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994 results for “2d”
Fig. 2.14 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.14. Picture of a quickly composed image of a mite at 20× magnification.
2D numerical modelling: the case study of the Arno River at Greve junction
<p>files description:</p> <p>- "nuova mesh_barra.2dm" is the input mesh file for BASEMENT;</p> <p>- "veg. surveys.docx" shows the data collected during the vegetation surveys activities.</p>
Supplementary data for article 'Estimating and abstracting the 3D structure of feline bones using neural networks on X-ray (2D) images'
<p>3D DICOM volumes (CT scans) of feline femora, PNGs generated from them as DRRs using MeVisLab, and STLs generated from the DICOM volumes with MIMICS or MeshLab. Software to work with these files can be found at http://doi.org/10.5281/zenodo.3829423</p>
Dedalus output from 2D incompressible MHD fluid on a magnetized beta plane
<p>This dataset includes all output from simulations of the 2D incompressible MHD fluid on a magnetized beta plane that were used in the paper:</p> <blockquote> <p>Parker, J. B. and Constantinou, N. C. (2019). Magnetic eddy viscosity of mean shear flows in two-dimensional magnetohydrodynamics. Phys. Rev. Fluids, 4, 083701. DOI: 10.1103/PhysRevFluids.4.083701</p> </blockquote> <p>Simulations were performed using the spectral code Dedalus.</p>
Experiments, drainage, 2D Hele Shaw cell
<p>Drainage experiment in a horizontal two_dimensional Hele-Shaw cell</p>
Spectral induced polarization of calcite precipitation: 2D experimental pore scale observation
<p>This data will be available after publication.</p>
Domus, Moleta dels Flares (Lésera), Forcall, Castellón, Comunitat Valenciana, España, Europa │Proyecto de digitalización 3D, 2D
<p>Proyecto de digitalización <strong>2D</strong> y <strong>3D</strong> de la domus de Lésera, (Moleta del Flares, yacimiento arqueológico) realizado por <strong>AD&D 4D, </strong>proyecto de <strong>recreación virtual</strong> de la domus de Lesera, (Moleta dels Frares, yacimiento arqueológico) realizado por Balam Consultores SL con patrocinio de <strong>Generalitat Valenciana </strong>y el <strong>Ayuntamiento de Forcall.</strong></p> <ul> <li>Fotografía panorámica 360º 27.130 x 3564 puntos 16 bits Tiff <strong>Ref: </strong><a href="https://zenodo.org/record/4075918/files/20200110MoletaDelsFlares-DomusPano360.tif?download=1">20200110MoletaDelsFlares-DomusPano360.tif</a></li> <li>Video 4K (3840x2160 puntos) 10 bits mov <strong>Ref</strong>: <a href="https://zenodo.org/api/files/27fbfa78-979c-4cf2-b362-06daea9d5b45/20200930L%C3%A9sera%20Video4K.7z">20200930Lésera Video4K.7z</a></li> <li>Fichas interactivas pdf (Español, English, Valenciano) <strong>Ref</strong>: <a href="https://zenodo.org/api/files/27fbfa78-979c-4cf2-b362-06daea9d5b45/FichasInteractivas-MoletaDelFlares-Lesesra-Domus2020.7z">FichasInteractivas-MoletaDelFlares-Lesesra-Domus20 ..</a></li> <li>Nube de puntos georreferenciada 24 M <strong>Ref</strong>: <a href="https://zenodo.org/api/files/27fbfa78-979c-4cf2-b362-06daea9d5b45/20200110MoletaDelsFlares-Domus24M.7z">20200110MoletaDelsFlares-Domus24M.7z</a></li> <li>Modelo digital de elevaciones 2 mm. pixel <strong> Ref</strong>: <a href="https://zenodo.org/api/files/27fbfa78-979c-4cf2-b362-06daea9d5b45/20201001MoletaDomusDEM2mm.7z">20201001MoletaDomusDEM2mm.7z</a></li> <li>Ortofotomapa 2 mm pixel <strong>Ref</strong>.: <a href="https://zenodo.org/api/files/27fbfa78-979c-4cf2-b362-06daea9d5b45/20201001MoletaDomusOrto2mm.7z">20201001MoletaDomusOrto2mm.7z </a></li> <li>Modelo 3D georreferenciado 48 Millones de polígonos <strong>Ref</strong>: <a