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6,452 results for “Application”
A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants
<p>This folder contain the data relative to the contact based pipe inspection presented on M. Tognon et al. "A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants." IEEE Robotics and Automation Letters 4.2 (2019): 1846-1851.</p>
Application Cases of Inverse Modelling with the PROPTI Framework - Data Set
<p><strong>Contents</strong></p> <p>Set of simulation data, supplementary for a paper submitted to (published: 15 June 2019) the Fire Safety Journal, with the title <a href="https://www.sciencedirect.com/science/article/pii/S0379711219300438">"Application Cases of Inverse Modelling with the PROPTI Framework"</a>. See also our project at <a href="https://www.researchgate.net/project/PROPTI-An-Generalised-Inverse-Modelling-Framework">ResearchGate</a>.</p> <p>This repository contains the complete input data for each IMP run of the mass loss calorimeter, shown in this paper. This comprises of the experimental data files, the templates for the simulation models and the input file for PROPTI.</p> <p>The data base files are provided. This includes the original ones created by PROPTI during the run, as well as the cleaned data base files, used to create the plots, and the extracted best parameter sets per generation. Plots, created during the IMP runs as means of monitoring the progress are also included.</p> <p>Furthermore, the repository contains a small collection of Jupyter notebooks which have been used to process the data base files and create the plots presented in this paper.</p> <p>The full factorial simulations were set up from within a Jupyter notebook. This notebook and the conducted simulations are also part of this repository.</p> <p>Data of the various TGA simulations are provided within a very <a href="https://zenodo.org/record/2538851#.XSXfAXtCSUk">similar repository</a>, linked to a <a href="https://www.researchgate.net/publication/328933654_PROPTI_-_A_Generalised_Inverse_Modelling_Framework">conference paper</a> (ESFSS 2018, Nancy, France).</p> <p>Finally, the simulation input files, PROPTI input, as well as the custom script for file handling in concert with OpenFOAM, are provided.</p> <p> </p> <p><strong>Technical Information</strong></p> <p>Each ZIP archive represents a sub-directory of the original directory. For the analysis scripts, the Jupyter notebooks, to work properly out of the box it is necessary to keep this structure. Thus, simply extract all archives into the same directory.</p> <p>Note: Size on disc, after extraction, is about 4.1 GB. Version 2 adds about 5.1 GB.</p> <p> </p> <p><strong>Version 2:</strong></p> <p>Version 2 contains new IMP runs that address an error in determining the normalised residual mass, see Jupyter Notebook "RevisedTargetAssessment.ipynb", as well as input from the reviewers. The IMP runs are denoted by "08" after the optimisation algorithm label, e.g. "MLC_FSCABC_08_new_75kw_Ins".</p>
Development, evaluation and application of NicheNet: datasets
<p>Here you can find the datasets used for development, validation and application of NicheNet, a method that links ligands to target genes to model intercellular communictation. The software to use NicheNet (i.e. R package nichenetr) is available at:</p> <p><a href="https://github.com/saeyslab/nichenetr">https://github.com/saeyslab/nichenetr</a></p> <p>Short description of individual files:</p> <p>* data for development of NicheNet: </p> <p>- lr_network.rds: network containing ligand-receptor interactions</p> <p>- signaling_network.rds: network containing signaling interactions</p> <p>- gr_network.rds: network containing gene regulatory interactions</p> <p>- weighted networks.rds: integrated weighted ligand-signaling and gene regulatory network</p> <p>* data for use of NicheNet:</p> <p>- ligand_tf_matrix.rds: matrix containing ligand-gene signaling scores</p> <p>- tf_target_matrix.rds: matrix containing gene-gene regulatory scores</p> <p>- ligand_target_matrix.rds: matrix containing ligand-target gene regulatory potential scores</p> <p>* datasets for validation of NicheNet:</p> <p>- expression_settings.rds: 111 expression datasets profiling the transcriptional response to a ligand</p> <p>* datasets to demonstrate application possibilities of NicheNet:</p> <p>- pemt_signature.txt: p-EMT geneset for which upstream regulators will be predicted</p> <p>- hnscc_expression.rds: single-cell expression data of malignant and non-malignant cells from head and neck squamous cell carcinoma tumors</p>
WASHTREET. Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.
