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22 results for “Nested Sampling”
Soil nutrients sampled in and around harvester ant nests in three habitats at the Jornada Basin LTER site, 1987
This dataset contains soil nutrient content measurements from harvester ant nests and reference soils in three nesting habitats at the Jornada Basin LTER site in 1987. The purpose of this investigation was to answer three general questions: 1. How does the modification of soil properties and the ratios of resources (e.g., water-N) by ants alter species assemblages of winter annual plants at the edge of the ant nests? 2. How does the "spring cleaning", clipping, predation or herbivory by ants affect success of the winter annual plants at the edge of ant nests? 3. Are there significant differences in the floristic assemblage and belowground standing crop (root biomass) between the edge of ant nest and the surrounding unaffected soils? Five ant nests and paired (non-nest) reference soils were sampled in three study locations. This dataset contains chemical analyses for the soil samples collected for each of the three sites, including total nitrogen, (ammonium, nitrate), inorganic phosphorus, and exchangeable cations (K+, Na+, Ca2+ and Mg2+). Also included is below ground biomass from five ant nests for each of the three sites. This study was completed in 1987.
Soil organic matter content sampled in and around harvester ant nests in three habitats at the Jornada Basin LTER site, 1987
This dataset contains soil organic matter content measurements from harvester ant nests and reference soils in three nesting habitats at the Jornada Basin LTER site in 1987. The purpose of this investigation was to answer three general questions: 1. How does the modification of soil properties and the ratios of resources (e.g., water-N) by ants alter species assemblages of winter annual plants at the edge of the ant nests? 2. How does the "spring cleaning", clipping, predation or herbivory by ants affect success of the winter annual plants at the edge of ant nests? 3. Are there significant differences in the floristic assemblage and belowground standing crop (root biomass) between the edge of ant nest and the surrounding unaffected soils? Five ant nests and paired (non-nest) reference soils were sampled in three study locations. This dataset contains percent (%) organic matter content measured by mass-loss on combustion in a muffle furnace. This study was completed in 1987.
Gravimetric soil water content sampled in and around harvester ant nests in three habitats at the Jornada Basin LTER site, 1987
This dataset contains gravimetric soil water content measurements from harvester ant nests and reference soils in three nesting habitats at the Jornada Basin LTER site in 1987. The purpose of this investigation was to answer three general questions: 1. How does the modification of soil properties and the ratios of resources (e.g., water-N) by ants alter species assemblages of winter annual plants at the edge of the ant nests? 2. How does the "spring cleaning", clipping, predation or herbivory by ants affect success of the winter annual plants at the edge of ant nests? 3. Are there significant differences in the floristic assemblage and belowground standing crop (root biomass) between the edge of ant nest and the surrounding unaffected soils? Five ant nests and paired (non-nest) reference soils were sampled in three study locations on a monthly schedule from January to May 1987. This dataset contains percent (%) soil water content in these samples determined using the gravimetric method. This study was completed in 1987.
