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369 results for “Datasets Benchmarking”
Evaluation of Spatiotemporal Fusion Methods Using Sentinel-2 And Sentinel-3: A New Benchmark Dataset And Comparison
<p>In Earth observation, data fusion is important to generate high temporal and spatial resolution images. Nevertheless, existing research on data fusion primarily concentrates on merging two sources of data (mostly MODIS and Landsat). Therefore, we offer the community a new benchmark dataset for evaluating data fusion using new European sensors (Sentinel-2 and Sentinel-3).</p> <p>The dataset is composed of three different sites located in different parts of the world to ensure the diversity of the ecosystem. The two components of the dataset are collected from operating missions ( Sentinel-2 and Sentinel-3). We also provide 10 bands for Sentinel-2 ranging from blue to SWIR, 4 bands at 10m resolution and 6 at 20m resolution. For Sentinel-3 16 bands are provided with a spatial resolution of 300m. The multiple bands allow for different applications for this dataset such as testing data fusion methods, etc.</p>
A benchmark dataset for the grazing flow over porous materials
<p>Wind-tunnel data of a grazing flow over porous wall-inserts to be used as a benchmark dataset for the development and validation of numerical modeling approaches of flows over and through porous media. </p> <p>The dataset contains several profiles along the streamwise extent of the wall-insert of the mean velocity magnitude, the turbulent intensity, and the turbulent length scale. Also included are some boundary-layer parameters of these profiles. These have been derived from single-component constant temperature hot-wire measurements.</p> <p>Additionally, the spectra of the unsteady wall-pressure fluctuations at several locations on the upper and lower surfaces of the porous wall-inserts are provided. These unsteady pressure measurements have been acquired using semi-infinite waveguide-type remote-microphone probes.</p> <p>Tested are two porous media with the same <em>diamond-lattice</em> pattern structure but different permeabilities and a reference solid-walled case. All three cases are tested at three inflow velocities: 15 m/s, 20 m/s, and 25 m/s.</p> <p> </p> <p>Modification in v3: Correction of permeability values in Table 1 on page 3 of <em>AIAA_Manuscript_GrazingFlowPorousMaterials_v3.pdf</em>.</p>
Alignment files for coverage benchmarks: Illumina and Nanopore sequencing datasets
<ul> <li><strong>cpara-illumina-noseq.bam</strong> and <strong>cpara-ont-noseq.bam</strong>: BAM files produced aligning the raw reads produced respectively by Illumina NextSeq and ONT Nanopore sequencing of an isolate of <em>C. parapsilosis</em> to evaluate the coverage calculations using real datasets.*</li> <li><strong>HG00258.bam</strong>: Exome sequencing from the 1000 Genomes Project (Clarke et al 2016 <a href="https://doi.org/10.1093/nar/gkw829">https://doi.org/10.1093/nar/gkw829</a>).</li> <li><strong>panel_01.bam</strong>: targeted sequencing of a Human gene panel of 16 genes.*</li> </ul> <p>* Sequences and qualities have been removed</p>
Code to generate figures 3 and 4 of: "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."
<p>Code to generate figures 3 and 4 of the manuscript titled "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."</p> <p> </p>
Expert Finding Benchmark Datasets (IR, CL and SW communities)
<p>This is the updated version of the original benchmark expert finding datasets proposed by the authors of this paper - <a href="https://doi.org/10.1145/2508497.2508501">https://doi.org/10.1145/2508497.2508501</a>. The current version is released as part of Neural Expert Finder (NEF), a novel expert finding approach utilizing transformer based pre-trained language models.</p>
Benchmark problems for transcranial ultrasound simulation: Datasets for intercomparison of compressional wave models
<p>This dataset contains the skull maps and modeling results associated with the forthcoming publication "Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models".</p>
Benchmark SIFET 2022 - Dataset 2: L'area urbana di Santa Marta
<p>Il secondo test field è stato individuato nella zona di Calle Larga Santa Marta e delle calli ad essa trasversali. La zona di Santa Marta, un'area residenziale di Venezia di recente edificazione, si caratterizza per la presenza di edifici di altezza compresa tra i 10 m e i 15 m circa, tra loro non molto distanti. La disposizione di questi oggetti architettonici fa si che l'area assuma la configurazione di un corridoio urbano.</p> <p>Tale area si presta dunque a valutare la possibilità di utilizzo di una tecnica di rilievo alternativa alla fotogrammetria tradizionale, poiché di difficile applicazione in questo contesto, e al rilievo laser scanning, in quanto i dati acquisiti sulla parte sommitale degli edifici risultano molto scarsi. </p> <p>Sono state effettuate due strisciate fotogrammetriche ponendo le camere ad altezze differenti, 2 m per il Set 1 e 4,5 m per il Set 2, mantenendo i medesimi punti di presa, in questo modo è stata acquisita una quantità di dati sufficiente anche per la parte alta dei fronti degli edifici. L'utilizzo delle camere sferiche risulta essere particolarmente vantaggioso per l'acquisizione di immagini ad altezze differenti poiché molto leggere e facilmente installabili su di un'asta telescopica .