<p><strong>WASHTREET</strong><strong> - </strong><strong>Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.</strong></p> <p>This dataset contains raw data and surface elevations results from the application of the Structure from Motion (SfM) photogrammetric technique in a 36 m<sup>2</sup> full-scale urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). This work is part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The accurately measurement of the surface elevations is needed for a proper representation of surface flow, which is key in the detachment and transport of solids in the model surface. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>)</p> <p>A detailed description of experimental procedure and data collected can be consulted in ‘<em>1_ExperimentalProcedure.pdf’</em>. Raw images taken as input for the SfM software are included in ‘<em>2_RawImages.zip’</em>. Then, the point cloud resulted is provided in ‘<em>3_SFM_RawPointCloud.ply</em>’. This point cloud was processed and the final elevation map with a resolution of 5 mm is included in ‘<em>4_SfM_ElevationMap(m).xyz</em>’.</p> <p>Further details of the physical model and hydraulic and sediment transport experiments can be consulted in the dataset <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET - Hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques are provided in the dataset <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13.<a href="http://doi.org/10.1038/s41597-020-0384-z"> https://doi.org/10.1038/s41597-020-0384-z</a></li> </ul>
An Attribute-Based Access Control model in RFID systems based on blockchain Decentralized Applications for healthcare environments (video demonstration)
<p>An Attribute-Based Access Control model in RFID systems based on blockchain Decentralized Applications for healthcare environments.</p>
"A geothermal application for GOCE..." accompanying data set
<p>Accompanying data to the paper</p> <blockquote> <p>Pastorutti, A., & Braitenberg, C., 2019. <strong>A geothermal application for GOCE satellite gravity data: modelling the crustal heat production and lithospheric temperature field in Central Europe</strong>, Geophysical Journal International, <a href="https://doi.org/10.1093/gji/ggz344">doi:10.1093/gji/ggz344</a></p> </blockquote> <p>which has been accepted for publication in <em>Geophysical Journal International</em> following peer review.</p> <p><strong>The paper version of record is available online at: <a href="https://doi.org/10.1093/gji/ggz344">doi.org/10.1093/gji/ggz344</a>.</strong></p>
Dataset of Paper "Material selection and prediction of solar irradiance in plastic devices for application of solar water disinfection (SODIS) to inactivate viruses, bacteria and protozoa"
<p>Datasets of Paper “Predictive evaluation of solar irradiance in solar disinfection water plastic containers”.</p> <p>Data of the transmission spectra of the polymers: PMMA, PET, PC and PP.</p> <p>Data of the extinction coefficient spectra of the polymers: PMMA, PP, PC and PET.</p> <p>Data of the spectral incident radiation as a function of the thickness for PMMA, PET, PC and PP containers.</p> <p>Data of the spectral incident radiation required for inactivation of <em>MS2</em> virus, <em>E. coli</em> bacteria and <em>C. parvum</em> protozoa in a PMMA, PET, PC and PP containers.</p>
SELIS platform for pan-European logistics applications datasets
<p>This publication comes as a result of the SELIS H2020 project (<a href="http://www.selisproject.eu">http://www.selisproject.eu</a>). The provided datasets are anonymized samples of the real data which describe some of the use cases we have encountered.</p> <p>In the interest of the Open Research Data Pilot, this publication (<a href="https://github.com/selisproject/selis-node-connectors">https://github.com/selisproject/selis-node-connectors</a>) provides a basic implementation of connectors used in real world applications for supply chain participants to connect and funnel their data to the SELIS Community Node (SCN). The prototype of the SELIS big data analytics and machine learning infrastructure is also openly available (<a href="https://github.com/selisproject/bda">https://github.com/selisproject/bda</a>).</p> <p>Along with the connectors, obfuscated/test data is also provided for each solution. The connectors all follow the same approach, which is parsing the data provided, transforming them into a SCN-compatible data exchange model and publishing them to the SCN under a specific configuration. The samples provided cover two use cases:</p> <p><strong>Adria Kombi Data set: </strong>This anonymized data set which includes 24 csv files describes a use case developed for the SELIS project. It involves the (as accurate as possible) estimation of time of arrival (ETA) for individual freight trains using historical data and live feeds.</p> <p><strong>SONAE Data set: </strong>This anonymized data set which includes 2 csv files describes a different use case developed for the same project. This specific use case involves the automatic generation of suggested order forecasts for a retailer (SONAE) given stock level data from different warehouses and information such as lead time (minimum time required from order till delivery) per product and supplier, delivery days etc.</p> <p> </p> <p><em>For a more detailed description of the data please refer to the README.md files included in the downloadable zipped directories.</em></p> <p> </p> <p> </p>
Cyclostrophic corrections of AVISO/DUACS surface velocities and its application to mesoscale eddies in the Mediterranean Sea
<p>We apply an optimised iterative method to retrieve with best accuracy the cyclogeostrophic corrections on fifteen years (2000-2015) of surface geostrophic velocity fields provided by AVISO/DUACS for the Mediterranean Sea. The initial gridded altimeter products were produced by SSALTO/DUACS and distributed by the Copernicus Marine Environment Monitoring Service (marine.copernicus.eu). </p> <p>Each netCFD file corresponds to the two cyclogeostrophic velocity components zonal u and meridional v. </p> <p>(ssu_adt_DYNED_MED_cyclo_2000_2015.nc & ssv_adt_DYNED_MED_cyclo_2000_2015.nc)</p> <p> </p>
Sound Events for Surveillance Applications
<p>The Sound Events for Surveillance Applications (SESA) dataset files were obtained from Freesound. The dataset was divided between train (480 files) and test (105 files) folders. All audio files are WAV, Mono-Channel, 16 kHz, and 8-bit with up to 33 seconds. # Classes: 0 - Casual (not a threat) 1 - Gunshot 2 - Explosion 3 - Siren (also contains alarms)</p>
Industry-scale Application and Evaluation of Deep Learning for Drug Target Prediction
<p>Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.</p>
Text-fig. 1. "Plant screen" scheme of complete results of the IPR-vegetation analysis derived from the database. in The Integrated Plant Record Vegetation Analysis: Internet Platform And Online Application
Text-fig. 1. "Plant screen" scheme of complete results of the IPR-vegetation analysis derived from the database.
