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139 results for “parameter estimation”
Fig. 3 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 3. Scatterplots of total body length in mm plotted against eye length in mm along the long axis. The upper plot shows measurements for zuphea and the lower plot for praniza. The body length cutoff values separating juvenile stages are shown as a dotted-green line. Gnathiids collected from emergence traps are seen as gold-filled squares and those collected from light traps are presented as purple-filled triangles. Differences in the ontological sampling bias of these two trap designs can be seen by comparing the two scatterplots. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 6 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 6. Histograms of trap counts by sample day and juvenile stage. The upper histograms show counts from emergence traps and the lower histograms show counts from light traps. Mean count for each histogram is shown as a dashed horizontal line. See text for an explanation of the number of sampling days shown in each plot.
Estimated particle parameters of an aluminium matrix composite
<p>The dataset consists of section profile parameters in tabular form (assumed to come from prolate spheroids) of ellipses fitted to measured particles of an aluminium matrix composite from metallographic analysis. The dataset can be used to reconstruct the trivariate spatial spheroid (prolate form) distribution.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 3. Estimate coefficients distribution of model on read rate as response of Terasort application
<p>Figure 3 shows estimate coefficients distribution of model on read rate as the response of Terasort application. The filled triangle point-up indicates the position of the minimum stable sampling time for the corresponding estimate coefficients as well as the number above it shows the exact position value. The dashed line represents the average estimated coefficient of the regression model.</p>
Figs 2-5 in Accessing camera trap survey feasibility for estimating Blastocerus dichotomus (Cetartiodactyla, Cervidae) demographic parameters
Figs 2-5. Individual discrimination of marsh deer males (Blastocerus dichotomus Illiger, 1815) using antler morphology: a spike-antlered male (2) and two different branched antlers (3 and 4). The arrows indicate the different horn tips. Marsh deer female accompanied by a fawn (5).
Fig. 1 in Accessing camera trap survey feasibility for estimating Blastocerus dichotomus (Cetartiodactyla, Cervidae) demographic parameters
Fig. 1. Map outlining the aerial and camera trap surveys conducted at the Jataí Ecological Station, state of SÃo Paulo, Brazil in order to obtain marsh deer demographic parameters.
Intermediate-mass black hole binary parameter estimation with next-generation ground-based detector networks
<p><strong>Reading the</strong> <strong>data</strong></p> <p>Each of the <em>alldetectors</em> and <em>CE40CE20ET</em> .zip file contains a folder with .pickle files. Each of these .pickle files corresponds to a point on the respective grid. The files contain dictionaries structured as follows:</p> <ul> <li>In the <em>skyareas </em>and <em>m1m2grid</em> folders, each dictionary stores the source-frame component masses, redshift, angular parameters, network SNR, network covariance matrix and 90% sky area.</li> <li>In the <em>Mzgrid</em> folders, each dictionary stores source-frame total mass, redshift, angular parameters, individual SNR and Fisher matrix for each detector in the network.</li> </ul> <p>The grids, detector network and parameters used to perform the Fisher calculations are described in the companion paper. The <em>converted_errors</em> folder contains network SNR, 1-sigma errors on source-frame masses, redshift, 90% sky areas for all the networks considered in the companion paper. These quantities are all angle-averaged.</p> <p>The <em>full_pe</em> .zip files cointain data comparing our Fisher results with full Bayesian PE runs for a few selected cases, as well as Jupyter notebooks to read those results (see Appendix B of the companion paper).</p>
Fig. 1 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 1. Map showing the location of the Central Catchment Nature Reserve on mainland Singapore. All 27 camera points are indicated with a red circle. Black squares indicate the three camera points added to the 1 km2 grid. The six cage traps are marked with a blue cross. Dotted circles indicate areas the last remaining patches of primary forest in Singapore.
Fig. 3 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 3. Map of the Central Catchment Nature Reserve showing the day and night fixes of the collared pig. The home ranges are calculated from the monthly 95% Kernel Density Estimate (KDE), while the aggregate home range was calculated from the 99% KDE from all six months. The Seletar Expressway (SLE) is pointed out on the map and the satellite overlay was adapted from Google Earth.
Fig. 2 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 2. The density map showing the number of activity centres per kilometer square, the locations of the camera points (circles), 143 out of 856 GPS locations from the collared pig (black dots) and the boundary of the Central Catchment Nature Reserve. Only a fraction of the GPS locations was plotted to prevent the colored pixels from being obscured. Each pixel is 1 km2. X and Y coordinates are in kilometers.
