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757 results for “twins”
Half of a twin capital with a griffin motif
he capital is considered to be one of the best examples of Romanesque masonry in Poland. The column head was found in 1980 in the courtyard of a baroque monastery and is one of many preserved fragments of the architectural decoration of the Romanesque monastic complex in Tyniec. Column capitals constitute a sizeable group amongst them. Researchers do not agree whether they were originally located in the church or in the cloisters. The capital has the shape of a truncated pyramid turned upside down. The surface of the capital is covered by ornamentation with zoomorphic and plant motifs. On the obverse, there is a representation of a griffin or a dragon with long pointed ears. Out of its mouth emerge vines ending in palmette leaves, among which there appears a winged figure of another animal. The reverse is covered by a composition of three palmette leaves inscribed in a heart-like shape. 4th quarter of the 11th century, Tyniec Museum of the Benedictine Abbey in Tyniec Inventory number: DA/167/2012 Source: Objaverse 1.0 / Sketchfab
Double Twin Cell
The various methods of expansion of the covered dwelling area, according to the progressive 'doubling', both in height and in depth, have determined the so-called 'typological process': from the 'matrix' type of elementary cells to the single-family row house or terrace-house type, up to the recent multi-family in-line house type (present sporadically in Pienza and only for the 'recast' of terraced houses). Single-cell homes, especially during the 19th century, expanded in height with the addition of a third floor, or merged together giving rise to online associations, to one or two apartments per floor. The stairs that connect the floors together are generally located in a central position. Although today the survey of the number of floors shows that most of the buildings consists of three floors above ground, probably the same houses originally did not exceed two floors. [Learn More](https://esg.pt/3dpast/platform/pienza/multiple-cells.html) Source: Objaverse 1.0 / Sketchfab
PETRONAS TWIN TOWER
Low Poly model PETRONAS TWIN TOWER Kuala Lumpur's dynamic, high-rise City Centre is known for upscale fashion malls, luxury hotels and expansive views from the futuristic Petronas Twin Towers. Overlooked by skyscrapers, the green expanse of the Kuala Lumpur City Center Park offers walking paths and musical fountains. Dining options range from stylish bistros offering Asian and European fare, to elegant rooftop bars serving innovative cocktails. Source: Objaverse 1.0 / Sketchfab
TWIN SEEDS Work Package 2 data
<p>Data collected within Work Package 2 of the Horizon Europe project TWIN SEEDS (Grant agreement ID: 101056793).</p> <p>The WP2 report "Emerging trends of Global Value Chains and Multinational Enterprises in the pandemic time", using as inputs these data is publicly accessible here: https://twinseeds.eu/wp-content/uploads/2023/12/WP2-Report.pdf</p>
Organization of drainage at Szczeliniec Wielki (SzW) and Szczeliniec Mały (SzM) twin mesas, Poland
<p>This research was funded by National Science Centre, Poland, research project no. 2021/41/N/ST10/00598.</p> <p><br>Published as:</p> <p>Migoń P., Duszyński F., Jancewicz K., Kotowska M., Porębna W., 2023. Surface-subsurface connectivity in the morphological evolution of sandstone-capped tabular hills – how much analogy to karst?, Geomorphology, vol. 440, no. 108884, 1–23. DOI https://doi.org/10.1016/j.geomorph.2023.108884</p>
TWIN SEEDS Work Package 5 data
<p><span>Data collected within Work Package 5 of the Horizon Europe project TWIN SEEDS (Grant agreement ID: 101056793).</span></p> <p><span>The WP5 report "Recent and emerging impact of GVCs and MNEs on employment and inequalities", using as inputs these data is publicly accessible here: <a href="https://twinseeds.eu/projects-outputs/reports/">https://twinseeds.eu/projects-outputs/reports/</a> </span></p>
TWIN SEEDS Work Package 4 data
<p><span>Data collected within Work Package 4 of the Horizon Europe project TWIN SEEDS (Grant agreement ID: 101056793).</span></p> <p><span>The WP4 report "Recent and emerging impact of GVCs and MNEs on employment and inequalities", using as inputs these data is publicly accessible here: <a href="https://twinseeds.eu/projects-outputs/reports/">https://twinseeds.eu/projects-outputs/reports/</a></span></p>
TWIN SEEDS Work Package 3 data
<p><span>Data collected within Work Package 3 of the Horizon Europe project TWIN SEEDS (Grant agreement ID: 101056793).</span></p> <p><span>The WP3 report "Recent and emerging impact of GVCs and MNEs on employment and inequalities", using as inputs these data is publicly accessible here: <a href="https://twinseeds.eu/projects-outputs/reports/">https://twinseeds.eu/projects-outputs/reports/</a> </span></p>
