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171 results for “time sampling”
Data from: Across space and time: a review of sampling and analytical biases in fossil data across macroecological scales
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Data from: Real-time assessment of hybridization between wolves and dogs: combining non-invasive samples with ancestry informative markers
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Increased time sampling in an evolve-and-resequence experiment with outcrossing Saccharomyces cerevisiae reveals multiple paths of adaptive change
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Data from: Timing of vegetation sampling does not influence associations between visual obstruction and turkey nest survival in a montane forest
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Data from: Ant mosaics in Bornean primary rain forest high canopy depend on spatial scale, time of day, and sampling method
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Details of the clinical and CSF findings in the enrolled patients with time-matched paired serum and CSF samples
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Comprehensive taxon sampling and vetted fossils help clarify the time tree of shorebirds (Aves, Charadriiformes)
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Timing of blood sample processing affects the transcriptomic and epigenomic profiles in CD4+ T-cells of atopic subjects
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Display of the Crab long-term (15 year time span) spectrum extracted from a sample of 50 randomly selected ScWs.
<p><a href="https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&instrument=isgri&osa_version=OSA10.2&radius=15&E1_keV=20&E2_keV=40&product_type=isgri_spectrum&catalog_selected_objects=1,2,3,4&selected_catalog=%7B%22cat_column_descr%22:%5B%5B%22meta_ID%22,%22%3Ci8%22%5D,%5B%22src_names%22,%22%7CS18%22%5D,%5B%22significance%22,%22%3Ef4%22%5D,%5B%22ra%22,%22%3Ef4%22%5D,%5B%22dec%22,%22%3Ef4%22%5D,%5B%22NEW_SOURCE%22,%22%3Ei2%22%5D,%5B%22ISGRI_FLAG%22,%22%3Ci8%22%5D,%5B%22FLAG%22,%22%3Ci8%22%5D,%5B%22ERR_RAD%22,%22%3Cf8%22%5D%5D,%22cat_column_list%22:%5B%5B1,2,3,4%5D,%5B%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22%5D,%5B116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477%5D,%5B84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844%5D,%5B26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285%5D,%5B-32768,-32768,-32768,-32768%5D,%5B2,2,2,2%5D,%5B0,0,0,0%5D,%5B0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203%5D%5D,%22cat_column_names%22:%5B%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22%5D,%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22%7D">https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&instrument=isgri&osa_version=OSA10.2&radius=15&E1_keV=20&E2_keV=40&product_type=isgri_spectrum&catalog_selected_objects=1,2,3,4&selected_catalog={%22cat_column_descr%22:[[%22meta_ID%22,%22%3Ci8%22],[%22src_names%22,%22|S18%22],[%22significance%22,%22%3Ef4%22],[%22ra%22,%22%3Ef4%22],[%22dec%22,%22%3Ef4%22],[%22NEW_SOURCE%22,%22%3Ei2%22],[%22ISGRI_FLAG%22,%22%3Ci8%22],[%22FLAG%22,%22%3Ci8%22],[%22ERR_RAD%22,%22%3Cf8%22]],%22cat_column_list%22:[[1,2,3,4],[%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22],[116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477],[84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844],[26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285],[-32768,-32768,-32768,-32768],[2,2,2,2],[0,0,0,0],[0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203]],%22cat_column_names%22:[%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22],%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22}</a></p>
Crab long-term (15 year time span) lightcurve extracted froma sample of 50 randomly selected ScWs in 30-100 keV band, usingOSA10.2.
