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470 results for “Evaluation dataset”
When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics - datasets and results
<p>This directory contains the data and results that were used and obtained during the realization of the following paper:</p> <blockquote> <p>When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics. Rinde R.S. van Lon and Tom Holvoet. Journal of Autonomous Agents and Multi-Agent Systems (2017).</p> </blockquote> <p>At the time of writing the paper is not yet accepted, some of the details (e.g. title, date of publication) are subject to change. The code that has been used can be found in this repository: https://github.com/rinde/vanLon17-JAAMAS-code</p> <p>Note that unpacked, all files are about 5 GB in total.</p> <p>This directory has the following structure:</p> <p>- makefile - script that can be used to run the analyses. The most important commands are:<br> - all - runs all analyses.<br> - paper-deps - runs only the analyses that were directly used in the paper.<br> - additional-analysis - runs several additional analyses.</p> <p>- readme.txt - this file.</p> <p>- results - contains all R-scripts used to analyse the data<br> - data/main - contains the results of the main result.<br> - data/mas-tuning - contains the results of the MAS tuning experiments.<br> - data/optaplanner-tuning-gendreau - contains the results of the OptaPlanner tuning experiments.<br> - data/sensitivity - contains the results of the simulator sensitivity experiments (using OptaPlanner algorithms).</p> <p>- scenarios<br> - gendreau2006 - contains the dataset from the Gendreau et al. [1] paper, used for the OptaPlanner tuning experiments.<br> - mas-tuning-dataset - contains the dataset that was used for tuning the MAS. This was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code<br> - vanLonHolvoet15-adapted-to-4-hours - contains the dataset that was used for the main experiment. This is an adapted version of the dataset from [2]. This version has a duration of 4 hours and requires a real-time simulator. It was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code </p> <ul> <li>[1] Michel Gendreau, Francois Guertin, Jean-Yves Potvin, and René Séguin. Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick- ups and deliveries. Transportation Research Part C: Emerging Technologies, 14 (3):157–174, 2006. ISSN 0968090X. doi:10.1016/j.trc.2006.03.002.</li> <li>[2] Rinde R. S. van Lon and Tom Holvoet. Towards systematic evaluation of multi-agent systems in large scale and dynamic logistics. In Qingliang Chen, Paolo Torroni, Serena Villata, Jane Hsu, and Andrea Omicini, editors, PRIMA 2015: Principles and Practice of Multi-Agent Systems: 18th International Conference, Bertinoro, Italy, October 26-30, 2015, Proceedings, pages 248–264. Springer In- ternational Publishing, Cham, 2015. ISBN 978-3-319-25524-8. doi:10.1007/978- 3-319-25524-8 16.</li> </ul>
Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics - datasets and results
<p>This directory contains the data and results that we used and obtained during the realization of the following paper:</p> <blockquote> <p>Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics. Rinde R.S. van Lon, Juergen Branke, and Tom Holvoet. Genetic Programming and Evolvable Machines (2017).</p> </blockquote> <p>The code that has been used can be found on GitHub (https://github.com/rinde/vanLon17-GPEM-code), an archive is available at https://doi.org/10.5281/zenodo.260130.</p> <p>This repository contains the following files:</p> <ul> <li>evo.zip (expands to 21.2 GB) A zip file containing all results of the training experiment and tuning experiment.</li> <li>JAAMAS.zip (expands to 227.6 MB) A zip file containing the main results of the JAAMAS experiment, also available at https://doi.org/10.5281/zenodo.209760</li> <li>realtime.zip (expands to 913.5 MB) A zip file containing the main results</li> <li>dataset10k.zip (expands to 4.9 GB) Contains the scenarios used for the training and tuning experiments.</li> <li>overview.zip (expands to 42 MB) Contains the following files: <ul> <li>overview/experiment-overview.csv - table containing basic overview of the 40 main evolutionary runs that were performed for this paper.</li> <li>overview/heuristics/ - folder with a tree visualization for every evolved heuristic.</li> <li>overview/makefile - executes the analysis scripts that were used in the paper (requires the R programming language, https://www.r-project.org/). All tables and figures will be placed in results/generated/.</li> <li>readme.txt - this file.</li> <li>overview/results/ - contains the scripts</li> <li>overview/results/data/ - empty directory in which evo.zip, JAAMAS.zip, and realtime.zip have to be unpacked for the analysis scripts to work.</li> </ul> </li> </ul>
Datasets for "An Evaluation of Bayesian Approaches based on Uniformly Most Powerful Tests for Detecting Violations of Local Independence"
<p>These files contain the results of the simulation studies reported in the main text. A detailed documentation is included as pdf file.</p>
Dataset WP4 P1.2. IBC evaluation activities
<p>Evaluation demo ImmersiaTV</p>
When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics - datasets and results v1.1.0
