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369 results for “Datasets Benchmarking”
Dataset for benchmark models (Final 2017-10-19)
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Designing a single cell ATAC-Seq (scATAC-Seq) dataset to benchmark scATAC-Seq analysis methods
GEO Series GSE223319. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
OP3: single-cell multimodal dataset in PBMCs for perturbation prediction benchmarking
GEO Series GSE279945. Homo sapiens. 102 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
International Virtual Permeability Benchmark 3D Image Dataset of the Fiber Tow Microscopic Sample
<p>This microscopic 3D sample representing a part of the fiber tow of the woven composite reinforcement was used in the first phase of the international Virtual permeability benchmark. Its results are reported in [1-3]. While the permeability had been investigated before for various porous media, this benchmark exercise was the first contribution in the field of real fibrous microstructures.</p> <p>The 3D image was extracted from a 3D X-ray microscope scan of a composite sample prepared at Leibniz-Institut für Verbundwerkstoffe GmbH (IVW, Kaiserslautern, Germany). The segmentation of the 3D image was performed at the Research Institute in Civil Engineering and Mechanics (GeM) of Ecole Centrale de Nantes (Nantes, France).<br> <br> The <em><strong>segmented</strong></em> binary volume image data (.raw file) has the following characteristics:</p> <ul> <li>dimensions of 1003 x 124 x 973 voxels;</li> <li>0.521<sup>3</sup> µm<sup>3</sup> voxel size;</li> <li>8-bit;</li> <li>little-endian byte order.</li> </ul> <p>The provided corresponding <em><strong>non-segmented</strong></em> volume image has the following characteristics:</p> <ul> <li>dimensions of 1003 x 124 x 973 voxels;</li> <li>0.521<sup>3</sup> µm<sup>3</sup> voxel size;</li> <li>16-bit;</li> <li>little-endian byte order.</li> </ul> <p>The microscopic volume contains about 400 fibers of a single tow and covers the entire tow through-thickness dimension. Its average fiber volume fraction is 56.5%, though it varies locally along the fiber direction. The structure of the tow represents 3 yarns twisted together. The tow originates from the glass twill-weave fabric HexForce 01102 by HEXCEL.</p> <p> </p> <p>[1] <em>Elena Syerko, Tim Schmidt, David May, Christophe Binetruy, Suresh G. Advani et al. Benchmark Exercise on Image-Based Permeability Determination of Engineering Textiles: Microscale Predictions // Composites Part A: Applied Science and Manufacturing – 2023: 107397.</em></p> <p>[2] <em>Elena Syerko, Christophe Binetruy, Tim Schmidt, David May. </em> <em>Micro-Scale Results from the Benchmark Exercise on the Image-Based Permeability Prediction of Composite Reinforcements // Proceedings of </em> <em>IBSim-4i – Image-Based Simulation for Industry 2022</em><em>, London, UK – 2022.</em></p> <p>[3] <em> David May, Elena Syerko, Tim Schmidt, Christophe Binetruy, Luisa Rocha da Silva, Stepan Lomov, Suresh Advani. Benchmarking Virtual Permeability Predictions of Real Fibrous Microstructure // Proceedings of the American Society for Composites – 36th Technical Conference, Texas, USA – 2021.</em></p> <p> </p>
Dump of RDF dataset used by PO for a Graph Database benchmark, 2022
<p>This dataset represents a newer version of the NQUADS files in RDF from Publication Offices used for benchmarking graph databases. </p> <p> </p>
Dataset for "A random-walk benchmark for single-electron circuits"
<p>Data used to generate the figures in the publication "A random-walk benchmark for single-electron circuits".</p>
ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints. Datasets, Benchmark Results, and Torch Files.
