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1,048 results for “Performance evaluation”
Data from: Agent-based versus correlative models of species distributions: Evaluation of predictive performance with real and simulated data
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Data from: Evaluating the target‐tracking performance of scanning avian radars by augmenting data with simulated echoes
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Performance evaluation of a laboratory developed PCR test for quantitation of HIV-2 viral RNA
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Evaluation of tracking performance and robustness for a hybrid locomotion controller
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Performance evaluation artefacts for in-memory encryption using the advanced encryption standard
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Data from: Critically evaluating the theory and performance of Bayesian analyis of macroevolutionary mixtures
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Data from: An experimental evaluation of the interplay between geometry and scale on cross-flow turbine performance
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Genotypic data from: Lab-based evaluation of the reproductive performance of trojan (MYY) brook trout (Salvelinus fontinalis)
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Seasonally optimized calibrations improve low-cost sensor performance: Long-term field evaluation of PurpleAir sensors in urban and rural India
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Controlled User Study: Usability and Efficiency Evaluation of the Parallel Performance Catalogue Extension for the Palladio-Bench
<p>This repository contains the raw data for the user Study we conduction for the theses of Denis Zaharaive:</p> <p> </p> <p> Controlled User Study: Usability and Efficiency Evaluation of the Parallel Performance Catalogue Extension for the Palladio-Bench</p>
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>
Experiment Dataset for L. Zhu, G. Casale, I. Perez, Fluid approximation of closed queueing networks with discriminatory processor sharing, Performance Evaluation (2020): 102094
<p>This dataset provides the results of the validation experiments for transient, steady-state and response time distribution<br> analysis published in L. Zhu, G. Casale, I. Perez, Fluid approximation of closed queueing networks with discriminatory processor sharing, Performance Evaluation (2020): 102094.</p>
Novel health system strategies for tuberculin skin testing at primary care clinics: performance assessment and health economic evaluation.
<p>Datasets for journal paper.</p>
Evaluating the performance of probabilistic algorithms for phylogenetic analysis of big morphological datasets: a simulation study
<p>Reconstructing the tree of life is an essential task in evolutionary biology. It demands accurate phylogenetic inference for both extant and extinct organisms, the latter being almost entirely dependent on morphological data. While parsimony methods have traditionally dominated the field of morphological phylogenetics, a rapidly growing number of studies are now employing probabilistic methods (maximum likelihood and Bayesian inference). The present-day toolkit of probabilistic methods offers varied software with distinct algorithms and assumptions for reaching global optimality. However, benchmark performance assessments of different software packages for the analyses of morphological data, particularly in the era of big data, are still lacking. Here, we test the performance of four major probabilistic software under variable taxonomic sampling and missing data conditions: the Bayesian inference-based programs MrBayes and RevBayes, and the maximum likelihood-based IQ-TREE and RAxML. We evaluated software performance by calculating the distance between inferred and true trees using a variety of metrics, including Robinson-Foulds (RF), Matching Splits (MS), and Kuhner-Felsenstein (KF) distances. Our results show that increased taxonomic sampling improves accuracy, precision, and resolution of reconstructed topologies across all tested probabilistic software applications and all levels of missing data. Under the RF metric, Bayesian inference applications were the most consistent, accurate, and robust to variation in taxonomic sampling in all tested conditions, especially at high levels of missing data, with little difference in performance between the two tested programs. The MS metric favored more resolved topologies that were generally produced by IQ-TREE. Adding more taxa dramatically reduced performance disparities between programs. Importantly, our results suggest that the RF metric penalizes incorrectly resolved nodes (false positives) more severely than the MS metric, which instead tends to penalize polytomies. If false positives are to be avoided in systematics, Bayesian inference should be preferred over maximum likelihood for the analysis of morphological data.</p>
Data from: Evaluating the performance of selection scans to detect selective sweeps in domestic dogs
Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analyzed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used, and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks.
