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10,553 results for “measurements”
N2O raw data from static greenhouse gas chamber measurements
<p>This dataset contains N<sub>2</sub>O concentration measurements of a 2 years measurement campaign for greenhouse gas fluxes from agricultural soils.</p> <p>The format of the data is ready to be fed into the gasfluxes R package on CRAN to calculate fluxes for each individual chamber measurement (identical IDs are referred to one single measurement, the ID contains the measurement day, treatment and replicate).</p> <p>The data is originally published in Krauss et al. 2017 and further used for improvements of the flux calculation procedure in Hüppi et a. 2018 (see references)</p>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2018_DiDonFranceesco
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign the magnet S0805 and S0808 were used. The tests were conducted for several bottom configurations (smooth bottom; thin sand d<sub>50</sub>=0.24 mm; coarse sand d<sub>50</sub>=0.56 mm; and mixed sand 70% thin sand and 30% coarse sand). The goals of such tests were: to study the effects of the type of magnets and to carry out a preliminary analysis the ferrofluid behavior over sandy bottom.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2017_Privitera
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign magnet type S0805 and a number of magnets equal to 2,3 and 4 were used. The tests were conducted both over a fixed bed (Perspex<sup>©</sup>) and in the presence of mobile beds. The goals of such tests were: to study of the velocity profiles for some fixed and mobile bottoms; to study the effects of the number of magnets on the ferrofluid behavior; preliminary analysis of the bed shear stress over sandy bottom.</p>
Examples dataset in conformity assessment with measurement uncertainty
<p>Thie dataset summarizes examples that can be used to validate the software developed as part of 17SIP05 CASoft project, which aims at popularizing the use of the methodology described by the reference document JCGM106:2012 for decision-making in conformity assessment problems. </p>
Sanitation Poverty: a multidimensional measure
<p>This file provides the primary data (database file, in *csv) and achieved results (results file, in *csv) published in the paper "Monitoring and Targeting the Sanitation Poor: A Multidimensional Approach", Natural Resources Forum, 2018 (under review).</p> <p>The paper discusses the adequacy and applicability of one approach that is increasingly adopted for multidimensional poverty measurement at the household level, the Alkire-Foster methodology. Drawing on this method, we identify and combine a set of direct household-related water and sanitation deprivations that a person experiences at the same time. This new multidimensional measure is useful for gaining a better understanding of the context in which WaSH services are delivered. It captures both the incidence and intensity of WaSH poverty, and provides a new tool to support monitoring and reporting, as well as targeting and planning. For illustrative purposes, one small town in Mozambique is selected as the initial case study.<br> </p>
Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset
<p>The dataset used in the paper entitled "Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset" published in IEEE Transactions for Network and Service Management. </p>
Dataset From: Measuring Inner Layer Capacitance with the Colloidal Probe Technique
<p>The dataset for the publication "Measuring Inner Layer Capacitance with the Colloidal Probe Technique". doi:10.3390/colloids2040065</p> <p>Files containing data have .dat extension and are in text format.</p>
Signalgun Signature measured in Water and Air (Tank Experiment)
<ul> <li>the experiments are performed in and above a water tank</li> <li>signatures of a Stalker R1 2.5'' gun fired at different elevations above the water surface are measured</li> <li>the Stalker R1 2.5'' gun has a caliber of 0.38 in (9 mm) and is fired with blank bullets</li> <li>signatures are measured in water and air with B&K 8105 hydrophones</li> <li>the signal recording is triggered with a hydrophone that is directly attached to the Stalker R1 2.5'' gun (it indicates when the gun is fired) - the measured signals are aligned to the main peak of the trigger signal</li> <li>recordings are averaged/stacked measurements of 4 shots with the Stalker R1 2.5'' gun at each source elevation "zs"</li> <li>the filename indicates the source type ("signalgun_"), where the signal is measured ("water_" or "air_") and the source elevation above the water surface ("zs_cm.txt")</li> <li>more information can be found in the referenced publication and the sketch of the experimental set up</li> </ul>
S15 Watergun Signature measured in Water and Air (Tank Experiment)
<ul> <li>the experiments are performed in a water tank (see photo)</li> <li>signatures of an S15 watergun fired at different water depths are measured</li> <li>S15 watergun has one cylindrical gun port and is fired at 130 bar</li> <li>signatures are measured in water and air with B&K 8105 hydrophones</li> <li>signatures in air are measured at normal incidence, directly above the source (yr = 0 cm, no "yr" in filename) and at different horizontal offsets from the source (varying "yr" as indicated in the filename)</li> <li>channel 3 is the trigger channel and indicates when the S15 watergun is fired</li> <li>recordings are averaged/stacked measurements of 3 shots with the S15 watergun at each source depth "zs"</li> <li>the filename indicates the source type ("watergun_S15_"), where the signal is measured ("water_" or "air_") and the source depth in water ("zs_cm.txt"), and the horizontal offset ("yr") for the specific experiments</li> <li>more information can be found in the referenced publication and the sketch of the experimental set up</li> </ul>