<p>This directory contains the data and results that were used and obtained during the realization of the following paper:</p> <p>When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics. Rinde R.S. van Lon and Tom Holvoet. Journal of Autonomous Agents and Multi-Agent Systems (2017).</p> <p>This is version v1.1.0. At the time of writing the paper is not yet accepted, some of the details (e.g. title, date of publication) are subject to change. The code that has been used can be found in this repository: https://github.com/rinde/vanLon17-JAAMAS-code </p> <p>This directory has the following structure:</p> <p>- makefile - script that can be used to run the analyses. The most important commands are:<br> - all - runs all analyses.<br> - paper-deps - runs only the analyses that were directly used in the paper.<br> - additional-analysis - runs several additional analyses.</p> <p>- readme.txt - this file.</p> <p>- results - contains all R-scripts used to analyse the data<br> - data/main - contains the results of the main result.<br> - data/mas-tuning - contains the results of the MAS tuning experiments.<br> - data/optaplanner-tuning-gendreau - contains the results of the OptaPlanner tuning experiments.<br> - data/sensitivity - contains the results of the simulator sensitivity experiments (using OptaPlanner algorithms).</p> <p>- scenarios<br> - gendreau2006 - contains the dataset from the Gendreau et al. [1] paper, used for the OptaPlanner tuning experiments.<br> - mas-tuning-dataset - contains the dataset that was used for tuning the MAS. This was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code<br> - vanLonHolvoet15-adapted-to-4-hours - contains the dataset that was used for the main experiment. This is an adapted version of the dataset from [2]. This version has a duration of 4 hours and requires a real-time simulator. It was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code </p> <p>[1] Michel Gendreau, Francois Guertin, Jean-Yves Potvin, and René Séguin. Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick- ups and deliveries. Transportation Research Part C: Emerging Technologies, 14 (3):157–174, 2006. ISSN 0968090X. doi:10.1016/j.trc.2006.03.002.</p> <p>[2] Rinde R. S. van Lon and Tom Holvoet. Towards systematic evaluation of multi-agent systems in large scale and dynamic logistics. In Qingliang Chen, Paolo Torroni, Serena Villata, Jane Hsu, and Andrea Omicini, editors, PRIMA 2015: Principles and Practice of Multi-Agent Systems: 18th International Conference, Bertinoro, Italy, October 26-30, 2015, Proceedings, pages 248–264. Springer In- ternational Publishing, Cham, 2015. ISBN 978-3-319-25524-8. doi:10.1007/978- 3-319-25524-8 16.</p>
Dataset related to the paper "Role of enhancement modifications in evaluating tumor response to immunotherapy in metastatic renal cell carcinoma"
<p>Dataset related to the paper "Role of enhancement modifications in evaluating tumor response to immunotherapy in metastatic renal cell carcinoma"</p>
Dataset: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models
<p>This dataset corresponds to the following publication:<br>Steinert, N., J., et al. 2023: <i>Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models</i>, Environmental Research Letters, 10.1088/1748-9326/ad10d7<br>https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>Global permafrost regions are undergoing significant changes due to global warming, whose assessments often rely on permafrost extent estimates derived from climate model simulations. These assessments employ a range of definitions for the presence of permafrost, leading to inconsistencies in the calculation of permafrost area. This dataset contains permafrost area calculations using 10 different definitions for detecting permafrost presence based on either ground thermodynamics, soil hydrology, or air-ground coupling from an ensemble of 32 Earth System Models.</p><p>This dataset includes two file archives:<br>1. 32 CMIP6 models, 10 permafrost definitions, historical period (1850-2014), annual data<br>2. 32 CMIP6 models, 10 permafrost definitions, SSP5-85 period (2015-2100), annual data</p><p>This dataset represents source data for the following publication. Please refer to this reference for a more detailed description of the definitions used in this dataset:<br>Steinert, N., J., et al. 2023: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models, https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>The results show that variations between permafrost-presence definitions result in substantial differences of up to 18 million km2, where any given model could both over- or underestimate the present-day permafrost area. Ground-thermodynamic-based definitions are, on average, comparable with observations but are subject to a large inter-model spread. The associated uncertainty of permafrost area estimates is reduced in definitions based on ground-air coupling. However, their representation of permafrost area strongly depends on how each model represents the ground-air coupling processes. The definition-based spread in permafrost area can affect estimates of permafrost-related impacts and feedbacks, such as quantifying permafrost carbon changes. For instance, the definition spread in permafrost area estimates can lead to differences in simulated permafrost-area soil carbon changes of up to 28%. This dataset therefore supports an emphasis on the importance of consistent and well-justified permafrost-presence definitions for robust projections and accurate assessments of permafrost from climate model outputs.</p><p>For any questions regarding the dataset, please free feel to contact Norman J. Steinert (nste@norceresearch.no, normanst@ucm.es)</p>