Supplementary data for Nested sampling cross-checks using order statistics
<p>This is the raw data for tables 1 and 2 in <a href="https://arxiv.org/abs/2006.03371">Nested sampling cross-checks using order statistics</a>. The file names for the MultiNest results in table 1 are:</p> <pre><code>MN_{PROBLEM}_{number of dimensions}d_efr_{MultiNest efr parameter}.txt</code></pre> <p>The file names for the PolyChord results in table 2 are:</p> <pre><code>PC_{PROBLEM}_{number of dimensions}d_nr_{PolyChord number of repeats}.txt</code></pre> <p>where PROBLEM specifies one of four test functions described in Appendix C</p> <ul> <li>gaussian = Gaussian</li> <li>mixture = Gaussian-log-gamma mixture</li> <li>rosenbrock = Rosenbrock function</li> <li>shells = Gaussian shells</li> </ul> <p>Each file contains 100 rows (corresponding to 100 runs) and 8 columns</p> <ol> <li>log evidence</li> <li>error log evidence</li> <li>KS statistic from test on all iterations</li> <li>number of iterations</li> <li>p-value from all iterations</li> <li>Greatest KS statistic from tests on chunks of iterations</li> <li>Iteration of start of chunk at which greatest KS statistic occurred</li> <li>Bonferroni corrected p-value from greatest KS statistic from tests on chunks of iterations</li> </ol>
A NICER View of the Massive Pulsar PSR J0740+6620 Informed by Radio Timing and XMM-Newton Spectroscopy: Nested Samples for Millisecond Pulsar Parameter Estimation
<p>Posterior sample files associated with the preprint "A <em>NICER</em> View of the Massive Pulsar PSR J0740+6620 Informed by Radio-Timing and <em>XMM-Newton</em> Spectroscopy" by Riley et al. (2021; <a href="https://arxiv.org/abs/2105.06980">arXiv:2105.06980 [astro-ph.HE]</a>; submitted to ApJL).</p> <p>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p> <p>Please refer to the README for detailed information.</p> <p> </p> <p> </p>
A NICER View of PSR J0030+0451: Nested Samples for Millisecond Pulsar Parameter Estimation
<p>Posterior sample files associated with "A <em>NICER</em> View of PSR J0030+0451: Millisecond Pulsar Parameter Estimation" by Riley et al. (2019; <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab481c">DOI: 10.3847/2041-8213/ab481c</a>).</p> <p>Also included are model modules and scripts in the Python language using the X-PSI framework.</p> <p>Please refer to the README for detailed information.</p>
Data for: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3
<p>We present all of the data across our SNR and abundance study for the molecules O2 and O3 for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-20, and the abundance values range in log space in steps of 0.5 and/or 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input O2 and O3 values (abundance case), the retrieved O2 and O3 values (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for O2 and O3. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input O2': str, {'Input O3': str}})</p>
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-16, and the abundance values range from log10(VMR) = -3.5 to -1.5 in steps of 0.5 and 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input H2O value (abundance case), the retrieved H2O value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for H2O. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input H2O': str})</p>
Text-fig. 2. Cracked chrysophycean-cysts and nest-like accumulation of the pennate diatom Gomphonema bohemicum REICHELT et FRICKE 1902 embedded in a bituminous and clayish matrix, SEM-photograph, sample Sf (no number), seam 1 roof. in Siliceous Microfossils From The Oligocene Tripoli-Deposit Of Seifhennersdorf
Text-fig. 2. Cracked chrysophycean-cysts and nest-like accumulation of the pennate diatom Gomphonema bohemicum REICHELT et FRICKE 1902 embedded in a bituminous and clayish matrix, SEM-photograph, sample Sf (no number), seam 1 roof.
