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139 results for “parameter estimation”
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
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Data for: Estimation of genetic parameters for the implementation of selective breeding in commercial insect production
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All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
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Estimating epidemiological parameters of highly pathogenic avian influenza in common terns using exact Bayesian inference
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Data from: An ASC-based unitary matrix pencil method for parameter estimation of group target
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Data for "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models"
<p>This repository contains all geo-physical catchment properties used in the publication "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models".</p>
Fig. 2. A B in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 2. A B-spline solid is a closed object whose shape can be adjusted by moving control points (dark points) that deforms the local portion of the object near the control point. The initial cylindrical shape in A is adjusted (B and C) by pulling out the points at the ends and drawing the points in the middle closer to the axis.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012) with uncertainty on the a priori plasma parameters obtained from the Plasma Line</p> <p>- Part VII) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
TA B L E 2 Estimates of pairwise sequence divergence (cyt-b gene) in pale-bellied Micronycteris, where M. minuta is divided in three clades. Below the diagonal: pairwise distance using the Kimura 2-parameter model (percentage). On the diagonal: within-clade distance using the Kimura 2-parameter model (percentage). Above the diagonal: pairwise p-distance values. Number of specimens sequenced in parenthesis. *Chimeric sequence obtained from two paratypes (Siles et al., 2013). in Revision of the pale-bellied Micronycteris Gray, 1866 (Chiroptera, Phyllostomidae) with descriptions of two new species
TA B L E 2 Estimates of pairwise sequence divergence (cyt-b gene) in pale-bellied Micronycteris, where M. minuta is divided in three clades. Below the diagonal: pairwise distance using the Kimura 2-parameter model (percentage). On the diagonal: within-clade distance using the Kimura 2-parameter model (percentage). Above the diagonal: pairwise p-distance values. Number of specimens sequenced in parenthesis. *Chimeric sequence obtained from two paratypes (Siles et al., 2013).
Data from: Field measurements give biased estimates of functional response parameters, but help explain foraging distributions
1. Mechanistic insights and predictive understanding of the spatial distributions of foragers are typically derived by fitting either field measurements on intake rates and food abundance, or observations from controlled experiments, to functional response models. It has remained unclear, however, whether and why one approach should be favoured above the other, as direct comparative studies are rare. 2. The field measurements required to parameterize either single or multi-species functional response models are relatively easy to obtain, except at sites with low food densities and at places with high food densities, as the former will be avoided and the second will be rare. Also, in foragers facing a digestive bottleneck, intake rates (calculated over total time) will be constant over a wide range of food densities. In addition, interference effects may further depress intake rates. All of this hinders the appropriate estimation of parameters such as the 'instantaneous area of discovery' and the handling time, using a type II functional response model also known as 'Holling's disc equation'. 3. Here we compare field- and controlled experimental measurements of intake rate as a function of food abundance in female bar-tailed godwits Limosa lapponica feeding on lugworms Arenicola marina. 4. We show that a fit of the type II functional response model to field measurements predicts lower intake rates (about 2.5 times), longer handling times (about 4 times) and lower 'instantaneous areas of discovery' (about 30 to 70 times), compared with measurements from controlled experimental conditions. 5. In agreement with the assumptions of Holling's disc equation, under controlled experimental settings both the instantaneous area of discovery and handling time remained constant with an increase in food density. The field data, however, would lead us to conclude that although handling time remains constant, the instantaneous area of discovery decreased with increasing prey densities. This will result into highly underestimated sensory capacities when using field data. 6. Our results demonstrate that the elucidation of the fundamental mechanisms behind prey detection and prey processing capacities of a species necessitates measurements of functional response functions under the whole range of prey densities on solitary feeding individuals, which is only possible under controlled conditions. Field measurements yield 'consistency tests' of the distributional patterns in a specific ecological context.