Model data for "The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation", submitted to Earth System Science Data
<p>© Crown Copyright, Met Office</p><p>The E1hrClimMon files contain the monthly mean diurnal cycles of TOA radiative fluxes (all-sky and clear-sky) for amip experiment of two configurations of HadGEM3: GC3.1 and GC5.0. The monthly mean diurnal cycle is constructed by averaging each UTC hourly mean over the entire month. The HadGEM3 OLR diagnostics used in this study differ from those submitted to CFMIP3. The OLR diagnostics submitted to CFMIP3 contain a correction that accounts for the surface temperature adjustment by the boundary layer scheme in model time steps between radiation time steps. This OLR diagnostic adjustment is introduced to conserve energy, but it significantly distorts the diurnal cycle of OLR. For comparison with the GERB obs4MIPs products, the OLR without this correction is recommended.</p><p>The COSP file contains the average monthly climatologies for the variables cfadLidarsr532 and clisccp for the amip simulations of GC3.1 and GC5.0.</p>
[Dataset] Evaluation of the Empirical Scaling Factor of Joule Heating Rates in TIE-GCM with EISCAT Measurements
<p>Dataset used for "Evaluation of the Empirical Scaling Factor of Joule Heating Rates in TIE-GCM with EISCAT Measurements"</p>
Dataset: "Auralization of Electric Vehicles for the Perceptual Evaluation of Acoustic Vehicle Alerting Systems"
<p>This repository contains audio examples and measurement data accompanying the paper: </p> <blockquote> <p>Müller L. & Kropp W. 2024. Auralization of electric vehicles for the perceptual evaluation of acoustic vehicle alerting systems. Acta Acustica, 8, 27. https://doi.org/10.1051/aacus/2024025</p> </blockquote> <p>The Matlab code for the corresponding auralization model can be found at: <a href="https://github.com/leonpaulmueller/evat" target="_blank" rel="noopener">https://github.com/leonpaulmueller/evat</a></p> <p> </p> <p><strong>Content</strong></p> <ul> <li><code>audio_examples.zip</code> <ul> <li>avas - Measured and synthesized AVAS source signals</li> <li>passby - Measured and auralized binaural EV passages at roadside observer position. The generated signals use the same vehicle velocity as the corresponding measurements.</li> <li>tire - Measured and synthesized tire/road noise source signals</li> </ul> </li> <li><code>measurements.zip</code> <ul> <li>ambience - binaural ambience measurements</li> <li>avas - AVAS source signal measurements</li> <li>passby - Binaural pass-by measurements, including velocity data and isolated AVAS and tire/road noise signals</li> <li>tires - tire/road noise measurements</li> </ul> </li> </ul> <p> </p> <p>For consistency with the paper, we use the following aliases for the three evaluated vehicles:</p> <ul> <li>Vehicle A: Tesla Model Y 2021</li> <li>Vehicle B: Volkswagen ID.3 Pro Performance 2021</li> <li>Vehicle C: Nissan Leaf 2018</li> </ul>
Underlying dataset for figures published in "A tool for evaluating heterogeneity in avidity of polyclonal antibodies"
<p>The dataset consists of all underlying data associated with Li et al. Front Immunol. 2023, 16;14:1049673. (doi: 10.3389/fimmu.2023.1049673.). Each folder corresponds to data shown in a single figure, with the figure number indicated in the folder names. Please refer to the ReadMe.pdf for information on the content of the figure-specific folders.</p>
The dataset for Evaluation of plume-induced continental crust growth rate in early Earth: Insight from integrated petrological-thermo-mechanical modeling
<p>All data of model results for the paper <em>Evaluation of plume-induced continental crust growth rate in early Earth: Insight from integrated petrological-thermo-mechanical modeling</em>.</p> <p>Contents of this dataset includes:</p> <p>A list for models and data of model results are provided in the zip file. </p> <p>The files suffixed *.grd are grid datas that can be draw by GMT.</p> <p>The files suffixed *.info are datas of the evolution of volume of the continental crust and TTGs in the model.</p> <p>The files suffixed *.prn are datas of P-T condtions of continental crust generation by partial melting.</p> <p>The files suffixed *.py are python codes for visualization of the volume of continental crust and P-T datas.</p>
Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"
<p>Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"</p>
The compiled dataset on the physicochemical properties of snow used for evaluation
<p>This repository contains scirpts and files used to generate the results of the study titled "Simulations of Snow Physicochemical Properties in Northern China using WRF-Chem" from Xia Wang, Lei Geng and Chun Zhao. All the original simulation and observation data used by Xia Wang to evaluate the physical and chemical properties of snow. This comprehensive dataset includes detailed information on various snow characteristics, such as snow cover, snow depth, and the presence of black carbon (BC), dust, and nitrates.<span> </span></p>
