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150 results for “Data fusion”
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
Research data supporting "MicroRNA Detection by DNA-Mediated Liposome Fusion"
<p>Raw research data supporting the publication:</p> <p>Jumeaux C., et al., 2017, "MicroRNA detection by DNA-mediated liposome fusion", ChemBioChem</p> <p>DOI: 10.1002/cbic.201700592</p>
X-ray tomography (CT) image data of tungsten fusion energy heat exchange components
<p>X-ray tomography (CT) image data of tungsten fusion energy heat exchange components.</p> <p>The dataset includes images of four samples:</p> <ul> <li>CCFE_MB_ROI (Culham Centre for Fusion Energy thermal break concept monoblock, region of interest sample)</li> <li>IPP_Wf-Cu (Max-Planck-Institut für Plasmaphysik tungsten fibre / copper matrix coolant pipe)</li> <li>ITER_HHFT_ROI (ITER reference monoblock which has undergone high heat flux testing, region of interest sample)</li> <li>ITER_MB_ROI (ITER reference monoblock, region of interest sample)</li> </ul> <p>This data was used originally for the following publication (please cite if re-using the data) where further details on the data may be obtained:</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Each of the sample directories include reconstructed slices in Tiff format. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). CCFE_MB_ROI also includes raw radiographs; scan & reconstruction parameter settings file.</p> <p>A Neutron CT version of this data is available for comparison: <a href="https://doi.org/10.5281/zenodo.3533418">https://doi.org/10.5281/zenodo.3533418</a></p> <p>Image-based simulation (IBSim) meshes were generated directly from these datasets: <a href="https://doi.org/10.5281/zenodo.3533422">https://doi.org/10.5281/zenodo.3533422</a></p>
Neutron tomography (CT) image data of tungsten fusion energy heat exchange components
<p>Neutron tomography (CT) image data of tungsten fusion energy heat exchange components.</p> <p>The dataset includes three sets of images:</p> <ul> <li>ITER_171T-WA-0002_MB (ITER reference monoblock)</li> <li>CCFE_ThBr_MB (Culham Centre for Fusion Energy thermal break concept monoblock)</li> <li>ROIsamples_Stack (A stack of four region of interest samples*)</li> </ul> <p>The region of interest samples within the stack are as below:</p> <ul> <li>CCFE_ThBr_ROI (Culham Centre for Fusion Energy thermal break concept monoblock)</li> <li>IPP_Wf-Cu_p5_s1 (Max-Planck-Institut für Plasmaphysik tungsten fibre / copper matrix coolant pipe)</li> <li>ITER_HHFT_ROI (ITER reference monoblock which has undergone high heat flux testing)</li> <li>ITER_17IT-WA-0002_ROI (ITER reference monoblock)</li> </ul> <p>This data was used originally for the following publication (please cite if re-using the data) where further details on the data may be obtained:</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Each of the sample directories include reconstructed slices in Tiff format. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads).</p> <p>CCFE_ThBr_ROI and ROIsamples_Stack include raw radiographs; dark and flat field images; scan & reconstruction parameter settings file.</p> <p>ITER_171T-WA-0002_MB includes data relating to the modulation transfer function (MTF) measurement.</p> <p>An X-Ray CT version of the ROI data is available for comparison: <a href="https://doi.org/10.5281/zenodo.3533420">https://doi.org/10.5281/zenodo.3533420</a></p> <p>Image-based simulation (IBSim) meshes were generated directly from these datasets: <a href="https://doi.org/10.5281/zenodo.3533422">https://doi.org/10.5281/zenodo.3533422</a></p>
Precipitation hydrogen isoscape for East China from 1969 to 2017 generated based on data fusion of iGCMs simulations
<p>The dataset includes the stable hydrogen isotope of precipitation for East China over the 1969-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. This dataset was built based on the Convolutional Neural Network (CNN) method, fusing observations and isotope-equipped general circulation models (iGCMs) simulations of hydrogen isotope composition. Some physical-based ancillary data are also introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion.</p>
Data of "Effect of sample dimensions on the stiffness of PA12 Lattice materials fabricated using Powder Bed Fusion"
