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Dataset results
1,549 results for “benchmarks”
Organelle Genome Utilities benchmark data
<p>test_Lamiaceae.zip: Benchmark result of Lamiaceae plastid data. Includes original sequences and analyze result from OGU.<br>test_1M_out.zip: Benchmark result of 1 million random GenBank records. Includes original sequences and analyze result from OGU.<br>test_308_angiosperm.zip: Benchmark result of 308 angiosperm family's plastid genomes. Includes original sequences and analyze result from OGU.<br>test_rodents.zip: Benchmark result of 307 mitochondria genomes from rodents. Includes original sequences and analyze result from OGU.<br>CDS and spacer tree of angiosperm plastid data.zip: Sequences and maximum likelihood trees of angiosperm plastid CDS and spacers. Built with IQTREE2.</p> <p>OGU-source code.zip: three versions of source code of OGU. </p>
Dataset of a multiphase flow and reactive transport benchmark for radioactive waste disposal
<p>The files include the full dataset (tables and figures) of the comparion the results of a multiphase flow and reactive transport<br>benchmark for radioactive waste disposal. The codes INVERSE-FADES-CORE V2, DuMuX , TOUGHREACT and<br>iCP were benchmarked with 6 test cases of increasing complexity, starting with conservative tracer transport under variably<br>unsaturated conditions and ending with water flow, gas diffusion, minerals and cation exchange.</p>
Metadata dataset: benchmark datasets for modelling
<p>The main goal of the Soil Mission MARVIC project is to develop a framework for designing harmonized context-specific Monitoring, Reporting and Verification (MRV) systems for carbon farming, in support of the EU Carbon Removals and Carbon Farming (CRCF) regulation.</p> <p>The scope of this report (MARVIC Deliverable 2.1) is to provide a metadata dataset of benchmark sites (BS) that are relevant to the calibration and validation of models used within the MARVIC test cases. The dataset provided by Deliverable 2.1 describes the main characteristics of each site, such as pedoclimatic conditions, management practices applied, soil chemical, physical, and biological parameters, and details the measured variables that have been collected over time. </p> <p>This dataset of metadata is used within MARVIC to determine which modelling approaches can be used in each of the test cases across work packages. </p>
OpenMapCD: A Multimodal Benchmark Dataset for Change Detection Between Optical Remote Sensing and Map Data
<p><strong>Overview: </strong></p> <ol> <li>OpenMapCD, the <strong>first large-scale multimodal dataset</strong> for change detection on optical remote sensing imagery and map (OpenStreetMap) data, <strong>supporing basic binary change detection and further semantic change detection</strong></li> <li>OpenMapCD is highly geographically diverse, with <strong>1288</strong> benchmark samples with 1024x1024 pixels from <strong>40 </strong>regions across six continents and out-of-distribution data in two areas in Japan</li> <li>Advancing land-cover mapping, binary change detection and semantic change detection tasks, and GIS system updating<br><br></li> </ol> <p><strong>Research Paper: <br></strong></p> <ul> <li>Arxiv paper: <a href="https://arxiv.org/abs/2310.02674v3">https://arxiv.org/html/2310.02674v3</a></li> <li>TGRS paper: <a href="https://ieeexplore.ieee.org/document/10551264">https://ieeexplore.ieee.org/document/10551264</a></li> </ul> <p><strong><br>Project Page:</strong><br>The benchmark code is available at: <a href="https://github.com/ChenHongruixuan/ObjFormer">https://github.com/ChenHongruixuan/ObjFormer</a><br><br><strong>Reference:</strong></p> <pre><code>@ARTICLE{Chen2024ObjFormer, author={Chen, Hongruixuan and Lan, Cuiling and Song, Jian and Broni-Bediako, Clifford and Xia, Junshi and Yokoya, Naoto}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer}, year={2024}, volume={62}, number={}, pages={1-22}, doi={10.1109/TGRS.2024.3410389} }</code></pre>
Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling (Simulation results)
