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369 results for “Datasets Benchmarking”
Process Behavior Corpus and Benchmarking Datasets
<p>A corpus of process behaviors and benchmarking datasets for semantics-aware process mining tasks.</p> <p>Files:</p> <ul> <li><strong>process_behavior_corpus.csv</strong>: the text corpus, which contains the behavior allowed by process models as sequences of activities (column <em>string_traces)</em>.</li> <li><strong>T_SAD.csv:</strong> A benchmark dataset generated from the corpus to assess the following task: Given a trace σ, decide if σ is a valid execution of the underlying process or not, without knowing the behavior allowed in the process.<br>Each row contains a trace (column <em>trace</em>) with a corresponding label (column <em>anomalous</em>) indicating whether the trace represents a valid execution of the underlying process. The set of activities that can occur in the process are also given (column <em>unique_activities</em>).</li> <li><strong>A_SAD.csv:</strong> A benchmark dataset generated from the corpus to assess the following task: Given an eventually-follows relation ef = a ≺ b of<br>a trace σ, decide if ef represents a valid execution order of the two activities a and b that are executed in a process or not, without knowing the behavior allowed in the process.<br>Each row contains an eventually-follows relation (column <em>eventually_follows</em>) with a corresponding label (column <em>out_of_order</em>) indicating wether the two activities of the relation were executed in an invalid order (TRUE) or in a valid order (FALSE) according to the underlying process (model). The set of activities that can occur in the process are also given (column <em>unique_activities</em>).</li> <li><strong>S_NAP.csv:</strong> A benchmark dataset generated from the corpus to assess the following task: Given an event log L and a prefix p_k of length k, with 1 < k, predict the next activity a_k+1<br>Each row contains a trace prefix (column <em>prefix</em>) with a corresponding next activity (column <em>next</em>) indicating the activity that should be performed next after the last activity of the prefix according to the trace from which the prefix was generated. The set of activities that can occur in the process are also given (column <em>unique_activities</em>).</li> <li><strong>S-PMD.csv:</strong> A benchmark dataset generated from the corpus to assess the following tasks: <ul> <li>Given a set of possible activities (column <em>unique_activities</em>), generate a difectly follows graph (column <em>dfg</em>) that captures the trace semantics of the process model. </li> <li>Given a set of possible activities (column <em>unique_activities</em>), generate a simple process tree (column <em>pt</em>) that captures the trace semantics of the process model.</li> </ul> </li> </ul> <p>Reference and legal info:</p> <p>The corpus and the benchmark datasets are generated using the SAP-SAM dataset:</p> <p>Kampik, T., Warmuth, C., Sola, D., Schäfer, B., Axworthy, L., Ivarsson, E., Ouda, K., & Eickhoff, D. (2022). SAP Signavio Academic Models (0.5.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7012043" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7012043</a><br><br>The SAP-SAM dataset is published with a specific license (see "Rights"), which, therefore, also applies to the data published in this record.</p> <p><strong>THE DATASETS AND ASSOCIATED EVALUATION EXPERIMENTS ARE DESCRIBED IN <a href="https://arxiv.org/pdf/2407.02310">THIS</a> PAPER.</strong></p> <p><strong>IN <a href="https://github.com/a-rebmann/llms4pm">THIS</a> REPOSITORY YOU FIND THE CODE AND RAW RESULTS OF EVALUATION EXPERIMENTS USING VARIOUS OPEN SOUCE LLMs TO SOLVE THE TASKS</strong></p>
Datasets collected for benchmarking in spatial transcriptomics
<p>Datasets collected for benchmarking in spatial transcriptomics. In addition, the code for benchmarking (March 2025 version, svg-benchmark-main.zip) is also located here and can be accessed on the GitHub website <a href="https://github.com/XiDsLab/svg-benchmark">https://github.com/XiDsLab/svg-benchmark</a>.</p>