Digital Twin or Digital Model: An Analysis of Definitions along the Product Lifecycle - Research data
<p>This research data contains the statements of the authors Grieves, Stark and Tao with regard to selected characteristics of Digital Twins. According to these statements different case studies along the product life cycle are classified as Digital Twin or Digital Model.</p> <p>Version 2 added a change in characteristic 2.</p>
Raw data collection for the publication P. Pötschke, T. Villmow, B. Krause and B. Kretzschmar, Influence of Twin-screw Extrusion Conditions on MWCNT Length and Dispersion and Resulting Electrical and Mechanical Properties of Polycarbonate Composites
<p>This data collection contains the raw data for the publication <br>Petra Pötschke, Tobias Villmow, Beate Krause and Bernd Kretzschmar, Influence of Twin-screw Extrusion Conditions on MWCNT Length and Dispersion and Resulting Electrical and Mechanical Properties of Polycarbonate Composites, <strong>polymers </strong>2024, 16(19), 2694. <a href="https://doi.org/10.3390/polym16192694">https://doi.org/10.3390/polym16192694</a></p> <p>The data are sorted according to the figures and tables in which they are used.</p> <p>The description in Table 1 is taken from the reference:<br>Villmow, T.; Kretzschmar, B.; Pötschke, P. Influence of screw configuration, residence time, <br>and specific mechanical energy in twin-screw extrusion of polycaprolactone/multi-walled carbon nanotube composites. Compos. Sci. Technol. 2010, 70, 2045-2055. doi: https://doi.org/10.1016/j.compscitech.2010.07.021.</p> <p>Figure 15 is adapted from the references:<br>Krause, B.; Boldt, R.; Pötschke, P. A method for determination of length distributions of multiwalled carbon nanotubes before and after melt processing. Carbon 2011, 49, 1243-1247, https://doi:10.1016/j.carbon.2010.11.042.<br>and<br>Liebscher, M.; Domurath, J.; Krause, B.; Saphiannikova, M.; Heinrich, G.; Pötschke, P. Electrical and melt rheological characterization of PC and co-continuous PC/SAN blends filled with CNTs: Relationship between melt-mixing parameters, filler dispersion, and filler aspect ratio. Journal of Polymer Science Part B: Polymer Physics 2018, 56, 79-88, https://doi:10.1002/polb.24515.<br>The data are reused with permissions. </p> <p>The raw data (TEM images) used for the calculation of the carbon nanotube length distributions and mean carbon nanotube length values shown in Figs. 4, 11, 13, 16, and 17 are publically available at: Krause, B. (2024). Transmission electron microscopy (TEM) images of multiwalled carbon nanotubes (MWCNT) detached from polycarbonate (PC) composites [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11400466</p> <p>Version v2:</p> <p>Compared to the submitted figure, in the final version in<strong> Fig. 9 </strong>the sample using the side feeder (PC-H-05) was removed and two samples extruded at 15 kg/h and 750 rpm (PC-H-25) and 1000 rpm (PC-H-26) were added. In the text-file the unit of GPa for the elastic modulus was corrected to MPa. </p> <p>Compared to the submitted Table, in the final version of <strong>Table 2 </strong>the electrical resistivity values were given in more detail including the standard deviation. The column title of sigma break was changed to sigma max, which is more correct for these stress-strain diagrams.</p> <p>Compared to the submitted Table, in the final version of <strong>Table 3</strong> the column title of sigma break was changed to sigma max, which is more correct for these stress-strain diagrams.The title of the first column was set to "screw" instead of "feeding". </p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution"
<p>These datasets have been used to produce Figure 3, 4 and 5 of manuscript: "Coherent phase transfer for real-world twin-field quantum key distribution". The files contain two arrays: time in seconds and normalised intensity.</p> <p>Explanation for "Data_fig3_XXX.txt": the files contains the raw data used to produce Fig. 3. The following timespans have been used:</p> <p>Data_fig3_stabilised.txt: t_start= 0.29 s, t_stop=0.292 s</p> <p>Data_fig3_unstabilised.txt: t_start=0.00225 s, t_stop=0.00424 s</p> <p>Explanation for "Data_fig4_XXX.txt" files: the procedure to obtain the phase deviation from the normalised interference is detailed in the text (Methods section). Datasets with different sampling rate have been combined to obtain the phase deviation on the long and short term.</p> <p>Explanation for "Data_fig5.txt": the files contains the raw data used to produce Fig. 5.</p>
