Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
139
datasets available to search
ShareScore release 0.7.1
Dataset results
139 results for “parameter estimation”
Dataset for the article : Non-invasive estimation of in vivo optical properties and hemodynamic parameters of domestic animals: a preliminary study on horses, dogs, and sheep
<div> <div> <div> <div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This dataset includes all the necessary data to understand and replicate the figures and tables presented in the article titled "Non-invasive Estimation of In Vivo Optical Properties and Hemodynamic Parameters of Domestic Animals: A Preliminary Study on Horses, Dogs, and Sheep." Specifically, it contains the raw measurement curves, along with the optical and hemodynamic parameters derived from the analysis of these raw data.</p> </div> </div> </div> </div> </div> </div>
Stellar Parameter Estimation for Half a Million LAMOST M Dwarfs based on Cycle-StarNet
Open the record for dataset details and reuse information.
Parameter estimation catalogs for binary neutron star mergers detected with next-generation gravitational wave detectors
<div> <p>Next-generation gravitational wave (GW) observatories, such as the Einstein Telescope (ET) and the Cosmic Explorer, will provide access to the population of binary neutron star (BNS) mergers throughout cosmic history and yield precise parameter estimates. Here, we publish the results of a comprehensive study evaluating BNS merger detection prospects using the ET alone or in a network of current or next-generation detectors up to redshift equal to 1. We publicly release all the parameter estimation for 10 years of observations of BNSs in the form of catalogs. These catalogs are made available to the community for multi-messenger studies, multi-probe cosmology, and nuclear study to constrain the neutron star (NS) equation of state (EOS). They can be used to focus on specific events (for example golden events with high signal-to-noise ratio) or for statistical studies on the BNS populations. </p> <p>Our simulations assessed the perspectives for detecting the optical emission of BNS mergers in the era of next-generation detectors, considering how uncertainties in BNS population properties, NS mass distribution, and the EOS might affect the detection rate and parameter estimation. The study is published in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo, Hazra, Dupletsa, Branchesi et al. 2024</a> arXiv:2411.02342 (submitted to A&A).</p> </div> <h3>BNS merger rate</h3> <p>As shown in <a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.4877S/abstract" target="_blank" rel="noopener">Santoliquido et al. (2021)</a>, the common envelope ejection efficiency parameter, α, determines one of the main sources of uncertainty for the number of BNS mergers per year. In order to evaluate the impact of the uncertainties of the BNS merger rate normalization on our results, we generate two catalogues of BNS mergers assuming α to be either <strong>0.5</strong> or <strong>1.0</strong>. </p> <h3>NS mass distribution</h3> <p>We draw the component masses of the NS binaries, M_1 and M_2, from two different mass distributions: <strong>Gaussian</strong> and<br><strong>uniform</strong> mass distributions. The Gaussian distribution is centred at 1.33 M⊙ with a standard deviation of 0.09 M⊙. The uniform mass distribution ranges in [1.1 M⊙, M_max], where M_max depends on the selected EOS.</p> <h3>Equation of state (EOS)</h3> <p>Since the NS EOS affects both the GW and EM signals expected from BNS mergers, we consider<br>two different EOSs, namely the <strong>APR4</strong> and <strong>BLh</strong> microscopic EOSs.</p> <h3>Detector configuration</h3> <p>Given the two values of α (0.5 and 1.0), the two mass distributions (uniform and Gaussian), and the two EOSs (BLh and APR4), we have a total of 8 different population sets, which constitute our injections for the gravitational signal analysis. For each of these datasets, we consider the following GW detector configurations:</p> <ul> <li>ET in its triangular design of 10 km arms, located in Sardinia, alone and operating together with (<strong>ET_delta_10_cryo</strong>): <ul> <li>the current ground-based network LIGO-Hanford, LIGO-Livingston, Virgo, KAGRA, LIGO-India (<strong>LVKI</strong>) </li> <li>one L-shaped CE with 40 km arms, located in the USA (<strong>1CE</strong>)</li> <li>2 CEs, both with 40 km arms, one in the USA and one in Australia (<strong>2CE</strong>)</li> </ul> </li> <li>ET in its 2L-shaped interferometer configuration of 15 km arms misaligned at 45 deg (one located in Sardinia and the other in the Netherlands); we consider the same networks as above, using the 2L-configuration instead of the triangular one (<strong>ET_2L_15_cryo_45deg</strong>). </li> </ul> <p>We thus have eight different detector networks giving a total of 64 simulations available in this repository. </p> <h3>Catalog description</h3> <p>The parameter estimation of the injected GW signals by the various detector networks is obtained through the Fisher matrix software <strong>GWFish</strong> (<a