PhyClone: Benchmarking datasets
<p>Dataset archives for the data used in the PhyClone benchmarking experiments.</p>
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
<p><strong>Project Page</strong></p> <p><a href="https://open-earth-map.org/">https://open-earth-map.org/</a></p> <p><strong>Paper</strong></p> <p><a href="https://arxiv.org/abs/2210.10732">https://arxiv.org/abs/2210.10732</a></p> <p><strong>Overview</strong></p> <p>OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. OpenEarthMap consists of 5000 aerial and satellite images with manually annotated 8-class land cover labels and 2.2 million segments at a 0.25-0.5m ground sampling distance, covering 97 regions from 44 countries across 6 continents. OpenEarthMap fosters research including but not limited to semantic segmentation and domain adaptation. Land cover mapping models trained on OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications.</p> <p><strong>Reference</strong></p> <pre><code>@inproceedings{xia_2023_openearthmap, title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping}, author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6254-6264} }</code></pre> <p><strong>License</strong></p> <p>Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data <a href="https://open-earth-map.org/attribution.html">here</a>. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0</a> International License.</p> <p><strong>Note for xBD data</strong></p> <p>The RGB images of xBD dataset are not included in the OpenEarthMap dataset. Please download the xBD RGB images from <a href="https://xview2.org/dataset">https://xview2.org/dataset</a> and add them to the corresponding folders. The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders.</p> <p><strong>Code</strong></p> <p>Sample code to add the xBD RGB images to the distributed OpenEarthMap dataset and to train baseline models is available <a href="https://github.com/bao18/open_earth_map">here</a>.</p> <p><strong>Leaderboard</strong></p> <p>Performance on the test set can be evaluated on the <a href="https://codalab.lisn.upsaclay.fr/competitions/9121">Codalab webpage</a>.</p>
CTO Dataset: A Clinical Trial Outcome Benchmark
<p><strong>DEPRECATED: Please see new dataset link for more information: <a href="https://huggingface.co/datasets/chufangao/CTO">https://huggingface.co/datasets/chufangao/CTO</a> </strong></p> <p>Supplementary files and predicted labels for <a href="https://github.com/chufangao/CTOD/">https://github.com/chufangao/CTOD/</a></p> <p>Please see the github for additional information.</p> <p> </p>
scExtract's benchmark datasets
Open the record for dataset details and reuse information.
Datasets used to benchmark ShiftScan (DSF data analysis tool)
Open the record for dataset details and reuse information.
Model and Benchmark Datasets of PhenoScreen
Open the record for dataset details and reuse information.
Benchmarking datasets used in the manuscript "VeChat: Correcting errors in noisy long reads using variation graphs"
<p>This is the raw long-read sequencing data used for benchmarking experiments in the manuscript "VeChat: Correcting errors in noisy long reads using variation graphs". The name of the dataset is labeled as : simulated/real; ecoli/metagenome; ploidy/name; pacbio/ont; sequencing error rate; average sequencing coverage per haplotype.</p>
Datasets for MuSiC Deconvolution benchmarking tutorial suite.
<p>These are the datasets for the MuSiC deconvolution benchmarking tutorial suite.</p>
Materials Science Optimization Benchmark Dataset for Multi-fidelity Hard-sphere Packing Simulations
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, 438371 random hard-sphere packing simulations representing 279 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 can be 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.
