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369 results for “Datasets Benchmarking”
Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset
<p><b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b></p><p>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.</p>
EO4WildFires: An Earth Observation multi-sensor, time-series machine-learning-ready benchmark dataset for wildfire impact prediction
<p>This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover.</p> <p>This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,742 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture.</p> <p>The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data.</p> <p>Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three (3) different preliminary experiments, in order to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated.</p>
TauBench: A Dynamic Benchmark for Graphics Rendering (Dataset Reference Frames)
<p>TauBench is a dynamic graphics rendering benchmark dataset, targeted especially towards rendering methods relying on the reuse of temporal data. The dataset is available at <a href="https://doi.org/10.5281/zenodo.5729573">https://doi.org/10.5281/zenodo.5729573</a>, and this upload provides path traced reference frames for it in PNG format. The images are rendered with <a href="https://github.com/vga-group/tauray">Tauray</a> at 16384 samples per pixel (spp), at both 1080p and 2160p resolutions. Frame indices start from 0 and are <em>not</em> padded with leading zeroes.</p> <p>More information about TauBench is also available at <a href="https://webpages.tuni.fi/vga/taubench">https://webpages.tuni.fi/vga/taubench</a>.</p>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
FIND: A Function Interpretation Dataset and Benchmark for Evaluating Interpretability Methods
<p><strong>FIND</strong> is an interactive dataset for evaluating AI interpretability methods on black box functions. </p> <p>This dataset contains all function files for the <strong>FIND</strong> benchmark and JSON files with associated metadata. The utilities provided in the associated <strong>FIND</strong> <a href="https://github.com/multimodal-interpretability/FIND">GitHub Repository</a> support running and evaluating interpretation of the functions with user-defined interpreters.</p>
Dataset related to the article "Laboratory-scale hydraulic fracturing dataset for benchmarking of Enhanced Geothermal System simulation tools"
<p>Experimental results from hydraulic fracturing experiments performed in granite and marble samples of size 30 cm × 30 cm × 45 cm under well-defined boundary conditions.</p> <p>Datasets include:</p> <ul> <li>pressure versus flow-rate response</li> <li>acoustic emission data from a dense network of 32 seismic sensors</li> <li>detailed description of the experimental set-up and adopted test protocol</li> <li>mechanical and petrophysical properties of the samples</li> <li>python code for seismic data processing</li> </ul> <p>This complete collection of data, obtained within the framework of European Union’s Horizon 2020 project GEMex, is rare in its kind and indispensable for verification of model assumptions and constitutive relationships of numerical codes used for designing field-scale hydraulic fracturing experiments.</p>
anTraX: high throughput video tracking of color-tagged insects (benchmark datasets)
<p>Datasets used to benchmark anTraX tracking software. Each dataset contains the raw videos, a configured anTraX session with all parameters required to reproduce the tracking results from the paper, as well as the tracking output for the first video in each dataset.</p> <p> </p> <p> </p>
Heteroplasmy Benchmark Dataset - mitochondrial DNA mixture model - MiSeq - U5-H1-M1-M2-M3-M4-M5 - FASTQ
<p>mtDNA mixture model of 2 mtDNA sequences belonging to haplogroups U5 and H1. Run on Illumina MiSeq with 3 different polymerases (Clontech, Herculase, NEB Taq), and different DNA extraction protocols - Paired-end Fastq files</p> <p>M1 = Mixture 1:2 i.e. 50%</p> <p>M2 = Mixture 1:10 i.e. 10%</p> <p>M3 = Mixture 1:50 i.e. 2%</p> <p>M4 = Mixture 1:100 i.e. 1%</p> <p>M5 = Mixture 1:200 i.e. 0.5%</p>
Industrial Benchmark Dataset for Customer Escalation Prediction