Data for Bell-jar performance evaluation report
<p>During the SI-Hg performance evaluation of elemental mercury gas generators on the market three generators were tested, e.g., PSA 10.536 elemental Hg generator, bell-jar and Tekran Model 3425. Key characteristics were determined e.g.; the stabilisation period, short-term drift, precision, i.e., reproducibility and repeatability of the concentration generated, linearity, bias, sensitivity to sample gas pressure, sensitivity to surrounding temperature and sensitivity to electrical voltage. All three generators could be tested according to the calibration protocol developed within the project. The results obtained with the different gas generator clearly shows the importance of a metrological calibration. All three candidate generators show a different bias for the setpoint compared to the calibrated output. </p><p>The data obtained during the performance evaluation of the bell-jar is published in this repository. The files of the following experiments can be found here:</p><ul><li>m1<ul><li>Measurement data online mercury analyser comparison VSL and bell-jar 2023-02-20</li><li>Bell_jar_calibration_m1</li></ul></li><li>m2<ul><li>Measurement data online mercury analyser comparison VSL and bell-jar 2023-02-28</li><li>Bell_jar_calibration_m2</li></ul></li><li>m3<ul><li>Measurement data online mercury analyser comparison VSL and bell-jar 2023-03-09</li><li>Bell_jar_calibration_m3</li></ul></li><li>short-term drift<ul><li>m1<ul><li>Calibration bell-jar short term drif_m1</li><li>Bell_jar_drift_m1</li></ul></li><li>m2<ul><li>Calibration bell-jar short term drif_m2</li><li>Bell_jar_drift_m2</li></ul></li><li>m3<ul><li>Calibration bell-jar short term drif_m3</li><li>Bell_jar_drift_m3</li></ul></li><li>m4<ul><li>Calibration bell-jar short term drif_m4</li><li>Bell_jar_drift_m4</li></ul></li></ul></li><li>stability<ul><li>Measurement data online mercury analyser comparison VSL and bell-jar stability</li></ul></li></ul>
Raw results of the simulation experiments performed to evaluate different Aloha-based schemes for LoRa-based DtS uplink transmissions
<p>This zip file contains the gnuplot and data files needed to generate the figures showing the experimental results presented in [1].</p> <p>[1] S. Herrería-Alonso, M. Rodríguez-Pérez, R. F. Rodríguez-Rubio and F. Pérez-Fontán, "Improving Uplink Scalability of LoRa-Based Direct-to-Satellite IoT Networks," in <em>IEEE Internet of Things Journal</em>, vol. 11, no. 7, pp. 12526-12535, April 2024, doi: 10.1109/JIOT.2023.3333934.</p>
Raw results of the numerical experiments performed to evaluate different MAC schemes for LoRaWAN networks
<p>This zip file contains the scripts, gnuplot and data files needed to generate the figures showing the numerical results presented in [1].</p> <p>[1] S. Herrería-Alonso, A. Suárez-González, M. Rodríguez-Pérez and C. López-García, "Enhancing LoRaWAN scalability with Longest First Slotted CSMA," in <em>Computer Networks</em>, vol. 216, article number 109252, Oct. 2022, doi: 10.1016/j.comnet.2022.109252.</p>
Raw results of the experiments performed to evaluate irregular repetition slotted ALOHA with multiuser detection
<p>This zip file contains the data files needed to generate the figures of the numerical results presented in [1].</p> <p> </p> <p>[1] M. Fernández-Veiga, M.E. Sousa-Vieira, A. Fernández-Vilas, R.P. Díaz-Redondo. "Irregular repetition slotted ALOHA with multiuser detection: A density evolution analysis". Computer Networks, 234, 109921, 2023.</p>
Performance Evaluation of Seven Remote Sensing Datasets for TRB Cropland Area Estimation (Accuracy Metrics and Temporal Trends)
<p>This dataset contains accuracy assessments and trend analyses for cropland area estimation using seven remote sensing datasets in the TRB region. The data is organized into the following structure:<br>1. Accuracy Evaluation ("RMSE+Rt+MPE" directory)</p> <p>Contains individual evaluation files for each of the seven remote sensing products<br>File naming convention: [DatasetName]_Evaluation.csv<br>Each file contains four columns:</p> <p>County: Administrative region<br>Rt: Temporal correlation<br>RMSE: Root Mean Square Error<br>MPE: Mean Percentage Error</p> <p>2. Area Change Trends ("Trend" directory)</p> <p>Contains trend analysis files for seven remote sensing products and reference observations (OBS)<br>File naming convention: [DatasetName/OBS]_trend.csv<br>Each file contains three columns:</p> <p>County: Administrative region<br>Slope: Trend slope coefficient<br>P_value: Statistical significance value</p> <p>3. Spatial Correlation (Rs.csv)</p> <p>Single file containing annual spatial correlation coefficients (Rs) for all seven remote sensing datasets.</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.