Semantic Clones Dataset for TrainTicket Microservices benchmark
<p>It contains Component Call Graphs pairs for the analysis of TrainTicket benchmark (release 0.1.0).</p> <p>It classifies the pairs into semantic clones and non-clones. It contains 27,222 total CCGs pairs.</p> <p>This dataset is published as part of the paper titled as "Detecting Semantic Clones In Microservices Using Components".</p> <p> </p> <p>This version contains correction for 6 pairs are classified as clones. (Highlighted in yellow)</p> <p>This correction is detected using our automatic approach. Check it out in the paper.</p> <p> </p>
GraalVM Compiler Benchmark Results Dataset 2022
<p>This is the 2022 fragment of the GraalVM Compiler Benchmark Results Dataset. Check the other objects in this collection for documentation and scripting support.</p>
GraalVM Compiler Benchmark Results Dataset 2021
<p>This is the 2021 fragment of the GraalVM Compiler Benchmark Results Dataset. Check the other objects in this collection for documentation and scripting support.</p>
GraalVM Compiler Benchmark Results Dataset 2020
<p>This is the 2020 fragment of the GraalVM Compiler Benchmark Results Dataset. Check the other objects in this collection for documentation and scripting support.</p>
GraalVM Compiler Benchmark Results Dataset 2019
<p>This is the 2019 fragment of the GraalVM Compiler Benchmark Results Dataset. Check the other objects in this collection for documentation and scripting support.</p>
GraalVM Compiler Benchmark Results Dataset 2018
<p>This is the 2018 fragment of the GraalVM Compiler Benchmark Results Dataset. Check the other objects in this collection for documentation and scripting support.</p>
GraalVM Compiler Benchmark Results Dataset 2016
<p>This is the 2016 fragment of the GraalVM Compiler Benchmark Results Dataset. Check the other objects in this collection for documentation and scripting support.</p>
GraalVM Compiler Benchmark Results Dataset 2017
<p>This is the 2017 fragment of the GraalVM Compiler Benchmark Results Dataset. Check the other objects in this collection for documentation and scripting support.</p>
DECIMER V2 Benchmark Datasets
<p><strong>A comprehensive benchmark of the DECIMER Image Transformer was conducted using all publicly available OCSR benchmark datasets and DECIMER test datasets. </strong></p> <p><strong>USPTO: A set of 5,719 images of chemical structures and the corresponding MOL files (US Patent Office) obtained from the OSRA online presence </strong></p> <p><strong>UOB: The dataset of 5,740 images and MOL files of chemical structures developed by the University of Birmingham, United Kingdom, and published alongside MolRec</strong></p> <p><strong>CLEF: The Conference and Labs of the Evaluation Forum test set of 992 images and molfiles published in 2012 </strong></p> <p><strong>JPO: A subset (450 images and MOL files) of a dataset based on data from the Japanese Patent Office, obtained from the OSRA online presence. Note that this dataset contains many labels (sometimes with Japanese characters) and irregular features, such as variations in the line thickness. Additionally, some images have poor quality and contain a lot of noise.</strong></p> <p><strong>RanDepict250k: A set of 250,000 chemical structure depictions generated with RanDepict (1.0.8) using RanDepict’s depiction feature fingerprints to ensure diverse depiction parameters. None of the depicted molecules is present in the DECIMER training data. The images here are all 299 x 299 pixels in size.</strong></p> <p><strong>RanDepict250k_augmented: A set of the same 250,000 images from the RanDepict250k dataset. Additional augmentations (examples: mild rotation, shearing, insertion of labels and reaction arrows around the structures, insertion of curved arrows in the structure) were added to the images using RanDepict. The images here are all 299 x 299 pixels in size.</strong></p> <p><strong>DECIMER hand-drawn: A set of 5,088 chemical structure depictions which were manually drawn by a group of 24 volunteers. The drawn molecules have been picked using the MaxMin algorithm from all molecules in PubChem so that the set represents a big part of the chemical space.</strong></p> <p><strong>Indigo: 50,000 images generated by Staker et al. using Indigowhich were collected from the supplementary information. All images have a resolution of 224 x 224 pixels.</strong></p> <p><strong>USPTO_big: 50,000 images from the USPTO from Staker et al. which were collected from the supplementary information. All images have a resolution of 224 x 224 pixels.</strong></p> <p><strong>Img2Mol test set: A set of 25,000 chemical structure depictions used by Clévert et al. for testing . All images have a resolution of 224 x 224 pixels.</strong></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.