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300 results for “tracers”
Datasets for the article "Moisture source controls on water isotopes in Antarctic precipitation - insights from water tracers in ECHAM6-wiso"
<p>This is the dataset used for the manuscript Qinggang Gao, Louise C Sime, Alison J Mclaren, et al. Moisture source controls on water isotopes in Antarctic precipitation -insights from innovative water tracers in ECHAM6-wiso. <em>ESS Open Archive .</em> December 10, 2024. DOI: 10.22541/essoar.173386109.93218804/v1.</p> <p>The corresponding code used for the manuscript can be found at https://github.com/l975421700/a_basic_analysis.</p>
Unpublished data: Quantifying CO2 Emissions and Carbon Sequestration from Digestate-Amended Soil Using Natural 13C Abundance as a Tracer
<p>Unprocessed data of CO2 evolution measured daily on cavity ring-down spectroscopy analyser (G2201-i CRDS isotopic CO2/CH4 analyser, Picarro, Santa Clara, CA, USA).</p>
Data for JGR-Atmospheres Paper: Stratospheric Hydration Processes in Tropopause-Overshooting Convection Revealed by Tracer-Tracer Correlations from the DCOTSS Field Campaign
<p>Airborne 1-second data merger of observations from the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign. This merger includes subjective feature identifications analyzed in the paper referenced in the title. </p>
Archive of NASA-Unified WRF model daily forecasting simulations for DOE TRACER IOP
<pre># Copyright 2022 NASA GSFC All rights reserved. # Creative commons attribution 4.0 international license NASA-Unified WRF model daily simulations for DOE TRACER IOP Document updated: 22 June 2022 Point of contact: Takamichi Iguchi (ESSIC UMD, Code612 NASA GSFC), takamichi.iguchi@nasa.gov Toshi Matsui (ESSIC UMD, Code612 NASA GSFC), toshihisa.matsui-1@nasa.gov Contents: ./READMEtracer.txt # this file ./namelist.wps.tracer_iop_31.template # namelist.wps file to configure WRF Pre-Processing System (WPS) ./namelist.input.real.tracer_iop_31.template # namelist.input file for NU-WRF model real.exe ./namelist.input.wrf.tracer_iop_31.template # namelist.input file for NU-WRF model wrf.exe ./${YYYY}${MM}${DD} # these directories contain files produced from 48-hours NU-WRF forecasting from 00UTC on ${YYYY}${MM}${DD}: pyplot_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for Composite radar reflectivity (dBZ) # PBL height (m) + 10-m horizontal wind (850hPa-level wind in plots before 06/02/2022), # OLR TOA (W m-2), and 5-mins-accumulated IC+CG lighting flash extent density (flash km-2) # Note that this composite dBZ is calculated from NSSL 2-moment microphysics for S-band, # not from POLARRIS radar simulator pyplot.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting accprecip_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for 1, 3, 6-hours, and total accumulated surface precipitation (mm) accprecip.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting polarris_zh_zdr_rh_vr_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ), # differential reflectivity (dB), cross-polar correlation (-), and # radial velocity (m s-1) at 0.5 degree elevation angle polarris_zh_zdr_rh_vr.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting polarris_zh_4sweeps_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ) # at 0.5, 1.8, 4.0 and 8.0 degree elevation angles polarris_zh_4sweeps.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting # following files are produced 3 days late # day1 represent the first 0-24hr forecast, day2 represents the 24-48hr forecast. CFAD_con_day?.png # Convective part of Contoured Frequency of Altitude Diagrams CFAD_str_day?.png # Stratiform part of Contoured Frequency of Altitude Diagrams QVP_con_day?.png # Convective part of QVP-like domain-mean radar profiles QVP_str_day?.png # Stratiform part of QVP-like domain-mean radar profiles RadarFrac_day?.png # Composite Radar Horizontal Fraction (0-1) by different minimum reflectivity thresholds</pre>
Dataset of 4D conserved tracers for convection simulated by large eddy model and cloud resolving model
<p>There are conserved tracers and active flag for convection used for diagnosis of bulk entrainment rate for four convection cases in this dataset. Total water and moist static energy are selected as tracer for shallow convection (BOMEX and RICO) and deep convection (GATE and KWAJEX), respectively. The two variables simulated by large eddy model for shallow convection and cloud resolving model for deep convection are four-dimension variables with horizontal scales, vertical altitude, and time. </p> <p>The size of domain simulated for BOMEX and RICO is 6.4 km with horizontal grid spacing of 100 m, and that GATE and KWAJEX is 256 km with horizontal grid spacing of 1 km. Besides, vertical layers in the simulation are 75 levels with spacing of 40m for BOMEX and 100 levels with spacing of 40m for RICO. For KWAJEX and GATE, the model was set up with 64 levels vertically, which gradually increases from 75 m at the surface to a spacing of 400 m through the troposphere and a larger spacing of 1 km in the Newtonian damping region. The model is integrated for 6 hours for BOMEX, 24 hours for RICO, 52.25 days for KWAJEX, and 20 days for GATE. Here, the range of time in these variables The four-dimension variables are saved every 3 seconds for shallow convection, and every 6 minutes for deep convection for two consecutive days.</p>
Fig. 3 in Mercury and stable isotopes ( N and C) as tracers during the ontogeny of Trichiurus lepturus
Fig. 3. Relationship between δ15N and δ13C in the muscle of sub-adult and adult specimens of Trichiurus lepturus. Bars represent the standard deviation.