Novel benzotriazole-nanomaterials for maritime applications: comparative anti-corrosion performance, environmental behavior, and hazard
<div>The present dataset contains chemical, characterization, and ecotoxicological data upon exposure of benzotriazole (BTA) in the soluble form and nanoforms (Mg-Al LDH-BTA and Zn-Al LDH-BTA). Chemical analyses data include the quantification of relevant anions (nitrites, nitrates, phosphates and chlorides, determined through High-Performance Liquid Chromatography) and target metals (Al, Mg and Zn) determined through Inductively Coupled Plasma Optical Emission Spectrometry). Characterization data include DLS (size) and zeta potential analysis. Ecotoxicological data includes acute and chronic endpoints determined in a wide-range of marine species.</div> <div> </div> <div> </div> <div> </div>
Figure 3 in Application of multifactorial discriminant analysis in the morphostructural differentiation of wild and cultured populations of Vieja Azul (Andinoacara rivulatus)
Figure 3. Cluster from Mahalanobis distances for cultured and wild populations of both sexes. HP: Cultured females; HS: wild females; MP: cultures males; MS: wild males.
Figure 2 in Application of multifactorial discriminant analysis in the morphostructural differentiation of wild and cultured populations of Vieja Azul (Andinoacara rivulatus)
Figure 2. Plot of the individual observation discriminant scores obtained with the canonical discriminant function. HP: Cultured females; HS: wild females; MP: cultures males; MS: wild males.
Figure 1 in Application of multifactorial discriminant analysis in the morphostructural differentiation of wild and cultured populations of Vieja Azul (Andinoacara rivulatus)
Figure 1. (a) Location of 25 anatomic landmark points designed on the left-side view of Andinoacara rivulatus; (b) 32 truss characters making up a truss network. 1- Commissure of the mouth; 2- most cranial point of the upper premaxilla; 3- origin of pelvic fin; 4- origin of dorsal fin; 5- origin of anal fin; 6- most cranial point of the base of the tenth spine of the dorsal fin; 7- ending of anal fin; 8- ending of dorsal fin; 9- ventral origin of caudal fin; 10- dorsal origin of caudal fin; 11- most cranial point of caudal peduncle; 12- most caudal point of caudal peduncle; 13- ending of pectoral fin; 14- end of operculum; 15- cranial edge of the eye; 16- caudal edge of the eye; 17- preoccipital (most posterior aspect of neurocranium); 18- below operculum; 19- origin of pectoral fin; 20- lower end of the head; 21- anal opening; 22- most cranial point of the lower premaxilla; 23- ending of 1st dorsal fin ray; 24- ending of the last anal fin ray; 25- ending of the pelvic fin radius.
Fig. 1 in Evaluating the use of a low dose fipronil bait in reducing black-tailed prairie dog (Cynomys ludovicianus) fleas at reduced application rates
Fig. 1. Flea prevalence (0.0–1.0) during pre-treatment and post-treatment for (A) captured black-tailed prairie dogs (Cynomys ludovicianus); and (B) active burrows. Vertical bars indicate the 95% confidence intervals (95% CI).
Fig. 4 in Applications and limitations of micro-XCT imaging in the studies of Permian radiolarians: A new genus with bi-polar main spines
Fig. 4. Dendrogram of the cluster analysis under the corresponding analysis based on nine genera (performed by software "R" and the EZR).
Fig. 3 in Applications and limitations of micro-XCT imaging in the studies of Permian radiolarians: A new genus with bi-polar main spines
Fig. 3. Schematic drawings on internal structure of Permian and Mesozoic spumellarians with bi-polar main spines (detailed information including source is summarized in Table 2). A, B. Protopsium. C. Pegoxystris. D. Falcispongus. E. Spongoxystris. F. Dalongicaepa Xiao and Suzuki gen. nov. G. Spongotortilispinus. H. Spongopallium. I, J. Archaeospongoprunum. K. Palaeospongurus. L. Paroertlispongus.
Fig. 2 in Applications and limitations of micro-XCT imaging in the studies of Permian radiolarians: A new genus with bi-polar main spines
Fig. 2. Micro-XCT cross-sectional images (A, B), SEM micrographs (C–E), and TLM micrographs (F) of the spumellarian Tetraspongodiscus stauracanthus Feng in Feng et al., 2006 from Changhsingian of the Rencunping section (A–D) and Dongpan (E), South China and upper Capitanian of West Texas, USA (F). A. LGFEC SR 33-201. B. LGFEC SR 33-202. C. LGFEC SR 36-011. D. LGFEC SR 33-022. E. DP 4/4860 (from Feng et al. 2006; copyright is permitted from Elsevier). F. S3-2A (from Noble and Jin 2010). Different cross sections show clear hoops with few primary beams and a possible median bar (A1 and B1), the centrical internal spicule (A2), and the embedded proximal part of the main spines (B2). Scale bars 50 μm.
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.