href="https://zenodo.org/api/files/27fbfa78-979c-4cf2-b362-06daea9d5b45/20200110MoletaDelsFlares-Domus48MGeo.7z">20200110MoletaDelsFlares-Domus48MGeo.7z</a></li> <li>Impresión 3D domus y recreación stl: <a href="https://zenodo.org/api/files/27fbfa78-979c-4cf2-b362-06daea9d5b45/20201105_Impresi%C3%B3n_Domus.7z">20201105_Impresión_Domus.7z</a></li> <li>Aplicación interactiva de realidad virtual mediante Unreal Engine 4 (optimizada para Oculus Rift S, funciona en cualquier plataforma Steam VR. Requisitos mínimos: i5 4550 o equivalente, GeForce GTX 1060 o equivalente, 8GB RAM). Domus recreada en 3D a partir de la hipótesis de su tercera fase (s. I d.C.) con mobiliario y decoración propia de la época y zona. 1,7 millones de polígonos, texturas 1K a 4K <strong>Ref</strong>: <a href="https://zenodo.org/api/files/27fbfa78-979c-4cf2-b362-06daea9d5b45/RecreacionDomusLeseraVR.rar">RecreacionDomusLeseraVR.rar </a></li> </ul> <p> </p> <p><strong>Online:</strong></p> <p><a href="https://sketchfab.com/3d-models/domus-moleta-dels-flares-forcall-castellon-439ca323bc9a43bca6f3775c6e043f67">Visor de Modelo 3D</a> │Sketchfab</p> <p><a href="https://skfb.ly/6WGEF">Visor de Modelo 3D Recreación</a> │Sketchfab</p> <p><a href="https://www.pointbox.xyz/clouds/5f8b368f9cad5d7421eeae37">Nube de Puntos Densa</a> │ Point Box</p> <p><a href="https://www.youtube.com/watch?v=yIOjo3ua7to">Video 4K</a> │ YouTube</p> <p> </p> <p>Sistema de referencia geodésico: <strong>ETRS 89 UTM 30 (EPSG 25830)</strong><br> Altitudes Ortométricas referidas al nivel medio del mar de Alicante.</p> <p>Modelo de Geóide <strong>EGM08</strong> con sobrecorreción de nivelación.</p>
Assessing corrosion resistance of 2D nanomaterial-based coatings on stainless steel substrates
<p><span>Two dimensional (2D) materials have elicited considerable interest in the past decade due to a diverse array of novel properties ranging from high surface to mass ratios, a wide range of band gaps (insulating boron nitride to semiconducting transition metal dichalcogenides), high mechanical strength and chemical stability. Given the superior chemo-thermo-mechanical properties, 2D materials may provide transformative solution to a familiar yet persistent problem of significant socio-economic burden: the corrosion of stainless steel (SS). With this broader perspective, we investigate corrosion resistance properties of stainless steel coated with 2D nanomaterials; molybdenum disulfide (MoS<sub>2</sub>), boron nitride (BN), bulk graphite in 3.5 wt. % aqueous NaCl solution. The nanosheets were prepared by a novel liquid phase exfoliation technique and the coatings were made by a paint brush to achieve uniformity. Open circuit potential (OCP) and potentiodynamic plots indicate the best corrosion resistance is provided by the MoS<sub>2</sub> and the BN coatings. Superior performance of the coating is attributed low electronic conductivity, large flake size, and uniform coverage of SS substrate, which likely impeded the corrosive ions from the solution to diffuse through the coating. </span></p>
Meshes and initial data for 2D and 3D experiments with PyNosh
<p>Data files used in Experiments with PyNosh.</p>
Currently available 3D activity cliffs and 2D-analogs of 3D-cliff compounds
<p>Three dimensional activity cliffs (3D-cliffs) were systematically determined based on currently available X-ray structures in PDB. The list of all 236, 292, 595 3D-cliffs that were identified from the K<sub>i</sub>, IC<sub>50</sub>, and K<sub>i</sub>/IC<sub>50</sub> sets, respectively, is provided. In addition, on the basis of matched molecular pairs, the 2D structural analogs of 3D-cliff compounds identified from ChEMBL database (release 19) are given.</p>
Video data of spontaneous responses of common marmosets (Callithrix jacchus) on 3D and 2D cricket stimuli.