Fig. 1 in Mercury and stable isotopes ( N and C) as tracers during the ontogeny of Trichiurus lepturus
Fig. 1. Northern Rio de Janeiro, in south-eastern Brazil. The sampling area where the Trichiurus lepturus specimens were collected is marked with a dashed polygon.
Fig. 2 in Mercury and stable isotopes ( N and C) as tracers during the ontogeny of Trichiurus lepturus
Fig. 2. Length (cm), weight (g), total mercury concentration (THg) in dry and wet weight basis and isotopic signatures (δ15N and δ13C) of sub-adult and adult specimens of Trichiurus lepturus, considering dry season, rainy season, and all sampling periods. Data is shown as a mean and standard deviation. The scale for length, weight, and THg is different for the two ontogenetic phases.
Tracer gas (SF6 and Xe) migration data collected in 2018 as a part of the Diffusion experiment
<p>The dataset contains the results of the gas sampling performed following two injections of a mixture of the tracer gases (SF<sub>6</sub> and Xe). Two tracer gas injections were conducted in 2018 in Carroll, New Hampshire. Tracer gas injections were performed beneath the water table in two chemical-explosion generated cavities to compare the migration of sulfur hexafluoride (SF<sub>6</sub>) and xenon (Xe) through an explosion-generated fracture network and to study the influence of ground water on gas transport. A mixture of tracer gases (50% of SF<sub>6</sub> and 50% of Xe) was injected into each cavity. The first gas injection took place on September 8, 2018 and gas sampling continued until September 20, 2018. The second injection was performed on October 31, 2018 and gas sampling continued until November 8, 2018. The data were collected using an automatic sampling system. The data analysis was performed by using a gas chromatograph (Shimadzu GC-8A) with a Thermal Conductivity Detector (TCD). The gas concentrations measured at the surface from 4 sampling locations are provided.</p> <p>In addition, we provide measurements of the barometric pressure and temperature recorded using an Onset HOBO pressure transducer placed in the vicinity of the injection site approximately 1 m above ground and collecting pressure and temperature samples every 15 minutes for the duration of the experiment. The pressure and temperature data is provided in Tables S2 and S4.</p> <p> </p>
Velocity and concentration measurements of passive tracer above gravelly seabeds under the wave influence
<p><strong>General description:</strong><br>This dataset was compiled and generated by Helena Stirnweiß as part of a series of Particle Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF) experiments conducted at the Institute of Fluid Mechanics at the University of Rostock. All data included herein is original and was acquired under controlled laboratory conditions. </p> <p>The experiments were conducted for 21 individual configurations of 3 different wave scenarios (description can be found in 'overview_wavescenarios.csv') and 7 seabed models (description can be found in 'overview_seabedmodels.csv'). The folders where the data is stored are named accordingly '[name of seabed model]_[name of wave scenario]'.</p> <p>Horizontal (u [m/s]) and vertical (w [m/s]) velocities were measured simultaneously to the concentration (c [l/l]) of a tracer fluid released from the seabed. The collected data was analyzed and phase averages and phase-resolved covariances were derived and are given for the field of view of each configuration as .npy-files in the respective folder. <br>Time-averaged and horizontally averaged profiles were determined for the concentration and all covariances in dependence on the bottom distance. The profiles are stored as .npy-files in the respective Folders. </p> <p>Mass mixing length (l_c) and Eddy diffusivity (D_t) profiles were derived for each configuration from the measured data as described in the corresponding article. The variables are given in dependence on the bottom distance as .npy-files in the respective folder. Slopes of the vertical mass mixing length profiles and Eddy diffusivity profiles from linear regression are given in 'slopes_turbmodels.csv'. </p> <p>TIME-RESOLVED DATA IS NOT PROVIDED IN THIS DATASET DUE TO EXTENSIVE DATA SIZE but will be shared upon request. Please contact the creators.</p> <p> </p> <p><strong>Description of .npy files in .zip-folders:</strong></p> <p>The time-averaged, horizontally averaged profiles (named '[c/RS/TF/TKE]_[optional: names of covariates]_time_averaged_[name of seabed model]_[name of wave scenario].npy') are given in each folder.