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232 results for “time measurement”
Time-series measurements of size fractionated primary production (>3 micron) in the subtropical North Pacific Ocean
<p>Filter size-fractionated (>3 μm) particulate 14C-based rates of primary production were measured at six discrete depths (5, 25, 45, 75, 100, and 125 m) throughout the euphotic zone. Seawater samples from each depth were subsampled into triplicate 30-mL polycarbonate centrifuge tubes from a pre-dawn cast, inoculated with 70 µL of NaH14CO3-, then incubated over the full photoperiod (~12-14 hours) on a floating in situ array at the corresponding depths where the water was collected. At the end of the incubation period (after sundown), 25 mL of each sample was vacuum-filtered first onto a 25-mm diameter 3-μm pore size polycarbonate membrane.</p>
Time-series measurements of size fractionated primary production (14C-assimilation) in the subtropical North Pacific Ocean
<p>Filter size-fractionated (>0.2-3 μm) particulate 14C-based rates of primary production were measured at six discrete depths (5, 25, 45, 75, 100, and 125 m) throughout the euphotic zone. Seawater samples from each depth were subsampled into triplicate 30-mL polycarbonate centrifuge tubes from a pre-dawn cast, inoculated with 70 µL of NaH14CO3-, then incubated over the full photoperiod (~12-14 hours) on a floating in situ array at the corresponding depths where the water was collected. At the end of the incubation period (after sundown), 25 mL of each sample was vacuum-filtered first onto a 25-mm diameter 3-μm pore size polycarbonate membrane, then the filtrate was vacuum-filtered onto a 25-mm diameter 0.2-μm pore size polycarbonate membrane filter. The rates reported here are for the >0.2-3 μm size fraction.</p>
Time series measurements of leucine incorporation as a measure of bacterial production in the subtropical North Pacific Ocean
<p>Samples were collected at or in the vicinity of Station ALOHA on 24 different cruises. On each cruise, rates of 3H-Leu incorporation into protein were measured. Seawater samples for measurements of production were collected from pre-dawn vertical hydrocasts at 6 discrete depths (5, 25, 45, 75, 100, 125 m) using polyvinyl chloride sampling bottles affixed to a rosette sampler. Polyethylene amber bottles (125 ml capacity) were subsampled from the CTD rosette bottles, and duplicate acid-cleaned 12 ml polycarbonate centrifuge tubes were filled from each depth. Each polycarbonate tube was inoculated with 20 nmol L-1 (final concentration) 3,4,5-3H-leucine. An additional 1.5 ml per depth was subsampled into 2 ml microcentrifuge tubes containing 100 µl of 100% (w/v) ice-cold trichloroacetic acid (TCA) to serve as a killed blank. The polycarbonate sample tubes were capped and incubated in situ over the photoperiod to measure rates of 3H-Leu incorporation in both dark (through use of black cloth bags) and light on a free-drifting array. At the end of the photoperiod, triplicate 1.5 ml subsamples were removed from each tube and added to 2 ml microcentrifuge tubes containing 100 µl of 100% ice-cold TCA; these tubes were stored frozen until analysis. Samples were processed following a modified method of the microcentrifuge method. </p>
Time series measurements of 14C-based primary production (>0.2 um) in the subtropical North Pacific Ocean
<p>Over a 3-year period (April 2010-April 2013), we measured primary production from vertical profiles at near-monthly time scales in the subtropical North Pacific Ocean. Production measurements were based on <sup>14</sup>C-assimilation into >0.2 um plankton biomass. Seawater for the productivity measurements was collected from predawn CTD hydrocasts into acid-cleaned 500-ml polycarbonate bottles. A total of four replicate 500 ml bottles were subsampled per depth and each bottle was spiked with ~1.85 MBq <sup>14</sup>C-bicarbonate. One hundred milliliters from one replicate per depth was immediately vacuum filtered through a polycarbonate filter as a time zero blank. These filters were placed in 20 ml glass scintillation vials and stored at -20oC until shore-based laboratory processing. The remaining three bottles were hung on a free-drifting array, deployed before dawn, and incubated at their initial collection depths throughout the photoperiod (typically 11-13 hours). After sunset the array was recovered, and 100 ml subsamples of all bottles were filtered under gentle vacuum onto 0.2 um polycarbonate filters that were then placed in scintillation vials and frozen. The total radioactivity added to each sample bottle was determined by subsampling 250 µl aliquots into scintillation vials containing 500 µl of β-phenylethylamine. At the shore-based laboratory, filters were acidified, passively vented, and the resulting radioactivity was determined using liquid scintillation counting.</p>
