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10,554 results for “measurements”
Supraglacial debris thickness measurements from Miage Glacier, Italy
<p>Two datasets containing 245 point measurements of supraglacial debris thickness on Miage Glacier, Italy. Measurements were made by manual excavation in 2006 and 2007 by Lesley Foster and in 2018 by Rebecca Stewart. </p> <p>The datasets are included here in two formats; (1) a delimited text file, and (2) a .kmz file for Google Earth. The data in each file type are identical. A Google Earth map showing an overview of the data coverage is also included as a jpg.</p> <p>The debris thickness to the ice surface was measured as the distance to the ice from a horizontal reference placed on the unmodified surrounding surface bridging the excavation. Debris thickness data are reported to the nearest 0.01 m.</p> <p>Most of the measurements made in 2007 were recorded along transects of the glacier surface. Each 100-m transect has been given the same grid reference for identification of these data.</p> <p> </p> <p> </p> <p>×</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>×</p> <p> </p> <p> </p> <p> </p> <div class="wayback1996-RTmodal"> <div> </div> <div> </div> × <div> </div> <div> </div> <div> </div> </div>
Electrical Resistivity Tomography measurements of a limestone wall during fires
<p>The dataset comprises median reisistivity from resistivities acquiered during fires taking place in an underground limestone quarry. It comprises 24 lines, corresponding to each of the time an ERT image was acquired (the time = 0 is the time the fire was ignited); and 9 columns corresponding to each depth (median calculated on a 2-cm thick interval).</p>
Data S1. Laboratory behavioral data from: Measuring the fitness advantage conferred by autotomy in the wild
<p>Data S1. Laboratory behavioral data. The composition is summarized in figure S1.</p> <p>Autotomy, the self-amputation of body parts, serves as an anti-predator defense in many taxonomic groups of animals. However, its adaptive value has seldom been quantified. Here, we propose a novel modeling approach for measuring the fitness advantage conferred by the capability for autotomy in the wild. Using a predator-prey system where a land snail autotomizes and regenerates its foot specifically in response to snake bites, we conducted a laboratory behavioral experiment and a 3-year multi-event capture–mark–recapture (CMR) study. Combining these empirical data, we developed a hierarchical model and estimated the basic life history parameters of the snail. Using samples from the posterior distribution, we constructed the snail's life table as well as that of a snail variant incapable of foot autotomy. As a result of our analyses, we estimated the monthly encounter rate with snake predators at 3.3% (95% CI: 1.6–4.9%), the contribution of snake predation to total mortality until maturity at 43.3% (15.0–95.3%), and the fitness advantage conferred by foot autotomy at 6.5% (2.7–11.5%). This study demonstrated the utility of the multi-method hierarchical modeling approach for the quantitative understanding of the ecological and evolutionary processes of anti-predator defenses in the wild.</p>
Data S2. CMR data from: Measuring the fitness advantage conferred by autotomy in the wild
<p>Data S2. CMR data. The composition is summarized in tables S2, S3, and S10.</p> <p>Autotomy, the self-amputation of body parts, serves as an anti-predator defense in many taxonomic groups of animals. However, its adaptive value has seldom been quantified. Here, we propose a novel modeling approach for measuring the fitness advantage conferred by the capability for autotomy in the wild. Using a predator-prey system where a land snail autotomizes and regenerates its foot specifically in response to snake bites, we conducted a laboratory behavioral experiment and a 3-year multi-event capture–mark–recapture (CMR) study. Combining these empirical data, we developed a hierarchical model and estimated the basic life history parameters of the snail. Using samples from the posterior distribution, we constructed the snail's life table as well as that of a snail variant incapable of foot autotomy. As a result of our analyses, we estimated the monthly encounter rate with snake predators at 3.3% (95% CI: 1.6–4.9%), the contribution of snake predation to total mortality until maturity at 43.3% (15.0–95.3%), and the fitness advantage conferred by foot autotomy at 6.5% (2.7–11.5%). This study demonstrated the utility of the multi-method hierarchical modeling approach for the quantitative understanding of the ecological and evolutionary processes of anti-predator defenses in the wild.</p>
Replication Package for the paper: Evaluating the Agreement among Technical Debt Measurement Tools: Building an Empirical Benchmark of Technical Debt Liabilities
<p>This is the replication package for the Archetypal Analysis conducted in the paper "Evaluating the Agreement among Technical Debt Measurement Tools: Building an Empirical Benchmark of Technical Debt Liabilities" accepted at Springer's EMSE Journal.</p> <p>It contains:</p> <ul> <li>The dataset with TD measurements from three TD tools for 25 Java projects</li> <li>The dataset with TD measurements from three TD tools for 25 JS projects</li> <li>The script to run the Archetypal Analysis on the two datasets</li> </ul>