<div> <div># ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints. Datasets, Benchmark Results, and Torch Files.</div> <br> <div>## Table of Contents</div> <br> <div>- [Overview](#overview)</div> <div>- [Folder Structure](#folder-structure)</div> <div>- [Contents](#contents)</div> <div>- [Licenses](#licenses)</div> <br> <div>## Overview</div> <div>This project contains three datasets along with stored results from the conducted benchmark analysis and torch files for running or reproducing active learning experiments.</div> <br> <div>## Folder Structure</div> <br> <div>```plaintext</div> <div>conBatchBAL_datasets/</div> <div>├── benchmark_results/</div> <div>├── benchmark_torch_files/</div> <div>├── build6k/</div> <div>├── mnist6k/</div> <div>└── nieman17k/</div> <div>```</div> <br> <div>## Contents:</div> <div>- benchmark_results/: This directory contains the results and config files for reproducing the experiments presented in the paper.</div> <br> <div>- benchmark_torch_files/: This folder contains the required torch (and json) files to run/reproduce active learning experiments.</div> <br> <div>- build6k/: This folder contains approximately 6000 aerial images of buildings in Rotterdam with their corresponding energy efficiency class and geolocation.</div> <br> <div>- mnist6k/: This folder contains approximately 6000 images of digits *artificially* geolocated in Rotterdam. The geolocations correspond to the buildings contained on the *build6k* dataset.</div> <br> <div>- nieman17k/: This folder contains approximately 17000 aerial images of buildings in Rotterdam with their corresponding typology class and geolocation.</div> <br> <div>**Additional readme files are included in each directory.**</div> <br> <div>## Licenses</div> <br> <div>- build6k/</div> <div>The build6k dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) [LICENSE](https://creativecommons.org/licenses/by/4.0/), allowing for use, distribution, and modifications with proper attribution.</div> <br> <div>- mnist6k/</div> <div>The mnist6k dataset is released under the CC BY-SA 3.0 [LICENSE](https://creativecommons.org/licenses/by-sa/3.0/).</div> <br> <div>- nieman17k/</div> <div>The nieman17k dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) [LICENSE](https://creativecommons.org/licenses/by/4.0/), allowing for use, distribution, and modifications with proper attribution.</div> <br> <div>- Benchmark Results and Torch Files</div> <div>The benchmark results and Torch files generated as part of this project are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) [LICENSE](https://creativecommons.org/licenses/by/4.0/), allowing for use, distribution, and modifications with proper attribution.</div> <br> <div>**License details are included separately in each directory**</div> </div>
A benchmark dataset of Solidity smart contracts
<p>A benchmark dataset contains 4,364 real-world Solidity smart contracts, which are manually labeled with ten types of vulnerabilities.</p> <ul> <li>DC (DelegateCall).</li> </ul> <p>The <em>address.delegatecall() </em>function allows a smart contract to dynamically load external contracts from <em>address</em> at runtime. If the attacker can control the external contract and affect the current contract status, the contract is vulnerable to DC.</p> <ul> <li>IOU (Arithmetic/Integer Overflow and Underflow).</li> </ul> <p>An arithmetic overflow or underflow, often called Integer Overflow or Underflow (IOU), occurs when an arithmetic operation attempts to create a numeric variable value that is larger than the maximum value or smaller than the minimum value of the variable type. If the arithmetic operation may pass a variable type’s maximum or minimum value and is performed without using SafeMath, the contract is vulnerable to IOU.</p> <ul> <li>NC (Nested Call).</li> </ul> <p>The function containing the loop has a high risk of exceeding its gas limitation and causing an out-of-gas error. If the attacker can control the loop iteration and causes the out-of-gas error, the contract is vulnerable to NC, </p> <ul> <li>RE (Reentrancy).</li> </ul> <p>The contract vulnerable to RE uses the <em>call()</em> function to transfer ether to an external contract. The external contract can reenter the vulnerable contract by fallback function. If the state variable change is after the <em>call()</em> function, the reentrance will cause status inconsistency. </p> <ul> <li>TD (Timestamp Dependency).</li> </ul> <p>The contract uses the <em>timestamp</em> as the deciding factor for critical operations, e.g., sending ether. If the attacker can get ether from the contract by manipulating the timestamp or affecting the critical operations, the contract is vulnerable to TD.</p> <ul> <li>TO (TxOrigin).</li> </ul> <p>If the contract only uses <em>tx.origin</em> to verify the caller's identification for critical operations, it is vulnerable to TO.</p> <ul> <li>TOD (Transaction Order Dependency).</li> </ul> <p>The contract may send out ether differently according to different values of a global state variable or different balance values of the contract. If the attackers can get ether from the contract by manipulating the transaction sequences, the contract is vulnerable to TOD.</p> <ul> <li>UcC (Unchecked Call).</li> </ul> <p>The contract uses the function <em>call()</em> or <em>send()</em> without result checking. If the <em>send()</em> or <em>call() </em> function fails and leads to status inconsistency, the contract is vulnerable to UcC.</p> <ul> <li>Unprotected Suicide).</li> </ul> <p>If an attacker can self-destruct the contract by calling the <em>selfdestruct</em>(address) function, the contract is vulnerable to UpS. </p> <ul> <li>FE (Frozen Ether).</li> </ul> <p>If the contract can receive ether but cannot transfer it by itself, it is vulnerable to FE. <br> </p> <p><strong>For the purpose of protection for smart contracts, the dataset can be available after request.</strong></p>
HySpecNet-11k: A large-scale hyperspectral benchmark dataset
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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.