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1,048 results for “Performance evaluation”
Dataset of Paper: "Evaluation of membranes performance for microplastic removal in a simple and low-cost filtration system"
<p>Dataset of Paper: "Evaluation of membranes performance for microplastic removal in a simple and low-cost filtration system":</p> <ul> <li>Zeta potential curves as a function of pH</li> <li>Results of transmembrane pressure (TMP) and flow rate of the filtration experiments. Average transmembrane pressure drop (ΔTMP) and flux (J) after 10 min of operation.</li> <li>Total number of unremoved microplastic particles per litre (NTMP), average size (DMP), and mass removal efficiency (MRE%) of each membrane when filtering water with 100 mg/L of PA and PS</li> </ul>
Establishing diversity in synthetic time series for prediction performance evaluation
<p>This dataset enables practitioners to evaluate their time series prediction algorithms on various types of time series</p>
Data from: Critically evaluating the theory and performance of Bayesian analyis of macroevolutionary mixtures
Bayesian analysis of macroevolutionary mixtures (BAMM) has recently taken the study of lineage diversification by storm. BAMM estimates the diversification-rate parameters (speciation and extinction) for every branch of a study phylogeny and infers the number and location of diversification-rate shifts across branches of a tree. Our evaluation of BAMM reveals two major theoretical errors: (i) the likelihood function (which estimates the model parameters from the data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior distribution of diversification-rate shifts across branches) is incoherent. Using simulation, we demonstrate that these theoretical issues cause statistical pathologies; posterior estimates of the number of diversification-rate shifts are strongly influenced by the assumed prior, and estimates of diversification-rate parameters are unreliable. Moreover, the inability to correctly compute the likelihood or to correctly specify the prior for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the number and location of diversification-rate shifts using BAMM.
Experimental data for the publication: "Evaluating scintillator performance in time-resolved, hard X-ray studies at synchrotron light sources"
<p>In accordance with the expectations outlined in <em><strong>Clarifications of EPSRC expectations on research data management</strong></em> (09/10/14) this data has been made publicly available to complement the open access publication "Evaluating scintillator performance in time-resolved, hard X-ray studies at synchrotron light sources". </p> <p>There are six data sets, corresponding to the six experimental data sets presented in the article. In each data set, which may be identified by their file names and reference to the article, the 1st column is the RF trigger - to - ICCD exposure delay in [ns], and the second column in the intensity recorded on the ICCD in [counts]. This intensity accounts for any online and offline processing outlined in the article, such as on-CCD exposures, dark frame correction etc. </p>
Evaluation of tracking performance and robustness for a hybrid locomotion controller
<p>Legged locomotion is a complex control problem that requires both accuracy and robustness to cope with real-world challenges. Legged systems have traditionally been controlled using trajectory optimization with inverse dynamics. Such hierarchical model-based methods are appealing due to intuitive cost function tuning, accurate planning, generalization, and most importantly, the insightful understanding gained from more than one decade of extensive research. However, model mismatch and violation of assumptions are common sources of faulty operation. Simulation-based reinforcement learning, on the other hand, results in locomotion policies with unprecedented robustness and recovery skills.<br>Yet, all learning algorithms struggle with sparse rewards emerging from environments where valid footholds are rare, such as gaps or stepping stones. In this work, we propose a hybrid control architecture that combines the advantages of both worlds to simultaneously achieve greater robustness, foot-placement accuracy, and terrain generalization. Our approach utilizes a model-based planner to roll out a reference motion during training. A deep neural network policy is trained in simulation, aiming to track the optimized footholds. We evaluate the accuracy of our locomotion pipeline on sparse terrains, where pure data-driven methods are prone to fail. Furthermore, we demonstrate superior robustness in the presence of slippery or deformable ground when compared to model-based counterparts. Finally, we show that our proposed tracking controller generalizes across different trajectory optimization methods not seen during training. In conclusion, our work unites the predictive capabilities and optimality guarantees of online planning with the inherent robustness attributed to offline learning.</p>