Observational studies on preventive measures and treatments for Covid-19
<p>In the course of our PubMed searches and preprints from MedRxiv, we identified a number of observational studies on preventive measures and treatments for Covid-19 that we have included in our systematic review.</p> <p>This file is updated regularly.</p>
PsPM-VC1F: SCR, ECG, PPU, respiration and pupil measurements delay from a fear conditioning task with visual CS, performed during MRI scanning
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU), respiration, pupil size response (PSR) and eyetracker measurements for 21 healthy unmedicated participants (11 females and 10 males, age range: 19 - 34 years, mean age: 25.5 +/- 4.1) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with visual CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were tilted Gabor patches, and plaids consisting of two overlaid Gabor patches, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were asked to rate pairs of CS patches with respect to which of the two stimuli they liked less. Then they were asked to report their subjective estimate of how likely they were to receive a shock after this CS in the future, on a visual analogue scale of 0-100.</p>
Seafloor Density Measurements, Prediction, and Associated Uncertainty for "Predicting global marine sediment density using the random forest regressor machine learning algorithm"
<p>Global seafloor density prediction results using the random forest regressor machine learning algorithm. </p> <p>Dataset S1. Seafloor density measurements. Columns are labeled with a header and include associated drilling project and measurement type for each sample. File format: CSV text file</p> <p>Dataset S2. Seafloor density prediction results from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p> <p>Dataset S3. Seafloor density prediction standard deviation from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p>
Cloud radar, micro rain radar, parsivel and pluvio measurements at Ny-Ålesund for 7 Feb 2018, 16 March 2018 and 16 April 2018
<p>This data set contains netcdf files of the University of Cologne's cloud radar MiRAC-A, micro rain radar, parsivel and pluvio installed at the Arctic research site AWPIEV at Ny-Ålesund. Data are available for 3 days: 7 Feb 2018, 16 March 2018 and 16 April 2018.</p> <p>The MiRAC-A hourly files (“mirac-a_nya_compact_*_P01_ZEN.nc” include (among other variables):</p> <p>float Ze(time, range) ;</p> <p>Ze:long_name = "Equivalent radar reflectivity factor Ze" ;</p> <p>Ze:units = "mm^6/m^3" ;</p> <p>float vm(time, range) ;</p> <p>vm:long_name = "Mean Doppler velocity" ;</p> <p>vm:units = "m/s" ;</p> <p>vm:comment = "negative values indicate falling particles towards the radar" ;</p> <p>float sigma(time, range) ;</p> <p>sigma:long_name = "Spectral width of Doppler velocity spectrum" ;</p> <p>sigma:units = "m/s" ;</p> <p> </p> <p>The Micro Rain Radar daily files (“*_nya_mrr_improtoo_0-101.nc”) include (among other variables):</p> <p>float Ze(time, range) ;</p> <p>Ze:description = "reflectivity of the most significant peak" ;</p> <p>Ze:units = "dBz" ;</p> <p> </p> <p>The parsivel daily files (“sups_nya_dm00_l1_any_v00_*.nc”) include (among other variables):</p> <p>float N(dclasses, time) ;</p> <p>N:fill_value = NaN ;</p> <p>N:units = "log10(m-3 mm-1)" ;</p> <p>N:long_name = "particle concentration per diameter class" ;</p> <p>float dclasses(dclasses) ;</p> <p>dclasses:units = "mm" ;</p> <p>dclasses:long_name = "volume equivalent diameter class center" ;</p> <p> </p> <p>The pluvio daily files (“pluvio_nya_*.nc”) include (among other variables):</p> <p>r_accum_NRT(dim) ;</p> <p>r_accum_NRT:description = "accumulated precipitation NRT" ;</p> <p>r_accum_NRT:units = "mm" ;</p> <p> </p> <p>All available variables are listed and described in the header of the corresponding netcdf files.</p>
TVCSnow 2017-2018 tundra snow depth probe measurements
<p>Snow depth measurements were recorded in March 2018 as part of Environment and Climate Change Canada's 2017-2018 Trail Valley Creek Snow Experiment (TVCSnow 17/18). These snow depths were collected to investigate the relationship between snow microstructure and airborne and ground-based passive microwave radiometer measurements of snow in a tundra environment to develop improved methods for retrieving tundra snow water equivalent and atmospheric profiles of temperature and humidity. Snow depths were recorded 50 km north of the town of Inuvik, Northwest Territories. Measurements took place from March 16th to 23rd 2018 in and around the Trail Valley Creek research station. The snow depth measurements were recorded with an automatic snow depth probe (magnaprobe).</p> <p> </p> <p>Open Government Licence - Canada<br> (https://open.canada.ca/en/open-government-licence-canada)</p>
GPNp and GPNo glass microluminescence measurement for different X-ray doses and probed at different depth.
