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369 results for “Datasets, Benchmarking”
maDLC Tri-Mouse Benchmark Dataset - Test Images
<p>see https://benchmark.deeplabcut.org/ for more information.</p>
Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data"
<p>"<span>Benchmarking automated detection and classification approaches for long-term acoustic monitoring of endangered species: a case study on gibbons from Cambodia</span>"</p> <div> <p><span>Recent advances in deep learning and transfer learning have revolutionized our ability for the automated detection of acoustic signals from long-term soundscape recordings. Here, we provide a benchmark for the automated detection of southern yellow-cheeked crested gibbon (<em>Nomascus gabriellae</em>) calls recorded in Jahoo, Cambodia. For the benchmarking, we compared the performance of support vector machines (SVMs), a quasi-DenseNet architecture (Koogu), transfer learning with ResNet50 models trained on the ‘ImageNet’ dataset (ResNet), and transfer learning with embeddings from a global birdsong model (BirdNET). We also investigated the impact of varying the number of training samples on the performance of these models. Transfer learning models based on <span>BirdNET embeddings had superior performance with a smaller number of training samples, whereas Koogu and ResNet models only had acceptable performance with a larger number of training samples (>200 gibbon samples). We deployed the BirdNET-based model over </span>> 130,000 hours<span> of continuous soundscape data, which, after manual review, resulted in >12,000 verified true positive detections. We found that gibbon calling events occurred mostly in the early morning hours between 05:00 to 0:600 local time. We had fewer gibbon detections during the monsoon period and found substantial variation in spatial patterns of calling events across months and years. </span>We show that automated detection can be used to investigate long-term spatial and temporal patterns of gibbon calling events. Reliable automated detection approaches are a critical first step for using passive acoustic monitoring to assess endangered gibbon populations at ecologically relevant temporal- and spatial-scales. </span></p> <p> Detailed instructions regarding use are provided on GitHub.</p> </div> <p>Link to GitHub: https://github.com/DenaJGibbon/benchmark-gibbon-calls.</p> <p>Please cite both if you use these data: </p> <p>Clink, D., Cross-Jaya, H., Kim, J., Ahmad, A. H., Hong, M., Sala, R., Birot, H., Agger, C., Vu, T. T., Thi, H. N., Chi, T. N., & Klinck, H. (2024). Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data" [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12706803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12706803</a></p> <p>Clink DJ, Cross-Jaya H, Kim J, Ahmad AH, Hong M, Sala R, Birot H, Agger C, Vu TT, Thi HN, Chi TN. Benchmarking for the automated detection and classification of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data. bioRxiv. 2024:2024-08.</p>
PRONTO heterogeneous benchmark dataset
<p>The PRONTO heterogeneous benchmark dataset is based on an industrial-scale multiphase flow facility. It includes data from heterogeneous sources, including process measurements, alarm records, high frequency ultrasonic flow and pressure measurements, an operation log and video recordings. The study collected data from various operational conditions with and without induced faults to generate a multi-rate, multi-modal dataset. The dataset is suitable for developing and validating algorithms for fault detection and diagnosis (FDD) and data fusion. </p> <p>When using the dataset please cite the following publication:</p> <p>A. Stief, R. Tan, Y. Cao, J. R. Ottewill, N. F. Thornhill, J. Baranowski, A heterogeneous benchmark dataset for data analytics: Multiphase flow facility case study, Journal of Process Control, 79 (2019) 41–55, DOI: <a href="https://doi.org/10.1016/j.jprocont.2019.04.009">https://doi.org/10.1016/j.jprocont.2019.04.009</a></p> <p>The dataset has been used in the following works:</p> <p>A. Stief, R. Tan, Y. Cao, J. R. Ottewill. Analytics of heterogeneous process data: Multiphase flow facility case study. IFAC-PapersOnLine, 51(18):363–368, 2018. DOI: <a href="https://doi.org/10.1016/j.ifacol.2018.09.327">https://doi.org/10.1016/j.ifacol.2018.09.327</a></p> <p>A. Stief, J. R. Ottewill, R. Tan, Y. Cao. Process and alarm data integration under a two-stage Bayesian framework for fault diagnostics. IFAC-PapersOnLine, 51(24):1220–1226, 2018. DOI: <a href="https://doi.org/10.1016/j.ifacol.2018.09.696">https://doi.org/10.1016/j.ifacol.2018.09.696</a></p> <p>A. Stief, J. R. Ottewill, J. Baranowski. Investigation of the diagnostic properties of sensors and features in a multiphase flow facility case study. in: 12<sup>th</sup> IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019</p> <p>M. Lucke, X. Mei, A. Stief, M. Chioua, N. F. Thornhill. Variable selection for fault detection and identification based on mutual information of multi-valued alarm series, in: 12<sup>th</sup> IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019</p> <p>R. Tan, T. Cong, N. F. Thornhill, J. R. Ottewill, J. Baranowski. Statistical monitoring of processes with multiple operating modes, in: 12<sup>th</sup> IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019.</p>
A benchmark Arabic dataset for question classification with AAFAQ taxonomy
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Mental health network of Gipuzkoa dataset (2015) for benchmark analysis