<p>The degree to which nonhuman animals recognize 2D images as representing the corresponding real objects remains debated. The common marmoset monkey (<em>Callithrix jacchus</em>) is often cited as a species which spontaneously shows natural behaviors to 2D images, e.g. grabbing behaviors to insects and fear responses to snakes. In this study, ten marmosets from two different groups were tested with a live cricket, a 3D plastic model, a monochrome image and two video recordings of the cricket.<br> The monkeys showed the grabbing behavior to the real cricket and the 3D plastic model, but to none of the 2D images. Our experiment suggests that depth information is the most important factor eliciting predatory behavior from the marmosets. In behavioral experiments, monkeys' responses toward 2D images of real objects should be carefully interpreted.</p> <p>All session videos are uploaded here with a 'LOG.txt' file which has sessions & timestamps when coded behaviors occurs.</p>
Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving
<p><em>Accepted by NeurIPS 2024 Datasets and Benchmarks Track</em></p> <p>We introduce the RePair puzzle-solving dataset, a large-scale real world dataset of fractured frescoes from the archaelogical campus of Pompeii. Our dataset consists of over 1000 fractured frescoes. The RePAIR stands as a realistic computational challenge for methods for 2D and 3D puzzle solving, and serves as a benchmark that enables the study of fractured object reassembly and presents new challenges for geometric shape understanding. Please visit <a href="https://repairproject.github.io/RePAIR_dataset/">our website</a> for more dataset information, access to source code scripts and for an interactive gallery viewing of the dataset samples.</p> <div> <h3>Access the entire dataset</h3> <p>We provide a compressed version of our dataset in two seperate files. One for the 2D version and one for the 3D version.</p> <p>Our full dataset contains over one thousand individual fractured fragments divided into groups with its corresponding folder and all compressed into their individual sub-set format regarding whether they are 2D or 3D. Regarding the 2D dataset, each fragment is saved as a .PNG image and each group has the corresponding ground truth transformation to solve the puzzle as a <strong><em>.TXT</em></strong> file. Considering the 3D dataset, each fragment is saved as a mesh using the widely <strong><em>.OBJ</em></strong> format with the corresponding material (<strong><em>.MTL</em></strong>) and texture (<strong><em>.PNG</em></strong>) file. The meshes are already in the assembled position and orientation, so that no additional information is needed. All additional metadata information are given as <strong><em>.JSON</em></strong> files.</p> <p> </p> <h1>Important Note</h1> <p><strong>Please be advised that downloading and reusing this dataset is permitted only upon acceptance of the following license terms.</strong></p> <p><em><strong>The Istituto Italiano di Tecnologia (IIT) declares, and the user (“User”) acknowledges, that the "RePAIR puzzle-solving dataset" contains 3D scans, texture maps, rendered images and meta-data of fresco fragments acquired at the Archaeological Site of Pompeii. IIT is authorised to publish the RePAIR puzzle-solving dataset herein only for scientific and cultural purposes and in connection with an academic publication referenced as Tsemelis et al., "Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving", NeurIPS 2024. Use of the RePAIR puzzle-solving dataset by User is limited to downloading, viewing such images; comparing these with data or content in other datasets. User is not authorised to use, in particular explicitly excluding any commercial use nor in conjunction with the promotion of a commercial enterprise and/or its product(s) or service(s), reproduce, copy, distribute the RePAIR puzzle-solving dataset. User will not use the RePAIR puzzle-solving dataset in any way prohibited by applicable laws. RePAIR puzzle-solving dataset therein is being provided to User without warranty of any kind, either expressed or implied. User will be solely responsible for their use of such RePAIR puzzle-solving dataset. In no event shall IIT be liable for any damages arising from such use.</strong></em></p> </div>
TINKER_WP3_2D&3D images - profile scans dataset_221123
<p>2D and 3D images of PCBs showing the gap 2D and 3D information. Moreover, profile measurements are also included in x and y axes.</p>
Orthophotos and 2D hydraulic modelling results used for habitat suitability modelling of the River Inn section (river km 35.3-48) in SE Germany