<p><strong>Silver-doped phosphate glass - Photosensitivity - Ionizing radiation</strong></p> <p><em><strong>- Glass composition of GPNp: </strong>31% P2O5, 20.6% Ga2O3, 46.4% Na2O, 2% Ag2O</em></p> <p><em><strong>- Glass composition of GPNo: </strong>56% P2O5, 28% Ga2O3, 14% Na2O, 2% Ag2O</em></p> <p>The data presented have been corrected for instrument function. Measurement under excitation at 405 nm (100 mW, TEM00, OBIS COHERENT) - LABRAM 800-HR instrument - Micro objective 100x NA 0.9.</p> <p><strong>-</strong><strong> Measurements performed by</strong> : Théo Guérineau, - Contribution of Anna Vedda & Francesca Cova (University of Milano-Bicocca - Milan, Italy) for glass irradiation</p> <p><strong>-</strong><strong> Measurement Date: </strong>February 2018 at ICMCB, Bordeaux, France.</p>
Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series
<p>The datasets contain extreme time series obtained from the post-processed 3D wave fields simulated using HOS-Ocean, a high-order spectral model (HOSM) that solves the deterministic propagation of nonlinear wave fields in deep water (Ducrozet et al., 2016).</p> <p>Voermans. (2020). Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4028014</p> <p> </p>
Measured Commercial LED Spectra - Brendel (2020)
<p><strong>Source URL</strong>: <a href="https://www.rit.edu/cos/colorscience/re_AsanoObserverFunctions.php">https://community.acescentral.com/t/spectral-images-generation-and-processing/2590/8</a><br> <br> Measured spectra of 29 commercially available LEDs spanning the range from 380 to 660 nm peak wavelength. The file contains one SPD per line. Each spectrum is given in 176 samples from 350 to 700 nm normalised to a unity power integral.</p>
Light and temperature measurements and untargeted proteomic measurements
<p class="BodyText1">The right timing of animal physiology and behavior ensures the stability of populations and ecosystems. In order to predict anthropogenic impacts on these timings, more insight is needed into the interplay between environment and molecular timing mechanisms. This is particularly true in marine environments.</p> <p class="BodyText1">Using high-resolution, long-term daylight measurements from a habitat of the marine annelid <i>Platynereis dumerilii</i>, we find that temporal changes in UVA/deep violet intensities, more than longer wavelengths, can provide annual time information, which differs from annual changes in photoperiod. We developed experimental setups that resemble natural daylight illumination conditions, and automated, quantifiable behavioral tracking. Experimental reduction of UVA/deep violet light (app. 370-430nm) under long photoperiod (LD16:8) significantly decreases locomotor activities, comparable to the decrease caused by short photoperiod (8:16). In contrast, altering UVA/deep violet light intensities does not cause differences in locomotor levels under short photoperiod. This modulation of locomotion by UVA/deep violet light under long photoperiod requires c-opsin1, an UVA/deep violet-sensor employing G<sub>i</sub>-signalling. C-opsin1 also regulates the levels of rate-limiting enzymes for monogenic amine synthesis and of several neurohormones, including PDF, Vasotocin (Vasopressin/Oxytocin) and NPY-1.</p> <p class="BodyText1">Our analyses indicate a complex inteplay between UVA intensities and photoperiod as indicators of annual time.</p>
DAS data of Penn State FORESEE array during the COVID-19 measures
<p>This repository contains data used in the paper "Seismic noises recorded by infrastructure fiber optics reveal the impact of COVID-19 measures on human activities, The Seismic Record, submitted"</p>
What Can MR Spectroscopy Measures of Occipital GABA tellabout Visual Plasticity in Human Adult?: Exp3 Dataset