<p>Simulation result data for Environment Agency benchmark test 5, Thamesmead hypothetical flood, and Carlisle 2005 case studies, using uniform DG2, adaptive MWDG2, adaptive HWFV1, non-uniform DG2, non-uniform FV1 and non-uniform ACC solvers. </p> <p>Model results are archived in 3 zip files:</p> <ul> <li>EA5.zip contains results of Environment Agency test 5 (Néelz and Pender, 2013)</li> <li>Thamesmead.zip contains results of Thamesmead hypothetical flood (Liang et al., 2008)</li> <li>Carlisle.zip contains results of Carlisle 2005 flooding (Neal et al., 2009)</li> </ul> <p>The results are stored with the following file extensions:</p> <ul> <li>".wd" for 2D flood inundation maps in ESRI ASCII format</li> <li>".stage" for water depth or water level time-series at staging points in tabulated text format</li> <li>".velocity" for velocity time-series at staging points in tabulated text format</li> </ul> <p>Model outputs are stored under directories named for each solver.</p> <p><strong>References</strong></p> <p>Néelz, S., & Pender, G. (2013). Benchmarking the latest generation of 2D hydraulic modelling packages. <em>Environment Agency: Bristol, UK</em>.</p> <p>Liang, Q., Du, G., Hall, J. W., & Borthwick, A. G. (2008). Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver. <em>Journal of Hydraulic Engineering</em>, <em>134</em>(11), 1603–1610. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:11(1603)</p> <p>Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., & Horritt, M. S. (2009). Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations. <em>Journal of Hydrology</em>, <em>368</em>(1–4), 42–55. https://doi.org/10.1016/j.jhydrol.2009.01.026</p> <p> </p>
CLDF dataset derived from List and Prokić's "Benchmark Database of Phonetic Alignments" from 2014
<p>Cite the source of the dataset as:</p> <blockquote> <p>List, Johann-Mattis and Jelena Prokić. (2014). A benchmark database of phonetic alignments in historical linguistics and dialectology. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC), 26 — 31 May 2014, Reykjavik. 288-294.</p> </blockquote>
Benchmark dataset for arby
<p>Datasets for the benchmarks performed on the reduce_basis function of the arby project <a href="https://arby.readthedocs.io/">https://arby.readthedocs.io/</a></p>
New upper bounds for some instances from benchmark for vector packing problem
<p>This dataset is a result of the research: Đorđe Stakić, Miodrag Živković, Ana Anokić, "A Reduced Variable Neighborhood Search Approach to the Heterogeneous Vector Bin Packing Problem", Information Technology and Control, 2021, 50(4), 808-826, <a href="https://doi.org/10.5755/j01.itc.50.4.29009">https://doi.org/10.5755/j01.itc.50.4.29009</a> Files are given by algorithm described in it. </p> <p>This dataset consists of 14 solutions with better bounds for instances described in paper: Heßler, K., Gschwind, T., Irnich, S. Stabilized branch-and-price algorithms for vector packing problems. European Journal of Operational Research, 2018, 271(2), 401-419. <a href="https://doi.org/10.1016/j.ejor.2018.04.047">https://doi.org/10.1016/j.ejor.2018.04.047</a> </p> <p>File structure: </p> <p>Instance name: UB: solution (bins with indices of items)</p> <ul> <li>CL_04_100_06: 627</li> <li>CL_04_100_08: 642</li> <li>CL_04_200_01: 1293</li> <li>CL_05_100_06: 314</li> <li>CL_05_100_08: 321</li> <li>CL_05_100_10: 327</li> <li>CL_05_200_02: 627</li> <li>CL_05_200_03: 633</li> <li>CL_05_200_04: 630</li> <li>CL_05_200_05: 632</li> <li>CL_05_200_06: 627</li> <li>CL_05_200_07: 634</li> <li>CL_05_200_08: 635</li> <li>CL_05_200_10: 632</li> </ul>
CrowdSpeech and Vox DIY: Benchmark Dataset for Crowdsourced Audio Transcription
<p>We collect and release CrowdSpeech — the first publicly available large-scale dataset of crowdsourced audio transcriptions. e show its applicability on an under-resourced language by constructing VoxDIY — a counterpart of CrowdSpeech for the Russian language.</p>
zEPHYR - Large On Shore Wind Turbine Benchmark
<p>Large On Shore Wind Turbine Benchmark - This benchmark collects data for the validation of wind turbine noise prediction methods to be applied in realistic weather conditions. It includes metmast data for the weather prediction model validation, acoustic measurements and an approached model of the SWT2.3-93 wind turbine used during the test campaign, as well as the corresponding<br> CAD.</p>
360 MEMEX Benchmarks: Lisbon