Performance Evaluation of Objective Quality Assessment Methods for Omnidirectional Images Under Emerging Compressions
<p>This dataset provides extensive data from large-scale subjective experiments, encompassing MOS values, eye-tracking data, and raw subjective scores, collected from two laboratories Brno University of Technology and Czech Technical University in Prague (BUT and CTU). This new dataset serves as a comprehensive foundation for future research endeavors into novel objective quality metrics for omnidirectional IQA (OIQA).</p> <p> </p> <p><strong>▷ </strong> contact: xsimka01@vut.cz</p>
Evaluating robot workspaces with performance maps
<p><strong>Evaluating robot workspaces with performance maps</strong></p> <p><em>Aline Kluge-Wilkes, Presley Demuner Reverdito</em></p> <p>The following data set was created while validating a proposed method to evaluate robot workspaces with different performance metrics. </p> <p>The underlying paper will be presented at the CIRP CATS 2024, the 10th Conference on Assembly Technology and Systems, hosted in Karlsruhe from 24-26.April.2024 and published thereafter.</p> <p>Deploying mobile robots in line-less and mobile assembly systems without predefined formations necessitates understanding the robot's capabilities and determining the tasks it can perform. The data set concerns performance maps to assess the feasibility of assembly tasks within the robot's workspace. These maps offer quantifiable metrics to compare the suitability of base placements for mobile robots concerning the feasibility of specific assembly tasks.</p> <p>Performance maps are a discretised representation of a particular robot's distribution of selected performance metrics. The current implementation focuses on calculating manipulability, dexterity, and condition number. Using a URDF robot model and task poses as input, the metrics are computed across distributed poses within the robot's workspace at a specified resolution, forming the performance map.</p> <p>The data set contains the application of the performance map to exemplary tasks and robots.</p> <p>A Design of Experiments (DoE) was conducted to investigate the influence of three predictor variables on robot manipulation performance. The predictor variables considered in the study were as follows:</p> <p>1. Resolution (R): Two levels of resolution, 0.08m and 0.12m, were examined to assess their impact on robot performance representation.</p> <p>2. Number of poses (N): Two different quantities of poses, 25 and 50, were studied to understand how they affect the representation of the robot's capabilities.</p> <p>3. Robot Model: Two distinct robot models, the ABB IRB1600-1.2 and the ABB IRB120-0.58, were chosen for the experiments.</p> <p> </p> <p>Eight experiments were designed and executed, each with a unique combination of the predictor variables. These experiments are summarised in the Excel sheet "DOE-tests-and-results". The first table overviews the eight experiments. The following eight tables indicate the positions and the results: the respective calculated robot's performance metrics. </p> <p>The .json files specify the poses for which the respective performance metrics (dexterity index, reciprocal condition number, and manipulability index) per robot were calculated.</p> <p>The included .png files visualise the distribution of each index for each conducted experiment in a performance map. The maps were created with Matplotlib. </p> <p> </p> <p>As a comparison, the "capability maps" of both robot models with both resolutions --- generated with the ROS (Robot Operating System) library Reuleaux (http://wiki.ros.org/reuleaux) --- are provided. The generated files are given in .h5 format. The respective files are named as "CM_...". </p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Acknowledgement:</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy --- EXC-2023 Internet of Production --- 390621612.</p> <p><br><br> </p>
Evaluation data for: Adaptive sampling by citizen scientists improves species distribution model performance: a simulation study
<p>All of the evaluation data for the simulations in the paper: Adaptive sampling by citizen scientists improves species distribution model performance: a simulation study. We considered the impact of five adaptive sampling methods on the performance of species distribution models (SDMs), please see the paper for more information. Contained in this repository are the evaluation metrics (AUC, mean square error (MSE) and correlation) for SDMs before and after adaptive sampling has taken place. The MSE and correlation evaluation metrics were calculated against the true distributions of the species. These files are those with "combined_outputs" in the titles. The repository also contains the observations of all the species in the simulations both before and after adaptive sampling (the files with "all_observations" in the title.</p> <p>These datasets are to be used with the plotting and evaluation scripts in the GitHub repository associated with the paper.</p>
