element analysis (XFEM) modeling the fracture process of 2D materials with anisotropic fracture toughness. Modification from the classic XFEM, this updated script is based on the maximum energy release rate criterion, and anisotropic fracture toughness in sine form and hexagonal symmetry is included. By modifying the ratio of maximum and minimum fracture toughness, model geometry, boundary conditions, and pre-crack location and direction, the fracture patterns, as well as the local fracture parameters including stress intensity factors and energy release rate at the crack tip can be calculated. Moreover, in this script, the distance of the crack extension at every step is irrelevant to the mesh size of the geometry, thus accuracy and computation efficiency is enhanced.</p>
AALTO - Power Angular Measurements and Ray Tracing Simulations at Sub-THz Frequencies in Corridor - DATA
<p>The data set includes simulation results from radio propagation modelling of TERAWAY links (at 90, 95 and 100 GHz) in realistic university corridor environment. The modelling is performed using a Ray Tracing Tool developed in MATLAB environment at Aalto University. Ray tracing technique used in this tool is based on Image Theory (IT) algorithm. Unlike a quasi three-dimensional environment, it supports ray tracing in full three dimension.</p> <p>This data set contains propagation modelling results of the TERAWAY link. Output data includes (but is not limited to): Multipath component IDs, Path Distance (meter), Angle of Arrival AoA (degree), Angle of Departure AoD (degree), Direction of Arrival DoA (degree), Direction of Departure DoD (degree), E-Field (Volt/meter), H-Field (Ampere/meter), Phase (Radians), Power (Watts), Number of reflections a path experienced, Number of diffractions a path experienced, information that is it ground reflected path or not, Receiver location (x and y coordinates), information that is it rooftop path or not.</p>
AALTO - Channel Characterization at Sub-THz Band with Measurements and Ray Tracing in Indoor Case - DATA
<p>The data set includes simulation results from radio propagation modelling of TERAWAY links (at 90, 95 and 100 GHz) in realistic university corridor environment. The modelling is performed using a Ray Tracing Tool developed in MATLAB environment at Aalto University. Ray tracing technique used in this tool is based on Image Theory (IT) algorithm. Unlike a quasi three-dimensional environment, it supports ray tracing in full three dimension.</p> <p>This data set contains propagation modelling results of the TERAWAY link. Output data includes (but is not limited to): Multipath component IDs, Path Distance (meter), Angle of Arrival AoA (degree), Angle of Departure AoD (degree), Direction of Arrival DoA (degree), Direction of Departure DoD (degree), E-Field (Volt/meter), H-Field (Ampere/meter), Phase (Radians), Power (Watts), Number of reflections a path experienced, Number of diffractions a path experienced, information that is it ground reflected path or not, Receiver location (x and y coordinates), information that is it rooftop path or not.</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>
Smart Meter Water Consumption Measurements – Additional Evaluation Data
<p><strong>Data</strong><br> We collected the cumulative water consumption data in Germany using the commercially available smart meters <em>Hydrus 1.3, DN 20,00</em> from <em>Diehl Metering</em>. For evaluation purpose we further collected some label data on activity events and on out-of-house periods: <br> <em>Activity Data:</em> During defined time spans, we performed two typical household activities associated with water consumption and recorded the time at which they were performed. Specifically, we turned on various taps for 30 seconds and flushed the toilets.<br> We avoided concurrent water consumption and always waited at least 5~minutes between two activities.<br> <em>Out-of-house Data</em>: A separate data set is also collected in two test households. Besides the readings from the smart water meters, the absence times of the residents were also collected using the diary method. We handed the residents a log form in which they recorded those time points when no residents were in the house. The residents were also asked to note whether a washing machine or a dishwasher was running during a period of absence.</p> <p>Further information on data collection and description of the dataset can be found in Section 3 and Section 6 of the original article, which is available at the following DOI: <strong>TBD.</strong></p> <p><strong>Structure</strong><br> The <em>Zenodo</em> Archive has two folders, which distinguishes the two different sets of evaluation data. <br> The <em>Activity Data</em> contains subfolders for each of the examined frame. Each of these subfolders contains general information about the frame (e.g., household ID), the smart meter readings (<em>smartmeter.csv</em>) and a list of performed activities (<em>activities.csv</em>).<br> The <em>Out-of-house Data</em> contains two subfolders for the sampled households. These subfolders in turn contain a <em>periods.csv</em> file with the recorded times of presence, a <em>smartmeter.csv </em>file with the smart meter readings during the survey, and a general <em>info</em> document.</p>
Brief introduction to parameterization of downward longwave radiation based on long-term baseline surface radiation measurements in China
<p>This short vidio is used to briefly introduce the contents of the article "Parameterization of downward longwave radiation based on long-term baseline surface radiation measurements in China".</p>
Measurements of Wiretap Encoded Image Transmissions over Multi-Mode Fibers
<p><strong>Securing Data in Multimode Fibers by Exploiting Mode-Dependent Light Propagation Effects</strong></p> <p>This dataset accompanies the publication "Securing Data in Multimode Fibers by Exploiting Mode-Dependent Light Propagation Effects" (S. Rothe, K.-L. Besser, D. Krause, R. Kuschmierz, N. Koukourakis, E. Jorswieck, J. Czarske. Research, vol. 6: 0065, Jan. 2023. <a href="https://doi.org/10.34133/research.0065">DOI:10.34133/research.0065</a>).</p> <p>It contains the measurements of the transmissions of the logo of TU Dresden, encoded by polar wiretap codes of different rates.<br> Each measurement contains the received signals of both the legitimate receiver (Bob) and the eavesdropper (Eve).</p> <p> </p> <p>The code to analyze the measurement files and reproduce the results from the above paper can be found at <a href="https://github.com/klb2/mmf-physec">https://github.com/klb2/mmf-physec</a>.</p>
Data from GB-SAR measurements on Lazaun rock glacier (Val Senales, South Tyrol, northern Italy)
<p>The *.csv files report the kinematic data extracted from the displacement maps obtained from the processing of ground-based SAR measurements of Lazaun rock glacier (Senales Valley, Northern Italy) and presented in the paper <strong><em>“Unprecedented observation of hourly rock glacier velocity with Ground-Based SAR</em></strong><strong><em>”</em></strong> (Bertone et al., 2023, submitted). The data were used to display the time series reported in the paper. The data refer to two field campaigns named <em>survey1</em> (<em>from 09/08/2018 to 18/08/2018</em>), and <em>survey2 </em>(<em>from 13/09/2018 to 03/10/2018</em>).</p> <ul> <li><em>TS_August_moving.cs</em>v: includes the survey 1 displacement data of moving points on the rock glacier.</li> <li><em>TS_August_stable.csv</em>: includes the survey 1 displacement data of stable points outside the rock glacier.</li> <li><em>TS_September_moving.csv</em>: includes the survey 2 displacement data of moving points on the rock glacier.</li> <li><em>TS_ September_stable.csv</em>: includes the survey 2 displacement data on stable points outside the rock glacier.</li> </ul> <p>Each *.csv file contains the following fields:</p> <ul> <li>“<em>displacement</em>”: cumulative displacement (mm);</li> <li>“<em>pointName</em>”: name of point where the time series are computed, as displayed in figure 3;</li> <li>“<em>time</em>”: time of acquisition, in <em>day</em>/<em>month</em>-<em>hour</em>:<em>minute </em>format;</li> <li>“<em>velocity</em>”: velocity (mm/hour);</li> <li>“<em>X</em>” and “<em>Y</em>”: coordinates of points (EPGS 32632) where the time series were extracted.</li> </ul> <p>The polygon representing the area of the Lazaun rock glacier is provided by the shapefile <em>Polygon_RG_Lazaun.shp.</em></p> <p>The velocity distribution on the Lazaun rock glacier as obtained in suvey1 and survey2 is provided by raster files. The velocities were obtained along the LOS and are in mm/day. The two raster files, named <em>raster_survey_1.tif</em> and <em>raster_survey_2.tif</em>, are completed by two files of the same name with the colored overlay (<em>raster_survey_1.lyr</em> and <em>raster_survey_2.lyr</em>).