Costless correction of chain based nested sampling parameter estimaion in gravitational wave data and beyond (supplementary material)
<p>These are the nested sampling inference products used to obtain the results for <span><a href="https://arxiv.org/abs/2404.16428">arXiv:2404.16428</a>. The chains are given as pickled dataframes as there are over 1000 runs provided here. The script for reproducing the plots in the paper is also given. </span></p> <p><span>Folders:</span></p> <p><span>outdir_simulated_BBH - contains 200 runs on the same simulated signal. 'samples' contains the full sets of weighted samples from each run and 'full_dfs' contains the dataframes with the weighted samples AND the phantom points from the run. The samples in 'full_dfs' with chain_no=0 are the weighted samples, and all others are phantoms. </span></p> <p><span>outdir_test - contains the single run on the above simulated signal which was performed with num_repeats=25*ndims=100. </span></p> <p><span>outdir_coverage - contains 1000 runs on different simulated signals, with parameters drawn from the prior. The drawn values are saved as '{}_params.npy' for each run.</span></p>
Fractal sampling point nesting
<b>Description: </b><p>This dataset contains a table showing the nesting of sampling points within the fractal sampling design at the SAFE project and an R code file used to create the nesting from a GIS file of the core sampling locations.<br><br>There are five fractal levels across 17 sites: six experimental blocks (A-F), two edge transects (LFE, VJR) and three sets of triplets of sites to sample specific habitats (Old growth: OG1 - 3, Oil palm: OP1 - 3, Twice logged forest: LF1 - 3). Within each site, there are sampling points at five fractal scales with fractal order 1 as the smallest scale and fractal order 5 being the largest scale. This file identifies the child - parent relationships of points across the fractal sampling levels. For more details, see <a>https://www.safeproject.net/dokuwiki/safe_gis/safe_fractal</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=179">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Fractal_point_nesting.xlsx, Nested_fractal_table_maker.R</p><p><b>Fractal_point_nesting.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Fractal sampling point nesting</b> (described in worksheet Fractal_point_nesting)</p><p>Description: This table contains one row for each fractal order 1 sampling point (finest scale) and then identifies the parent sampling points at the higher fractal scales (2-5). Note that not all sites have a central fractal order 5 point.</p><p>Number of fields: 9</p><p>Number of data rows: 579</p><p>Fields: </p><ul><li><b>Site</b>: Site ID prefix (Field type: ID)</li><li><b>Habitat</b>: Focal habitat of sampling site (Field type: Categorical)</li><li><b>Logging</b>: Logging history of sampling site (Field type: Categorical)</li><li><b>Frag_Area</b>: Estimated area of fragment (Field type: Numeric)</li><li><b>FirstOrder</b>: First order fractal point (Field type: Location)</li><li><b>SecondOrder</b>: Second order fractal parent (Field type: Location)</li><li><b>ThirdOrder</b>: Third order fractal parent (Field type: Location)</li><li><b>FourthOrder</b>: Fourth order fractal parent (Field type: Location)</li><li><b>FifthOrder</b>: Fifth order fractal parent (not applicable for all sites) (Field type: Location)</li></ul></li></ol><p><b>Nested_fractal_table_maker.R</b></p><p>Description: This R code creates the table stored in Fractal_point_nesting from the SAFE Core sampling stations GIS layer. </p><p><b>Date range: </b>2010-01-01 to 2020-01-01</p><p><b>Latitudinal extent: </b>4.6350 to 4.7716</p><p><b>Longitudinal extent: </b>116.9474 to 117.7031</p>
Behavioral probabilities and sample videos for "Behavioral evidence for nested central pattern generator control of Drosophila grooming"
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Data from: Fine-tuning the nested structure of pollination networks by adaptive interaction switching, biogeography and sampling effect in the Galápagos Islands
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Data from: Dispersal of stream salmonids from nests and stocking sites: patterns, variability, and sampling bias
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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
Evaluating relationships between ecological processes that occur concurrently is complicated by the potential for such processes to covary. Ground-nesting birds rely on habitat characteristics that provide concealment from predators; this protection often is provided by vegetation at the nest. Recently, researchers have raised concern that measuring vegetation at nest fate introduces a bias, as vegetation at successful nests is measured later in the growing season. This bias can lead to an erroneous conclusion that plant height is positively associated with nest survival. However, if the features that provide concealment are invariant during the incubation period, no bias should be expected, and the timing of measurement is less influential. We used data collected from 98 nests to evaluate whether there is evidence that such a bias exists in a study of wild turkey (Meleagris gallopavo) nesting in a forest ecosystem. We modelled nest survival as a function of visual obstruction and other covariates of interest. At unsuccessful nests, we collected visual obstruction readings at both the date of nest failure and the projected hatch date and compared survival estimates generated using both sets of vegetation data. In contrast to studies in other systems, we found little evidence that the timing of vegetation sampling influenced conclusions regarding the association between visual obstruction and survival; model selection and estimates of nest survival were similar regardless of when vegetation data were collected. The dominant hiding cover at most of our nests was provided by evergreen shrubs; slow growth of these plants likely prevent appreciable changes in visual obstruction during incubation. When considered with a growing body of literature, our results suggest that the influence of timing of sampling depends on the study system. When designing future studies, investigators should consider the structures that provide nest concealment and whether phenology is confounded with nest survival.
