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470 results for “Evaluation dataset”
TraVLR: Now You See It, Now You Don't! A Bimodal Dataset for Evaluating Visio-Linguisic Reasoning
<p>TraVLR dataset for evaluation of visio-linguistic reasoning and cross-modal transfer. Four reasoning tasks are supported: spatiality, cardinality, numerical comparison and quantifiers. For some of the datasets, we provide two versions which differ only in their training sets: one with 8k training examples, and another with 32k training examples. The validation sets, in-distribution and out-of-distribution test sets are identical. </p>
Data from: Thermal failure of diamond tools indicated by diamond degradation: Damage evaluation and property prediction on small image datasets
<p>High temperature induced diamond degradation often leads to the failure of diamond tools. In this work, diamond samples holding different degrees of thermal damage were prepared by heating and sintering. The influence of diamond particle size and processing temperature was investigated through mechanical testing and micromorphology observation, meanwhile, a dataset containing 2870 SEM images showing diamonds with different degrees of degradation was constructed. By modification of VGG16 network, classification models and regression models were developed for thermal damage evaluation and sample property prediction. Training strategies including transfer learning and data augmentation were implemented and verified essential on the small dataset, where drop-out showed no positive effects. Two classification models (3-class and 65-class) were constructed and trained for damage evaluation. Visualized damage feature maps exported from Grad-CAM revealed the influential mechanism of thermal damage on diamonds, which proved the effectiveness of the classification models as well. Under the optimized training strategies, regression models were built for sample property prediction. The models towards toughness index, bending strength loss, relative density and Rockwell hardness were examined. Comparing the output results with real property values in test sets, the first two models matched well, and the latter two showed the opposite. It verified the validity of the regression models for property prediction as they were all established based on diamond damage image datasets. The loss in bending strength loss prediction model was smaller than that of toughness index, indicating bending strength easier to be shorten than impact toughness for diamond/metal composites suffering thermal impacts.</p>
Machine activity detection evaluation dataset.
<p>Dataset for the evaluation of the one-shot time series machine state detector.</p>
Tropical Cyclone Evaluation Methodology Dataset
<p>This dataset includes Automated Tropical Cyclone Forecast (ATCF) format ASCII files. These data are a subset of files from the Hurricane Forecast Improvement project (HFIP) Stream 1.5 evaluation covering three years of retrospective forecasts from 2010-2012. The best track analysis (b-decks), operational baselines (a-deck), and experimental Stream1.5 model output included can be used as example data to perform tropical cyclone model forecast evaluations, following Brown et al, 2023: User-responsive diagnostic verification methods for evaluating tropical cyclone track and intensity forecasts.</p>
TCHES artefact dataset for paper : Efficient Regression-Based Linear Discriminant Analysis for Side-Channel Security Evaluations
<p>This dataset allows reproducing the results of the CHES 2023 paper : "Efficient Regression-Based Linear Discriminant<br> Analysis for Side-Channel Security Evaluations".</p> <p>It contains the side-channel measurements necessary to do so.</p> <p>Scripts and readme is available at the CHES artefact site : <link not yet alive></p> <p> </p>
DCASE 2023 Challenge Task 2 Evaluation Dataset
<p><strong>Description</strong></p> <p>This dataset is the "evaluation dataset" for the <a href="https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">DCASE 2023 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring"</a>.</p> <p>The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel audio that includes both a machine's operating sound and environmental noise. The duration of recordings varies from 6 to 18 sec, depending on the machine type. The following seven types of real/toy machines are used:</p> <ul> <li>Vacuum</li> <li>ToyTank</li> <li>ToyNscale</li> <li>ToyDrone</li> <li>bandsaw</li> <li>grinder</li> <li>shaker</li> </ul> <p> </p> <p><strong>Definition</strong></p> <p>We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.".</p> <ul> <li>"Machine type" indicates the type of machine, which in the development dataset is one of seven: fan, gearbox, bearing, slide rail, valve, ToyCar, and ToyTrain.</li> <li>A section is defined as a subset of the dataset for calculating performance metrics.</li> <li>The source domain is the domain under which most of the training data and some of the test data were recorded, and the target domain is a different set of domains under which some of the training data and some of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, signal-to-noise ratio, etc.</li> <li>Attributes are parameters that define states of machines or types of noise.</li> </ul> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset consists of seven machine types. For each machine type, one section is provided, and the section is a complete set of training and test data. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training, (ii) ten clips of normal sounds in the target domain for training. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.</p> <p> </p> <p><strong>Recording procedure</strong></p> <p>Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.</p> <p> </p> <p><strong>Directory structure</strong></p> <p>- /dev_data </p> <p> - /raw<br> - /Vacuum<br> - /test <br> - /section_00_0001.wav <br> - ... <br> - /section_00_0200.wav <br> - /ToyTank (The other machine types have the same directory structure as Vacuum.) <br> - /ToyNscale<br> - /ToyDrone<br> - /bandsaw<br> - /grinder<br> - /shaker</p> <p><strong>Condition of use</strong></p> <p>This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite all the following papers. We will publish a paper on the description of the DCASE 2023 Task 2, so pleasure make sure to cite the paper, too.</p> <ul> <li>Noboru Harada, Daisuke Niizumi, Yasunori Ohishi, Daiki Takeuchi, and Masahiro Yasuda. <em>First-shot anomaly detection for machine condition monitoring: A domain generalization baseline. In arXiv e-prints: 2303.00455</em>, 2023. [<a href="https://arxiv.org/pdf/2303.00455.pdf">URL</a>]</li> <li>Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> In Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022), 31-35. Nancy, France, November 2022, . [<a href="https://arxiv.org/pdf/2205.13879.pdf">URL</a>]</li> <li>Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [<a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Harada_6.pdf">URL</a>]</li> </ul> <p> </p> <p><strong>Contact</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> <li>Noboru Harada, <a href="mailto:noboru@ieee.org">noboru@ieee.org</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>