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Benchmark dataset for pore-scale CO2-water interaction
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CrossLoc Benchmark Datasets
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Urbanev: An open benchmark dataset for urban electric vehicle charging demand prediction
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Data from: Application of a 1H brain MRS benchmark dataset to deep learning for out-of-voxel artifacts
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ASA³P example & benchmark datasets
<p>These are the benchmark and example datasets used in Schwengers et al. 2020, PLOS CompBio - 10.1371/journal.pcbi.1007134.</p> <p><a href="https://github.com/oschwengers/asap">https://github.com/oschwengers/asap</a></p>
MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities
<p><strong>Abstract:</strong></p> <p>We introduce the <strong>MLM (Multiple Languages and Modalities)</strong> dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. The second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single-task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalizing on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding. </p> <p><strong>Introduction:</strong><br> Multiple Languages and Modalities comprises data points on 236k human settlements for evaluating and optimizing multitask learning systems. MLM presents a dataset with a high level of diversity in terms of modality and language. For each entity, we have extracted text summaries, images, coordinates, and their respective triple classes. Text summaries are available in three languages (English, French, and German) with each entity having between one and three language entries. </p> <p>Human settlements from all continents are provided in the overall dataset (MLM) with 72% located in Europe. Two further versions of the dataset - MLM-irle and MLM-irle-gr - were generated for use in the benchmark evaluation for multitask systems described in the paper (see above). MLM-irle-gr (ie geo-representative) was generated to serve organizations that focus on the European Union by providing a geographically balanced coverage of human settlements in this region. MLM-irle-gr contains data on 24k human settlements across the EU weighted in relation to the population count for each of the 28 countries.</p> <p>MLM contains the following fields:</p> <pre><code>---------------------------------------------------------------------- # field-label description ---------------------------------------------------------------------- 1. id a unique identifier 2. label textual label 3. coordinates longitude, latitude geo-location value 4. summaries list of textual summaries related to the entity 5. images list of images related to the entity 6. classes list of associated triple class ----------------------------------------------------------------------</code></pre> <p>MLM - Details by Dataset Version:</p> <pre><code>----------------------------------------------------------- Num. of MLM MLM-irle MLM-irle-gr ----------------------------------------------------------- Entities 236496 218681 22501 Images 412422 314533 31621 Summaries 497899 462328 47508 Triple classes 1685 1655 452 -----------------------------------------------------------</code></pre> <p><strong>Availability:</strong></p> <p>All three versions of MLM listed in the table directly above are available for direct download and use. To support findability and sustainability, the MLM dataset is published as an on-line resource at<em> <a href="https://doi.org/10.5281/zenodo.3885753">https://doi.org/10.5281/zenodo.3885753</a></em>. A separate page with detailed explanations and illustrations is available at <em><a href="http://cleopatra.ijs.si/goal-mlm/">http://cleopatra.ijs.si/goal-mlm/</a> </em>to promote ease-of-use. The project GitHub repository contains the complete source code for the system and the generation script is available at <em><a href="http://github.com/GOALCLEOPATRA/MLM">https://github.com/GOALCLEOPATRA/MLM</a></em>. Documentation adheres to the standards of <em>FAIR Data principles</em> with all relevant metadata specified to the research community and users. It is freely accessible under the Creative Commons Attribution 4.0 International license, which makes it reusable for almost any purpose. </p> <p><strong>Updating and Reusability:</strong><br> MLM is supported by a team of researchers from the University of Bonn, the Leibniz Information Center for Science and Technology, and Jožef Stefan Institute. The resource is already in use for individual projects and as a contribution to the project deliverables of the Marie Skłodowska-Curie CLEOPATRA Innovative Training Network. In addition to the steps above that make the resource available to the wider community, the usage of MLM will be promoted to the network of researchers in this project. Use among researchers and practitioners in digital humanities will be promoted by demonstrations and presentations at domain-related events. Activities are planned for the Digital Methods Summer School run by the University of Amsterdam. The range of modalities and languages present in the dataset also extend its application to research on multimodal representation learning, multilingual machine learning, information retrieval, location estimation, and the Semantic Web. MLM will be supported and maintained for three years in the first instance. A second release of the dataset is already scheduled and the generation process outlined above is designed to enable rapid scaling.</p>
A benchmark dataset for Manipuri Meetei-Mayek handwritten character recognition