Plankton Temperature Measurements - Data Management - University of Tennessee - Mock Data
<p><strong>Comparison of conochilus unicornis (CONI) and conochilus hippocrepus (CHIP) depth and colony size over time at three different locations. </strong></p> <p>This contains colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonNamePond_v7.27.2012.csv">SEH_PlanktonNamePond_v7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempA_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from site A and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempB_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Site B and the data on which these data were gathered wereTHESE DATES.</p> <p><strong>Metadata</strong></p> <p>Date: Day that samples were collected</p> <p>Time_Day_Night: Gives time that the sample was gathered in the day</p> <p>Temp_C: Temperature of the water containing the plankton.</p> <p>CONI: Conochilus unicornis - species of plankton</p> <p>CHIP: Conochilus hippocrepis - plankton</p> <p>XXXX_ColonySize_mm: Average diameter (mm) of 5 randomly chose plankton colonies in the sample </p> <p>Temp: TemperatureYSI probe taken once at each depth</p> <p>ChlorophyllA_Units: Chlorophyll A values</p>
HMDiR: an HRTF dataset measured on a mannequin wearing XR devices
<p>The HMDiR dataset (Head-Mounted-Display acoustic Impulse Responses) comprehends HRIR measurements for 1200 locations collected over a Neumann KU-100 mannequin fitted with a variety of HMDs used for virtual, augmented, or mixed reality. The database can be used to run subjective and objective studies on the impact of wearing headgear on spatial audio perception and source localization. Data is provided in <em>.mat</em> format, readable in MATLAB.</p> <p>Source locations measured comprehend 200 azimuth angles from 0º to 358.2º in steps of 1.8º and six elevation rings from -36º to +54º in steps of 18º. Distance is 1 meter. The data was measured in November 2017 using ScanIR v2.</p> <p>The headsets measured were the following:</p> <ul> <li>Microsoft Hololens (2016 version)</li> <li>Metavision Meta-2</li> <li>HTC Vive</li> <li>Samsung GearVR SM-R322 </li> <li>Oculus Rift CV1</li> </ul> <p>The data is available as <em>.mat </em>files prepared in two different formats:</p> <ul> <li><strong>MARL format:</strong> complete individual-entry-based structure, includes metadata information for elevation, azimuth, distance, headset type, location, measurement signal and sample rate</li> <li><strong>Compact format:</strong> simplified matrix for only HRIR data packed as a 6x200 matrix. Rows spanning elevations from -36º to +54º from top-to-bottom. Columns spanning azimuth positions from 0º to 358.2º (clockwise) left-to-right.</li> </ul> <p>If you make use of this dataset for academic purposes, please cite the following white paper:</p> <p><em>Andrea Genovese, and Agnieskza Roginska, HMDiR: an HRTF dataset measured on a mannequin wearing XR devices, Audio Engineering Society Conference: 2019 AES International Conference on Immersive and Interactive Audio. Audio Engineering Society. </em></p>
Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements
<p>The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled “Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.” The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017).</p> <p> </p> <p>Software versions used:</p> <ul> <li>Image Processing Workbench v2.2.0 (Marks et al., 2017)</li> <li>Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019)</li> </ul> <p> </p> <p><strong>NOTE:</strong> Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file.</p> <p> </p> <p><strong>topo.nc</strong> – Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are:</p> <ul> <li>dem – digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017)</li> <li>mask – basin mask for the Boise River Basin</li> <li>veg_height – vegetation height in meters from the National Land Cover Database (Homer et al., 2015)</li> <li>veg_type – vegetation type from the National Land Cover Database</li> <li>veg_tau – vegetation fractional transmissivity derived from the vegetation type</li> <li>veg_k – vegetation emissivity derived from the vegetation type</li> </ul> <p> </p> <p><strong>maxus.nc</strong> – maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002)</p> <p> </p> <p><strong>Station data:</strong></p> <ul> <li>Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF.</li> <li>metadata.csv – metadata for 40 stations</li> <li>air_temp.csv – 38 stations</li> <li>cloud_factor.csv – 7 stations</li> <li>precip.csv – 21 stations</li> <li>vapor_pressure.csv – 19 stations</li> <li>wind_direction.csv – 14 stations</li> <li>wind_speed.csv – 14 stations</li> </ul> <p> </p> <p><strong>smrf_config.ini</strong> – Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.</p>
FT3 Anonymous (1) 4-key fagottino: measurements, photos.
