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7,195 results for “SET”
Data set for 2024GL112954
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Data set for 2024JC021754
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SEQC2 CNV benchmark sets
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Data Set from Quasi-static Cyclic Tests of Scoured 2×3 Pile-Group Foundations for Bridge Structures
<p>This data set is from Quasi-static cyclic tests of scoured 2×3 pile-group foundations for bridge structures, as a data paper submitted to Earthquake Spectra (Submission ID: EQS-24-0190)</p>
The NNLO and aN3LO MSHT20xNNPDF40 combined PDF sets
<p>These PDF sets are obtained by combining 100 Monte Carlo replicas obtained from the MSHT20 and NNPDF4.0 PDF sets. Variants coresponding to nnlo (QCD), an3lo (QCD), nnlo (QCD+QED), and an3lo (QCD+QED) are available in the LHAPDF6 format. All variants correspond to as=0.118.</p>
Integrals for "Quantum Simulations of Chemistry in First Quantization with any Basis Set"
<p>Integrals used to study scalings and carry out resource estimations. We did not upload the matrices for basis sets with 32000 functions because of large matrix sizes (8 GB per matrix). These integrals correspond to matrix elements of the Hamiltonian in the original basis (Eq. (II.1) and Eq.(III.6)) in this version of the manuscript: (<a href="https://arxiv.org/abs/2408.03145v2">arXiv:2408.03145v2</a> [quant-ph]), and not in the Pauli string representation. </p> <p>UPDATE: in the previous version of matrices in dual plane wave basis, kinetic energy + electron nuclear term gave the correct one-body contribution, but each individual file had an error in it. In this version, we updated the correct kinetic energy and electron nuclear matrices. Notice that electron-nuclear files are just diagonal elements so you need to convert it to a matrix to get correct kinetic + electron nuclear term. Also, dual plane waves electron-electron matrix includes a factor of 1/2 in it. FCIDUMP files are the same as in previous version.</p>
SC2ReSet: StarCraft II Esport Replaypack Set
<p><strong>SC2ReSet: StarCraft II Esport Replaypack Set</strong></p> <p>This repository contains raw data that was used to generate <a href="https://doi.org/10.5281/zenodo.5503997">SC2EGSet: StarCraft II Esport Game State Dataset</a></p> <p><strong>License Information</strong></p> <p>Raw StarCraft II data is subject to processing under the Blizzard end user license agreement (EULA), and in special cases Blizzard AI and Machine Learning License may be applied. Please refer to the materials listed below.</p> <ol> <li><a href="https://www.blizzard.com/en-us/legal/fba4d00f-c7e4-4883-b8b9-1b4500a402ea/blizzard-end-user-license-agreement">https://www.blizzard.com/en-us/legal/fba4d00f-c7e4-4883-b8b9-1b4500a402ea/blizzard-end-user-license-agreement</a></li> <li><a href="https://blzdistsc2-a.akamaihd.net/AI_AND_MACHINE_LEARNING_LICENSE.html">https://blzdistsc2-a.akamaihd.net/AI_AND_MACHINE_LEARNING_LICENSE.html</a></li> </ol> <p><strong>Citation</strong></p> <p>If you use this dataset you can chose to cite the projects and/or articles presented below.</p> <p><a href="https://www.researchgate.net/publication/373767449_SC2EGSet_StarCraft_II_Esport_Replay_and_Game-state_Dataset"><strong>Dataset Description Article</strong></a></p> <p>To cite the article that introduces <a href="https://doi.org/10.5281/zenodo.5575796">SC2ReSet</a> and <a href="https://doi.org/10.5281/zenodo.5503997">SC2EGSet</a> use this:</p> <blockquote> <p><br>@article{Białecki2023,<br> author = {Bia{\l}ecki, Andrzej<br> and Jakubowska, Natalia<br> and Dobrowolski, Pawe{\l}<br> and Bia{\l}ecki, Piotr<br> and Krupi{\'{n}}ski, Leszek<br> and Szczap, Andrzej<br> and Bia{\l}ecki, Robert<br> and Gajewski, Jan},<br> title = {SC2EGSet: StarCraft II Esport Replay and Game-state Dataset},<br> journal = {Scientific Data},<br> year = {2023},<br> month = {Sep},<br> day = {08},<br> volume = {10},<br> number = {1},<br> pages = {600},<br> issn = {2052-4463},<br> doi = {10.1038/s41597-023-02510-7},<br> url = {https://doi.org/10.1038/s41597-023-02510-7}<br>}<br> </p> </blockquote>
Model Counting Competition 2024: Full Instance Set
