Dunhuang dance is an artistic treasure of traditional Chinese culture, with a long history and an important component of Dunhuang culture. Its digital preservation, display, and research are of great significance. To promote the digitalization process and development of Dunhuang dance, this study proposes to combine Dunhuang dance with 3D human pose estimation technology to construct a Dunhuang dance 3D action database. This database divides Dunhuang dance into 7 themes, 83 basic movements, and 16 long movements. Good results have been achieved in quantitative, qualitative, and manual evaluations, laying the foundation for the preservation, application, and development of Dunhuang dance; This provides new ideas for the research, promotion, and inheritance of Dunhuang culture. The future use of this database can be applied to generative artificial intelligence, digital exhibitions and performances of Dunhuang dance culture, education and research of Dunhuang dance, digital media and entertainment of Dunhuang dance, etc.

Volume measurement of for example a tumor in a 3D image dataset is an important and often performed task. The problem is to segment the tumor out of this volume in order to measure its dimensions. This problem is complicated by the fact that the tumors are often connected to vessels and other organs. According to the present invention, an automated method and corresponding device and computer software are provided, which analyze a volume of interest around a singled out tumor, and which, by virtue of a 3D distance transform and a region drawing scheme advantageously allow to automatically segment a tumor out of a given volume.

This dataset is about books. It has 6 rows and is filtered where the book subjects is Three-dimensional imaging in astronomy. It features 9 columns including author, publication date, language, and book publisher.

This three-dimensional (3-D) building massing model of New York City is one of the many digital tools provided by the Office of Technology and Innovation. There are three file formats available to download via the buttons below. For more information on all the tools available, view the OTI's Digital Tools web page.

Dataset of shape factor between two cylinders randomly oriented in three-dimensional space computed using finite element method.

The Voxel dataset is a constructed dataset of 3D shapes designed to present a unique problem for ML and NAS tools. Instead of a photo of a 3D object, we exploit ML's ability to work across N number of 'colour' channels and use this dimension as a third dimension for images. This dataset is one of the three hidden datasets used by the 2024 NAS Unseen-Data Challenge. The images include 70,000 generated 3D Images of seven different shapes that we generated by creating a 20x20x20 grid of points in 3d space, and randomly generated different 3D shapes (see below) and recorded which of the points the shape collided with, generating the voxel like shapes in the dataset. The data has a shape of (n, 20, 20, 20) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing). For each class (shape), we generated 10,000 samples evenly distributed between the three sets. The three classes and corresponding numerical labels are as follows: Sphere: 0, Cube: 1, Cone: 2, Cylinder: 3, Ellipsoid: 4, Cuboid: 5, Pyramid: 6

NumPy (.npy) files can be opened through the NumPy Python library, using the numpy.load() function by inputting the path to the file into the function as a parameter. The metadata file contains some basic information about the datasets, and can be opened in many text editors such as vim, nano, notepad++, notepad, etc

Dataset of the article 'Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements' (https://doi.org/10.1017/jfm.2024.432). The codes processing data here are on https://github.com/erc-nextflow/3D-GAN. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 949085, NEXTFLOW). Views and opinions expressed are, however, those of the authors only, and do not necessarily reflect those of the European Union or the ERC. Neither the European Union nor the granting authority can be held responsible for them. A.C.M. acknowledges financial support from the Spanish Ministry of Universities under the Formación de Profesorado Universitario (FPU) programme 2020. R.V. acknowledges financial support from ERC (grant agreement no. 2021-CoG-101043998, DEEPCONTROL).