<p><a href="https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&E1_keV=30&E2_keV=100&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&catalog_selected_objects=1,2,3,4&detection_threshold=7.5&instrument=isgri&osa_version=OSA10.2&product_type=isgri_lc&time_bin=10000&time_bin_format=sec&query_status=ready&query_type=Real&radius=15&use_scws=no&selected_catalog=%7B%22cat_column_descr%22:%5B%5B%22meta_ID%22,%22%3Ci8%22%5D,%5B%22src_names%22,%22%7CS18%22%5D,%5B%22significance%22,%22%3Ef4%22%5D,%5B%22ra%22,%22%3Ef4%22%5D,%5B%22dec%22,%22%3Ef4%22%5D,%5B%22NEW_SOURCE%22,%22%3Ei2%22%5D,%5B%22ISGRI_FLAG%22,%22%3Ci8%22%5D,%5B%22FLAG%22,%22%3Ci8%22%5D,%5B%22ERR_RAD%22,%22%3Cf8%22%5D%5D,%22cat_column_list%22:%5B%5B1,2,3,4%5D,%5B%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22%5D,%5B116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477%5D,%5B84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844%5D,%5B26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285%5D,%5B-32768,-32768,-32768,-32768%5D,%5B2,2,2,2%5D,%5B0,0,0,0%5D,%5B0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203%5D%5D,%22cat_column_names%22:%5B%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22%5D,%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22%7D">https://www.astro.unige.ch/cdci/astrooda_?src_name=Crab&RA=83.633080&DEC=22.014500&E1_keV=30&E2_keV=100&T1=2003-03-15T23:27:40.0&T2=2018-03-16T00:03:15.0&T_format=isot&catalog_selected_objects=1,2,3,4&detection_threshold=7.5&instrument=isgri&osa_version=OSA10.2&product_type=isgri_lc&time_bin=10000&time_bin_format=sec&query_status=ready&query_type=Real&radius=15&use_scws=no&selected_catalog={%22cat_column_descr%22:[[%22meta_ID%22,%22%3Ci8%22],[%22src_names%22,%22|S18%22],[%22significance%22,%22%3Ef4%22],[%22ra%22,%22%3Ef4%22],[%22dec%22,%22%3Ef4%22],[%22NEW_SOURCE%22,%22%3Ei2%22],[%22ISGRI_FLAG%22,%22%3Ci8%22],[%22FLAG%22,%22%3Ci8%22],[%22ERR_RAD%22,%22%3Cf8%22]],%22cat_column_list%22:[[1,2,3,4],[%221A+0535%2B262%22,%224U+0517%2B17%22,%22Crab%22,%22H+0614%2B091%22],[116.77162170410156,7.909553527832031,1232.6466064453125,8.148767471313477],[84.72620391845703,77.66708374023438,83.63129425048828,94.25425720214844],[26.314136505126953,16.48775863647461,22.01542854309082,9.165886878967285],[-32768,-32768,-32768,-32768],[2,2,2,2],[0,0,0,0],[0.0002800000074785203,0.0002800000074785203,0.0002800000074785203,0.0002800000074785203]],%22cat_column_names%22:[%22meta_ID%22,%22src_names%22,%22significance%22,%22ra%22,%22dec%22,%22NEW_SOURCE%22,%22ISGRI_FLAG%22,%22FLAG%22,%22ERR_RAD%22],%22cat_coord_units%22:%22deg%22,%22cat_frame%22:%22fk5%22,%22cat_lat_name%22:%22dec%22,%22cat_lon_name%22:%22ra%22}</a></p>
In-time particle simulation (Itpas) - sample data
<p>Itpas is a Lagrangian particle dispersion model, online coupled to the weather forecast model COSMO.</p> <p>This archive contains a sample data set of the Itpas output and the associated meteorological data fields. </p> <p>The model domain covers an area from eastern Germany to central Poland including the cities of Berlin and Poznan (lower left corner: 51°N 12°E, upper right corner: 54.4°N 19.3°E) and has a size of 167 x 150 grid cells with a spatial resolution of 2.8 km and 50 vertical levels.<br> The data is assigned to May 31, 2017 form 08 UTC to 23 UTC</p> <p>l.nc -> Atmospheric data produced by the COSMO-model</p> <p>part_t001200_p001.nc<br> part_t001260_p001.nc -> Trajectories emitted in the morning from 08:50 to 09:45 UTC</p> <p>part_t001380_p001.nc<br> part_t001440_p001.nc -> Trajectories emitted at noon from 11:40 to 12:30 UTC</p> <p>mean_traj.txt -> position of the mean trajectory [time rot_lon rot_lat z_sea z_surface]<br> stda_traj.txt -> standard deviation of the mean trajectory</p> <p>The data can be used as sample data for the following software:<br> https://github.com/mttfst/trajectory-plot<br> https://github.com/mttfst/trajectory-cross-section</p>
Divergence time estimation of genus Tribolium by extensive sampling of highly conserved orthologs
<p><i>Tribolium castaneum</i>, the red flour beetle, is among the most well-studied eukaryotic genetic model organisms. <i>Tribolium</i> often serves as a comparative bridge from highly derived <i>Drosophila</i> traits to other organisms. Simultaneously, as a member of the most diverse order of metazoans, Coleoptera, <i>Tribolium</i> informs us about innovations that accompany hyper diversity. However, understanding the tempo and mode of evolutionary innovation requires well-resolved, time-calibrated phylogenies, which are not available for <i>Tribolium</i>. The most recent effort to understand <i>Tribolium</i>phylogenetics used two mitochondrial and three nuclear markers. The study concluded that the genus may be paraphyletic and reported a broad range for divergence time estimates. Here we employ recent advances in Bayesian methods to estimate the relationships and divergence times among <i>Tribolium castaneum</i>, <i>T. brevicornis</i>, <i>T. confusum</i>, <i>T. freemani</i>, and <i>Gnatocerus cornutus </i>using 1368 orthologs conserved across all five species and an independent substitution rate estimate. We find that the most basal split within <i>Tribolium</i> occurred ~86 Mya [95% HPD 85.90–87.04 Mya] and that the most recent split was between <i>T. freemani</i> and <i>T. castaneum</i> at ~14 Mya [95% HPD 13.55-14.00]. Our results are consistent with broader phylogenetic analyses of insects and suggest that Cenozoic climate changes played a role in the <i>Tribolium </i>diversification.</p>