<p>The dataset contains all instances that the organizers of the competition received or collected during the preparation phase of the Model Counting Competition 2024. The dataset includes short benchmark descriptions (00_description.{pdf,txt}) by the submitters/collectors (00_authors.txt).</p> <p>For more details, we refer to the upcoming report.</p> <p>Contributors are listed in the dataset.</p> <p>[Version 2: We accidentally included Track2-4 instances instead of Track1 instances in Track1. We fixed this. Now, <span>mc2024-track1-mc_collected.tar</span> correctly contains the Track1 instances.]</p>
Data set of Magnetic stability of magnetite particles with single vortex structure during low temperature oxidation
<p>Data set of Magnetic stability of magnetite particles with single vortex structure during low temperature oxidation</p>
Three-Dimensional Segmentation Assisted with Clustering Analysis for Surface and Volume Measurements of Equine Incisor in Multidetector Computed Tomography Data Sets
<p>The dataset contains computed tomography (CT) images of head horses with annotations of 12 segments corresponding to areas with teeth. Imaged animals: 49 horses. Measured animal features such as surface area, and volume are included. The study was supported by the National Science Centre, Poland as a part of the project<br>Miniatura 6 No 2022/06/X/ST6/00431. </p>
Testing the Acoustic Localisation Positioning System-algorithm: Data sets
<p>The data is from 3 separate experimental setups: Tracking a device of constant speed using the Audio1 (single processor) software (B.1); and tracking a faster device using the al-Qt (multi processor) software (B.2) The experiments were also recorded on video. (see Video folder). For the experiments in B.1 a visualisation of the data has been made with the help of a MATLAB script which also included. The dimensions of the room and loudspeaker spacings were identical for both set-ups, namely 3 x 2 meters, with the loudspeakers at the corners of the rectangle. the loudspeakers were situated on the floor, approximately level with the tracked devices.</p> <p>Setup C collects debug log examples of experiments with very short capture cycles with 2 mics and 2 loudspeakers running ALPS al-Qt.</p> <p>The source code for Audio is available from: <a href="https://github.com/spatmus/alps/tree/master/Audio1">https://github.com/spatmus/alps/tree/master/Audio1</a><br> The source code for al-Qt is available from: <a href="https://github.com/spatmus/alps/tree/master/al-Qt">https://github.com/spatmus/alps/tree/master/al-Qt</a><br> A binary of al-Qt for macOS 11, can be found in the assets folder of the release <a href="https://github.com/spatmus/alps/releases">https://github.com/spatmus/alps/releases</a></p>
Data set (stata format) + do file for : Risk misperceptions of structured financial products with worst-of payout characteristics revisited
<p>This file contains the data set (in stata format) and do file associated with the paper entitled "Risk misperceptions of structured financial products with worst-of payout characteristics revisited"</p>
2021 DATA SET OF TRAFFIC COUNTES BASED ON CITIZEN SENSORS TO MONITOR DAILY TRAFFIC SOUTHERN INNER BYPASS RING IN LJUBLJANA
<p>Traffic counters for Southern inner bypass ring in Ljubljana, Slovenia. Corresponding roads: Aškerčeva road, Zoisova road, Karlovška road and Roška road.</p>
A set of aa changes located in spike
<p>This file contains an acknowledgement to the contributors of the SARS-CoV-2 sequences used in this study, in addition to a table documenting a set of aa changes located in Spike protein of SARS-COV-2</p>
Data set supplementing "Interactive versus static decision support tools for COVID-19: An experimental comparison"
<p>This is the de-identified data set used to conduct the analyses in the preprint submitted to JMIR Public Health and Surveillance under the title "Interactive versus static decision support tools for COVID-19: An experimental comparison" (https://doi.org/10.2196/preprints.33733).</p> <p>This data set contains the appraisal of 196 respondents (without decision support, with static or with interactive decision support) to appropriate social and care-seeking behavior for seven fictitious descriptions of patients. Additionally, this data contains participants'</p> <ul> <li>gender</li> <li>educational background</li> <li>previous medical training</li> <li>affinity for technology</li> <li>experience with COVID-19 related medical questions</li> <li>perceived threat from COVID-19</li> <li>answers to a COVID-19 knowledge test</li> <li>accuracy</li> <li>decision certainty (after deciding)</li> <li>mental effort</li> <li>ratings of <ul> <li>the decision support tool's usefulness </li> <li>ease of use</li> <li>trust</li> <li>future intention to use the tool</li> </ul> </li> </ul>