Dataset of Evaluating the effects of biomass burning on severe haze formation in a megacity of Sichuan Basin, Southwestern China
<p><span>This dataset contain the concentrations of different chemical compositions, including organic aerosol, nitrate, sulfate, ammonium, chloride, BC, etc. The time series of different OA sources and meteorological parameters are also contained in the dataset.</span></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>
Dataset: Low-Basicity 5-HT6 Receptor Ligands from the Group of Cyclic Arylguanidine Derivatives and Their Antiproliferative Activity Evaluation
<p>Biochromatographic data for publication: Low-Basicity 5-HT6 Receptor Ligands from the Group of Cyclic Arylguanidine Derivatives and Their Antiproliferative Activity Evaluation</p> <p> </p>
Genomic datasets used for evaluation of k-mer representations and indexes
<p>This record contains genomic datasets, including subsampled <em>k</em>-mer sets for some datasets (files with names containing _subsampled_). Namely, it provides the following datasets:</p> <ul> <li>Two <em>E. coli</em> pan-genomes, obtained as the union of the <em>E. coli</em> genomes from the 661k collection. One contains <em>all</em> genomes (without quality filtering) and for the other (<em>HQ</em>) we applied high-quality filtering.</li> <li><em>S. pneumoniae</em> pan-genome: 616 genomes, as provided in RASE DB <em>S. pneumoniae</em> <a href="https://github.com/c2-d2/rase-db-spneumoniae-sparc/">https://github.com/c2-d2/rase-db-spneumoniae-sparc/</a></li> <li><em>SARS-CoV-2</em> pan-genome, downloaded from GISAID <a href="https://gisaid.org/">https://gisaid.org/</a> (access upon registration) on Jan 25, 2023 (GISAID version 2023/01/23, 14,682,066 genomes, 430 Gbp).</li> <li>Metagenomic sample SRS063932 (Illumina raw reads) of human microbiome with accession SRX023459, download from <a href="https://www.hmpdacc.org/hmp/HMASM/">https://www.hmpdacc.org/hmp/HMASM/</a>. The fastq files were converted to FASTA files using `seqtk seq -A -C`.</li> <li>Human RNA-seq Illumina raw reads with accession SRX348811, downloaded using the prefetch tool from the SRA toolkit and then converted into the FASTA format by<br>`fastq-dump --split-3 --fasta`.</li> <li>Human genome Illumina raw reads with accession SRX016231, downloaded using the prefetch tool from the SRA toolkit and then converted into the FASTA format by<br>`fastq-dump --split-3 --fasta`.</li> <li>Human genome assembly chm13v2.0 (T2T), downloaded from <a href="https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz">https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz</a>.</li> <li>Two MiniKraken datasets (4GB and 8GB), downloaded from <a href="https://ccb.jhu.edu/software/kraken/">https://ccb.jhu.edu/software/kraken/</a>, with the 31-mers dumped using Jellyfish 1.1.12.</li> </ul> <p>The resulting FASTA files (apart from the human genome assembly chm13v2.0 and MiniKraken datasets) were converted to unitigs by GGCAT v1.1.0<br>by `ggcat build -k {kmer-size} -m 200 -j 5 -s {min-freq} -o {preprocessed_unitigs} {input_FASTA}`, where we used $k=128$ and `{min-freq}`=1 for pan-genomes and $k=32$ and `{min-freq}`=2 for dataset from raw reads.</p> <p>Finally, the subsampled files `{dataset}_subsampled_k{$k$}_r0.1.fa.xz` contain 10% randomly chosen distinct canonical $k$-mers from the whole $k$-mer set of the given dataset. The FASTA file contains one subsampled <em>k</em>-mer per sequence.</p>
Continuous and proactive software architecture evaluation: An IoT case -- Dataset generated from iFogSim
<p>There will always be a trade-off between using the simulators and physical IoT devices in experimentation and data generation. This is due to the high cost of the actual deployment of IoT devices as compared to simulators. However, some companies, such as Amazon, IBM, and Intel, are motivating the need for having IoT simulation instrumenting what-if test scenarios, typically used during the architecture analysis and refinement stages to evaluate the response and sensitivity of the architecture to these tests. </p> <p>Additionally, many researchers are currently looking for an IoT dataset that provides QoS for IoT architectures. This work provides a dataset well-tested for the most important quality attributes when evaluating IoT architectures.</p> <p>In particular, this work used iFogSim to generate QoS of various IoT architectures in the form of Response Time, Energy consumption, and network usage. After that, MOA framework was used to generate the Forecast QoS values using different time series forecasting algorithms.</p>
Data collected from user evaluation of dataset search using similarity methods
<p>The data in this dataset was collected from a user evaluation of dataset search using similarity methods in data catalogs. It is linked to the dataset metadata in the data catalog used for the evaluation, which is available in https://doi.org/10.5281/zenodo.4433464.</p>
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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.