<p>This is part of a set of synchronized images recorded at three major cities (Barcelona, Lisbon and Paris). Images are uploaded to the Mapillary server and accessed by the scripts provided in https://gitlab.com/feriret/memex-benchmarking</p> <p> </p> <p>Do not hesitate to contact the authors for any further information</p>
360 MEMEX Benchmarks: Paris
<p>This is part of a set of synchronized images recorded at three major cities (Barcelona, Lisbon and Paris). Images are uploaded to the Mapillary server and accessed by the scripts provided in https://gitlab.com/feriret/memex-benchmarking</p> <p> </p> <p>Do not hesitate to contact the authors for any further information</p>
The LUBM4OBDA Benchmark for Tabular Sources
<p>The LUBM4OBDA Benchmark for tabular sources extends <a href="http://github.com/oeg-upm/lubm4obda">LUBM4OBDA</a>, which originally considered relational databases, to tabular data. It includes:</p> <ul> <li>The data in CSV and Apache Parquet formats for scaling factor 1, 10, 100 and 1000.</li> <li>The RML mappings (without meta knowledge).</li> </ul>
The Robot Tracking Benchmark (RTB)
<p>The Robot Tracking Benchmark (RTB) is a synthetic dataset that facilitates the quantitative evaluation of 3D tracking algorithms for multi-body objects. It was created using the procedural rendering pipeline BlenderProc. The dataset contains photo-realistic sequences with HDRi lighting and physically-based materials. Perfect ground truth annotations for camera and robot trajectories are provided in the BOP format. Many physical effects, such as motion blur, rolling shutter, and camera shaking, are accurately modeled to reflect real-world conditions. For each frame, four depth qualities exist to simulate sensors with different characteristics. While the first quality provides perfect ground truth, the second considers measurements with the distance-dependent noise characteristics of the Azure Kinect time-of-flight sensor. Finally, for the third and fourth quality, two stereo RGB images with and without a pattern from a simulated dot projector were rendered. Depth images were then reconstructed using Semi-Global Matching (SGM).</p> <p>The benchmark features six robotic systems with different kinematics, ranging from simple open-chain and tree topologies to structures with complex closed kinematics. For each robotic system, three difficulty levels are provided: easy, medium, and hard. In all sequences, the kinematic system is in motion. While for easy sequences the camera is mostly static with respect to the robot, medium and hard sequences feature faster and shakier motions for both the robot and camera. Consequently, motion blur increases, which also reduces the quality of stereo matching. Finally, for each object, difficulty level, and depth image quality, 10 sequences with 150 frames are rendered. In total, this results in 108.000 frames that feature different kinematic structures, motion patterns, depth measurements, scenes, and lighting conditions. In summary, the Robot Tracking Benchmark allows to extensively measure, compare, and ablate the performance of multi-body tracking algorithms, which is essential for further progress in the field.</p>
The ICDAR 2003 Informal Competition for the Recognition of On-line Words: The Unipen-ICROW-03 benchmark set - Version 0.0
<p>Proposal for an informal benchmark on word recognition. See for the related ImUnipen collection<br> of word images from on-line vectorial handwriting data: https://zenodo.org/record/1195059</p> <p>At the time (ICDAR 2003) there was not a lot of interest so the project was not pursued.</p> <p>Lambert Schomaker - February 2023</p> <p>_______________________________________________________________________________</p> <p>The ICDAR 2003 Informal Competition for the Recognition of On-line Words:<br> The Unipen-ICROW-03 benchmark set <br> Version 0.0</p> <p>Lambert Schomaker / International Unipen Foundation</p> <p>The ICROW suite of test files for the recognition of isolated on-line<br> free-style (handprint, mixed and cursive) words has been<br> composed. Different tablets, nationalities and languages<br> are involved. Only the ASCII set is used within word labels.