Performance evaluation of BARtab Holze et al., 2023
<p>Comparison of synthetic barcode extraction and quantification pipelines BARtab.</p> <p>tool-comp-results.tar.gz:</p> <p>Comparison of BARtab, pycashier and TimeMachine/Rewind on population-level data. </p> <p>Input data: https://figshare.com/articles/dataset/FateMap_Paper_datasets_3_Goyal_et_al_2021_Biorxiv_/22806494</p> <p> </p> <p>tools-comp-sc-results.tar.gz:</p> <p>Comparison of BARtab and FateMap/Rewind on single-cell level data.</p> <p>Input data: fastq files from https://figshare.com/articles/dataset/FateMap_Paper_datasets_2_Goyal_et_al_2021_Biorxiv_/22802888?file=40535864 and barcode.tsv.gz files from GEO GSM7434409, GSM7434410, GSM7434411, GSM7434412.</p> <p> </p> <p>Code for both analyses: https://github.com/DaneVass/bartools_manuscript_code/tree/main/tools-comparison</p>
Satellite Precipitation Products data performance evaluation with observed datasets
<p>Datasets used for evaluation of Satellite Precipitation Products (SPPs). We used Three open-source datasets from different sources for our evaluation of the SPPs applicability in real-time flood forecasting.</p>
Quality and Utility of European Cardiovascular and Orthopaedic Registries for the Regulatory Evaluation of Medical Device Safety and Performance Across the Implant Lifecycle: A Systematic Review - Dataset
<p><strong>Background: </strong>The European Union Medical Device Regulation (MDR) requires manufacturers to undertake post-market clinical follow-up (PMCF) to assess the safety and performance of their devices following approval and Conformité Européenne (CE) marking. The quality and reliability of device registries for this Regulation have not been reported. As part of the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, we identified and reviewed European cardiovascular and orthopaedic registries to assess their structures, methods, and suitability as data sources for regulatory purposes.</p> <p><strong>Methods: </strong>Regional, national and multi-country European cardiovascular (coronary stents and valve repair/replacement) and orthopaedic (hip/knee prostheses) registries were identified using a systematic literature search. Annual reports, peer-reviewed publications, and websites were reviewed to extract publicly available information for 33 items related to structure and methodology in six domains and also for reported outcomes.</p> <p><strong>Results: </strong>Of the 20 cardiovascular and 26 orthopaedic registries fulfilling eligibility criteria, a median of 33% (IQR: 14%-71%) items for cardiovascular and 60% (IQR: 28%-100%) items for orthopaedic registries were reported, with large variation across domains. For instance, no cardiovascular and 16 (62%) orthopaedic registries reported patient/ procedure-level completeness. No cardiovascular and 5 (19%) orthopaedic registries reported outlier performances of devices, but each with a different outlier definition. There was large heterogeneity in reporting on items, outcomes, definitions of outcomes, and follow-up durations.</p> <p><strong>Conclusion: </strong>European cardiovascular and orthopaedic device registries could improve their potential as data sources for regulatory purposes by reaching consensus on standardised reporting of structural and methodological characteristics to judge the quality of the evidence as well as outcomes.</p>
Dataset: Testing for effects of growth rate on isotope trophic discrimination factors and evaluating the performance of Bayesian stable isotope mixing models experimentally: a moment of truth?
<p><span>Discerning assimilated diets of wild animals using stable isotopes is well established where potential dietary items in food webs are isotopically distinct. With the advent of mixing models, and Bayesian extensions of such models (Bayesian Stable Isotope Mixing Models, BSIMMs), statistical techniques available for these efforts have been rapidly increasing. The accuracy with which BSIMMs quantify diet, however, depends on several factors including uncertainty in tissue discrimination factors (TDFs; <em>Δ</em>) and identification of appropriate error structures. Whereas performance of BSIMMs has mostly been evaluated with simulations, here we test the efficacy of BSIMMs by raising domestic broiler chicks (<em>Gallus gallus domesticus</em>) on four isotopically distinct diets under controlled environmental conditions, ideal for evaluating factors that affect TDFs and testing how BSIMMs allocate individual birds to diets that vary in isotopic similarity. For both liver and feather tissues,<em> δ</em><sup>13</sup>C and <em>δ </em><sup>15</sup>N values differed among dietary groups. <em>Δ</em><sup>13</sup>C of liver, but not feather, was negatively related to the rate at which individuals gained body mass. For <em>Δ</em><sup>15</sup>N, we identified effects of dietary group, sex, and tissue type, as well as an interaction between sex and tissue type</span><span><span>, </span></span><span><span>with f</span></span><span>emales having higher liver <em>Δ</em><sup>15</sup>N relative to males. For both tissues, BSIMMs allocated most chicks to correct dietary groups, especially for models using combined TDFs rather than diet specific TDFs, and those applying a multiplicative error structure. These findings provide new information on how biological processes affect TDFs and confirm that adequately accounting for variability in consumer isotopes is necessary to optimize performance of BSIMMs. Moreover, they demonstrate experimentally that these types of models reliably characterize consumed diets when appropriately parameterized.<span> </span></span></p>
Raw results of the simulation experiments performed in the educacional study evaluating a guaranteed service
<p>This zip file contains the simulation results, gnuplot script and data files needed to generate the figures showing the simulation results presented in [1].</p> <p>[1] Suárez-González A., Rodríguez Rubio R., López-Ardao J.C., López-García C, Herrería-Alonso S. (2021). Educational case study: evaluation of guaranteed service on low earth orbit satellite networks. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 215-219. DOI: https://doi.org/10.46354/i3m.2021.emss.029</p>
Raw results of the simulation experiments performed to evaluate the Time-to-idle Control Variate ferformance in the single queue case
<p>This zip file contains the simulation results, gnuplot script and data files needed to generate the figures showing the simulation results presented in [1].</p> <p>[1] Suárez-González A., López-García C., López-Ardao J.C., Rodríguez Rubio R., Rodríguez Pérez M. (2021). "Time-to-idle Control Variate Performance in the Single Queue Case". Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 147-151. DOI: https://doi.org/10.46354/i3m.2021.emss.020</p>
Jupyter Notebooks for "Evaluating CephFS Performance vs. Cost on High-Density Commodity Disk Servers" 10.1007/s41781-021-00071-1
<p>Jupyter notebooks used to create plots in article DOI 10.1007/s41781-021-00071-1</p> <p>Title "Evaluating CephFS Performance vs. Cost on High-Density Commodity Disk Servers"</p> <p>Journal "Computing and Software for Big Science"</p>
NDN Content Store and Caching Policies: Performance Evaluation - Dataset
<p>This is a dataset results from the evaluation of the following caching replacement policies: Random, Least Recently Used (LRU), Least Frequently Used (LFU), and First In First Out (FIFO) applied to NDN. It was used for the paper "NDN Content Store and Caching Policies: Performance Evaluation", DOI: https://doi.org/10.3390/computers11030037 .</p> <p>The simulation tool used is the ndnSIM-2.7.</p> <p>Includes:</p> <p>1. The actual dataset from simulations (for both NDN Testbed and Abilene).</p> <p>2. The C++ ndnSIM scenarios (for both NDN Testbed and Abilene).</p> <p>3. Matlab scripts for data extraction.</p> <p>4. Simple Bash scripts for automating the execution</p> <p> </p>
Identification of Performance Changes at Code Level (Jetty Evaluation Dataset)
<p><em>This is the anonymous reviewing version; the source code repository will be added after the review.</em></p> <p>This dataset provides the results of measuring jetty with the 1,000 artificial regressions with Peass and with JMH. The creation of the artificial regressions and the measurement is defined here: https://anonymous.4open.science/r/jetty-evaluation-6F58/ (Repository named jetty-evaluation, GitHub link will be provided after review) An example regression is contained in <a href="https://anonymous.4open.science/r/jetty-experiments-202D">https://anonymous.4open.science/r/jetty-experiments-202D</a> We obtained these data from measurement on Intel Xeon CPU E5-2620 v3 @ 2.40GHz.</p> <p>The dataset contains the following data:</p> <ul> <li>regression-results-peass-0.tar.xz (Results of the measurement with Peass, part 0)</li> <li>regression-results-peass-1.tar.xz (Results of the measurement with Peass, part 1)</li> <li>regression-results-peass-2.tar.xz (Results of the measurement with Peass, part 2)</li> <li>regression-results-peass-3.tar.xz (Results of the measurement with Peass, part 3)</li> <li>regression-results-jmh.tar.xz (Results of the measurement with JMH)</li> <li>tree-results.tar.xz (Metadata of the trees)</li> </ul> <p>To get the data in a usable format, extract the peass data to one folder (the folder will be named <strong>$PEASS_RESULT_FOLDER</strong>):</p> <pre><code class="language-bash">mkdir peass for file in *; do echo $file; tar -xf $file; done for i in {0..3}; do mv $i/* .; done</code></pre> <p>This will yield to a folder containing 1000 folders named regression-$i, where each consists of</p> <ul> <li>deps.tar.xz: The regression test selection results</li> <li>logs.tar.xz: The logs of the test executions</li> <li>results: The traces of the regression test selection and a file named changes_*testcase.json, which contains statistical details of the measured performance change (if present)</li> <li>jetty.project_peass: Detailed measurement data and logs of individual JVM starts</li> </ul> <p>To analyse the Peass results, run</p> <pre><code class="language-bash">cd scripts/peass ./analyzeChangeIdentification.sh $PEASS_RESULTS_FOLDER ./analyzeFrequency.sh $PEASS_RESULTS_FOLDER</code></pre> <p>This will take some time, since partial results need to be unpacked for analysis. The first script will create the following results:</p> <p>and the second will yield the following results:</p> <pre><code>Correct Measurement: 587 Not selected changes: 146 Wrong measurement result: 267 Wrong analysis (should be 0): 0 Overall: 1000 Share of changed method on correct measurements: 0.109571 0.11238 32 Method call count on correct measurement: 15638.4 32853.7 32 Average tree depth on correct measurements: 1.1022 2.6875 32 Share of changed method on wrong measurements: 0.17692 0.180365 968 Method call count on wrong measurement: 711415 180438 968 Average tree depth on wrong measurements: 1.23239 2.42252 968</code></pre> <p>To analyze the JMH data, first extract the metadata (the folder will be named <strong>$TREEFOLDER</strong>):</p> <pre><code class="language-bash">tar -xf tree-results.tar.xz</code></pre> <p>Afterwards extract the JMH results (the folder will be named $<strong>JMH_RESULTS_FOLDER</strong>):</p> <pre><code class="language-bash">tar -xvf regression-results-jmh.tar.xz</code></pre> <p>This will yield a folder containing a measurement for each regression with two files:</p> <ul> <li>basic.json: The performance measurement result of the basic version</li> <li>regression-$i.json: The performance measurement result of the version containing the regression</li> </ul> <p>Afterwards, run the analysis in the jetty-evaluation repository:</p> <pre><code class="language-bash">cd scripts/jmh ./analyzeFrequency.sh $JMH_RESULTS_FOLDER $TREEFOLDER</code></pre> <p>Since the regression are injected in the call tree of the benchmark, there are now unselected changes. The analysis will yield the following results:</p> <pre><code>Share of changed method on correct measurements: 0.184631 0.271968 587 Method call count on correct measurement: 14628.2 4979.6 587 Share of changed method on wrong measurements: 0.180981 0.235614 267 Method call count on wrong measurement: 14902.2 4333.58 267</code></pre> <p> </p>
Dataset for the article "PERFORMANCE EVALUATION OF AN IMAGING RADIATION PORTAL MONITOR SYSTEM"
<p>The dataset includes root files used in generating the main figures for the article "PERFORMANCE EVALUATION OF AN IMAGING RADIATION PORTAL MONITOR SYSTEM" by Jana Vasiljević, Alf Göök and Bo Cederwal. The article will be submitted to MDPI Applied Sciences.</p> <p> </p>
Segmentation performance evaluation results and R scripts for generating figures and tables.
<p>This archive contains the evalutation results of 24 segmentation models and the R scripts to produce figures and tables published in Kloster et al. 2022: Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.</p> <p>Please refer to the manuscript for further details.</p>
Genotypic data from: Lab-based evaluation of the reproductive performance of trojan (MYY) brook trout (Salvelinus fontinalis)
<p>Evaluating the efficacy of the use of trojan male brook trout with two Y chromosomes (M<sub>YY</sub>) requires a better understanding of reproductive performance. We measured the reproductive performance of hatchery age-0 and age-1 M<sub>YY</sub> brook trout compared to hatchery XY males using laboratory crosses. Offspring of XY males had higher survival than offspring of age-1 M<sub>YY</sub> one day post-fertilization but not offspring of age-0 M<sub>YY</sub>. We found no detectable differences in survival from eyed-egg to the juvenile-fry stage. However, size-at-age differed, where offspring of age-0 M<sub>YY</sub> were 3.6% smaller in length and 25.2% smaller in weight than those of XY males. For crosses fertilized by both M<sub>YY</sub> and XY males, we found that a significantly higher proportion of offspring within families were sired by M<sub>YY </sub>versus XY males. These results show, under controlled conditions, evidence for possible fitness advantage for M<sub>YY</sub> under sperm competition, but a possible fitness disadvantage associated with early growth of their offspring. Overall, our results hold promise for the use of M<sub>YY</sub> brook trout to serve as an effective eradication tool. </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.