href="https://ui.adsabs.harvard.edu/abs/2023A%26C....4200671D/abstract" target="_blank" rel="noopener">Dupletsa et al. 2023</a>). The Fisher analysis method approximates the likelihood with a multivariate Gaussian distribution. All the parameters [M_1, M_2, dL, ι, RA, DEC, Ψ, phase, tc, Λ_1, Λ_2] are considered for the Fisher matrix derivation. The uncertainties on parameters coming from the covariance matrix (the inverse of the Fisher matrix) are given at 1σ. We implement a duty cycle of 85% for each of the L-shaped detectors, and for each of the three nested detectors composing the triangle. </p> <ul> <li><strong>Signals_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the parameters describing a GW event and the corresponding network signal-to-noise ratio (SNR) <ul> <li><strong>mass_1: </strong>primary mass of the binary in [Msol] (in detector frame) (M_1)</li> <li><strong>mass_2:</strong> secondary mass of the binary in [Msol] (in detector frame) (M_2)</li> <li><strong>luminosity_distance:</strong> the luminosity distance of the merger in [Mpc]</li> <li><strong>dec:</strong> declination angle in [rad]. It varies in [−𝜋/2,+𝜋/2]</li> <li><strong>ra:</strong> right ascension in [rad]. It varies in [0,2/𝑝𝑖]</li> <li><strong>theta_jn:</strong> the angle between the line of observation and the total angular momentum (orbital, spin and GR corrections) of the binary [rad] (it reduces to the so-called inclination angle or <strong>iota</strong> if the spin component is absent); it ranges in [0,𝜋]</li> <li><strong>psi:</strong> the polarization angle in [rad]; it ranges in [0,𝜋]</li> <li><strong>geocent_time:</strong> merger time as GPS time in [s]</li> <li><strong>phase:</strong> the initial phase of the merger in [rad]; it ranges in [0,2𝜋]</li> <li><strong>redshift: </strong>the redshift of the merger</li> <li><strong>lambda_1: </strong>dimensionless tidal polarizabilty of primary component</li> <li><strong>lambda_2:</strong> dimensionless tidal polarizabilty of secondary component</li> <li><strong>network_SNR:</strong> network SNR for a the given event</li> </ul> </li> <li><strong>Errors_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the <div> <div>parameter errors for each event. The first column is <strong>network_SNR</strong>, the following columns repeat the injected parameters as above and the relative errors <strong>err_<em>{parameter}</em></strong><em>. </em>The last column is the error on sky localisation (<strong>err_sky_location</strong>) at 90% credible interval. </div> </div> </li> </ul> <h3>Further details </h3> <p>Further details on the assumptions we made to produce these catalogs can be found in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo et al. 2024</a>, while further details on GWFish can be found on <a href="https://colab.research.google.com/github/janosch314/GWFish/blob/main/gwfish_tutorial.ipynb" target="_blank" rel="noopener">this tutorial</a>. We also provide the jupyter notebook <strong>paper_plots.ipynb</strong>, to reproduce Figs. 10, 11, 13, D.1, D.5, D.6. </p>
Matlab code and datasets for: Auditory model-based parameter estimation and selection of the most informative experimental conditions
<p>The dataset and the matlab code used for the publication: Auditory model-based parameter estimation and selection of the most informative experimental conditions</p>
Gravitational wave parameter estimation dataset
<p>Dataset for 'Gravitational Wave Parameter Estimation Using Machine Learning' workshop at OzGrav Winter School - 2023, held in University of Western Australia, Perth.</p>
Estimated Oxygen Extraction Versus Dynamic Parameters for Perioperative Hemodynamic Optimization
ClinicalTrials.gov study NCT04053595. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Bayesian adaptive Markov Chain Monte Carlo estimation of genetic parameters
Open the record for dataset details and reuse information.
Data from: Estimating quantitative genetic parameters in wild populations: a comparison of pedigree and genomic approaches
Open the record for dataset details and reuse information.
Data from: Accurately estimating correlations between demographic parameters: A comment on Deane et al. (2023)
Open the record for dataset details and reuse information.
Data from: Colonization and persistence of urban ant populations as revealed by joint estimation of kinship and population genetic parameters
Open the record for dataset details and reuse information.
Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images - Simulation Data
Open the record for dataset details and reuse information.
Convex hull estimation of mammalian body segment parameters
Open the record for dataset details and reuse information.
Data from: Estimation of genetic parameters associated with frosty pod rot (Moniliophthora roreri) and cacao production in Mexico
Open the record for dataset details and reuse information.
Data from: Field measurements give biased estimates of functional response parameters, but help explain foraging distributions
Open the record for dataset details and reuse information.
Data from: Penalized likelihood methods improve parameter estimates in occupancy models
Open the record for dataset details and reuse information.