</p> <p>The two velocity maps obtained from survey1 and survey2 are also provided as complete georeferenced figures in geotiff format and named <em>figure_survey_1.tif</em> and<em> figure_survey_2.tif.</em></p> <p>All the geospatial data are provided in the EPSG 32632 spatial reference.</p> <p> </p> <p><strong>AIR TEMPERATURE</strong></p> <p>The LAZ_AIRT_survey1.csv and LAZ_AIRT_survey2.csv files contain the air temperature measured about 250 m North from the Lazaun rock glacier by a Gemini Tinytag TGP 4020 data logger connected to an external probe installed in a passive radiation shield. The data cover the two GB-SAR field campaigns (survey 1 from 09/08/2018 to 18/08/2018, and survey 2 from 13/09/2018 to 03/10/2018).</p> <p>The csv files contain the following fields:</p> <ul> <li>Date: time of acquisition, in <em>day/month/year</em> <em>hour:minute</em> format;</li> <li>Mean: the mean air temperature recorded by the datalogger during the corresponding hour;</li> <li>Maximum: the maximum air temperature recorded by the datalogger during the corresponding hour;</li> <li>Minimum: the minimum air temperature recorded by the datalogger during the corresponding hour;</li> </ul>
Data release for "Measurements of neutrino oscillation parameters from the T2K experiment using 3.6E21 protons on target"
<p>This archive contains the electronic version in ROOT format of the measurements of oscillation parameters in the paper "Measurements of neutrino oscillation parameters using 3.6 \times 10^{21} protons on target with the T2K experiment". Its arxiv identifier is <a href="https://arxiv.org/abs/2303.03222">arXiv:2303.03222 [hep-ex]</a>, and Published in <a href="https://doi.org/10.1140/epjc/s10052-023-11819-x"><em>Eur. Phys. J. C</em> <strong>83</strong>, 782 (2023)</a>.</p> <p>**************************************<br>***** Results included in this release<br>**************************************<br>Both Bayesian and frequentist results are provided, with details of each analysis provided in the paper. All published oscillation parameters are provided, with 2D confidence/credible regions and 1D DeltaChi^2 and posterior probability density distributions. The Bayesian and frequentist results are separated in two different files ("Bayesian_DataRelase.root" and "Frequentist_DataRelease.root"), and an a tag in the TGraph and histogram names also allow to differentiate them: "cred" for credible interval from the Bayesian analysis, "conf" for confidence interval from the frequentist analysis. For the 1D distributions, the posteriors are Bayeisan results and the DeltaChi^2 are frequentist results.</p> <p>Results for each mass hierarchy hypothesis are provided, denoted "NH" for normal hierarchy and "IH" for inverted hierarchy. The Bayesian file also includes the results marginalised over the mass hierarchy, denoted by the tag "both" in the object names.<br>The Bayesian and frequentist results use different conventions for the mass splitting in the inverted hierarchy: the Bayesian results are in term of #Deltam^{2}_{32} for both normal (NH) and inverted (IH) hierarchies, whereas the frequentist results are plotted versus #Deltam^{2}_{32} for the NH, and |#Deltam^{2}_{31}| for the IH.</p> <p>When employed, the constraint on theta13 from reactor experiment results corresponds to the value in the PDG 2019 summary table: sin^2(theta_13)=(2.18+-0.07) x 10^{-2}. This is commonly referred to as "the reactor constraint".<br>Results marked "woRC" are without this reactor constraint, and "wRC" are with the reactor constraint.</p> <p>A glossary is provided at the end of this readme.</p> <p>Two example ROOT macros ("Bayesian_example.cpp" and "Frequentist_example.cpp") showcase how to extract information from the data release. These produce pdf files of the results that can be directly compared to the "*ref.pdf" files for validation.</p> <p>**************************************<br>***** Objects inside the ROOT files<br>**************************************<br>The ROOT objects contained inside the files are named first with an identifier of which parameter(s) are being shown, followed by the reactor constraint tag, followed by the mass hierarchy tag.