</p> <p>The set contains:</p> <p> 13119 written words<br> 884 unique lexical word entries<br> 72 writers </p> <p>Language: Dutch, English, Italian.<br> Nationalities: Dutch, Irish, Italian, + mixed</p> <p>The benchmark test is a good estimator for <br> "walk-up" recognition performance.</p> <p>[Note: some of the writers (NIC-Pc95*.dat set) are present in the<br> UNIPEN R01/V07 distribution, but the actual words are unseen <br> outside of the Int. Unipen Foundation.]</p> <p>Please note the Copyright notice in the <br> accompanying file 'Copyright'</p> <p>Wed Jul 16 21:20:10 CEST 2003</p> <p>Lambert Schomaker</p> <p>---------------------------------------------------------------------------</p> <p>Instructions for the ICDAR 2003 informal competition for<br> the recognition of on-line words.</p> <p>1 - unpack the .tgz file<br> 2 - use the UNIPEN files as input for your recognizer.<br> 3 - report, for each writer, a file <writer-id>.res</p> <p> Example: do-my-recognizer < NIC-Hi93b-marc.dat > NIC-Hi93b-marc.res</p> <p>Format of the .res file.</p> <p>No XML for this moment: simplicity does it.</p> <p>We assume that the recognizer is able to produce a top-10 list<br> of likely words, sorted from most likely to least likely.<br> The output for each word is on a single line. The correct<br> target word is in the first column.</p> <p><targetword 1> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp><br> <targetword 2> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp></p> <p>Example with two words:</p> <p>summertime slumbertime slipknot summertime somatome spumante simulative semitone schoolmate sermonette semimature<br> Aberdeen Adamson Aberdeen Addison Armageddon Abyssinian Araban Albanian Alabamian Abraham Adelaide</p> <p><br> 4 - pack the *.res files in a .tgz or .zip file and send them<br> to schomaker@ai.rug.nl<br> All *.dat files need to be processed.</p> <p>LS.<br> </p> <p> </p>
Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations
<p>Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.</p> <p>For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.</p>
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>
ESSD benchmark output data
<p>This dataset contains the ESSD benchmark output data. Visit <a href="https://github.com/EUPP-benchmark/ESSD-benchmark">https://github.com/EUPP-benchmark/ESSD-benchmark</a> for more information.</p> <p>This dataset is provided as supplementary material with:</p> <ul> <li>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</li> </ul> <p>Please cite this article if you use (a part of) this code for a publication.</p> <p><br> Description of the methods used to get the ESSD benchmark output data<br> -------------------------------------------------------------------------------------------------------</p> <p> - ANET: NN post processing method using ensemble member encoders and dynamic attention<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-ANET">https://github.com/EUPP-benchmark/ESSD-ANET</a><br> <br> - AR-EMOS: EMOS with heteroscedastic autoregressive error adjustments<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-AR-EMOS">https://github.com/EUPP-benchmark/ESSD-AR-EMOS</a><br> <br> - ASRE: Accounting for systematic and representativeness errors<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-ASRE">https://github.com/EUPP-benchmark/ESSD-ASRE</a><br> <br> - DRN: Distributional regression network<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-DRN">https://github.com/EUPP-benchmark/ESSD-DRN</a><br> <br> - DVQR: D-vine copula based postprocessing<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-D-Vine-Copula">https://github.com/EUPP-benchmark/ESSD-D-Vine-Copula</a><br> <br> - EMOS: Ensemble model output statistics<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-EMOS">https://github.com/EUPP-benchmark/ESSD-EMOS</a><br> <br> - RC: Reliability Calibration (IMPROVER)<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-reliability-calibration">https://github.com/EUPP-benchmark/ESSD-reliability-calibration</a><br> <br> - MBM: Member-By-Member postprocessing<br> Repository: <a href="https://github.com/EUPP-benchmark/ESSD-mbm">https://github.com/EUPP-benchmark/ESSD-mbm</a></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>
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.