MAESTRO Real - Multi-Annotator Estimated Strong Labels; Evaluation dataset
<p>Evaluation dataset for the DCASE challenge 2023 task 4 B - Sound Event Detection with Soft Labels.</p> <p>The evaluation dataset contains 26 real-life audio files from 5 different acoustic scenes.</p>
TAU Urban Acoustic Scenes 2023 Mobile, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2023 Mobile evaluation dataset consists of 1-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Traveling by a tram - <em>tram</em></li> <li>Traveling by a bus - <em>bus</em></li> <li>Traveling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul>
Datasets supporting the publication: Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador
<p>##################################</p> <p><strong>Datasets supporting the publication:</strong></p> <p><em>Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador.</em></p> <p>International Journal of Remote Sensing. <a href="https://doi.org/10.1080/01431161.2023.2205983">https://doi.org/10.1080/01431161.2023.2205983</a></p> <p>C. Scott Watson<sup>a</sup>*, John R. Elliott<sup>a</sup>, Marco Córdova<sup>b</sup>, Jonathan Menoscal<sup>b</sup>, Santiago Bonilla-Bedoya<sup>c</sup></p> <p><em><sup>a</sup>COMET, School of Earth and Environment, University of Leeds, LS2 9JT, UK</em></p> <p><em><sup>b</sup>Facultad Latinoamericana de Ciencias Sociales, FLACSO, Quito, Ecuador</em></p> <p><em><sup>c</sup>Research Center for the Territory and Sustainable Habitat, Universidad Tecnológica Indoamérica,Machala y Sabanilla, 170301, Quito, Ecuador</em></p> <p><strong>-Please refer to the publication for details on the production of each dataset.<br> -Please cite the publication and this dataset repository when using the data.</strong></p> <p>##################################</p> <p><strong>Data:</strong></p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>J1_mosaic_max.tif</td> <td>Mosaicked Jilin-1 multi-spectral night-time image of Quito, Ecuador. Acquisition: 8th July 2021 at ~10:30 UTC (05:30 local time)</td> </tr> <tr> <td>corine_landcover_S2_20210705T153621_20210705T154215_T17MQV.tif</td> <td>Land cover classification applied to a Sentinel-2 image (5th July 2021)</td> <td> </td> </tr> <tr> <td>corine_landcover_symbology_qgis.txt</td> <td>Land cover classification symbology for QGIS</td> </tr> <tr> <td>light_type_classification.tif</td> <td>Light type classification: class 1 = LED, class 10 = HPS.</td> </tr> <tr> <td>classified_light_locations.shp</td> <td>Classified light source (point) locations</td> </tr> </tbody> </table>
Datasets for " Evaluation of the TOF-ACSM-CV for PM1.0 and PM2.5 measurements during the RITA-2021 field campaign"
<p>For the description of the dataset, please see the readme file included in the compressed file.</p>
Model dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation"
<p>This is the global 1-deg CLM5-SNICAR model simulation dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation".</p>
Dataset: Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production Systems
<p>This online repository contains the accompanying data for the ETFA publication "Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production Systems".<br> <br> This involves all tables which contain the values of the maturity evaluation criteria of the five examined subject DSLs.</p>
A real dataset for evaluating hybrid clustering algorithm
<p>This is a real dataset for testing our hybrid clustering algorithm, and details can be found in our manuscript.</p>
Datasets S1 ~ S6 for evaluating hybrid clustering algorithm
<p>These datasets are used to evaluate our hybrid clustering algorithm. For details of our algorithm, please refer to https://github.com/junhaiqi/Hybrid_clustering.git.</p>
Dataset in support of the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e
<p>This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e.</p> <p>The newly created module providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models is called "CloudJ_mod.f90." The modules from the Cloud-J code itself are gathered in the "ModsCloudJ_mod.f90" file.</p> <p>The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651</p>
Dataset supporting the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e
<p>This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e.</p> <p>The newly created module providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models is called "CloudJ_mod.f90." The modules from the Cloud-J code itself are gathered in the "ModsCloudJ_mod.f90" file in the CloudJ sub-folders.</p> <p>The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651</p>
Data from: HExT, a software supporting tree-based screens for hybrid taxa in multilocus datasets, and an evaluation of the homoplasy excess test
Open the record for dataset details and reuse information.
Economic evaluation of sea-level rise adaptation strongly influenced by hydrodynamic feedbacks: Dataset 1
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Evaluating the performance of probabilistic algorithms for phylogenetic analysis of big morphological datasets: a simulation study
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Data from: Evaluating the ability of Bayesian clustering methods to detect hybridization and introgression using an empirical red wolf dataset
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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.