<p> Dataset of FT3 Anonymous (1) 4-key fagottino containing (partial) external and internal measurements and photos. </p> <p> </p>
FT35 Dupré prototype 0-key fagottino: basic measurements, photos
<p> Dataset of FT35 Dupré prototype 0-key fagottino containing basic measurements and photos.</p> <p> </p>
Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ
<p>This repository contains data reported in the below study:</p> <p>Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ. <em>Remote Sensing of Environment</em>.</p> <p>Each text file contains the data-set used to produce the relevant figure (see file name). You can find the data to produce A.4. online at https://avaa.tdata.fi/web/smart/smear/ </p> <p>Please pay attention to the following before using this data.</p> <ol> <li><strong>Figure2_lampRadPanel_Wm2srnm.txt</strong>: Note that the shapes are of interest here. The magnitude is not the same as the incident light at top of canopy, as these spectra were measured in a laboratory. See paper section A.1. for more details. </li> <li><strong>Figure3_LEDIFspectra_Wm2srnm.txt</strong>: This data contains the whole observed spectrum including the non-fluorescence regions, which were saturated (warped) in the visible. The fluorescence region is approximately > 650 nm. </li> <li><strong>Figure4_AQYspectra_nm.txt</strong>: As with Figure3 the whole spectrum is included here.</li> <li><strong>FigureA3_repLEDIFspectra_[pmay/psep/usep]._nm.txt</strong>: Data from which the mean spectra (Figure3) were calculated, including the uncorrected red spectra. I have split these by canopy type to avoid name conflicts.</li> </ol> <p> </p>
FT2 Adler (2) 13-key tenoroon: measurements, photos, endoscopic video
<p> Dataset of FT2 Adler (2) 13-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
Dataset for "Method to retrieve cloud condensation nuclei number concentrations using lidar measurements"
<p>This repository contains the source data for the manuscript "<strong>Method to retrieve cloud condensation nuclei number concentrations using lidar measurements</strong>" published in <em>Atmospheric Measurement Techniques</em>. In situ measured data from five filed campaigns and corresponding theoretical simulated CCN number concentrations, lidar extinction and backscatter are included.</p>
Computed Basic Statistics of Hydraulics and Discharge Measures at USGS River Monitoring Stations
<p>The shared table contains basic statistics (average, standard deviation, minimum, maximum, and coefficient of variation [%]) river channel hydraulics and discharge records of the 4472 USGS river monitoring stations. The required raw data are free to access by the USGS-<em>National Water Information System</em> (<a href="https://waterdata.usgs.gov/nwis">https://waterdata.usgs.gov/nwis</a>). Hydraulics and discharge records that measured at each USGS monitoring site, given a long time period, were assembled, assessed, and finally used for computing the basic statistics. </p>
FT12 Castlas 7-key fagottino: measurements, photos, endoscopic video
<p>Dataset of F12 Castlas 7-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. </p>
ScienceDex guides
Understand access before you commit
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.