<p>This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices. </p> <p>The dataset accompanies the publication "System Design for a Data-driven and Explainable Customer Sentiment Monitor" (submitted). We provide an anonymized version of data collected over a period of two years.</p> <p>The dataset should fuel the research and development of new machine learning algorithms to better cope with real-world data challenges including sparse and noisy labels, and concept drifts. Additional challenges is the optimal fusion of enterprise and log based features for the prediction task. Thereby, interpretability of designed prediction models should be ensured in order to have practical relevancy. </p> <p><strong>Supporting software</strong></p> <p>Kindly use the corresponding <a href="https://github.com/annguy/customer-sentiment-monitor">GitHub repository</a> (https://github.com/annguy/customer-sentiment-monitor) to design and benchmark your algorithms. </p> <p> </p> <p><strong>Citation and Contact</strong><br> </p> <p>If you use this dataset please cite the following publication:</p> <p><br> </p> <pre><code>@ARTICLE{9520354, author={Nguyen, An and Foerstel, Stefan and Kittler, Thomas and Kurzyukov, Andrey and Schwinn, Leo and Zanca, Dario and Hipp, Tobias and Jun, Sun Da and Schrapp, Michael and Rothgang, Eva and Eskofier, Bjoern}, journal={IEEE Access}, title={System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data}, year={2021}, volume={9}, number={}, pages={117140-117152}, doi={10.1109/ACCESS.2021.3106791}}</code></pre> <p> </p> <p>If you would like to get in touch, please contact an.nguyen@fau.de.<br> </p>
Use Case Points Benchmark Dataset
<p>This dataset was gathered by us from three software houses. This is real-life dataset. Use Case points method as originated by Karner was used for counting a steps or number of actors. Data are based on different languages, various problem domains. ISBSG style for language, domain and application type were adopted.</p> <p><br> Attributes are used as follows:<br> Project_No - only project ID for identification purposes<br> Simple Actors - Number of actor classify according UCP - simple actors.<br> Average Actors - Number of actor classify according UCP - average actors.<br> Complex Actors - Number of actor classify according UCP - complex actors.<br> UAW - Unadjusted Actor weight, computed by using UCP equation. <br> Simple UC - Number of use cases classified as simple - UCP number of steps is used.<br> Average UC - Number of use cases classified as average - UCP number of steps is used.<br> Complex UC - Number of use cases classified as complex - UCP number of steps is used.<br> UUCW - Unadjusted UseCase Weight - computed by using UCP equation.<br> TCF - Technical Complexity FactorECF - Enviromental Complexity Factors<br> Real_P20 - Real_P20 - Real Effort in Person hours, decided by productivity factor (PF = 20).<br> Real_Effort_Person_Hours - Real Effort (development time) in person-hours.<br> Sector - Problem domain of projectLanguage - Programming language used for project.<br> Methodology - Development methodology used for project development.<br> ApplicationType - Classification of project type - provided by donator. <br> DataDonator - anonymized acronym for data donator.<br> </p>
MatSim Dataset and benchmark for one-shot visual materials and textures recognition
<p><strong>The MatSim Dataset and benchmark</strong></p> <p>Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.</p> <p>MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).</p> <p>Based on the paper: <a href="https://arxiv.org/pdf/2212.00648.pdf">One-shot recognition of any material anywhere using contrastive learning with physics-based rendering</a></p> <p> </p> <p><strong>Benchmark_MATSIM.zip: </strong>contain the benchmark made of real-world images as described in the paper</p> <p><strong>Dataset Generation Scripts.zip: </strong>Contain the Blender (4.1) Python scripts used for generating the dataset<br><br><a href="https://zenodo.org/record/7390166/files/MatSim_object_train_split_1.zip?download=1"><strong>MatSim_object_train_split_1,2,3....zip:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.</p> <p><strong>MatSimTrainObjectsNearField_.zip </strong>Contain train sets with near fieldlight sources</p> <p><strong><a href="https://zenodo.org/record/7390166/files/MatSim_Vessels_Train_1.zip?download=1">MatSim_Vessels_Train_1,2,3....zip </a></strong><a href="https://zenodo.org/api/files/020f90b2-7c41-44ad-86e3-69257884a569/MatSim_object_train_split_1.zip"><strong>:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.<br><br><strong>*Note: these are subsets of the dataset; the full dataset can be found at:</strong><br><a href="https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX">https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX</a></p> <p>or<br><a href="https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF">https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF</a></p> <p> </p>
SPIDER - Lumbar spine segmentation in MR images: a dataset and a public benchmark