<br>For the frequentist results, there's an additional "FC" tag, marking if critical DeltaChi^2 values have been computed with Feldman-Cousins ("FC") or using Wilks' theorem (constant DeltaChi^2).</p> <p>**************************************<br>*** 2D regions<br>**************************************<br>Objects of the form<br>gr2D_varX_varY_<wRC,woRC>_<NH,IH,both>_<conf,cred><68,90,955,997>(_N)<br>are TGraphs corresponding to the 2D confidence ("conf") or credible ("cred") regions for the 2 variables (varX, varY). N is the iterator for different TGraphs corresponding to the same region; these occur when confidence regions are discontinuous (for example when deltaCP loops over from +pi to -pi).<br>68, 90, 955, 997 are the percentage credible/confidence levels.</p> <p>The best fit markers are also provided for the 2D results:<br>gr2D_varX_varY_<wRC,woRC>_<NH,IH,both>_bestfit</p> <p>The best fit markers and contour lines are computed for each MH *separately*, i.e. assuming DeltaChi^2 is 0 at the minimum or that the total posterior probability integrates to 1 in the mass hierarchy considered. There is only one exception, some 2D regions for (sin^2(theta_23), dcp) are also provided using a best fit over both MH to allow for comparisons with other experiments using this convention. This special set of contours has an extra tag "globalMH" in its name to distinguish it from the others.</p> <p>For larger confidence/credible exclusion regions (e.g. 99.7%) and when the Bayesian analysis shows the result for dm2 for both hierarchies, the regions may be split in to discontinuous regions. They are named "_0" and "_1", and the value on the y-axis denotes dm^{2}_{23}, from which the hierarchy can be deduced. The examples show examples of how this can be acheived.</p> <p>**************************************<br>*** 1D plots<br>**************************************<br>Objects of the form<br>h1D_var<chi2,posterior>_<wRC,woRC>_<NH,IH><br>are TH1D of the DeltaChi^2 ("chi2") or posterior probability ("posterior") for oscillation parameter "var".</p> <p>The Bayesian and frequentist results use different conventions with respect to the mass hierarchy:<br>- 1D DeltaChi^2 plots use a global minimum over both hierarchies<br>- Each 1D posterior probability plot integrates to unity *individually*</p> <p>**************************************<br>***** Additional notes for frequentist results<br>**************************************<br>Most of the 2D frequentist regions were computed using the standard DeltaChi^2 values (from the Gaussian case), and not the Feldman-Cousins method. They therefore have only approximate coverage.<br>For the 2D distributions, only {sin^2(theta_23), deltaCP} with reactor constraint were computed using the Feldman-Cousins method, and are expected to have proper coverage. To distinguish them from other confidence regions, a tag "FC" is included in the name of the corresponding TGraph.<br>Additionally, those extra regions using Feldman-Cousins method are provided with two conventions regarding the best fit used to evaluate them. The TGraphs with an extra tag "globalMH" use a best fit over both MH hypothesis. The ones without this extra tag use the best fit obtained in each MH to compute the confidence regions for this MH.</p> <p>For the 1D plots, critical delta chi2 values obtained with the Feldman-Cousins method are provided for theta23 and deltaCP (with reactor constraint "wRC" case only):<br>grCritical_{variable}chi2_wRC_{MH}_conf{CL}<br> variable: th23, dCP<br> MH: NH, IH<br> CL: 68, 90, 955, 997</p> <p>To obtain the FC-corrected confidence interval in those 2 cases for a given confidence level, take the intersection of grCritical with the corresponding 1D histogram. This is shown in the example macros.</p> <p>**************************************<br>***** Additional notes for Bayesian results<br>**************************************<br>For plots involving the mass splitting, the choice of hierarchy is given by the sign:<br> dm32>0 is normal hierarchy (Delta m^2_{32} > 0)<br> dm32<0 is inverted hierarchy (Delta m^2_{32} < 0)</p> <p>For the Jarlskog invariant, the prior on deltaCP is either flat in deltaCP, or flat in sindeltaCP ("flatsindcp")</p> <p>Note that the posteriors have not been smoothed, and may contain small discontinuities due to MCMC statistical uncertainties, e.g. in "h1D_dCPposterior_wRC_IH" around delta CP=-1.47.