</p> <p>Durante questo test le camere sferiche sono state orientate mantenendo gli assi ottici ortogonali alle facciate degli edifici; in corrispondenza degli slarghi presenti lungo la calle e ogniqualvolta fosse necessario compiere una rotazione di 90° è stato adottato uno schema di presa cruciforme, con riferimento all'andamento principale. Per il Set 1 sono state acquisite 113 immagini con la camera GoPro MAX 360 e 114 immagini con le camere Nikon KeyMission 360 e Ricoh Theta Z1; per il Set 2 sono state acquisite 106 immagini con la camera GoPro MAX 360, 105 immagini con le camere Nikon KeyMission 360 e 104 immagini con la camera Ricoh Theta Z1.</p>
Benchmark SIFET 2022 - Dataset 1: Il Chiostro dei Tolentini
<p>Il primo test field è rappresentato dal portico del Chiostro del complesso dei Tolentini, sede dell'Università IUAV di Venezia. La presenza di elementi come gli archi e le volte a crociera definisce il portico come un oggetto architettonico chiuso, di cui ogni parte è potenzialmente oggetto di rilievo, sia le pareti che la copertura.</p> <p>Il caso studio si presta a testare la possibilità di utilizzo di una metodologia operativa che consente una notevole riduzione dei tempi di acquisizione. Nel caso della fotogrammetria con camere sferiche è sufficiente una sola strisciata per acquisire tutte le informazioni necessarie alla generazione del modello fotogrammetrico del porticato; per avere le stesse informazioni utilizzando la fotogrammetria tradizionale si sarebbe invece reso necessario effettuare un numero molto maggiore di strisciate.</p> <p>Durante questo test le camere sferiche sono state orientate mantenendo l'asse ottico ortogonale alla parete in mattoni e al porticato: é stata effettuata una presa per ogni campata dei quattro lati mentre per le campate angolari sono state acquisite due immagini, in posizioni differenti, l'una ruotata di 90° rispetto all'altra. Con ogni camera sono state quindi acquisite 36 immagini.</p>
Benchmark SIFET 2022 - Dataset 3: Il Rio de S. Barnaba
<p>Il terzo test field individuato è stato il Rio de San Barnaba. Il Rio si caratterizza per avere lunghi tratti in cui gli edifici affacciano direttamente su un canale di larghezza variabile tra i 5 m e i 10 m. Anche in questo caso la disposizione degli edifici fa sì che si l'area assuma la configurazione di un corridoio urbano, la cui caratteristica fondamentale è la possibilità di essere percorso esclusivamente utilizzando una barca.</p> <p>Valutare la possibilità di utilizzo delle camere sferiche per il rilievo dei rii veneziani è un tema di reale interesse poiché la configurazione spaziale di tali luoghi non permette l'utilizzo delle tradizionali tecniche di rilievo: la fotogrammetria tradizionale risulta essere di difficile applicazione in questo contesto, e il rilievo laser scanning diventa una tecnica inutilizzabile non disponendo di una base stabile di appoggio. </p> <p>Durante questo test le camere sferiche sono state orientate mantenendo gli assi ottici ortogonali alle facciate degli edifici per il Set 1 e ruotandole di 90°, ovvero paralleli alle facciate degli edifici, per il Set 2. Per il Set 1 sono state acquisite 109 immagini (62 dell'area riportata nella sezione "Schema delle prese" e 47 delle zone immediatamente vicine, di completamento) con la camera Nikon KeyMission 360 e 110 immagini (63 dell'area riportata nella sezione "Schema delle prese" e 47 delle zone immediatamente vicine, di completamento) con le camere GoPro MAX 360 e Ricoh Theta Z1; per il Set 2 sono state acquisite 57 immagini con la camera Nikon KeyMission 360 e 56 immagini con le camere GoPro MAX 360 e Ricoh Theta Z1.</p>
Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity"
<p>Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity".</p> <p>Data and further information at GitHub repository https://github.com/kreutz-lab/dia-benchmarking (DOI: 10.5281/zenodo.6371925)</p>
Dataset of UAV thermal video sequences with annotations for MOTS benchmarking
<p>Instance segmentation dataset created for the research 'Monitoring Mammalian Herbivores via Convolutional Neural Networks implemented on Thermal UAV imagery'. It comprises 959 frames, 20.647 masks, and 239 tracks, and consists of 7 video sequences depicting aerial thermal imagery of cattle collected with a UAV (Parrot ANAFI Thermal) in two outdoor farms in the Netherlands. Data were acquired at three temperatures (10ºC, 19ºC, and 26.5ºC), under sunny and overcast weather conditions, at various angles of inclination (including nadir), and at heights ranging between 8-28 meters. Ground truth was labeled manually with the Computer Vision Annotation Tool <em>CVAT</em>.</p>