<div> </div> <div> <pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "Effect of sample dimensions on the stiffness of PA12 Lattice materials fabricated using Powder Bed Fusion", journal = "Additive Manufacturing", pages = " ", year = "2024", issn = "", doi = "https://doi.org/10.1016/j.addma.2024.104382", author = "L. Cobian, E. Maire, J. Adrien, U. Freitas, J.P. Fernandez-Blazquez, M.A. Monclus, J. Segurado"</pre> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 862015</p> </div>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Data for the conference poster "TiAl6V4 bistable mechanism produced by Laser Powder Bed Fusion"
<p>The dataset contains raw data for conference poster "TiAl6V4 bistable mechanism produced by Laser Powder Bed Fusion" presented at the 9th Metal Additive Manufacturing Conference.</p>
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network - CAMS data for experiments
<p>Contains data generated by the CAMS model (during a global reanalysis), used to train and evaluate the models presented article "Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network" - DOI of this article will be provided as soon as it is available.</p> <p>This data can be downloaded from the Copernicus Atmospheric Data Store (https://ads.atmosphere.copernicus.eu/#!/home), and is also hosted by the ICARE Data and Services Center (https://www.icare.univ-lille.fr/).</p> <p>This dataset only contains the specific data collection used for the experiments presented in aforementioned article. It is only a portion of the data available from these two websites.</p>
Data from: Human atlastin-3 is a constitutive ER fusion catalyst (lipid mixing data)
<p>Homotypic membrane fusion catalyzed by the atlastin (ATL) GTPase sustains the branched endoplasmic reticulum (ER) network in metazoans. Our recent discovery that two of three human ATL paralogs (ATL1/2) are C-terminally autoinhibited implied that relief of autoinhibition would be integral to the ATL fusion mechanism. An alternative hypothesis is that the third paralog ATL3 promotes constitutive ER fusion with relief of ATL1/2 autoinhibition used conditionally. However, published studies suggest ATL3 is a weak fusogen at best. Contrary to expectations, we demonstrate here that purified human ATL3 catalyzes efficient membrane fusion in vitro and is sufficient to sustain the ER network in triple knockout cells. Strikingly, ATL3 lacks any detectable C-terminal autoinhibition, like the invertebrate <em>Drosophila</em> ATL orthologue. Phylogenetic analysis of ATL C-termini indicates that C-terminal autoinhibition is a recent evolutionary innovation. We suggest that ATL3 is a constitutive ER fusion catalyst and that ATL1/2 autoinhibition likely evolved in vertebrates as a means of upregulating ER fusion activity on demand.</p>
Data for: Human atlastin-3 is a constitutive ER membrane fusion catalyst (phylogenetic and sequence analysis)
<p>Homotypic membrane fusion catalyzed by the atlastin (ATL) GTPase sustains the branched endoplasmic reticulum (ER) network in metazoans. Our recent discovery that two of the three human ATL paralogs (ATL1/2) are C-terminally autoinhibited implied that relief of autoinhibition would be integral to the ATL fusion mechanism. An alternative hypothesis is that the third paralog ATL3 promotes constitutive ER fusion with relief of ATL1/2 autoinhibition used conditionally. However, published studies suggest ATL3 is a weak fusogen at best. Contrary to expectations, we demonstrate here that purified human ATL3 catalyzes efficient membrane fusion in vitro and is sufficient to sustain the ER network in triple knockout cells. Strikingly, ATL3 lacks any detectable C-terminal autoinhibition, like the invertebrate <em>Drosophila</em> ATL ortholog. Phylogenetic analysis of ATL C-termini indicates that C-terminal autoinhibition is a recent evolutionary innovation. We suggest that ATL3 is a constitutive ER fusion catalyst and that ATL1/2 autoinhibition likely evolved in vertebrates as a means of upregulating ER fusion activity on demand.</p>
CAIRT Complete Data Fusion 2D - Input/Output Dataset
<p>This package contains input/output data of tool Complete_Data_Fusion_2D (CDF_2D)(https://doi.org/10.5281/zenodo.8290163) which performs the 2D data fusion of CAIRT with IASI-NG, Sentinel 5 (S5) simulated measurements, both flying on MetOp-SG A which CAIRT will be in formation with.</p>
Data from: Data fusion for integrative species identification using deep learning
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Data from: Human atlastin-3 is a constitutive ER fusion catalyst (lipid mixing data)
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Data from: Evolutionary innovation through fusion of sequences from across the tree of life
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Data for OFDVDnet: A sensor fusion approach for video denoising in fluorescence guided surgery