Time series measurements of nitrogen fixation in the subtropical North Pacific
<p>Rates of N<sub>2</sub> fixation were measured using the <sup>15</sup>N<sub>2</sub> isotopic tracer technique. Sampling occurred during near-monthly Hawaii Ocean Time-series cruises. Whole seawater samples from six discrete depths (5, 25, 45, 75, 100, and 125 m) were subsampled into acid-washed 4.3 L polycarbonate bottles. Between June 2005 and May 2012, 3 mL of <sup>15</sup>N<sub>2</sub> gas was injected into each bottle. Beginning in August 2012, the <sup>15</sup>N<sub>2</sub> gas was first dissolved into seawater and 100 mL of the resulting <sup>15</sup>N<sub>2</sub>-enriched water was added to 4.3 L polycarbonate sampling bottles. The resulting atom % enrichment of stocks of <sup>15</sup>N<sub>2</sub>-enriched seawater was measured using a membrane inlet mass spectrometer. Incubation bottles amended with the <sup>15</sup>N<sub>2</sub> tracer were attached to a free-drifting array and incubated at the discrete depths from which samples had been collected. The array was deployed before dawn and samples were incubated at in situ light and temperature for 24 h. After recovery of the array, the entire volume from each bottle was filtered onto a pre-combusted glass microfiber filter (Whatman 25 mm GF/F) and filters were placed onto pre-combusted pieces of foil in Petri dishes and stored frozen at -20°C. Filters were dried for 24 h at 60°C, pelleted, and the total mass of N and its isotopic signature on each filter were analyzed on an elemental analyzer-isotope ratio mass spectrometer (Carlo-Erba EA NC2500 coupled with ThermoFinnigan Delta S). </p>
Storm-Time Plasma Pressure Inferred from Multi-Mission Measurements and Its Validation using Van Allen Probes Particle Data
<p>Data associated with Space Weather Journal manuscript submission titled: "Storm-Time Plasma Pressure Inferred from Multi-Mission Measurements and Its Validation using Van Allen Probes Particle Data". This includes all the digital data that was used in constructing the Figures from the main text, along with files containing the fit set of coefficients and parameters for the model, and files describing the subset of magnetometer used for fitting the model. </p>
Highly time-resolved measurements of element concentrations in PM10 and PM2.5: Comparison of Delhi, Beijing, London, and Krakow
<p>Data presented in the manuscript "Highly time-resolved measurements of element concentrations in PM10 and PM2.5: Comparison of Delhi, Beijing, London, and Krakow" (https://doi.org/10.5194/acp-2020-618) by Rai et al. (2020).</p>
Data from: Accelerometers can measure total and activity-specific energy expenditure in free-ranging marine mammals only if linked to time-activity budgets
Energy expenditure is an important component of foraging ecology, but is extremely difficult to estimate in free-ranging animals and depends on how animals partition their time between different activities during foraging. Acceleration data have emerged as a new way to determine energy expenditure at a fine scale but this needs to be tested and validated in wild animals. This study investigated whether vectorial dynamic body acceleration (VeDBA) could accurately predict the energy expended by marine predators during a full foraging trip. We also aimed to determine whether the accuracy of predictions of energy expenditure derived from acceleration increased when partitioned by different types of at-sea activities (i.e. diving, transiting, resting and surface activities). To do so, we equipped 20 lactating northern (Callorhinus ursinus) and 20 lactating Antarctic fur seals (Arctocephalus gazella) with GPS, time-depth recorders and tri-axial accelerometers and obtained estimates of field metabolic rates using the doubly labelled water (DLW) method. VeDBA was derived from tri-axial acceleration, and at-sea activities (diving, transiting, resting and surface activities) were determined using dive depth, tri-axial acceleration and travelling speed. We found that VeDBA did not accurately predict the total energy expended by fur seals during their full foraging trips (R2 = 0·36). However, the accuracy of VeDBA as a predictor of total energy expenditure increased significantly when foraging trips were partitioned by activity and when activity-specific VeDBA was paired with time-activity budgets (R2 = 0·70). Activity-specific VeDBA also accurately predicted the energy expenditures of each activity independent of each other (R2 > 0·85). Our study confirms that acceleration is a promising way to estimate energy expenditures of free-ranging marine mammals at a fine scale never attained before. However, it shows that it needs to be based on the time-activity budgets that make up foraging trips rather than being derived as a single measure of VeDBA applied to entire foraging trips. Our activity-based method provides a cost-effective means to accurately calculate energy expenditures of fur seals using acceleration and time-activity budgets, that can be transfered to studies on other species.