<p>Dataset from Exp3 in "Proulx, Sébastien, Sheynin, Yasha, Hess, Robert, & Farivar, Reza. (2020). What Can MR Spectroscopy Measures of Occipital GABA tell about Visual Plasticity in Human Adult?. Zenodo. http://doi.org/10.5281/zenodo.4034898".</p> <p>Data was collected in 3-min measures: 3 pre-deprivation seperated by 3-min breaks and 3 deprivation measured at 30, 50 and 70 minutes into deprivation.</p> <p>The 'data' variable from the exp3Data.mat file stores percept durations that were logged then averaged within measures.</p> <p>The 'dataTot' variable from the exp3Data.mat file is the same data as in the 'data' variable, but processed differently as the fraction of a measure time (180min) a given percept is perceived.</p> <p>Metadata is included in the data and dataTot variable structure.</p>
Southern Ocean Cloud and Aerosol data set: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage
<p>Due to its remote location and extreme weather conditions, atmospheric in situ measurements are rare in the Southern Ocean. As a result, aerosol-cloud interactions in this region are poorly understood and remain a major source of uncertainty in climate models. This, in turn, contributes substantially to persistent biases in climate model simulations, numerical weather prediction models and reanalyses. It has been shown in previous studies that in situ and ground-based remote sensing measurements across the Southern Ocean are critical for complementing satellite data sets due to the importance of boundary layer and low-level cloud processes. These processes are poorly sampled by satellite-based measurements which are typically obscured by near-continuous overlying cloud cover observed in this region. Here we provide a comprehensive set of ship-based aerosol and meteorological observations collected on the TAN1802 voyage of R/V Tangaroa across the Southern Ocean, from Wellington, New Zealand, to the Ross Sea, Antarctica. The voyage was carried out from 8 February to 21 March, 2018. The compiled data set provides here includes measurements from a range of instruments, such as (i) meteorological conditions at the sea surface and profile measurements; (ii) the size and concentration of particles; (iii) trace gases dissolved in the ocean surface such as dimethyl sulfide and carbonyl sulfide; (iv) and remotely sensed observations of low clouds. We encourage the scientific community to use these measurements for further analysis and model evaluation studies, in particular, for studies of Southern Ocean clouds, aerosol and their interaction.</p>
North West England mobile methane concentration and isotope measurements
<p>This dataset contains mobile isotopic (<sup>13</sup>C/<sup>12</sup>C) methane measurements and associated data collected around the Fylde, Lancaster, Morecambe Bay, and Barrow-in-Furness in North West England. Data were collected between November 2016 and March 2017.</p> <p> </p> <p>For further details see the following papers:</p> <p><span>Takriti, M., Ward, S.E., Wynn, P.M., McNamara, N.P., 2023. Isotopic characterisation and mobile detection of methane emissions in a heterogeneous UK landscape. Atmospheric Environment 305, 119774. https://doi.org/10.1016/j.atmosenv.2023.119774</span></p> <p><span>Takriti, M., Wynn, P.M., Elias, D.M.O., Ward, S.E., Oakley, S., McNamara, N.P., 2021. Mobile methane measurements: Effects of instrument specifications on data interpretation, reproducibility, and isotopic precision. Atmospheric Environment 246, 118067. https://doi.org/10.1016/j.atmosenv.2020.118067</span></p> <p></p>
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>
Dataset for pilot evaluation of an enzymatic assay for rapid measurement of antiretroviral drug concentrations
<p>Liquid chromatography tandem mass spectrometry (LC-MS/MS) data, and REverSe TRanscrIptase Chain Termination (RESTRICT) assay data for pilot evaluation study of enzymatic RESTRICT assay for rapid measurement of antiretroviral drug concentrations among people receiving oral pre-exposure prophylaxis (PrEP).</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.