Three-dimensional (3D) urban structures play a critical role in informing climate mitigation strategies aimed at the built environment and facilitating sustainable urban development. Regrettably, there exists a significant gap in detailed and consistent data on 3D building space structures with global coverage due to the challenges inherent in the data collection and model calibration processes. In this study, we constructed a global urban structure dataset (GUS-3D), including building volume, height, and footprint information, at a 500 m spatial resolution using extensive satellite observation products and numerous reference building samples. Our analysis indicated that the total volume of buildings worldwide in 2015 exceeded 1 × 1012 m3. Over the 1985 to 2015 period, we observed a slight increase in the magnitude of 3D building volume growth (i.e., it increased from 166.02 km3 during the 1985–2000 period to 175.08 km3 during the 2000–2015 period), while the expansion magnitudes of the two-dimensional (2D) building footprint (22.51 × 103 km2 vs. 13.29 × 103 km2) and urban extent (157 × 103 km2 vs. 133.8 × 103 km2) notably decreased. This trend highlights the significant increase in intensive vertical utilization of urban land. Furthermore, we identified significant heterogeneity in building space provision and inequality across cities worldwide. This inequality is particularly pronounced in many populous Asian cities, which has been overlooked in previous studies on economic inequality. The GUS-3D dataset shows great potential to deepen our understanding of the urban environment and creates new horizons for numerous 3D urban studies.

The 3D Global Building Footprints (3D-GloBFP) dataset is the first global-scale building height dataset at the individual building footprint level for the year 2020, generated through the integration of multisource Earth Observation (EO) data and the extreme gradient boosting (XGBoost) model. The reliability and accuracy of 3D-GloBFP have been validated across 33 subregions, achieving R² values ranging from 0.66 to 0.96 and root-mean-square errors (RMSEs) between 1.9 m and 14.6 m. The dataset is divided into spatial grid-based tiles, each stored as an individual ShapeFile (.shp) containing estimated building heights (in meters) in attribute tables. See world_grid.shp and readme.txt at https://doi.org/10.5281/zenodo.11319912 for grid partitioning and naming details.

These data were collected as part of the Great Lakes Restoration Initiative (GLRI) project template 678-1 entitled "Evaluate immediate and long-term BMP effectiveness of GLRI restoration efforts at urban beaches on Southern and Western Lake Michigan". This project is evaluating the effectiveness of projects that are closely associated with restoration of local habitat and contact recreational activities at two GLRI funded sites in Southern Lake Michigan and one non-GLRI site in Western Lake Michigan. Evaluation of GLRI projects will assess whether goals of recipients are on track and identify any developing unforeseen consequences. Including a third, non-GLRI project site in the evaluation allows comparison between restoration efforts in GLRI and non-GLRI funded projects. Projections and potential complications associated with climate change impacts on restoration resiliency are also being assessed. Two of the three sites to receive evaluation represent some of the most highly contaminated beaches in the United States and include restoration BMPs which could benefit urban beaches and nearshore areas throughout the Great Lakes. The urban beaches chosen for evaluation are at various stages of the restoration process and located in Indiana (Jeorse Park Beach), Illinois (63rd Street Beach), and Wisconsin (North Beach). Evaluation of effectiveness of restoration efforts and resiliency to climate change at urban beaches will provide vital information on the success of restoration efforts and identify potential pitfalls that will help maximize success of future GLRI beach and nearshore restoration projects. Data used for evaluation include continuous monitoring and synoptic mapping of nearshore currents, bathymetry, and water quality to examine nearshore transport under a variety of conditions. In addition, biological evaluations rely upon daily indicator bacteria monitoring, microbial community and shorebird surveys, recreational usage, and other ancillary water quality data. The pre- and post-restoration datasets comprised of these physical, chemical, biological, geological, and social data will allow restoration success to be evaluated using a science-based approach with quantifiable measures of progress. These data will also allow the evaluation of the resiliency of these restoration efforts under various climate change scenarios using existing climate change predictions and models. This data release is comprised of three-dimensional point measurements of basic water-quality parameters in coastal Lake Michigan at 63rd Street Beach near Chicago, Illinois, on September 2, 2015. Water-quality parameters include temperature, specific conductance, pH, dissolved oxygen, turbidity, total chlorophyll, and phycocyanin concentration. These data were collected using a YSI EcoMapper autonomous underwater vehicle (AUV) equipped with a YSI 6600 V2-4 bulkhead housing a YSI 6560FR fast response temperature/conductivity probe, YSI 6589FR fast response pH sensor, YSI 6150 ROX optical dissolved oxygen sensor, YSI 6136 turbidity sensor, YSI 6025 chlorophyll sensor, and YSI 6131 BGA-PC phycocyanin (blue-green algae) sensor. All parameters were sampled at 1-second intervals as the AUV completed the pre-programmed survey pattern of the nearshore zone. The AUV was programmed to continually undulate between the water surface and 4 feet above the bottom (dive angle of 15 degrees) as it moved at 2 knots between programmed waypoints along it survey mission path. The resulting dataset allows for analysis of the three-dimensional distributions of water-quality parameters in Lake Michigan at 63rd Street Beach.