<br>All time-averaged data is stored in the following format.</p> <p>import numpy as np</p> <p>data = np.load(filename.npy, allow_pickle=True)<br>#data[0] -> z-dimensions in mm<br>##data[0][z]</p> <p>#data[1] -> respective quantity (c, Reynolds stresses (RS), turbulent fluxes (TF), turbulent kinetic energy (TKE))<br>##data[1][z]</p> <p> </p> <p>The phase-averages (named '[u/w/c]_phase_averaged_[name of seabed model]_[name of wave scenario].npy'), phase-resolved covariances of the fluctuations (named '[RS/TF]_[names of the covariates]_[name of seabed model]_[name of wave scenario].npy', note: RS stands for Reynolds stresses, TF stands for turbulent fluxes), and the turbulent kinetic energy (named 'TKE_[name of seabed model]_[name of wave scenario].npy') are given in each folder.<br>All phase-resolved data is stored in the following format.</p> <p>import numpy as np</p> <p>data = np.load(filename.npy, allow_pickle=True)<br>#data[0] -> x-dimensions in mm<br>##data[0][z, x]</p> <p>#data[1] -> z-dimensions in mm<br>##data[1][z, x]</p> <p>#data[2] -> respective quantity (c, u, w, Reynolds stresses (RS), turbulent fluxes (TF), turbulent kinetic energy (TKE))<br>##data[2][phi_idx, z, x]</p> <p>#(phase-averaging is performed with 100 phase bins -> phi_idx ranges from 0 to 99)</p> <p> </p> <p>Mass mixing length (lc) and eddy diffusivity (Dt) profiles were derived as described in the corresponding article and are given in each folder under '[lc/Dt]_prof_[name of seabed model]_[name of wave scenario].npy' in the following format:<br>data = np.load(filename.npy, allow_pickle=True)<br>#data[0] -> z-dimensions in mm<br>##data[0][z]</p> <p>#data[1] -> respective quantity (l_c in mm, D_t in m^2/s)<br>##data[1][z]</p>
Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data from two monitoring stations Bertha Ganter – Fort McKay and Barge Landing for 20 August 2013 to 2 September 2013. This data was used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125) for model output and observational data comparisons. The same data can be accessed and downloaded from "<a href="https://wbea.org/historical-monitoring-data/">https://wbea.org/historical-monitoring-data/</a>".</p>
IODP Expedition 385 Per-fluorocarbon tracers (PFT)
<p>Perfluorocarbon tracers (PFT) can be used to quantify the amount of core contamination due to drilling fluid. PFT is continuously pumped into the stream of drilling fluid to maintain a consistent concentration. Core subsamples are then measured in triplicate using µECD gas chromatography. Measured levels of PFT of the core outer surface, the outer core surface after undergoing a decontamination procedure, and the inner core are then compared to determine the degree of contamination.</p>
Dataset for the paper "Low-Cost Three-Quadrant Single Solar Cell I-V Tracer"
<p>Dataset related to the publication "Low-Cost Three-Quadrant Single Solar Cell I-V Tracer" in Applied Sciences 2022, <em>12</em>(13), 6623; <a href="https://doi.org/10.3390/app12136623">https://doi.org/10.3390/app12136623</a></p>
Middle East oil and gas methane emissions signature captured at a remote site using light hydrocarbon tracers
<p>Datasets of measured species mixing ratios during the Cape Greco winter campaign in 2021-2022 associated publication of the same name: "Middle East oil and gas methane emissions signature captured at a remote site using light hydrocarbon tracers". While CO2 values are given in ppm, other compounds units are ppb.</p>
Examples of PET tracers synthesized via arene C-H radiofluorination (A) Maximum intensity projection (MIP) PET images of [18F]Fenoprofen (42) demonstrate higher uptake in TPA-treated mouse ear
<p>Examples of PET tracers synthesized via arene C-H radiofluorination (A) Maximum intensity projection (MIP) PET images of [18F]Fenoprofen (42) demonstrate higher uptake in TPA-treated mouse ear (A-1) compared with control (A-2) mouse ear. (B) PET/CT images demonstrate preferential tumor (MCF-7) accumulation of 39, compared with longer blood circulation and higher non-specific binding of 41 at 1 hour post-injection. (C) Structures of the tracers used in the preceding panels are shown.</p>
Nutrient and tracer amounts for Tracer Additions for Spiraling Curve Characterization studies on arctic streams near Toolik Field Station, Alaska 2010 -2012.