</p> <p>Plots with "_bestfit" appended signify the point in the space with the highest posterior density, and is not necessarily the global minimum of the test-statistic.</p> <p>For the 1D posterior distributions, the user can freely calculate credible intervals from the distributions. It is recommended to start at the point of the highest posterior density, and moving down in posterior density to produce asymmetric credible intervals. The root macro "Bayesian_example.cpp" shows a method to do this.</p> <p>**************************************<br>***** Glossary<br>**************************************</p> <p>"RC" - Reaction Constraint from PDG 2019 sin^2(theta_13)=(2.18+-0.07) x 10^{-2}.<br>"wRC" - With Reactor Constraint<br>"woRC" - Without Reactor Constraint<br>"FC" - Feldman-Cousins<br>"NH" - Normal Hierarchy<br>"IH" - Inverted Hierarchy<br>"both" - Marginalised over normal and inverted hierarchy<br>"cred" - Credible interval<br>"conf" - Confidence interval<br> "68" - 68% (1 sigma)<br> "90" - 90%<br> "955" - 95.5% (2 sigma)<br> "997" - 99.7% (3 sigma)<br>"chi2" - DeltaChi^2 (-2lnL) for parameter<br>"Critical" - Critical DeltaChi^2 computed with Feldman-Cousins</p> <p>"th13" - sin^2(theta_13)<br>"th23" - sin^2(theta_23)<br>"dCP" - delta CP<br>"dm2" - Delta m^2_{23} (NH), |Delta m^2_{13} (IH)| for confidence intervals; used in frequentist analysis.<br>"dm32" - Delta m^{2_{23} regardless of hierarchy; in the Bayesian analysis Delta m^2_{23} is always plotted.<br>"jarlskog" - Jarlskog invariant, only in Bayesian analysis<br>"flatsindcp" - Flat in sin delta CP</p>
Supplementary code and data for the paper `From stage to page: language independent bootstrap measures of distinctiveness in fictional speech`
<p>The repository provides full data and processing / analysis pipeline for the paper <strong>'From stage to page: language independent bootstrap measures of distinctiveness in fictional speech</strong>'<br> <br> Rendered notebooks are also available through Github:</p> <p>1) <a href="https://github.com/perechen/difs-character-voices/blob/master/data/all_stars_clean.ipynb">Preparation, energy distance and exploration</a> (main)</p> <p>2) <a href="https://github.com/perechen/difs-character-voices/blob/master/03_analysis.md">Keyword curves & formal modeling</a></p> <p> </p> <p>- `00_dracor_get_data.R`. Script uses <a href="https://dracor.org/">DraCor</a> dedicated API to get texts spoken by characters</p> <p>- `01_distinctiveness_energy.ipynb` does the heavy lifting of data wrangling, cleaning and preprocessing, plus implements energy distance bootstrapping and does exploratory analysis</p> <p>- `02_logodds_curves.R` calculates keyword curves for characters<br> <br> - `03_analysis_and_models.R` explores keyword curves and does Bayesian models</p>
Urban flask measurements of CO2ff and CO to identify emission sources at different site types in Auckland, New Zealand
<p>As part of the CarbonWatch-NZ research programme, air samples were collected at 28 sites around Auckland, New Zealand to determine the atmospheric ratio (R<sub>CO</sub>) of excess (local enhancement over background) carbon monoxide to fossil CO<sub>2</sub> (CO<sub>2</sub>ff). Sites were categorised into seven types (background, forest, industrial, suburban, urban, downwind, and motorway) to observe R<sub>CO</sub> around Auckland. Flasks from motorway sites observed R<sub>CO</sub> of 14 ± 1 ppb/ppm and were used to evaluate traffic R<sub>CO</sub>. The similarity between suburban (14 ± 1 ppb/ppm) and traffic R<sub>CO</sub> suggests that traffic dominates suburban CO<sub>2</sub>ff emissions during daytime hours, the period of flask collection. The lower urban R<sub>CO</sub> (11 ± 1 ppb/ppm) suggests that urban CO<sub>2</sub>ff emissions are comprised of more than just traffic, with contributions from residential, commercial, and industrial sources, all with a lower R<sub>CO</sub> than traffic. Finally, the downwind sites were believed to best represent R<sub>CO</sub> for Auckland City overall (11 ± 1 ppb/ppm). We demonstrate that the initial discrepancy between the downwind R<sub>CO</sub> and Auckland's estimated daytime inventory R<sub>CO</sub> (15 ppb/ppm) can be attributed to an overestimation in inventory traffic CO emissions. After revision based on our observed motorway R<sub>CO</sub>, the revised inventory R<sub>CO</sub> (12 ppb/ppm) is consistent with our observations. </p>