Training Deep Learning Models to Estimate SWAT Parameters using Streamflow Observations
<p>This folder provides the simulation and observational data</p> <p>Simulation data using SWAT (1000 realz)<br> Train, Val, and Test splits (80/10/10)<br> Observational data for ARW (WY2000-2016)</p>
Parameters of dielectric properties and emissivity estimated for Earth arid areas.
<p>Dataset gives the parameters estimated from passive microwave observations of the dielectric properties and emissivities of arid areas at 10 to 89 GHz.</p>
Dataset: Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks
<p>Dataset for the paper E. Rinaldi, M. González Lastre, S. García Herreros, S. Ahmed, M. Khanahmadi, F. Nori, and C. Sánchez Muñoz (2023), <a href="https://arxiv.org/abs/2310.02309">¨Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks¨, arxiv: 2310.02309</a></p> <p>This dataset can be used to populate the [datapath] folder in the repository <strong>ParamEst-NN</strong> (<a href="https://github.com/CarlosSMWolff/ParamEst-NN">github.com/CarlosSMWolff/ParamEst-NN</a> ) and reproduce the results shown in the paper.</p> <p>The dataset consist of four folders:</p> <ol> <li><strong>Training trajectories.</strong> Records of quantum-jump trajectories simulated with the Monte-Carlo solver of the <a href="https://qutip.org/">QuTiP</a> library, used to train neural networks for the problem of quantum parameter estimation. The records consist of time delays between quantum jumps.</li> <li><strong>Models. </strong>Models trained with the training trajectories provided, and used to obtain the results shown in the paper.</li> <li><strong>Validation trajectories. </strong>Trajectories used to benchmark the trained models. For the 2D case, the same trajectories are provided as a single .npy file, and split in 10 separated batches inside a ```batches``` folder. These are the batches that we used to generate Bayesian estimations on a cluster using nested sampling (see README file of the <a href="http://github.com/CarlosSMWolff/ParamEst-NN">repository</a>).</li> <li><strong>Cached results. </strong>Here we provide pre-computed Bayesian estimations for the 2D multi-parameter estimation case using nested sampling.</li> </ol> <p> </p> <p><em>E.R. was supported by Nippon Telegraph and Telephone Corporation (NTT) Research during the early stages of this work.<br> C.S.M. acknowledges that the project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No.847648, with fellowship code LCF/BQ/PI20/11760026, and financial support from the MCINN project PID2021-126964OB-I00 (QENIGMA) and the Proyecto Sinérgico CAM 2020 Y2020/TCS- 6545 (NanoQuCo-CM).</em></p>
Figure 6 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 6. – Histological changes in testis development of Engraulis encrasicolus at the different reproductive maturity stages. A: Stage I, represented only by spermatogonia cells, inactive spermatogenesis, the number of the fields concerned in the biometric analysis N = 236. B: Stage II, active spermatogenesis with primary spermatogenium multiplication and tubular size increase, the number of the fields concerned in the biometric analysis N = 241. C: Stage III, active spermatogenesis, spermatozoa are concentrated in the light of the seminiferous tubules, the number of the fields concerned in the biometric analysis N = 125. D: Stage IV, spermiation, the number of the fields concerned in the biometric analysis N = 93. E: Stage V, seminiferous tubules are empty, spermatozoa are eliminated by release or resorption, the number of the fields concerned in the biometric analysis N = 988.
Figure 4 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 4. – Mean values of the gonadosomatic index (GSI, represented in percentage) in Engraulis encrasicolus male collected in the Gulf of Bejaia from October 2007 to May 2008. Data are represented as Mean ± SEM. (values indicates the number of fishes).
Figure 1 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 1. – Photomicrograph of male anchovy testicular tissue illustrating the seminiferous tubules in stage I.
Estimating demographic parameters for bearded seals, Erignathus barbatus, in Alaska using close-kin mark-recapture methods
Open the record for dataset details and reuse information.
R_JAGS code for estimation and analysis of species-area-relationship (SAR) parameters from NEON (National Ecological Observatory Network) data on plant surveys
Open the record for dataset details and reuse information.
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
<p>We present two different methods to estimate parameters within a partial differential equation (PDE) model of cancer invasion. The model describes the spatio-temporal evolution of three variables -- tumour cell density, extracellular matrix density and matrix degrading enzyme concentration -- in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with Approximate Bayesian Computation (ABC); the second is a two-stage gradient matching method based on smoothing the data with a Generalized Additive Model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.</p>
Filling the Gap: A Tool to Automate Parameter Estimation for Software Performance Models
<p>The data part of this release support the results <br /> presented in the paper <br /> "Filling the Gap: A Tool to Automate Parameter Estimation<br /> for Software Performance Models", by W. Wang, J. F. Perez, and G. Casale, accepted <br /> to QUDOS workshop 2015. </p>
ScienceDex guides
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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.