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197 results for “time estimation”
FIGURE 1. Strict consensus tree from a in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 1. Strict consensus tree from a PAUP parsimony heuristic search; 10,000 addition sequence replicates; bootstrap support shown above branches
FIGURE 2. Consensus tree from a in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 2. Consensus tree from a PHASE analysis; 10 million generations. Posterior probabilities shown above branches
We are what we eat, plus some per mill: Using stable isotopes to estimate diet composition in Gyps vultures over space and time
<p><span><span><span><span><span><span><span><span><span><span><span>1. Dietary studies in birds of prey involve direct observation and examination of food remains at resting and nesting sites. Although these methods accurately identify diet in raptors, they are time consuming, resource intensive and associated with biases that stem from the feeding ecology of raptors like <i>Gyps </i>vultures (<i>Gyps africanus</i> and <i>Gyps rueppelli</i>). Our study set out to estimate diet composition in <i>Gyps </i>vultures informed by stable isotopes that provide a good representation of assimilated diet from carrion resources in local systems. </span></span></span></span></span></span></span></span></span></span></span></p> <p>2. We hypothesized that differences in <i>Gyps </i>vulture diet composition is a function of sampling location, and that these vultures move between Serengeti National Park and Selous Game Reserve to forage. We also, theorized that grazing ungulates are the principal items in <i>Gyps</i> vulture diet. </p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Through a combination of linear and Bayesian models, diet derived from d<sup>13</sup>C in <i>Gyps</i> vultures consisted of grazing herbivores across study areas, with those in Serengeti National Park consuming higher proportions of grazing herbivores (> 87%). d<sup>13</sup>C differences in vulture feather subsets per site did not indicate vulture diet change, and in combination with blood d<sup>13</sup>C, vultures fed largely on grazers for ~159 days before they were sampled in both sites. Similarly, d<sup>15</sup>N values implied that <i>Gyps </i>vultures fed largely on herbivores across space and time. d<sup>34</sup>S ratios separated prey source for vultures between the two sites. d<sup>34</sup>S variation in vultures across sites resulted from differences in baseline (plant) d<sup>34</sup>S values, though it is not possible to match d<sup>34</sup>S to specific locations. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Our findings highlight the relevance of repeated sampling that considers tissues with varying isotopic turnover and emerging Bayesian techniques for dietary studies using stable isotopes. Findings also suggested limited vulture movement between the two local systems. However, more sampling coupled with telemetry is required to fully comprehend this observation and its implications to <i>Gyps</i> vulture ecology and conservation.</span></span></span></span></span></span></span></span></span></span></span></p>
Dataset for 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations'
<p> </p> <p>This dataset is associated with the research entitled: 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. It contains raw data from six low-cost sensor loggers and one anemometer, collected during an experiment consisting of a series of controlled releases conducted in October 2019 at the TADI (TotalEnergies Anomaly Detection Initiative) platform.</p> <p><strong>Dataset Structure:</strong></p> <p>- `time`: Timestamp, marking the exact time the data was collected.<br> - `CH4`: Methane concentration measured by the reference instrument in parts per million (ppm).<br> - `2611C`: Voltage variation measured by the Figaro TGS 2611C-00 MOS sensor in volts (V).<br> - `2600`: Voltage variation measured by the Figaro TGS 2600 MOS sensor in volts (V).<br> - `2611E`: Voltage variation measured by the Figaro TGS 2611E-00 MOS sensor in volts (V).<br> - `RH_DHT22`: Relative humidity measured by the DHT22 sensor in percentage (%).<br> - `RH_SHT75`: Relative humidity measured by the SHT75 sensor in percentage (%).<br> - `T_DHT22`: Air temperature measured by the DHT22 sensor in degrees Celsius (°C).<br> - `T_SHT75`: Air temperature measured by the SHT75 sensor in degrees Celsius (°C).<br> - `T_BMP180`: Air temperature measured by the BMP180 sensor in degrees Celsius (°C).<br> - `T_BMP280`: Air temperature measured by the BMP280 sensor in degrees Celsius (°C).<br> - `P_BMP180`: Atmospheric pressure measured by the BMP180 sensor in pascals (Pa).<br> - `P_BMP280`: Atmospheric pressure measured by the BMP280 sensor in pascals (Pa).<br> - `Release`: Number of the controlled release.</p> <p><strong>Acknowledgment:</strong></p> <p>When using this dataset, please reference the original research paper titled ‘Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’.</p> <p><strong>Contact Information:</strong></p> <p>Olivier Laurent (olivier.laurent@lsce.ipsl.fr)</p> <p> </p>
Correlating Outcomes With Biochemical Markers to Estimate Time-progression in Idiopathic Pulmonary Fibrosis (IPF)
ClinicalTrials.gov study NCT01071707. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care: Study 2
ClinicalTrials.gov study NCT06646133. IPD Sharing: YES. Countries: 1. Publications: 6.
We are what we eat, plus some per mill: Using stable isotopes to estimate diet composition in Gyps vultures over space and time
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Data from: Among- and within-population variation in flowering time of Iberian Arabidopsis thaliana estimated in field and glasshouse conditions
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Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
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Supplementary material for: nQMaker: estimating time non-reversible amino acid substitution models
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Data from: Estimating diversity through time using molecular phylogenies: old and species-poor frog families are the remnants of a diverse past.
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Data from: Estimating selection through male fitness: three complementary methods illuminate the nature and causes of selection on flowering time
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Data from: Fossils matter: improved estimates of divergence times in Pinus reveal older diversification
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Data from: Model misspecification confounds the estimation of rates and exaggerates their time dependency
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Data from: Accounting for heterogeneity when estimating stopover duration, timing and population size of red knots along the Luannan Coast of Bohai Bay, China
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Data from: Estimating field capacity from volumetric soil water content time series using automated processing algorithms
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Data from: Bayesian divergence-time estimation with genome-wide SNP data of sea catfishes (Ariidae) supports Miocene closure of the Panamanian isthmus
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Data from: Using survival models to estimate invertebrate prey identification times in a generalist stream fish
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The implications of lineage-specific rates for divergence time estimation
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Data from: A jungle tale: molecular phylogeny and divergence time estimates of the Desmopsis - Stenanona clade (Annonaceae) in Mesoamerica
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Allen Brain Atlas
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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.