Measures of urban form and mobility energy use indices for each census tract in the United States
<p>This dataset contains data on urban form (the configuration of the built environment) for each census tract in the United States, encompassing density (destination access), land use diversity (entropy), road network properties, road network capacity relative to the surrounding population, and public transit access. Metrics are measured around the centroid of each census tract in multiple given radii. The data also contain other publicly available metrics for each census tract that may be helpful, such as each tract's associated city, zipcode, and county name, area and water area, and centroid coordinates. Certain measures resemble those available in the U.S. Environmental Protection Agencies' Smart Location database or were derived from them, while others were compiled using additional data sources and the statistical model presented in the associated main article. Specifically, the data presented here contain travel energy use indices for each census tract, reflecting the estimated difference in daily land-based mobility energy use per capita relative to the baseline (the U.S. average) as a result of that environment's particular urban form. </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>
Length measurements of hatchery produced blue mussel larvae
<p>Length meaurments of blue mussel larvae (<em>Mytilus edulis</em>) produced at the Cartron Point Shellfish hatchery in collaboration with Atlantic Technological University, Ireland. </p>
Dataset for Manuscript: Comparing Urban Anthropogenic NMVOC Measurements with Representation in Emission Inventories - A Global Perspective
<p>Urban observations of individual NMVOCs and the calculated or reported emission ratios used for comparison to emission inventories.</p>
Syngnathus scovelli band iridescence measurements
<p>This is the raw data that corresponds to the manuscript "The development of a quantification method for measuring iridescence using sexually selected traits in the Gulf pipefish (<em>Syngnathus scovelli</em>)" (DOI: <a href="https://doi.org/10.3389/fmars.2023.1127790">10.3389/fmars.2023.1127790</a>). The two datasets contain the iridescence measurements for each individual band on the pipefish for each study outlined in the manuscript.</p> <ol> <li><strong>iridescence_across_lighting_RAW</strong>: contains every iridescence measurements for the study about lighting conditions, to see if lighting has any affect on the amount of iridescence measured from a photo.</li> <li><strong>pipefish_measures_iridescence_RAW</strong>: contatins all of the other iridescence measurements for the geographic study, for females from two Texas populations and two Florida populations, and the estrogen study, where we measured the development of iridescence on male pipefish in response to synthetic estrogen.</li> </ol>
Electrical Impedance Tomography (EIT) measurement monitoring the respiratory status
<p>This dataset was used in a study titled "Structural priors represented by discrete cosine transform improve EIT functional imaging" which is currently pending publication. The dataset consists of Electrical Impedance Tomography (EIT) measurements that were collected during the monitoring of respiratory status in tested subjects. The data is stored as .get files, which can be imported into the Matlab® workspace using the codes provided on GitHub at https://github.com/rongqing-chen/DCT-EIT.</p> <p>Using the dataset requires the citation of the following publication:</p> <ul> <li>Lovas A, Chen R, Molnár T, Benyó B, Szlávecz Á, Hawchar F, et al. Differentiating Phenotypes of Coronavirus Disease-2019 Pneumonia by Electric Impedance Tomography. Frontiers in Medicine. 2022;9. doi: 10.3389/fmed.2022.747570</li> <li>R. Chen, S. Krueger-Ziolek, A. Lovas, B. Benyó, S.J. Rupitsch, K. Moeller (2023) Structural priors represented by discrete cosine transform improve EIT functional imaging. PLoS ONE 18(5): e0285619. 10.1371/journal.pone.0285619</li> </ul>
LC-MS/MS plasma protein measurements from children with bacterial and viral infections - "MS-A"
<p>LC-MS/MS data generated from plasma samples from children with bacterial and viral infections. </p>
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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.