A new remote sensing benchmark dataset for machine learning applications : MultiSenGE
<p>[UPDATE] You can now access MultiSen (GE and NA) collection though this portal : <a href="https://doi.theia.data-terra.org/ai4lcc/?lang=en">https://doi.theia.data-terra.org/ai4lcc/?lang=en</a></p> <p>MultiSenGE is a new large-scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. It contains 8,157 patches of 256 * 256 pixels for Sentinel-2 L2A, Sentinel-1 GRD and a regional LULC topographic regional database. </p> <p>Every file has a specific nomenclature :</p> <ul> <li>Sentinel-1 patches: {tile}_{date}_S1_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Sentinel-2 patches: {tile}_{date}_S2_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Ground reference patches: {tile}_GR_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>JSON Labels: {tile}_{x-pixel-coordinate}_{y-pixel-coordinate}.json</li> </ul> <p>where <em>tile</em> is the Sentinel-2 tile number, <em>date</em> the date of acquisition of the patch, <em>x-pixel-coordinate</em> and <em>y-pixel-coordinate</em> are the coordinates of the patch in the tile.</p> <p>In addition, you can find a set of useful python tools for extracting information about the dataset on Github : <a href="https://github.com/r-wenger/MultiSenGE-Tools">https://github.com/r-wenger/MultiSenGE-Tools</a></p> <p>First experiments based on this <em>dataset</em> is in press in ISPRS Annals : <strong>Wenger, R., </strong>Puissant, A., Weber, J., Idoumghar, L., and Forestier, G.: MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 635–640, https://doi.org/10.5194/isprs-annals-V-3-2022-635-2022, 2022.</p> <p>Due to the large size of the dataset, you will only find the associated JSON files on this Zenodo repository. To download the Sentinel-1, Sentinel-2 patches and the reference data, please do so via these links: </p> <ul> <li>Sentinel-1 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz</a></li> <li>Sentinel-2 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz</a></li> <li>Ground reference patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz</a></li> <li>JSON files for each patch: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz</a></li> </ul>
TocoDecoy: a new approach to design unbiased datasets for training and benchmarking machine-learning scoring functions
<p>This dataset file contains TocoDecoy datasets generated based on the targets and active ligands of LIT-PCBA.</p> <p>1_property_filtered.zip :</p> <ul> <li>TD set: the ligand file name, 2D T-sne vectors, Smiles, molecular weight (MW), Wildman-Crippen partition coefficient (log P), number of rotatable bonds (RB), number of hydrogen-bond acceptors (HBA), number of hydrogen-bond donors (HBD), number of halogens (HAL), topology similarities of decoys to the seed active ligands, active label (active or inactive) and training set label (whether belongs to training set or test set) <strong>OF active ligands and their topologically dissimilar decoys</strong></li> <li>CD set: the decoy conformations with low docking scores generated by docking active ligands into protein pockets using Glide, Schrödinger.</li> </ul> <p> </p>
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
<p>Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding and requires careful hyperparameter tuning. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. It consists of a set of patches optimized to generalize across different models and applied to ImageNet data after preprocessing them with affine transformations. This process enables an approximate yet faster robustness evaluation, leveraging the transferability of adversarial perturbations.</p> <p>We release our dataset as a set of folders indicating the patch target label (e.g., `banana`), each containing 1000 subfolders as the ImageNet output classes.</p> <p>An example showing how to use the dataset is shown below.</p> <pre><code class="language-python"># code for testing robustness of a model import os.path from torchvision import datasets, transforms, models import torch.utils.data class ImageFolderWithEmptyDirs(datasets.ImageFolder): """ This is required for handling empty folders from the ImageFolder Class. """ def find_classes(self, directory): classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir()) if not classes: raise FileNotFoundError(f"Couldn't find any class folder in {directory}.") class_to_idx = {cls_name: i for i, cls_name in enumerate(classes) if len(os.listdir(os.path.join(directory, cls_name))) > 0} return classes, class_to_idx # extract and unzip the dataset, then write top folder here dataset_folder = 'data/ImageNet-Patch' available_labels = { 487: 'cellular telephone', 513: 'cornet', 546: 'electric guitar', 585: 'hair spray', 804: 'soap dispenser', 806: 'sock', 878: 'typewriter keyboard', 923: 'plate', 954: 'banana', 968: 'cup' } # select folder with specific target target_label = 954 dataset_folder = os.path.join(dataset_folder, str(target_label)) normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) transforms = transforms.Compose([ transforms.ToTensor(), normalizer ]) dataset = ImageFolderWithEmptyDirs(dataset_folder, transform=transforms) model = models.resnet50(pretrained=True) loader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=5) model.eval() batches = 10 correct, attack_success, total = 0, 0, 0 for batch_idx, (images, labels) in enumerate(loader): if batch_idx == batches: break pred = model(images).argmax(dim=1) correct += (pred == labels).sum() attack_success += sum(pred == target_label) total += pred.shape[0] accuracy = correct / total attack_sr = attack_success / total print("Robust Accuracy: ", accuracy) print("Attack Success: ", attack_sr) </code></pre> <p> </p>