<p>This is a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 studies of 218 patients with a history of low back pain. The data was collected from four different hospitals. There is an additional hidden test set, not available here, used in the accompanying SPIDER challenge on spider.grand-challenge.org. We share this data to encourage wider participation and collaboration in the field of spine segmentation, and ultimately improve the diagnostic value of lumbar spine MRI.</p> <p>Which MRI studies are assigned to the training and validation sets can be found in the overview file. This file also provides the biological sex for all patients and the age for the patients for which this was available. It also includes a number of scanner and acquisition parameters for each individual MRI study. The dataset also comes with radiological gradings found in a separate file for the following degenerative changes:</p> <p>1.    Modic changes (type I, II or III)</p> <p>2.    Upper and lower endplate changes / Schmorl nodes (binary)</p> <p>3.    Spondylolisthesis (binary)</p> <p>4.    Disc herniation (binary)</p> <p>5.    Disc narrowing (binary)</p> <p>6.    Disc bulging (binary)</p> <p>7.    Pfirrman grade (grade 1 to 5). </p> <p>All radiological gradings are provided per IVD level.</p> <div>This dataset, and the associated public benchmark, are described in this paper: <a href="https://www.nature.com/articles/s41597-024-03090-w" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03090-w</a></div> <div>The public segmenation challenge can be found here: <a href="https://spider.grand-challenge.org/" target="_blank" rel="noopener">https://spider.grand-challenge.org/</a></div> <div> </div> <div>When using this dataset, please cite this dataset with the correct DOI, and also cite the afformentioned paper.</div>
Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping
<p>We (Intelligent Mining and Analysis of Remote Sensing big data, IMARS) create a large-scale annotated dataset (Globe230k) for land use/land cover (LULC) mapping, which is annotated on Google Earth image of 1 m spatial resolution. Globe230k is annotated by numerous experts and students major in survey and mapping after necessary training, through visual interpretation on very high-resolution images, as well as in-situ field survey, under the guidance of the organized annotation pipeline. Globe230k has three superiorities:</p> <p>1) Large scale: the Globe230k includes 232,819 annotated images with the size of 512x512 and spatial resolution of 1 m, with more than 3x1010 annotated pixels, and it includes 10 first-level categories. </p> <p>2) Rich diversity: the annotated images are sampled from worldwide regions, with coverage area of over 60,000 km2, indicating a high variability and diversity. Besides, in order to ensure the category balance, we intentionally give more chance to the rare categories to be sampled, such as wetland, ice/snow, etc.</p> <p>3) Multi-modal: Globe230k not only contains RGB bands, but also include other important features for Earth system research, such as Normalized differential vegetation index (NDVI), digital elevation model (DEM), vertical-vertical polarization (VV) bands, vertical-horizontal polarization (VH) bands, which can facilitate the multi-modal data fusion research. Due to the large size of the multi-modal dataset (DEM 1.91G, NDVI 164G, VVVH 372G), these dataset are stored on Baidu Yunpan, the download link is :https://pan.baidu.com/s/12AKbiqOXSf4fnm7mYkCE0g?pwd=230k, the extraction code is 230k.</p> <p>The image patches and their corresponding annotated patches are respectively stored in "image_patch.zip" and "label_patch.zip" file. The RGB image is in forms of ".jpg", with size of 512x512, the pixel value is ranged from 0-255. The annotated patches is in forms of ".png", also with size of 512x512, the pixel value is ranged from 1-10, which respectively represent 1#cropland, 2#forest, 3#grass, 4#shrubland, 5#wetland, 6#water, 7#tundra, 8#impervious, 9#bareland, 10#ice/snow. The corresponding DEM, NDVI and VVVH patches are all in form of ".tif", with size of 512x512 (due to the different resolution of DEM, NDVI and VVVH patches, they are all uniformly resized to the same scale as the image patch). </p> <p>The total 232,819 pairs are officially divided into training set, validation set, and test set, based on ratio of 7:1:2, which can be find in "train_num.txt","val_num.txt","test_num.txt" file. Based on this division, the official baseline accuracy of several state-of-the-art semantic segmentation can be found in the related arcticle (https://spj.science.org/doi/10.34133/remotesensing.0078).</p> <p>We hope it can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.</p>
Benchmarking dataset for multiskilled workforce planning with uncertain demand