Data from: Long-term environmental monitoring for assessment of change: measurement inconsistencies over time and potential solutions
The importance of long-term environmental monitoring and research for detecting and understanding changes in ecosystems and human impacts on natural systems is widely acknowledged. Over the last decades a number of critical components for successful long-term monitoring have been identified. One basic component is quality assurance/quality control protocols to ensure consistency and comparability of data. In Norway, the authorities require environmental monitoring of the impacts of the offshore petroleum industry on the Norwegian continental shelf, and in 1996 a large-scale regional environmental monitoring program was established. As a case study, we used a sub-set of data from this monitoring to explore concepts regarding best practices for long-term environmental monitoring. Specifically, we examined data from physical and chemical sediment samples and benthic macro-invertebrate assemblages from 11 stations from six sampling occasions during the period 1996-2011. Despite the established quality assessment and quality control protocols for this monitoring program, we identified several data challenges, such as, missing values and outliers, discrepancies in variable and station names, changes in procedures without calibration, and different taxonomic resolution. Furthermore, we show that the use of different laboratories over time makes it difficult to draw conclusions with regard to some of the observed changes. We offer recommendations to facilitate comparison of data over time. We also present a new procedure to handle different taxonomic resolution so valuable historical data is not discarded. These topics have a broader relevance and application than for our case study.
Data from: Measuring stratigraphic congruence across trees, higher taxa, and time
The congruence between the order of cladistic branching and the first appearance dates of fossil lineages can be quantified using a variety of indices. Good matching is a prerequisite for the accurate time calibration of trees, while the distribution of congruence across large samples of cladograms has underpinned claims about temporal and taxonomic patterns of completeness in the fossil record. The most widely used stratigraphic congruence indices are the stratigraphic consistency index, the modified Manhattan stratigraphic measure, and the gap excess ratio (plus its derivatives; the topological gap excess ratio and the modified gap excess ratio). Many factors are believed to variously bias these indices, with several empirical and simulation studies addressing some subset of the putative interactions. This study combines both approaches to quantify the effects (on all five indices) of eight variables reasoned to constrain the distribution of possible values (the number of taxa, tree balance, tree resolution, range of first occurrence dates, center of gravity of first occurrence dates, the variability of first occurrence dates, percentage of extant taxa, and percentage of taxa with no fossil record). Our empirical data set comprised 647 published vertebrate and invertebrate cladograms spanning the entire Phanerozoic, and for these data we also modelled the effects of mean age of first occurrences (as a proxy for clade age), the taxonomic rank of the clade, and the higher taxonomic group to which it belonged. The center of gravity of first occurrence dates had not been investigated hitherto, and this was found to correlate most strongly with some measures of stratigraphic congruence in our empirical study (top-heavy clades had better congruence). The modified gap excess ratio was the index least susceptible to bias. We found significant differences across higher taxa for all indices; arthropods had lower congruence and tetrapods higher congruence. Stratigraphic congruence – however measured – also varied throughout the Phanerozoic, reflecting the taxonomic composition of our sample. Notably, periods containing a high proportion of arthropods had poorer congruence overall than those with higher proportions of tetrapods.