<p>The aerial RGB picture acquisition was performed on October 7 (bypass channel) and 11 (side channel) 2022 using a DJI-Matrice 210 V2 RTK drone. For the image acquisition, the drone mounted the DJI Zenmuse X5S RGB camera. The flight was realized at an altitude of about 120 m, ensuring a lateral and longitudinal overlap of the images of about 80%. Gound Control Points (GCPs) have been disposed along the study site, and their position georeferenced using a Emlid Reach RS2 RTK GPS system. After data collection, an RGB orthomosaic with a spatial resolution of 25 cm was generated, using PIX4Dmapper v4.7.5 (www.pix4d.com).</p><p>The hydrodynamic simulations were performed with the freeware software BASEMENT v3.2 (https://basement.ethz.ch), which solves the 2D shallow-water equations using a finite volume approach over two-dimensional unstructured meshes. Computational meshes were created using the QGIS plugin BASEmesh 2, with spatially varying element sizes, which were set to be finer in areas expected to be suitable for spawning and as nursery grounds, or when needed to more accurately represent the local morphological complexity.</p>
Reproducibility of "Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs"
<p>This file is the source data to ensure reproducibility of the paper "Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs". It contains the logs and results of all DFT calculations associated with transition states generated using the model proposed in the paper. It also includes code to reproduce the core findings of the paper, which can be done by running reproduce.sh. To accurately reproduce the results of the paper, use the v1.0.0 virtual environment from "https://github.com/seonghann/tsdiff".</p>
Label-free imaging of DNA interactions with 2D materials
<p>Raw images and data analysis related to the manuscript entitled "Label-free imaging of DNA interactions with 2D materials"</p>
Data for: Fractional Topological Charges in 2D Magnets
<p>We provide the data files and, where relevant, simulation files (written in go) for each of the four figures in the main text.</p> <p>To run the .go files, place them in the same directory with the .smp files and execute sbatch "simulation_file_name.smp". Note that "drivenDefect.smp" will only run if the initial magnetisation data file "m_200x1000y_centred_Delta_1.25_NegB0_arrIndB0_2.ovf" is placed in a subdirectory "initialDefectCentred" of the same directory. </p> <p>We use three different ways of presenting the data for magnetisation, topological charge density and the velocity data for Fig. 4c. Magnetisation data follow the format "{{x,y,z},{Mx,My,Mz}}", while topological charge density data follow "..{x,y,\rho_{top}}..". Note that the y index runs over the outer lists, while the x index runs over the inner lists, so to retrieve e.g. the 5th column and 3rd row of a data file, use dataFile[[5,3]], after importing the data file into "dataFile". For the velocity data, we use "{w,vx,vy}.." (note that only the vy data was used in Fig.1c).</p>
2D Germanane-MXene Heterostructures for Cations Intercalation in Energy Storage Applications
<p>Raw Data of the full Article "2D Germanane-MXene Heterostructures for CationsIntercalation in Energy Storage Applications"</p> <p> </p>
Thermal modeling of subduction zones with prescribed and evolving 2D and 3D slab geometries data
<p>Deforming subduction zone finite element model temperature, velocity, surface and flux field data as reported in the work:</p> <p>N. Sime, C. R. Wilson and P. E. van Keken<br> Thermal modeling of subduction zones with prescribed and evolving 2D and 3D slab geometries.</p>
Temperature, pressure, permebility datasets of a 2D hydrothermal model
<p>The numerical datasets are temperatures, pressures, permeabilities of a 2D natural-state hydrothermal system simulated using TOUGH2.</p> <p>The numerical datasets were used to evaluate a neural network proposed in the following article:</p> <p>Ishitsuka et al. Modeling unobserved geothermal structures using a physics-informed deep neural network with transfer learning of prior knowledge, under review.</p> <p>#----------------------------------------------------------------------------------------------</p> <p>(Dataset 1: Dataset_Reference.csv, Pretrain_Datasets.zip)</p> <p>The hydrothermal system had a 2D space with a horizontal width of 2 km and a depth of 1.6 km. The system consists of five geological units with different permeability, and had a fault-controlled permeable region extending vertically. The hydrothermal fluid flowed though the vertical permeable zone.</p> <p>(Dataset2: Dataset_Reference_Ungaran-Base.csv, Pretrain_Datasets_Ungaran-Base.zip)</p> <p>The permeability structure of the numerical model is constructed based on the hydrothermal model in Jatmiko et al. (2022). A 10 km long and 5 km deep region of the numerical model is divided into 5,000 elements at 100 m intervals. In the hydrothermal model, the hydrothermal upflow zone is around the summit of the mountain.</p> <p>Jatmiko BW et al. Resource assessment of Ungaran geothermal field using numerical model and monte carlo simulation. IOP Conference Series: Earth and Environmental Science 2022;1031:2021.</p> <p>#----------------------------------------------------------------------------------------------</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.