<p>These datasets are related to the Data Article entitled: “A benchmark dataset for the retail multiskilled personnel planning under uncertain demand”, submitted to the Data Science Journal. This data article describes datasets from a home improvement retailer located in Santiago, Chile. The datasets were developed to solve a multiskilled personnel assignment problem (MPAP) under uncertain demand. Notably, these datasets were used in the published article "Multiskilled personnel assignment problem under uncertain demand: A benchmarking analysis" authored by Henao et al. (2022). Moreover, the datasets were also used in the published articles authored by Henao et al. (2016) and Henao et al. (2019) to solve MPAPs.</p> <p>The datasets include real and simulated data. Regarding the real dataset, it includes information about the store size, number of employees, employment-contract characteristics, mean value of weekly hours demand in each department, and cost parameters. Regarding the simulated datasets, they include information about the random parameter of weekly hours demand in each store department. The simulated data are presented in 18 text files classified by: (i) Sample type (in-sample or out-of-sample). (ii) Truncation-type method (zero-truncated or percentile-truncated). (iii) Coefficient of variation (5, 10, 20, 30, 40, 50%).</p>
Validation and Benchmark Dataset for Discrete Element Method Simulations
<p>Verification and Benchmark Dataset for Discrete Element Method Simulations<br>v3 (05/02/2024)<br>Authors: Jose Salomon, Fernando Patino-Ramirez, Catherine O'Sullivan<br>https://doi.org/10.5281/zenodo.10160309<br>Contact: jjs19@ic.ac.uk<br>--------------------------------------------------------------------<br>Description of the repository:</p> <p>This repository contains a collection of datafiles and scripts that can be employed to validate and benchmark new or existing DEM codes. <br>Two validation cases/folders are considered "FCC_packing" and "Rolling_clump". The benchmark dataset is provided in the "Toyoura_sh" folder.<br>All datafiles and scripts are in the corresponding *.zip files. A detailed description of all cases can be found in the related article.</p> <p>In each of these folders, two sub-folders can be found: (1)"Data" and (2)"Scripts". These folders contain:</p> <p>1)"Data": contains the datafiles to perform the validation or benchmark. Two types of data/folders can be found here: "Raw" and "Filtered".<br>The "Raw" folder contains raw data only. The "Filtered" data contains the post-processed data employed to generate the plots found in the related article.<br>Plots in the related article can be reproduced by using the MATLAB files found in the corresponding data folder.</p> <p>2)"Scripts": contains the LAMMPS scripts used to generate the data files contained in "Data".<br>Indications about how to run these scripts can be found in the "README.txt" file in each folder.</p> <p>In order to reproduce the simulations of this repository, LAMMPS must be built including the "GRANULAR" and "RIGID" packages. Please check the README.txt files in each folder for details.</p>
Benchmark-Dataset FAN-01: Low pressure Axial Fan in a short Duct
<p>The case consists of a generic axial fan for industrial applications. Provided measurement data include instationary pressure probes in the rotor's tip gap, distributions of velocity and turbulent kinetic energy gained by laser Doppler anemometry, as well as acoustic results gained by microphones and beamforming.</p> <p>A detailed description of the dataset with references can be found in the PDF-File. The rotor geometry is available as IGS or Parasolid file. The measurement data is available, including the ones (LDA-data, pressure probes, acoustic microphones, üerformance) listed in the PDF description file.</p> <p><strong>Citation of the fan and the data:</strong></p> <p>Zenger, Florian, et al. <em>A benchmark case for aerodynamics and aeroacoustics of a low pressure axial fan</em>. No. 2016-01-1805. SAE Technical Paper, 2016.</p> <p><strong>Citation of the microphone array measurements:</strong></p> <p>Krömer, Florian J. <em>Sound emission of low-pressure axial fans under distorted inflow conditions</em>. FAU University Press, 2018.</p> <p><strong>Citation of the python scripts:</strong></p> <p>Junger, Clemens. <em>Computational aeroacoustics for the characterization of noise sources in rotating systems</em>. Diss. Technische Universität Wien, 2019.