TIMING AND PATIENTS' POSITION DURING CUFF BLOOD PRESSURE MEASUREMENT AFFECT MYOCARDIAL WORK PARAMETERS MEASURED BY ECHOCARDIOGRAPHY
<p><span>BACKGROUND. Although cuff blood pressure measurement is a critical parameter to calculate myocardial work (MW) noninvasively, there is no recommendation about when and how to measure it. Accordingly, we sought to evaluate the effects of the timing during the echo study and the patient's position on the scanning bed during the cuff blood pressure measurement on MW parameter calculations.</span></p> <p><span>METHODS. 101 consecutive patients (44 women, 66±14 years) undergoing clinically indicated echocardiography were prospectively enrolled. During the echocardiographic study, we measured the cuff blood pressure four times, using a fully automatic digital blood pressure monitor applied to the right arm and left in the same position throughout the stud: BP1 before the start of the echo study, with the patient lying in the supine position; BP2, after positioning the patients on his/her left side to start the echo study; BP3, at the time of the acquisition of the three apical views (4-, 2-chamber, and the long-axis) used to measured left ventricular global longitudinal strain (GLS); and BP4, at the end of the echo study with the patient again in the supine position.</span></p> <p><span>RESULTS. Systolic blood pressure (SBP) at BP1 was 147±21 mm Hg. Between BP1 and BP2, it dropped by 17±9 mm Hg (p<0.05). SBP at BP3 was significantly lower than BP2 (130±20 mm Hg vs. 122±18 mm Hg, p<0.05), and at BP4 was significantly lower than at BP1 (-9±13 mm Hg, p<0.05). The average GLS was -16±3%. Accordingly, the global work index was 1929±441 mmHg% at BP1, dropped to 1717±421 at BP2, decreased to 1602±351 mm Hg% at BP3, and increased to 1815±386 mmHg% at BP4 (p<0.001).</span></p> <p><span>CONCLUSIONS. The timing during the echocardiography study and the patient's position on the scanning bed are critical determinants of the measured cuff SPB and the resulting values of MW parameters. </span></p>
Bitstream Compaction iCE40 - Bitstreams and Reconfiguration Time Measurements
<p>The HDF5 file contains data from an experiment concerning bitstream compaction for Lattice iCE40 FPGAs.</p> <p>Four projects were synthesized with two different toolchains and afterwards compacted in two different levels, resulting in 24 bitstreams. The creation of each bitstream was repeated 100 times to guard against variations caused by errors in the toolchains or by the compaction. No such errors occured and the configuration is the same for every repetition.</p> <p>The reconfiguration time for each of the 24 bitstreams was measured 10.000 times.</p> <p>Toolchains:</p> <ul> <li>Lattice iCEcube2</li> <li>Open Source Toolchain (Yosys, nextpnr, Project Icestorm)</li> </ul> <p>Projects:</p> <ul> <li>empty: Completely empty project, that uses no FPGA resources.</li> <li>b12: A project from the ITC’99 benchmark suite.</li> <li>aes: A simple AES-128 encryption module.</li> <li>picosoc: RISC-V system on a chip</li> </ul> <p>Compaction levels:</p> <ul> <li>original: uncompacted bitstream</li> <li>builtin: compacted with two methods also available in iCEcube2</li> <li>compact: compacted with all five methods</li> </ul> <p>File structure:</p> <p>The data is organzied hierarchical. It is first split between bitstreams and measurements, then by toolchain, then by project and finally by compaction level.</p>
No consistent effect of daytime versus night-time measurement of thermal tolerance in nocturnal and diurnal lizards
<p>While essential in understanding impacts of climate change for organisms, diel variation remains an understudied component of temporal variation in thermal tolerance limits (i.e. the critical thermal minimum (CTmin) and maximum (CTmax)). For example, a higher Ctmax might be expected for an individual if the measurement is taken during the day (when heat stress is most likely to occur) instead of at night. We measured thermal tolerance (Ctmin and Ctmax) during both the daytime and night-time in 101 nocturnal and diurnal geckos and skinks in Hong Kong and in South Africa, representing six species, and covering a range of habitats. We found that period of measurement (day vs. night) only affected Ctmin in South Africa (but not in Hong Kong) and that Ctmax was unaffected. Body size and species were important factors for determining Ctmax in Hong Kong and Ctmin in South Africa, respectively. Overall, however, we did not find consistent diel variation of thermal tolerance and suggest that measurements of critical thermal limits may be influenced by timing of measurement – but that such effects, when present, are likely to be context-dependent.</p>
Run-time Performance Measurements to "Instanced Rendering of Parameterized 3D Glyphs with Adaptive Level-of-Detail using three.js"
Open the record for dataset details and reuse information.