<p>A benchmark dataset is always required for any classification or recognition system. To the best of our knowledge, no benchmark dataset exists for handwritten character recognition of Manipuri Meetei-Mayek script in <strong>public domain</strong> so far. Manipuri, also referred to as Meeteilon or sometimes Meiteilon, is a Sino-Tibetan language and also one of the Eight Scheduled languages of Indian Constitution. It is the official language and lingua franca of the southeastern Himalayan state of Manipur, in northeastern India. This language is also used by a significant number of people as their communicating language over the north-east India, and some parts of Bangladesh and Myanmar. It is the most widely spoken language in Northeast India after Bengali and Assamese languages. In this work, we introduce a handwritten Manipuri Meetei-Mayek character dataset which consists of more than 5000 data samples which were collected from a diverse population group that belongs to different age groups (from 4 years to 60 years), genders, educational backgrounds, occupations, communities from three different districts of Manipur, India (Imphal East District, Thoubal District and Kangpokpi District) during March and April 2019. Each individual was asked to write down all the Manipuri characters on one A4-size paper. The recorded responses are scanned with the help of a scanner and then each character is manually segmented from the scanned images. This dataset consists of segmented scanned images of handwritten Manipuri Meetei-Mayek characters (Mapi Mayek, Lonsum Mayek, Cheitap Mayek, Cheising Mayek, Khutam Mayek) of size 128X128 pixels in .JPG format as well as in .MAT format.</p>
Benchmarking datasets used in the manuscript "Strain-level metagenomic profiling using pangenome graphs with PanTax"
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Spatially Variant Super-Resolution (SVSR) benchmarking dataset
<p>The Spatially Variant Super-Resolution (SVSR) benchmarking dataset contains 1119 real low-resolution images that are degraded by complex noise of varying intensity and type and their corresponding real noise free X2 and X4 high-resolution counterparts, for evaluation of the robustness of real-world super-resolution methods. Additionally, the dataset is also suitable for evaluation of denoisers. The associated paper can be found here: <a title="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf" href="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf">https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf</a></p>
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data (minimal)
<p>This is a minimal version of the DynaBench dataset, containing the first 5% of the data. The full dataset is available at <a href="https://professor-x.de/dynabench">https://professor-x.de/dynabench</a></p><p><strong>Abstract:</strong></p><p>Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at <a href="https://professor-x.de/dynabench">https://professor-x.de/dynabench</a>.</p><p><strong>Technical Info</strong></p><p>The dataset is split into 42 parts (6 equations x 7 combinations of resolution/structure). Each part can be downloaded separately and contains 7000 simulations of the given equation at the given resolution and structure. The simulations are grouped into chunks of 500 simulations saved in the hdf5 file format. Each chunk contains the variable "data", where the values of the simulated system are stored, as well as the variable "points", where the coordinates at which the system has been observed are stored. For more details visit the DynaBench website at <a href="https://professor-x.de/dynabench/">https://professor-x.de/dynabench/</a>. The dataset is best used as part of the dynabench python package available at <a href="https://pypi.org/project/dynabench/">https://pypi.org/project/dynabench/</a>.</p>
GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts (Dataset 2)
<p>The following contains the datasets described in the paper: <strong>GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts</strong>, and the associated code can be found at <a href="https://github.com/Graph-COM/GDL_DS">https://github.com/Graph-COM/GDL_DS</a>. </p>
LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 3 of 3
<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 5 and reference. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference (this one) <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td> </td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing). </p>
LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 2 of 3
<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 3 and 4. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 (this one) <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td> </td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing). </p>
Reference Dataset for QfO Benchmark Webservice
<p>This dataset contains the reference data used for the QfO benchmark webservice workflow (https://github.com/qfo/benchmark-webservice). It is build from the QfO reference proteomes provided by UniProt. The dataset is specific to the release of the reference proteome. The version of this dataset reflects the version of the QfO Reference Proteome dataset.</p>
Benchmark dataset for protein function prediction that integrates various protein information
<p>This research was funded by the National Science Centre in Poland (grant number 2021/41/N/ST6/01919)</p>
ScienceDex guides
Understand access before you commit
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.