This is a dataset from three-dimensional constrained variational analysis (3DCVA). It can be used to generate large-scale forcing data for SCM/CRM/LES, or evaluate model results.

The Datasets folder contains 3 datasets, corresponding to the datasets mentioned in the article with 670, 2691 and 586 images. The Runs folder has all the runs used. Besides the images itself it also contains tags for each image under /entry_1/image_1/detector_and_photon_corrected/tags. The headings attribute gives the name of each tag. The Jonas Strict tag corresponds to the 670 images, the Redflamingo tag to the 2691 images and the Redflamingo_Ida_subset tag to the 586 images. The final dataset with 260 images can be obtained from Datasets/amoc6914-Redflamingo-manual-586.h5 and use the /model/diameterNM group to select the 260 images with size closest to 214 nm.

Here we present an AM simulation data generated from Autodesk Netfabb Local Simulation, a non-linear finite element thermo-mechanical solver for additive manufacturing (AM) processes. The dataset contains two simulation trials based on Ti-6Al-4V material under various processing conditions. The simulation software generates fine-scale thermal data of laser scanning path and corresponding mechanical properties data across all time frames. Each dataset contain timestep folders and all related thermal and mechanical properties data can be found within timestep folders.

To ensure the best possible achievement, the temperature is measured periodically using a mercury thermometer and it is range (20c - 22c). The ventilation is also good, which prevented the overheating of the camera. The camera is placed away from direct sunlight and to ensure good lighting, the brightness measured frequently using the Lux Light Meter application on Samsung galaxy note nine and it in range (76 Lux - 87 Lux). The Kinect camera is placed at a height of 0.75m. The recording was started thirty minutes after the camera had turned on. Children were asked to walk along a line, at normal speed, towards the Kinect camera. The cameras recorded color video and skeleton tracking videos ten times then choosing one suitable gait cycle. Each time the participant walks about two gait cycles in the range of [1.5m to 4m] in front of the camera. Then extracting one gait cycle to use in the following stages.

This data release contains a digital geospatial dataset generated by the U.S. Geological Survey under a cooperative agreement with the Water Replenishment District of Southern California to characterize the three-dimensional hydrogeology of the Los Angeles Coastal Plain. The geospatial dataset covers about 580 square miles of the largest coastal plain of southern California and contains the unit tops and extents of the three-dimensional chronostratigraphic model (3D CM) and supporting well data. The 3D CM was constructed in EarthVision using methods described in Development of a Groundwater-Simulation Model in the Los Angeles Coastal Plain, Los Angeles County, California; Chapter B (Ponti and Martin, 2021). Specifically, the geospatial database contains (1) a shapefile (point feature class) for the altitude of the top of each chronostratigraphic unit where it exists on a spatial grid in the 3D CM; and (2) a shapefile (point feature class) of wells used to construct the 3D CM including their corresponding name, location, source of well data, and depths of stratigraphic picks.

This dataset contains observer data and stimulus information for the below publication. Refer to this manuscript for more details.