Limits of perceived audio-visual spatial coherence as defined by reaction time measurements
<p>Data accompanying the paper "Limits of perceived audio-visual spatial coherence as defined by reaction time measurements" published in Frontiers in Neuroscience, May 2019.</p>
New high time resolution mutual impedance measurements
<p>=====================================================================================================<br> Author : L. Bucciantini<br> Date : 03/11/2022<br> Laboratory : CNRS-LPC2E, Orléans (France)<br> =====================================================================================================</p> <p>Dear reader, we thank you for your interest in our dataset. In this document, we describe what you will<br> find in it.</p> <p>In case you need help with using the dataset, or if you are interested in mutual impedance experiments,<br> do not hesitate to contact our team in Orléans (pierre.henri@cnrs-orleans.fr, pierre.henri@oca.eu)</p> <p>=====================================================================================================<br> Topic:<br> This dataset contains the outputs of numerical simulations and experimental tests performed<br> to test and validate two new mutual impedance instrumental modes: the chirp mode and the<br> multi-spectral mode. These modes have the objective of improving the time resolution of<br> mutual impedance measurements.</p> <p>These new instrumental modes are validated by comparing their measurements to the classic<br> instrumental mode of mutual impedance measuremetns, the so-called frequency sweep mode.</p> <p><br> Numerical model:<br> The numerical outputs are obtained from a numerical model based on the solution of the 1D-1V Vlasov-Poisson<br> system of equations. The scheme used to solve the model is the one developed<br> by Mangeney et al. (2002), A Numerical Scheme for the Integration of the Vlasov-Maxwell System of Equations. Journal of Computational Physics, (doi: https://doi.org/10.1006/jcph.2002.7071).<br> The 1D-1V Vlasov-Poisson version of this model is described in Henri, et al. (2010), Vlasov-Poisson<br> simulations of electrostatic parametric instability for localized Langmuir wave packets in the solar wind,<br> Journal of Geophysical Research (Space Physics), 115, 6106 (2010)</p> <p>Experimental testing facility:<br> The experimental outputs are obtained using the testing facility of CNRS-LPC2E space laboratory. It is<br> composed of a vacuum chamber, a pumping system and a plasma source. The pumps make vacuum in the<br> chamber (p=1e-5mbar). The plasma source generates a plasma flow that we use to test mutual impedance<br> instruments in a medium which is representative of Earth's ionosphere (100-2000 km of altitude).</p> <p>-----------------------------------------------------------------------------------------------------<br> Instrumental modes of interest</p> <p>frequency sweep (FS) mode : typical mode. Implemented for DFP-COMPLIMENT onboard Comet Interceptor</p> <p>chirp (CH) mode : new fast mode. It is based on the emission of only one period per frequency.</p> <p>multi-spectral (MS) mode : new fast mode. It is based on the simultaneous emission of multiple frequencies.</p> <p>-----------------------------------------------------------------------------------------------------<br> What is inside the dataset:</p> <p>The dataset is composed of 3 numerical and 6 experimental mutual impedance measurements.<br> The numerical measurements are obtained for the same plasma box but for different instrumental modes.<br> The experimental measurements are obtained for two different plasmas, monitored using the three different<br> instrumental modes.</p> <p>-----------------------------------------------------------------------------------------------------<br> List of directories:</p> <p><br> Numerical_CH, Numerical_MS: Each of these outputs corresponds to a mutual impedance measurement<br> Numerical_FS performed using a different instrumental mode.