FIG. 1 in The termites of the Mayombe Forest Reserve, Congo (Brazzaville): transect sampling reveals an extremely high diversity of ground-nesting soil feeders
FIG. 1. Proportions of feeding groups (a), nesting groups (b) and taxonomic groups (c) in MBR and MFR. Taxonomic groupings: m, Macrotermitinae; an, Anoplotermes-group Apicotermitinae; ap, Apicotermes-group Apicotermitinae; am, Amitermes-group; te, Termes-group Termitinae; cu, Cubitermes-group Termitinae; nas, Nasutitermes-group Nasutitermitinae; sub, Subulitermes -group Nasutitermitinae. The Foraminitermes- group Termitinae (one species in both regions) and Rhinotermitidae (one species in MFR only) have been excluded for plot clarity. Other functional groupings are as in table 1.
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: Model selection and parameter inference in phylogenetics using nested sampling
Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational effort to yield reliable estimates. One of the major challenges in phylogenetics is the estimation of the marginal likelihood. This quantity is commonly used for comparing different evolutionary models, but its calculation, even for simple models, incurs high computational cost. Another interesting challenge relates to the estimation of the posterior distribution. Often, long Markov chains are required to get sufficient samples to carry out parameter inference, especially for tree distributions. In general, these problems are addressed separately by using different procedures. Nested sampling (NS) is a Bayesian computation algorithm which provides the means to estimate marginal likelihoods together with their uncertainties, and to sample from the posterior distribution at no extra cost. The methods currently used in phylogenetics for marginal likelihood estimation lack in practicality due to their dependence on many tuning parameters and their inability of most implementations to provide a direct way to calculate the uncertainties associated with the estimates, unlike NS. In this paper, we introduce NS to phylogenetics. Its performance is analysed under different scenarios and compared to established methods. We conclude that NS is a competitive and attractive algorithm for phylogenetic inference. An implementation is available as a package for BEAST 2 under the LGPL licence, accessible at https://github.com/BEAST2-Dev/nested-sampling.
Current nest box designs may not be optimal for the larger forest dormice; pre-hibernation increase in body mass might lead to sampling bias in ecological data
<p>Biologists commonly use nest boxes to study small arboreal mammals, including forest dormouse (Dryomys nitedula). Hibernating dormouse species often experience pronounced seasonal variations in body mass, which might lead to sampling biases if it is not taken into account when designing nest boxes. In our study of forest dormouse, we noticed that the entrance hole of nest boxes had been gnawed on. We hypothesized that this behavior was exhibited by individual dormice who had higher body mass and, therefore, were unable to pass through the entrance holes. To test our hypothesis, we categorized individual dormice present inside nest boxes based on their body mass; then compared the seasonal body mass dynamics with the timing of the gnawing behavior. We also compared nest box occupancy by forest dormouse before and after the gnawing behavior. Interestingly, we found that the gnawing behavior was displayed exclusively when part of the dormouse population increased considerably in body mass, which supports our hypothesis. Additionally, nest box occupancy decreased significantly from 20% before to 4.6% after the gnawing behavior. We suggest that researchers use nest boxes with entrance holes larger than 4 cm in future studies of forest dormouse to prevent the possible exclusion of the conspecifics that have higher body mass before hibernation. This type of sampling bias can probably happen in studies of other species, such as fat dormouse, that similarly show pronounced seasonal variations in body mass. We recommend that biologists consider the seasonal body mass dynamics of the target species when designing nest boxes to minimize bias in ecological data and improve management actions.</p>
Data from: Model selection and parameter inference in phylogenetics using nested sampling
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