Data set 1997
<p>Data set of morbidity items from Canary Health Survey, 1997</p>
Anonymous Data Set
<p>Data set for "Handling Environmental Uncertainty in Design Time Access Control Analysis".</p>
Data Sets for Article: Exploration on Learning Molecular Docking with Deep Learning Models
<p>The MOL2 and CSV file of the clustered compounds from ChemDiv are available in <strong>Additional file 2</strong></p> <p>Docking scores of training set1 and the following traing set2 for each target were saved as csv files and provided in <strong>Additional file 3.</strong></p> <p>The SMILES, MOL2, SDF of DUD-E compounds and PDB of receptors used for validation are provided in <strong>Additional file 4</strong>.</p> <p>The SMILES of 500,000 compounds randomly selected from the ChEMBL database are provided in <strong>Additional file 5</strong></p> <p>The SMILES of compounds with activities from the ChEMBL database for each target are provided in <strong>Additional file 6</strong></p>
DOE1 and DOE2 - Sensor data set radial forging at AFRC testbed
<p><strong>Sensor data set, radial forging at AFRC testbed</strong></p> <p><strong>General information on the data set</strong></p> <p>Radial forging is widely used in industry to manufacture components for a broad range of sectors including automotive, medical, aerospace, rail and industrial. The Advanced Forming Research Centre (AFRC) at the University of Strathclyde, Glasgow, houses a GFM SKK10/R radial forge that has been used as a testbed for this project. Using two pairs of hammers operating at 1200 strokes/min, and providing a maximum forging force per hammer of 150 tons, the radial forge is capable of processing a range of metals, including steel, titanium and inconel. Both hollow and solid material can be formed with the added benefit of creating internal features on hollow parts using a mandrel. Parts can be formed at a range of temperatures from ambient temperature to 1200 °C.</p> <p>For the provided data set, a total of 95 parts were forged over two days of operation. Each part was forged with machine parameters outlined in the file <code>Met4FoF STRATH test plan v4.xlsx</code>. Deviations from the test plan occurred during the second batch and are outline in the sheet <code>Batch 2</code>. Each forged part was then measured using a CMM to provide dimensional output relative to a target specification and tolerances. T<strong>he CMM records 16 dimensional measurements</strong>.</p> <p>The aim of the measurement setup is to predict the quality (in terms of dimensional properties) of the forged part from the sensor measurements during the forging process.</p> <p><strong>Structure of the data</strong></p> <ul> <li>The sensor readings for the forging of the parts are provided in csv files in the folders <code>ScopeTraces_DOE1</code> and <code>ScopeTraces_DOE2</code> for DOE1 and DOE2 respectively. For DOE1 the scope traces are named <code>Scope0001.csv</code> to <code>Scope0050.csv</code>. For DOE2 there were deviations and the Scope Traces to billet mapping is outlined in <code>Met4FoF STRATH test plan v4.xlsx</code>. Each file contains the readings (columns) against time (rows). The first column displays the clock times (in milliseconds).</li> <li>A commentary on the sensors is provided in the file <code>ForgedPartDataStructureSummaryv3.xlsx </code>(NOTE: Some columns do not have sensor descriptions as this information is not available).</li> <li>The CMM data is provided in the file <code>CMM_DOE1.xlsx</code> and <code>CMM_DOE2.xlsx</code> for DOE1 and DOE2 respectively.</li> </ul>
Inhibitory control of frontal metastability sets the temporal signature of cognition
<p>Data and scripts used in the article "Inhibitory control of frontal metastability sets the temporal signature of cognition". The upload follows the structure of the article with one folder related to the monkey electrophysiology part (Fig. 1-4) and another one to the modelling part (Fig 5-7).</p> <p>Each folder contains a README file indicating the guidelines to access the data and use the code.</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.