<br> For MS and FS, inside the directory each folder corresponds to the<br> emission of a different frequency.</p> <p><br> Experimental : It contains a txt file with inside the mutual impedance spectra obtained<br> for two different plasmas</p> <p><br> -----------------------------------------------------------------------------------------------------<br> List of folders inside the directories:</p> <p>In the case of the numerical measurements, folders begin with the name "000" and have increasing index.</p> <p>------------------------------------------------------------------------------------------------------<br> List of files inside the folders for the numerical measurements:</p> <p>density_e.npz : electron density inside the box, in function of time (tempo)</p> <p>density_p.npz : ion density inside the box, in function of time (tempo)</p> <p>E.npz : electric field in the box, in function of time (tempo)</p> <p>qrho.npz : electric potential in the box, in function of time (tempo)</p> <p>qrho_imposed.npz : electric charge imposed at the emitting antennas, in function of time (tempo)</p> <p>tempo.npz : time-vector for density, electric field, electric potential and charge vectors</p> <p>TEST_Luca.dat : parameters describing the characteristics of the simulated plasma box</p> <p><br> [<br> Note : the previous files can be opened as follows</p> <p>import numpy as np</p> <p>vector_file_name = np.load('file_name.npz') # Use these for the .npz files<br> characteristics_of_the_box = np.genfromtxt('TEST_Luca.dat',skip_header=1)</p> <p>]</p> <p>------------------------------------------------------------------------------------------------------<br> List of files inside the folders for the experimental measurements:</p> <p>data.txt : frequencies and amplitudes associated to the measurements.</p> <p>[<br> Note : the previous file can be directly included into a python script</p> <p>]</p> <p>-------------------------------------------------------------------------------------------------------<br> Characteristics of the simulated plasma box (TEST_Luca.dat)</p> <p>nx : amount of spatial grid points</p> <p>xl : physical size of the spatial box, expressed in Debye length</p> <p>tt_w : time resolution for ion and electron density, electric field, electric potential and charge</p> <p>rap_m : ion-to-electron mass ratio</p> <p>R_p : ion-to-electron temperature ratio</p> <p>dt : time step used to evolve in time the numerical simulation</p> <p>emission : emission frequency</p> <p>power : amplitude of the electric charge imposed at the emitting antennas</p> <p><br> (Note : all parameters not listed here but present inside the TEST_Luca.dat file correspond to additional<br> functionalities of the model. For the use of this dataset, they can be discarded.)</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Real-time Anti-Factor Xa Measurements in Surgical Patients to Examine Enoxaparin Metabolism and Optimize Enoxaparin Dose
ClinicalTrials.gov study NCT02704052. IPD Sharing: NO. Countries: 1. Publications: 2.
Feasibility of the Comfort Measures Only Time Out (CMOT)
ClinicalTrials.gov study NCT05861323. IPD Sharing: NO. Countries: 1. Publications: 1.
Correlation of Different Time Measurements of the Surgical PLETH Index With Postoperative Pain
ClinicalTrials.gov study NCT04724564. IPD Sharing: NO. Countries: 1. Publications: 5.
Monitoring of Breathing Effort Through Pressure Time Product Measurement Using Airway Occlusion Pressure
ClinicalTrials.gov study NCT06040138. IPD Sharing: NO. Countries: 1. Publications: 17.
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International Brain Laboratory public data
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OpenNeuro
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