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Data for: Human atlastin-3 is a constitutive ER membrane fusion catalyst (phylogenetic and sequence analysis)
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Standard Bouguer anomaly model achieved by multi-source Bouguer gravity anomaly Bayesian data fusion algorithm in Sichuan-Yunnan region
<p>* Method: Based on the equivalent source inversion and Bayesian uncertainty quantization theory, a new multi-source gravity data fusion algorithm is developed, which effectively solves the multi-source data fusion problem with different noise and datum.</p> <p>* Standard Bouguer anomaly is Fused from WGM2012 Bouguer gravity anomaly model and 394 gravity profile data measured in Sichuan-Yunnan region. Fusion anomaly results can eliminate datum draft between multi-source gravity and reduce incoherent noise.</p> <p>* Spatial resolution of the standard Bouguer anomaly is about 20 kilometers.</p> <p>* Correcting deviations means the difference between the fused standard Bouguer anomaly model and the WGM2012 Earth gravity model.</p>
Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'
<p><strong>Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' along with the experimental results, and the methods used for comparison.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The data used in our experiments that we have the copyright of [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a>.<br> The licences valid for the elements of this repository are discussed under point "3. Licenses" below.</p> <p><strong>1. Structure</strong></p> <p>The repository contains the following items:</p> <ol> <li>"CODE_AND_RESULTS.zip" with the source codes and results of our method and the comparison methods,</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>We now focus on the structure of the file CODE_AND_RESULTS.zip.<br> It contains the following items:</p> <ol> <li>The directory "new_methods" contains the source code and results of the new methods proposed in our paper.</li> <li>The directory "comparison" contains the source code of the two approaches used for comparison: ACoL [<a href="https://doi.org/10.1109/CVPR.2018.00144">A</a>] and DANet [<a href="http://doi.org/10.1109/ICCV.2019.00669">B</a>].</li> <li>The folder "tools_and_metrics" holds additional libraries, software tools, and metrics using in our experiments. </li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>Inside the folder "new_methods," the following sub-folders are provided:</p> <ol> <li>"data" includes data loading code and code for how organizing the input data of the neural network.</li> <li>"expr" includes training code.</li> <li>"model" includes neural network model, basic network and additional modules, depending on the file name, including improved network, and comparison model.</li> <li>"utils" includes some used library functions and test codes when testing, including image segmentation, searching for the largest connected area and data visualization, etc. Verification on the WSADD dataset is done via test_airplane.py and on the DIOR dataset via val_model.py.</li> </ol> <p>In our experiments, we used two datasets:</p> <p>"WSADD" [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>], which is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a> under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.<br> The "<a href="https://doi.org/10.1109/CVPR.2018.00144">DIOR</a>" proposed in [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>].</p> <p><strong>2. References</strong></p> <p>[<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>] Z.-Z. Wu, T. Weise, Y. Wang, Y. Wang, Convolutional neural network based weakly supervised learning for aircraft detection from remote sensing image, <em>IEEE Access</em> 8 (2020) 158097-158106. doi:<a href="http://doi.org/10.1109/ACCESS.2020.3019956">10.1109/ACCESS.2020.3019956</a>. <br> [<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] Z.-Z. Wu. Weakly Supervised Airplane Detection Dataset: WSADD. May 2020. zenodo.org. doi:<a href="http://doi.org/10.5281/zenodo.3843229">10.5281/zenodo.3843229</a>.