Convex hull estimation of mammalian body segment parameters
<p><span>Obtaining accurate values for body segment parameters (BSPs) is fundamental in many biomechanical studies, particularly for gait analysis. Convex hulling, where the smallest-possible convex object that surrounds a set of points is calculated, has been suggested as an effective and time-efficient method to estimate these parameters in extinct animals, where soft tissues are rarely preserved. We investigated the effectiveness of convex hull BSP estimation in a range of extant mammals, to inform the potential future usage of this technique with extinct taxa. Computed tomography scans of both the skeleton and skin of every species investigated were virtually segmented. BSPs (the mass, position of the centre of mass and inertial tensors of each segment) were calculated from the resultant soft tissue segments, while the bone segments were used as the basis for convex hull reconstructions. We performed phylogenetic generalised least squares and ordinary least squares regressions to compare the BSPs calculated from soft tissue segments with those estimated using convex hulls, finding consistent predictive relationships for each body segment. The resultant regression equations can therefore be used with confidence in future volumetric reconstruction and biomechanical analyses of mammals, in both extinct and extant species where such data may not be available.</span></p>
Data from: Estimating quantitative genetic parameters in wild populations: a comparison of pedigree and genomic approaches
The estimation of quantitative genetic parameters in wild populations is generally limited by the accuracy and completeness of the available pedigree information. Using relatedness at genome-wide markers can potentially remove this limitation and lead to less biased and more precise estimates. We estimated heritability, maternal genetic effects and genetic correlations for body size traits in an unmanaged long-term study population of Soay sheep on St Kilda using three increasingly complete and accurate estimates of relatedness: (1) Pedigree 1, using observation-derived maternal links and microsatellite-derived paternal links; (2) Pedigree 2, using SNP-derived assignment of both maternity and paternity; and (3) whole-genome relatedness at 37,037 autosomal SNPs. In initial analyses, heritability estimates were strikingly similar for all three methods while standard errors were systematically lower in analyses based on Pedigree 2 and genomic relatedness. Genetic correlations were generally strong, differed little between the three estimates of relatedness and the standard errors declined only very slightly with improved relatedness information. When partitioning maternal effects into separate genetic and environmental components, maternal genetic effects found in juvenile traits increased substantially across the three relatedness estimates. Heritability declined compared to parallel models where only a maternal environment effect was fitted, suggesting that maternal genetic effects are confounded with direct genetic effects and that more accurate estimates of relatedness were better able to separate maternal genetic effects from direct genetic effects. We found that the heritability captured by SNP markers asymptoted at about half the SNPs available, suggesting that denser marker panels are not necessarily required for precise and unbiased heritability estimates. Finally, we present guidelines for the use of genomic relatedness in future quantitative genetics studies in natural populations.
The measurement of true initial rates is not always absolutely necessary to estimate enzyme kinetic parameters
<p>Dataset of Excel files used for the publication.</p>
A Data Set for State and Parameter Estimation in Power Systems
<p>This data set consists of data from three power system models of different scales (IEEE 14, IEEE 118 and <a href="https://doi.org/10.5281/zenodo.2642175">PanTaGruEl</a>). For each of these systems, 5 different cases are provided, they are sorted from the least to the most "advanced" system operations.</p> <p>Data are stored in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5</a> format (as H5T_NATIVE_FLOAT) which can be read by (mostly) any language (e.g. Python, Matlab or Julia).</p> <p><strong>Description of the different cases:</strong></p> <ul> <li><em>Case 1#</em> consists of 2000 samples. Each sample is obtained by: firstly, defining total active and reactive loads in the system which are then distributing to the buses and, secondly, dispatching generation (this is performed by running an OPF (Optimal Power Flow) with <a href="https://matpower.org/">Matpower</a>). The same distribution factors were used for every samples.</li> <li><em>Case 2#</em> is similar to <em>case 1#</em> with the addition of independent white noises to each bus load.</li> <li><em>Case 3#</em> differs from <em>case 1#</em> in that independent active and reactive bus loads are randomly drawn.</li> <li><em>Case 4# </em>is similar to <em>case 3#</em>, but some generators are randomly drawn to be in maintenance. This set of generators is independently generated for each sample.</li> <li><em>Case 5#</em> is similar to <em>case 4#</em>, plus the generation cost of each generator is randomly drawn from a predefined range. Costs are independently generated for each sample.</li> </ul> <p><strong>General Description:</strong></p> <p>Each data set case file contains the following elements:</p> <ul> <li>V (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): Voltage magnitudes,</li> <li>theta (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): Voltage phases,</li> <li>P (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Active</a> power injections (i.e. = generation - load),</li> <li>Q (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Reactive</a> power injections,</li> <li>idgen (<span class="math-tex">\(N_{\rm gen}\)</span> vector): index of generator buses,</li> <li>id_slack: index of the bus used as <a href="https://en.wikipedia.org/wiki/Slack_bus">slack bus</a>,</li> <li>epsilon (<span class="math-tex">\(N_{\rm line} \times 2\)</span> matrix): list of the lines in the system (Each row corresponds to a line. Entries are buses’ indices.),</li> <li>b (<span class="math-tex">\(N_{\rm line}\)</span> vector): line <a href="https://en.wikipedia.org/wiki/Admittance">susceptances</a>,</li> <li>g (<span class="math-tex">\(N_{\rm line}\)</span> vector): line <a href="https://en.wikipedia.org/wiki/Admittance">conductances</a>,</li> <li>bsh (<span class="math-tex">\(N_{\rm bus}\)</span> vector): shunt susceptances,</li> <li>gsh (<span class="math-tex">\(N_{\rm bus}\)</span> vector): shunt conductances.