Laysa Hedjar, Matteo Toscani, and Karl R. Gegenfurtner, "The importance of hue: color discrimination of three-dimensional objects and two-dimensional discs," Journal of the Optical Society of American A 42(5), 1-9 (2025), doi:10.1364/JOSAA.544380

Experimental participant data is available in thresholds.csv:

ptID: participant ID number

JND (DT units): just-noticable difference threshold in detection threshold units

stimulus type: 3D blob stimulus or 2D disc stimulus

quadrant: quadrant of DKL color space

color dimension: dimension of color difference (hue or chroma)

direction: direction of color difference (increasing or decreasing for chroma, clockwise [cw] or counter-clockwise [ccw] for hue)

Participant data used to scale DKL color space is available in detection_thresholds.csv:

ptID: participant ID number

JND (arbitrary DKL units): just-noticable detection threshold

angle: angle measured in DKL color space (Angle 0 is the positive L-M axis, Angle 90 is the positive S-(L+M) axis)

Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large-scale numerical models can be resource intensive. With the data and computer codes in this data release users can (1) reconstruct and run 115 General Simulation Models (GSMs) of groundwater flow, (2) calculate groundwater age metrics at selected GSM cells, (3) train a boosted regression tree model using the provided data, (4) predict three-dimensional continuous groundwater age metrics across the Glacial Principal Aquifer, and (5) predict tritium concentrations at wells for comparison with measured tritium concentrations. The computer codes in this data release are in the form of Python scripts and Jupyter Notebooks. Users will need to have these Python resources installed on their computers to run the codes. Instructions for creating the Python environment can be found in the file Creating the Python environment.txt. Users who would rather not run the scripts but who wish to obtain the final data sets can do so by downloading the file Output--Predictions.7z. Users who wish to reproduce the data sets in this release can do so by downloading, unzipping, and running the data workflow in Starn_GW_Residence_Time_Data_and_Scripts.7z. The codes in this file use relative pathnames, so the directory structure within this file should not be changed. The ".7z" file extension indicates 7-Zip files, http://www.7-zip.org Executables--MODFLOW and MODPATH executable files provided for convenience. These are Windows 64-bit versions. Step 1--Create General Simulation Models--Codes to create 115 GSMs Step 2--Data preparation--Calculate residence time distributions at selected GSM cells Step 3--Metamodel training--Train a boosted regression tree metamodel (XGBoost) Step 4--Metamodel prediction--Predict age metrics throughout the Glacial Aquifer Step 5--Tritium simulation --Calculate tritium concentration at selected wells

"Detection method for pairs of P and S waves of deep low-frequency earthquakes using a 3D array in the Tokai area of the Nankai subduction and its application to hypocenter determination" by Sadaomi Suzuki, Makoto Okubo, Kazutoshi Imanishi, and Naoto Takeda, submitted in G-Cubed.

The U.S. Geological Survey (USGS) used the Borehole data to characterize the hydrogeology in the Central Valley of California for the updated Central Valley Hydrologic Model (CVHM2). These data encompass the inputs and outputs for the three-dimensional (3D) hydrogeologic Framework for the Central Valley, California, a part of the Central Valley Hydraulic Framework version 2 (CVHM2) project. These files include the shapefiles with flow layers (Flow_model_grid.zip), a scatter dataset of the modeled Central Valley (PercentCoarse_CV_50ft.csv), and the well log information and kriging parameters for the Central Valley (Kriging_Table.xlxs).

Liaodactylus primus is the earliest filter-feeding pterosaur, discovered from the Late Jurassic Tiaojishan Formation in Jianchang County, Liaoning Province of China, dated to about 160 Million years ago (161.8±0.4 – 159.5±0.6 Ma). The discovery of Liaodactylus primus documents a specialized feeding strategy among pterosaurs in the Mesozoic Yanliao Biota in Northern China, which contributes to understanding ecological diversity in reptiles of the biota. Here we provide a dataset of the holotype of Liaodactylus primus (PMOL-AP00031) from X-ray Computed Tomography (X-ray CT), including original CT slice images, reconstructed three-dimensional CT models of the mandibles, animated videos, and morphological character matrix for phylogenetic analysis. Data from CT scanning the skull of Liaodactylus primus will contribute to further studies of pterosaurs regarding their morphology, ecology, and phylogeny. Three-dimensional virtual models of the mandible of Liaodactylus primus can be 3D printed, with broad applications in research, teaching, and public outreach.