<br> [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>] K. Li, G. Wan, G. Cheng, L. Meng, J. Han, Object detection in optical remote sensing images: A survey and a new benchmark, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em> 159 (2020) 296-307. doi:<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">10.1016/j.isprsjprs.2019.11.023</a>. <br> [<a href="https://doi.org/10.1109/CVPR.2018.00144">D</a>] X. Zhang, Y. Wei, J. Feng, Y. Yang, T. S. Huang, Adversarial complementary learning for weakly supervised object localization, in: <em>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</em> (CVPR'18), Jun. 18-22, 2018, Salt Lake City, UT, USA, IEEE Computer Society, 2018, pp. 1325-1334. doi:<a href="https://doi.org/10.1109/CVPR.2018.00144">10.1109/CVPR.2018.00144</a>. <br> [<a href="http://doi.org/10.1109/ICCV.2019.00669">E</a>] H. Xue, C. Liu, F. Wan, J. Jiao, X. Ji, Q. Ye, DANet: Divergent activation for weakly supervised object localization, in: <em>Proceedings of the IEEE/CVF International Conference on Computer Vision</em> (ICCV'19), Oct. 27-Nov. 2, 2019, Seoul, Korea, IEEE, 2019, pp. 6588-6597. doi:<a href="http://doi.org/10.1109/ICCV.2019.00669">10.1109/ICCV.2019.00669</a>.</p> <p><strong>3. Licenses</strong></p> <p>The following licenses apply for the files and folders in the archive "CODE_AND_RESULTS.zip":</p> <ul> <li>The files in the folder `new_methods` are under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder `comparison/ACoL` have been obtained from https://github.com/xiaomengyc/ACoL, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>We put our code and data under the </li> <li>The files in the folder "comparison/DANet" have been obtained from <a href="https://github.com/xuehaolan/DANet">https://github.com/xuehaolan/DANet</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/xuehaolan/">https://github.com/xuehaolan/</a>.</li> <li>The files in the folder "tools_and_metrics/detections_DIOR" are related to the repository <a href="https://github.com/rafaelpadilla/Object-Detection-Metrics">https://github.com/rafaelpadilla/Object-Detection-Metrics</a>, which is under the <a href="https://mit-license.org/">MIT License</a>, and therefore are under the same license.</li> <li>The files in the folder "tools_and_metrics/Nest-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/Nest">https://github.com/ZhouYanzhao/Nest</a>, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder "tools_and_metrics/PRM-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/PRM">https://github.com/ZhouYanzhao/PRM</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/ZhouYanzhao/">https://github.com/ZhouYanzhao/</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><strong>4. Contact</strong></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, wuzz@hfuu.edu.cn<br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, tweise@hfuu.edu.cn, tweise@ustc.edu.cn</p> <p>Institute of Applied Optimization, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China<br> </p>
Data from: Fission–fusion processes weaken dominance networks of female Asian elephants in a productive habitat
Dominance hierarchies are expected to form in response to socioecological pressures and competitive regimes. We assess dominance relationships among free-ranging female Asian elephants (Elephas maximus) and compare them with those of African savannah elephants (Loxodonta africana), which are known to exhibit age-based dominance hierarchies. Both species are generalist herbivores, however, the Asian population occupies a more productive and climatically stable environment relative to that of the African savannah population. We expected this would lower competition relative to the African taxon, relaxing the need for hierarchy. We tested whether 1) observed dominance interactions among individuals were transitive, 2) outcomes were structured either by age or by social unit according to 4 independent ranking methods, and 3) hierarchy steepness among classes was significant using David's score. Elephas maximus displayed less than a third the number of dominance interactions as observed in L. africana, with statistically insignificant transitivity among individuals. There was weak but significant order as well as steepness among age-classes but no clear order among social units. Loxodonta africana showed significant transitivity among individuals, with significant order and steepness among age-classes and social units. Elephas maximus had a greater proportion of age-reversed dominance outcomes than L. africana. When dominance hierarchies are weak and nonlinear, signals of dominance may have other functions, such as maintaining social exclusivity. We propose that resource dynamics reinforce differences via influence on fission–fusion processes, which we term "ecological release." We discuss implications of these findings for conservation and management when animals are spatially constrained.
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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.