</p> <p><strong>Related work and existing publications:</strong></p> <p>Schoder, Stefan, Clemens Junger, and Manfred Kaltenbacher. "Computational aeroacoustics of the EAA benchmark case of an axial fan." <em>Acta Acustica</em> 4.5 (2020): 22. <a href="https://doi.org/10.1051/aacus/2020021">https://doi.org/10.1051/aacus/2020021</a></p> <p>Schoder, Stefan, and Felix Czwielong. "Dataset fan-01: Revisiting the EAA benchmark for a low-pressure axial fan." <em>arXiv preprint arXiv:2211.12014</em> (2022). <a href="https://doi.org/10.48550/arXiv.2211.12014">https://doi.org/10.48550/arXiv.2211.12014</a></p> <p>Kaltenbacher, Manfred, and Stefan Schoder. "EAA Benchmark for an axial fan." <em>e-Forum Acusticum 2020</em>. 2020. <a href="https://hal.science/hal-03221387/document">https://hal.science/hal-03221387/document</a></p> <p>Tieghi, Lorenzo, et al. "Machine-learning clustering methods applied to detection of noise sources in low-speed axial fan." <em>Journal of Engineering for Gas Turbines and Power</em> 145.3 (2023): 031020. <a href="https://doi.org/10.1115/1.4055417">https://doi.org/10.1115/1.4055417</a></p> <p>Antoniou, E., Romani, G., Jantzen, A., Czwielong, F., & Schoder, S. (2023). Numerical flow noise simulation of an axial fan with a Lattice-Boltzmann solver. <em>Acta Acustica</em>, <em>7</em>, 65. <a href="https://doi.org/10.1051/aacus/2023060">https://doi.org/10.1051/aacus/2023060</a></p> <p><strong>Data curation and Questions about the Dataset</strong></p> <p>Data curated by Stefan Schoder, any questions related to the dataset to stefan.schoder@tugraz.at.</p> <p> </p>
MatSeg DataSet and Benchmark For Zero-Shot Material States Segmentation From images
<h2>This is an old version for the new version see <a href="../records/11331618">https://zenodo.org/records/11331618</a></h2> <p> </p> <p>A Dataset and Benchmark for zero-shot segmentation of materials states described in: “Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data” Described in <strong><a href="https://arxiv.org/pdf/2403.03309.pdf">https://arxiv.org/pdf/2403.03309.pdf</a> </strong></p> <p>See ReadMe in the zip file for technical details.</p> <p> </p> <h2><strong>MatSeg Benchmark </strong></h2> <p>A benchmark for zero-shot material state segmentation. The benchmark contains 820 real-world images with a wide range of material states and settings. For example: food states (cooked/burned..), plants (infected/dry.), to rocks/soil (minerals/sediment), construction/metals (rusted, worn), liquids (foam/sediment), and many other states in a class-agnostic manner. The goal is to evaluate the segmentation of material materials without knowledge or pretraining on the material or setting. The focus is on materials with complex scattered boundaries, and gradual transition (like the level of wetness of the surface). The annotation of the benchmark is point-based and similarity-based. Hence, for each image, we select several points and regions (Figure 4). We group the points of the same materials into the same label, we also define a group of points that have partial similarity. For example points in group A are more similar to points in group B than to points in group C (In case materials A and B are similar to each other but not identical). This approach allows us to capture the complexity of gradual transition and partial similarities in the world. While also enabling dealing with complex scattered and blurry shapes without needing to annotate the full shape which in many cases is unclear or very hard.</p> <p>Files <a href="../api/records/10801191/draft/files/MatSegBenchmarkPart1of3.zip/content" target="_blank" rel="noopener noreferrer">MatSegBenchmark</a>*.zip</p> <h2><strong>MatSeg synthetic Dataset Samples </strong></h2> <p>Synthethic dataset of images of materials spread on object surfaces and their segmentation map.</p> <p>The synthetic dataset is a very big, sample of the dataset as been uploaded.</p> <p>Files: MatSegSynthehticDataSample*.zip</p> <p>The full dataset can be found in this URLS:</p> <p><a href="https://e.pcloud.link/publink/show?code=kZHCcnZOfzqInb3anSl7xzFBoqCDmkr2JKV">https://e.pcloud.link/publink/show?code=kZHCcnZOfzqInb3anSl7xzFBoqCDmkr2JKV</a></p> <p><a href="https://icedrive.net/s/SBb3g9WzQ5wZuxX9892Z3R4bW8jw">https://icedrive.net/s/SBb3g9WzQ5wZuxX9892Z3R4bW8jw</a></p> <p> </p> <p>Generation Script for the synthetic data:</p> <p><a href="https://github.com/sagieppel/MatSeg-Synthethic-Dataset-Generation-Script">https://github.com/sagieppel/MatSeg-Synthethic-Dataset-Generation-Script</a></p> <p><a href="../records/10822596/files/sagieppel/MatSeg-Synthethic-Dataset-Generation-Script-3.zip?download=1">https://zenodo.org/records/10822596</a></p> <p> </p> <p> </p> <p> </p>
Zebra finch dataset for the paper: Benchmarking nearest neighbor retrieval of zebra finch vocalizations across development
<p>This is the dataset created in the paper "Benchmarking nearest neighbor retrieval of zebra finch vocalizations across development".</p>
Processed Datasets - Imputation in Well Log Data: A Benchmark