Role of the Cost Function for Material Parameter Estimation - Data Set
<p>##################################################################################################</p> <p><strong>Attention: We found an error in our optimisation template, related to the results that are provided in figure 2. We are working on fixing the issue and upload a corrected version in the upcoming days.</strong></p> <p> </p> <p> </p> <p> </p> <p>Dataset for the contribution "Role of the Cost Function for Material Parameter Determination" to the "<a href="https://www.femtc.com/events/2020/">Fire and Evacuation Modeling Technical Conference</a>" (FEMTC) 2020.</p> <p> </p> <p>It contains:</p> <ul> <li>the complete data of each IMP run,</li> <li>simulations of the best parameter sets of each IMP run,</li> <li>the IMP run for the reaction kinetics parameters,</li> <li>the validation simulations,</li> <li>the plots shown in the article and presentation, as well as</li> <li>the Jupyter Notebooks used for the data analysis.</li> </ul> <p>Note: The copper foil thickness is set to0.2 mm in all simulations, due to a typo in the FDS input file template and should have been 0.025 mm according to the <a href="https://www.sciencedirect.com/science/article/abs/pii/S0379711217300541">article describing CAPA II</a>. A simulation with the correct thickness showed very little divergence from the conducted simulations, thus the effect of the typo is regarded to be neglectable. Therefore, all files in this data repository are using the incorrect initial value. The mentioned simulations and a plot comparing both results are provided in this repository.</p> <p> </p> <p>Version 1.1:</p> <p>Added slides and article.</p>
Data from: Estimating the parameters of background selection and selective sweeps in Drosophila in the presence of gene conversion
We used whole-genome resequencing data from a population of Drosophila melanogaster to investigate the causes of the negative correlation between the within-population synonymous nucleotide site diversity (πS) of a gene and its degree of divergence from related species at nonsynonymous nucleotide sites (KA). By using the estimated distributions of mutational effects on fitness at nonsynonymous and UTR sites, we predicted the effects of background selection at sites within a gene on πS and found that these could account for only part of the observed correlation between πS and KA. We developed a model of the effects of selective sweeps that included gene conversion as well as crossing over. We used this model to estimate the average strength of selection on positively selected mutations in coding sequences and in UTRs, as well as the proportions of new mutations that are selectively advantageous. Genes with high levels of selective constraint on nonsynonymous sites were found to have lower strengths of positive selection and lower proportions of advantageous mutations than genes with low levels of constraint. Overall, background selection and selective sweeps within a typical gene reduce its synonymous diversity to ∼75% of its value in the absence of selection, with larger reductions for genes with high KA. Gene conversion has a major effect on the estimates of the parameters of positive selection, such that the estimated strength of selection on favorable mutations is greatly reduced if it is ignored.
Estimating correlations among demographic parameters in population models
<p>Estimating correlations among demographic parameters is critical to understanding population dynamics and life-history evolution, where correlations among parameters can inform our understanding of life-history trade-offs, result in effective applied conservation actions, and shed light on evolutionary ecology. The most common approaches rely on the multivariate normal distribution, and its conjugate inverse Wishart prior distribtion. However, the inverse Wishart prior for the covariance matrix of multivariate normal distributions has a strong influence on posterior distributions. As an alternative to the inverse Wishart distribution, we individually parameterize the covariance matrix of a multivariate normal distribution to accurately estimate variances (σ<sup>2</sup>) of, and process correlations (ρ) between, demographic parameters. We evaluate this approach using simulated capture-mark-recapture data. We then use this method to examine process correlations between adult and juvenile survival of black brent marked on the Yukon-Kuskokwim River Delta, Alaska (1988-2014). Our parameterization consistently outperformed the conjugate inverse Wishart prior for simulated data, where the means of posterior distributions estimated using an inverse Wishart prior were substantially different from the values used to simulate the data. Brent adult and juvenile annual apparent survival rates were strongly positively correlated (ρ = 0.563, 95% CRI 0.181 − 0.823), suggesting that habitat conditions have significant effects on both adult and juvenile survival. We provide robust simulation tools, and our methods can readily be expanded for use in other capture-recapture or capture-recovery frameworks. Further, our work reveals limits on the utility of these approaches when study duration or sample sizes are small.</p>
Data from: Separate block based parameter estimation method for Hammerstein systems
Different from the output-input representation based identification methods of two-block Hammerstein systems, this paper concerns a separate block based parameter estimation method for each block of a two-block Hammerstein CARMA system, without combining the parameters of two parts together. The idea is to consider each block as a subsystem and to estimate the parameters of the nonlinear block and the linear block separately (interactively), by using two least squares algorithms in one recursive step. The internal variable between the two blocks (the output of the nonlinear block, and also the input of the linear block) is replaced by different estimates: when estimating the parameters of the nonlinear part, the internal variable between the two blocks is computed by the linear function; when estimating the parameters of the linear part, the internal variable is computed by the nonlinear function. The proposed parameter estimation method possesses property of the higher computational efficiency compared with the previous over-parameterization method in which many redundant parameters need to be computed. The simulation results show the effectiveness of the proposed algorithm.
A Bayesian framework for estimating parameters of a generic toxicokinetic model for the bioaccumulation of organic chemicals by benthic invertebrates: proof of concept with PCB153 and two freshwater species.
<p>R Codes and corresponding .csv data files for estimating toxicokinetic model parameters for both chironomids and gammarids exposed to PCB153.</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.