Neural Policy Style Transfer with Twin-Delayed DDPG (NPST3)
<p>Neural Policy Style Transfer with Twin-Delayed DDPG (NPST3) dataset.</p> <p>The research leading to these results has received funding from: RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; ROBOASSET, ”Sistemas robóticos inteligentes de diagnóstico y rehabilitación de ter apias de miembro superior”, PID2020-113508RB-I00 funded by AGENCIA ESTATAL DE INVESTIGACION (AEI); and “Programa propio de investigación convocatoria de movilidad 2020” from Universidad Carlos III de Madrid. The original data used in this project was obtained from mocap.cs.cmu.edu. The original data was created with funding from NSF EIA-0196217.</p>
Supplementary Material of the paper entitled "Governing Digital Twin technology on smart and sustainable tourism"
<p>In this document, we provide some supplementary material of the paper entitled “Governing Digital Twin technology on smart and sustainable tourism’’.</p> <p>Rahmadian, E., Feitosa, D., Zwitter, A. (2022). Governing Digital Twin technology on smart and sustainable tourism.</p>
Data Augmentation for learning mechanical digital twins of voids in welding joints
<p>In Source-2_Data_Augmentation:</p> <p>Exercice1_augmentation.ipynb Jupyter Notebook for data warpping of defect images.</p> <p>Exercice2_augmentation_multimodale.ipynb Jupyter Notebook for multimodal data augmentaion (defect images and mechanical fields) via oversampling</p> <p>Exercice3_clustering.ipynb Data clustering using the k-medoids algorithm applied to mechanical dissimilarity of the defects.</p> <p>k_medoids.py is a python code of a kmedoids algorithm.</p> <p>in Data:</p> <p>All_images.npy (numpy file) contains the defect images.</p> <p>All_Stresses.npy (numpy) contains mechanical fields, All_Stresses[k,i,j,ic,it] is the instance number k of the component ic of the Cauchy stress tensor at time it. The mechanical problem is decribed in <a href="https://dx.doi.org/10.5802/crmeca.51">⟨10.5802/crmeca.51⟩</a>. <a href="https://hal.archives-ouvertes.fr/hal-03113503">⟨hal-03113503⟩.</a></p> <p>New_images_1.npy and New_Stresses_1.npy are augmented data for k=1.</p> <p>New_images_87.npy and New_Stresses_87.npy are augmented data for k=87.</p> <p>Dissimilarity_Stress.npy is the Frobenius norm of the distances between stress tensors (All_Stresses.npy).</p> <p> </p>
Fig. 2 in Interspecific Interactions as a Factor of Limitation of Geographical Distribution: Evidence Obtained by Modeling Home Ranges of Vole Twin Species Microtus Arvalis – M. Levis (Rodentia, Microtidae)
Fig. 2. Potential distribution of the East European vole (Microtus levis). Captions as in fig.1.
Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
<p>This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance for model predictive control; and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real-time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the digital twin instance for model predictive control to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 1 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 1 of 2 of data collection.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 2 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 2 of 2 of data collection.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: LIDAR Point Cloud of a Portion of UTEP Campus
<p>This Autodesk ReCap file is a combination of numerous individual LiDAR scans captured using a Leica Terrestial LiDAR system. The scan includes some black and white and some color scans. The area of campus generally focuses on the southwestern portion of campus including the Interdisciplinary Research Building, the Mining Minds roundabout, the Sun Bowl 2 Parking Lot, the University Bookstore, and the Sun Bowl Parking Garage, and roads including University Ave. and Sun Bowl Drive.</p>
X-ray diffraction data of twinned gamma-form of o-nitroaniline
<p>The diffraction data are of the gamma-form of o-Nitroaniline, C<sub>6</sub>H<sub>6</sub>N<sub>2</sub>O<sub>3</sub>. This compound is known to be polymorphic; the alpha-form is probably amorphous, while the beta- and gamma-forms are crystalline. Difficulties with the unit-cell determination of the gamma-form were reported as a consequence of twinning. These newly recorded diffraction data are of a twinned crystal.</p> <p>The raw data and processing with EVAL are described in details in IUCrData as a Raw Data Letter [Lutz & Kroon-Batenburg, IUCrData (2022].</p> <p>The data were recorded on a Bruker ApexII diffractometer and stored as .sfrm files. They were also converted with Bruker imagesum.py script as part of the APEXII software to full .cbf files.</p>
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.