Data from: Sex, size and timing: sampling design for reliable population genetics analyses using microsatellite data
1. Population genetics is used in a wide variety of fields such as ecology and biodiversity conservation. How estimated genetic characteristics of natural populations can be influenced by the sampling design has been a long-standing concern. Multiple simulation and empirical studies illustrated the influence of both sample size and polymorphism of markers. However, our review of studies on butterfly population genetics indicates no consensus on sample size for the estimation of genetic diversity or differentiation. Furthermore, other aspects of sampling design (sex ratio and timing of sampling) were not addressed and their potential impact on genetic parameter estimates rarely explored. 2. Using a large empirical dataset (with spatial and temporal replicates) collected on a butterfly species, Boloria aquilonaris, as well as simulated datasets reflecting (1) three scenarios of migration-genetic drift equilibrium and (2) one scenario of parameter stabilization after 100,000 generations, we quantified the impacts of three aspects of genetic sampling design (namely sample size, sex ratio, and timing of sampling) on the estimation of allele frequencies and its potential downstream impact on the estimation of genetic parameters. 3. With empirical data, we found that sample size and timing of sampling strongly affected the accuracy of allele frequencies and the downstream analyses, while sex ratio did not. Our results were consistent across spatial and temporal replicates. Also, with simulated data, we showed that the genetic sampling design had limited effect in systems where dispersal outweighs genetic drift, while it can have major consequences on our understanding of the genetic diversity and population differentiation in systems dominated by genetic drift (such as most study systems with conservation concerns). 4. We advocate for careful consideration of all aspects of the sampling design in population genetics studies, i.e. a sufficient number of samples, while ensuring similar sex ratio among sampling locations and collecting with timing appropriate to the question under study. This is particularly important when the study aims at species conservation.
Data from: Time for a rethink: time sub-sampling methods in disparity-through-time analyses
Disparity-through-time analyses can be used to determine how morphological diversity changes in response to mass extinctions, and to investigate the drivers of morphological change. These analyses are routinely applied to palaeobiological datasets, yet although there is much discussion about how to best calculate disparity, there has been little consideration of how taxa should be sub-sampled through time. Standard practice is to group taxa into discrete time bins, often based on stratigraphic periods. However, this can introduce biases when bins are of unequal size, and implicitly assumes a punctuated model of evolution. In addition, many time bins may have few or no taxa, meaning that disparity cannot be calculated for the bin and making it harder to complete downstream analyses. Here we describe a different method to complement the disparity-through-time tool-kit: time-slicing. This method uses a time-calibrated phylogenetic tree to sample disparity-through-time at any fixed point in time rather than binning taxa. It uses all available data (tips, nodes and branches) to increase the power of the analyses, specifies the implied model of evolution (punctuated or gradual), and is implemented in R. We test the time-slicing method on four example datasets and compare its performance in common disparity-through-time analyses. We find that the way you time sub-sample your taxa can change your interpretations of the results of disparity-through-time analyses. We advise using multiple methods for time sub-sampling taxa, rather than just time binning, to gain a better understanding disparity-through-time.
Pre-analytical validity of arterial blood gas samples: A prospective experimental study on changes derived from time delay and mechanical stress
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Supplementary Figure 1. Comparison of diagnostic sensitivity of African swine fever virus (ASFV) real-time PCR kits using artificially spiked samples (ASSs)
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Data from: Modelling distributions of fossil sampling rates over time, space and taxa: assessment and implications for macroevolutionary studies
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Data from: Sex, size and timing: sampling design for reliable population genetics analyses using microsatellite data
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Comparative analysis of time-based and quadrat sampling in seasonal population dynamics of intermediate hosts of human schistosomes
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Data from: The effect of fossil sampling on the estimation of divergence times with the fossilised birth death process
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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.