The Changing Seasonality of Arctic Stream Systems (CSASN) was active from 2010 to 2012. The CSASN goal was to quantify the relative influences of through flow, lateral inputs, and hyporheic regeneration on the seasonal fluxes C, N, and P in an arctic river network, and to determine how these influences might shift under seasonal conditions that are likely to be substantially different in the future. There were a number of tracer addition for spiraling curve characterization (TASCC) and Plateau nutrient additions at each sampling location. The data in this file contains nutrient concentrations from each sampling event during each TASCC and Plateau addition during the CSASN project. This file contains all the information about each nutrient addition during the entire project.
CSASN Nutients: Tracer addition for spiraling curve characterization from 2010 to 2012
The Changing Seasonality of Arctic Stream Systems (CSASN) was active from 2010 to 2012. The CSASN goal was to quantify the relative influences of through flow, lateral inputs, and hyporheic regeneration on the seasonal fluxes C, N, and P in an arctic river network, and to determine how these influences might shift under seasonal conditions that are likely to be substantially different in the future. There were a number of TASCC and Plateau nutrient additions at each sampling location. The data in this file contains nutrient concentrations from each sampling event during each tracer additions for spiraling curve characterization (TASCC )and Plateau nutrient addition during the CSASN project.
McMurdo Dry Valleys LTER: Nutrient tracer injection experiments in supraglacial streams on Canada Glacier, Antarctica, January 2019
This dataset contains a series of grab samples collected from nutrient tracer injection experiments conducted in supraglacial streams on Canada Glacier in Taylor Valley, Antarctica. The purpose of these experiments was to measure potential nutrient uptake in supraglacial stream networks. We chose three reaches and co-injected nutrient tracer(s) and a conservative tracer (NaCl). Tracers were injected at the top of each reach, allowed to mix with the stream water, and move downstream. Grab samples in this dataset were collected at the base of the reach over the course of these tracer experiments, which ran from 14-19 January 2019.
Lake Hoare Tracer Test Stream Chemistry
As part of the McMurdo Dry Valleys, Long Term Ecological Research (MCM-LTER) project in Antarctica, a LiCl tracer was injected into Andersen Creek in the Lake Hoare basin on 17 December 2012. The purpose of this study was to determine the fate of stream water below lake ice. Injection began at 20:30 hours and continued for two hours. Water samples were collected at half-hour intervals from 5 stream sites and 15 ice boreholes over a 4 hour period beginning at the start of injection. Samples were analyzed for major cations and major anions using an ion chromotograph at McMurdo Station. Results show that stream water moved West along the lake shoreline below the moat ice, and did not generate interflow below the perennial lake ice.
Statistics of turbulent tracer dispersion from UV camera observations of SO 2, Data set
<p>LES and radiation transport data sets used to produce the figures in the manuscript:</p> <p>Kylling, A., Ardeshiri, H., Cassiani, M., Dinger, A. S., Park, S.-Y., Pisso, I., Schmidbauer, N., Stebel, K., and Stohl, A.: Can statistics of turbulent tracer dispersion be inferred from camera observations of SO2 in the ultraviolet?, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-286, in review, 2019</p> <p>See README file for further description of files.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.