</li> </ul> <p><strong>Visualization:</strong></p> <p>The data set also includes bus coordinates.</p> <p><strong>Some theory:</strong></p> <p>The <a href="https://en.wikipedia.org/wiki/Incidence_matrix">incidence matrix</a> B is defined as</p> <p><span class="math-tex">\(B_{ij} = \left\{\begin{array}{l}-1,\; \text{if line $j$ starts at bus $i$,}\\1,\; \text{if line $j$ ends at bus $i$,}\\ 0,\; \text{otherwise.} \end{array}\right.\)</span></p> <p>(“Ends” and “starts” are purely conventional, but they have to be assigned to account for the direction power flows in the system. We use the first column of epsilon as "starts" and the second one as "ends".)</p> <p>The <a href="https://en.wikipedia.org/wiki/Nodal_admittance_matrix">admittance matrix</a> Y is obtained by</p> <p><span class="math-tex">\(y = g + ib,\\ y_{\rm sh} = g_{\rm sh} + ib_{\rm sh},\\ Y = B\,{\rm diag}(y)\,B^\top + {\rm diag}(y_{\rm sh}) .\)</span></p> <p>Defining the <a href="https://en.wikipedia.org/wiki/AC_power">complex</a> power injections and voltages, respectively, as</p> <p><span class="math-tex">\(S = P + iQ,\\ \underline{V} = V \cdot e^{i \theta}, \)</span></p> <p>where <span class="math-tex">\(\cdot\)</span> denotes the element-wise product. One has the following relation</p> <p><span class="math-tex">\(S = \underline{V} \cdot {\rm conj}(Y\, \underline{V}).\)</span></p> <p>This relation is equivalent to the <a href="https://en.wikipedia.org/wiki/Power-flow_study">power flow equations</a>.</p> <ul> </ul> <p> </p>
Data for "Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations"
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Data for Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection
<p> <span>The data presented are related to the paper entitled</span> <span>Optimal Estimation of </span><span>Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection</span><span>, available in </span><span>Energies journal. Here, data are included to show the results of an Under Frequency Load Shedding</span> <span>(UFLS) scheme that considers the injection of virtual inertia by a VSC-HVDC link. The data obtained</span> <span>in six cases that were considered and analyzed are shown. In this case, each case represents a different</span> <span>frequency response configuration in the event of generation loss, taking into account the presence or</span> <span>absence of a VSC-HVDC link, traditional and optimized UFLS schemes, as well as the injection of</span> <span>virtual inertia by the VSC-HVDC link. Data for each example contains: state of the relay, threshold,</span> <span>position in every delay, load shed, and relay configuration parameters. Data were obtained through</span> <span>Digsilent Power Factory and Python simulations. The purpose of this dataset is that other researchers</span> <span>can reproduce the results reported in our paper</span></p>
CLM-FATES parameter estimation using the 'calibrate, emulate, sample' approach - experimental results
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Observing scenarios simulations for HLVK-Configuration for O4 Runs, using 20 million injections. This simulation led to 17,009 BNS useful for training Parameter Estimations of EM counterparts of GW. (July 2024 edition).
<p>We have conducted a simulation of the HLVK-configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>Due to the large file sizes, we have split them into three parts and uploaded them to Zenodo with the following DOIs:<br><br></p> <ol> <li><strong>The first files is located :</strong> <code>runs_part_aa</code> and <code>runs_part_ab</code> : <a title="https://zenodo.org/doi/10.5281/zenodo.12693652" href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a></li> <li><strong>The second files is located : </strong><code>runs_part_ac</code> and <code>runs_part_ad</code> : <a href="../doi/10.5281/zenodo.12694779">https://zenodo.org/doi/10.5281/zenodo.12694779</a></li> <li><strong>The third files is located : </strong><code>runs_part_ae</code> and <code>runs_part_af</code> : <a href="../doi/10.5281/zenodo.12696695">https://zenodo.org/doi/10.5281/zenodo.12696695</a></li> </ol> <blockquote> <p>Download them or use this Python script from GitHub to download all of them by running the script: </p> <p> <a href="https://github.com/weizmannk/ObservingScenariosInsights/blob/main/chunk-xml/zenodo-process/download_split_chunk_data.py">hchunk-files-downloader</a>.</p> </blockquote> <p>After downloading them (6 files ), you will need to combine them in a single file using the following process:</p> <blockquote> <p>1.Combine the parts:<br><code>cat runs_part_* > runs.zip</code></p> </blockquote> <blockquote> <p>2.Verify the combined file:<br><code>ls -lh runs.zip</code><br><code>file runs.zip</code></p> </blockquote> <blockquote> <p>3.Unzip the combined file:<br><code>unzip runs.zip</code></p> </blockquote> <p> </p> <p>In the <code>runs</code> folder, we have three subfolders:</p> <ul> <li><code>O4</code>: This contains the <code>.fits</code> files for skymap localization and all GW parameters of the CBCs that passed the threshold cutoff of 8.</li> <li><code>statistics_results</code>: This contains the summary results of the statistical predictions of GW detections.</li> <li><code>subpopulations</code>: This folder is the split of BNS, NSBH, and BBH events. It is useful for those who need quick parameters of BNS and NSBH for their lightcurve simulations or EM counterpart statistical estimation.</li> </ul> <p> </p> <p>For more information, visit: <a href="https://github.com/lpsinger/observing-scenarios-simulations" target="_new" rel="noreferrer">Observing Scenarios Simulations</a></p> <p>Contact: <a rel="noreferrer">weizmann.kiendrebeogo@oca.eu</a> or <a rel="noreferrer">kiend.weizman7@gmail.com</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.