<p>Imputation of well log data is a common task in the field. However a quick review of the literature reveals a lack of padronization when evaluating methods for the problem. The goal of the benchmark is to introduce a standard evaluation protocol to any imputation method for well log data. </p> <p>In the proposed benchmark, three public datasets are used:</p> <ul> <li><strong>Geolink:</strong> The Geolink Dataset is another public dataset of wells in the Norwegian offshore. The data is provided by the company of the same name, <a href="https://www.geolink-s2.com/" target="_blank" rel="noopener">GEOLINK</a> and follows the NOLD 2.0 license. <br>This dataset contains a total of 223 wells. It also has lithology labels for the wells with a total of 36 lithology classes. [<a href="https://drive.google.com/drive/folders/1EgDN57LDuvlZAwr5-eHWB5CTJ7K9HpDP" target="_blank" rel="noopener">download original</a>]</li> <li><strong>Taranaki Basin:</strong> The Taranaki Basin Dataset is a curated set of wells and a convenient option for experimentation especially due to it is ease of accessibility and use.<br>This collection, under the CDLA-Sharing-1.0 license, contains well logs extracted from the <a href="https://geodata.nzpam.govt.nz/" target="_blank" rel="noopener">New Zealand Petroleum & Minerals Online Exploration Database</a> and <a href="http://pet.gns.cri.nz/" target="_blank" rel="noopener">Petlab</a>.<br>There are a total of 407 wells, of which 289 are onshore and 118 are offshore exploration and production wells. [<a href="https://developer.ibm.com/exchanges/data/all/taranaki-basin-curated-well-logs/" target="_blank" rel="noopener">download original</a>]</li> <li><strong>Teapot Dome:</strong> The Teapot Dome dataset is provided by the Rocky Mountain Oilfield Testing Center (RMOTC) and the US Department of Energy.<br>It contains different types of data related to the Teapot Dome oil field, such as 2D and 3D seismic data, well logs, and GIS data. The data is licensed under the Creative Commons 4.0 license. <br>In total, the dataset has 1,179 wells with available logs. The number of available logs varies across wells. There are only 91 wells with the gamma ray, bulk density, and neutron porosity logs, while only three wells have the complete basic suite. [<a href="http://s3.amazonaws.com/open.source.geoscience/open_data/teapot/rmotc.tar" target="_blank" rel="noopener">direct download</a>]</li> </ul> <p>Here you can download all three datasets already preprocessed to be used with our implementation, found <a href="https://github.com/uai-ufmg/well-log-imputation" target="_blank" rel="noopener">here</a>.</p> <p> </p> <h3>File Description:</h3> <p>There are six files for each fold partition for each dataset.</p> <ul> <li><code><em>datasetname_fold_k_well_log_metadata_train.json </em></code>: JSON file with general information of the slices of <strong>training </strong>partition of the fold <strong>k</strong>. Contains total number of slices and the number of slices per well.<em> </em></li> <li><em><code>datasetname_fold_k_well_log_metadata_val.json</code> </em>: JSON file with general information of the slices of <strong>validation </strong>partition of the fold <strong>k</strong>. Contains total number of slices and the number of slices per well. </li> <li><em><code>datasetname_fold_k_well_log_slices_train.npy</code>: </em>.npy (numpy) file ready to be loaded with the slices for <strong>training </strong>of the fold <strong>k </strong>already processed. When loaded<em> </em>should have shape of<em> (total_slices, 256, number_of_logs)</em></li> <li><em><code>datasetname_fold_k_well_log_slices_val.npy</code> </em>: .npy (numpy) file ready to be loaded with the slices for <strong>validation </strong>of the fold <strong>k </strong>already processed.</li> <li><em><code>datasetname_fold_k_well_log_slices_meta_train.json</code> : </em>JSON file with the slices info for all slices in the <strong>training </strong>partition of the fold <strong>k</strong>. For each slice, 7 data points are provided, the last four are discarded (it would contain other information that was not used). The first three are in order the: origin well name, the starting position in that well, and the end position of the slice in that well.</li> <li><em><code>datasetname_fold_k_well_log_slices_meta_val.json</code> </em>: JSON file with the slices info for all slices in the <strong>validation </strong>partition of the fold <strong>k</strong>.</li> </ul>
Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection
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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)
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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.