Benchmark Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
<p>This archive contains three benchmark datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.</p>
MELA Dataset: A Benchmark for Mediastinal Lesion Analysis (Training Set Part 1)
<p>MELA dataset is a benchmark for developing algorithms on mediastinal lesion analysis. We hope this large-scale dataset could facilitate the research and application of automatic mediastinal lesion detection and diagnosis. </p> <p>MELA dataset contains 1100 CT scans collected from patients with one or more lesions in the mediastinum. The MELA dataset is split into a subset of 770 CT scans for training, a subset of 110 CT scans for validation, and a test set of 220 CT scans for evaluation.</p> <p>Due to the size limit of zenodo.org, we split the MELA training set into 3 parts; this is the Training Set Part 1 of MELA dataset, including 260 CTs. Files include:</p> <ol> <li>Train1.zip: 130 CTs in NII format (nii.gz).</li> <li>Train2.zip: 130 CTs in NII format (nii.gz).</li> </ol>
MELA Dataset: A Benchmark for Mediastinal Lesion Analysis (Training Set Part 2)
<p>MELA dataset is a benchmark for developing algorithms on mediastinal lesion analysis. We hope this large-scale dataset could facilitate the research and application of automatic mediastinal lesion detection and diagnosis. </p> <p>MELA dataset contains 1100 CT scans collected from patients with one or more lesions in the mediastinum. The MELA dataset is split into a subset of 770 CT scans for training, a subset of 110 CT scans for validation, and a test set of 220 CT scans for evaluation.</p> <p>Due to the size limit of zenodo.org, we split the MELA training set into 3 parts; this is the Training Set Part 2 of MELA dataset, including 260 CTs. Files include:</p> <ol> <li>Train3.zip: 130 CTs in NII format (nii.gz).</li> <li>Train4.zip: 130 CTs in NII format (nii.gz).</li> </ol>
MELA Dataset: A Benchmark for Mediastinal Lesion Analysis (Training Set Part 3)
<p>MELA dataset is a benchmark for developing algorithms on mediastinal lesion analysis. We hope this large-scale dataset could facilitate the research and application of automatic mediastinal lesion detection and diagnosis. </p> <p>MELA dataset contains 1100 CT scans collected from patients with one or more lesions in the mediastinum. The MELA dataset is split into a subset of 770 CT scans for training, a subset of 110 CT scans for validation, and a test set of 220 CT scans for evaluation.</p> <p>Due to the size limit of zenodo.org, we split the MELA training set into 3 parts; this is the Training Set Part 3 of MELA dataset, including 250 CTs. Files include:</p> <ol> <li>Train5.zip: 130 CTs in NII format (nii.gz).</li> <li>Train6.zip: 120 CTs in NII format (nii.gz).</li> </ol>
Zagreb Calibration Benchmark Dataset
<p>Zagreb Calibration Benchmark Dataset is multi-modal high-resolution dataset geared at evaluating calibration solutions.</p>
Dataset for Surrogate Model Benchmarking for Dynamic Climate Impact Models
<p>The data represents time series of seasonal weather forecasts for rainfall and temperature. The dataset contains 10 forecasts of 6-month horizon from, two per year, from 2017 to 2021; start dates January 1 and July 1, respectively. Each forecast comprises 50 ensemble members. In total, this sums up to 91300 data points, each containing daily average rainfall, temperature.</p> <p>Each sample (row) comprises following features (columns):</p> <ul> <li><strong>datetime</strong>: Date of the forecast sample.</li> <li><strong>forecast</strong>: Identifier of the ensemble member, i.e. integer between 1 and total number of ensemblemembers.</li> <li><strong>precip</strong>: Averaged daily rainfall forecast in millimeters.</li> <li><strong>temp</strong>: Averaged daily temperature forecast in degree Celsius.</li> </ul> <p>Dataset created by The Weather Company, an IBM business. This service is based on data and products of the European Center for Medium-range Weather Forecasts (ECMWF-Archive and ECMWF-RT). Generated using Copernicus Climate Change Service information [2019 and ongoing]. ECMWF Archive data published under a Creative Commons Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/<br> Disclaimer: Neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.