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

ATOM3D is a unified collection of datasets concerning the three-dimensional structure of biomolecules, including proteins, small molecules, and nucleic acids. These datasets are specifically designed to provide a benchmark for machine learning methods which operate on 3D molecular structure, and represent a variety of important structural, functional, and engineering tasks. All datasets are provided in a standardized format along with a Python package containing processing code, utilities, models, and dataloaders for common machine learning frameworks such as PyTorch. ATOM3D is designed to be a living database, where datasets are updated and tasks are added as the field progresses.

Description from: https://www.atom3d.ai/

Lunar regolith simulants are manufactured in order to provide a higher volume, much less expensive and more available source of material, compared to real lunar regolith material, upon which to test various instruments and machines that are being designed to operate on the lunar surface. There are many sources of these materials. However, the three-dimensional (3D) shape of these materials has never been characterized and used to compare to each other and to real lunar regolith material brought back from the Apollo missions. The focus of this database is to provide 3D shape and size information for each of 17 lunar regolith materials (8 mare, 9 highland). Over 1.1 million particles are in this database, with their 3D shape stored as STL files. Geometric information about each particle is in the database, as well as the original X-ray CT images from which the particles were extracted.

Three-dimensional data arrays (collections of individual data matrices) are increasingly prevalent in modern data and pose unique challenges to pattern extraction and visualization. This article introduces a biclustering technique for exploration and pattern detection in such complex structured data. The proposed framework couples the popular plaid model together with tools from functional data analysis to guide the estimation of bicluster responses over the array. We present an efficient algorithm that first detects biclusters that exhibit strong deviations for some data matrices, and then estimates their responses over the entire data array. Altogether, the framework is useful to home in on and display underlying structure and its evolution over conditions/time. The methods are scalable to large datasets, and can accommodate a variety of dynamic patterns. The proposed techniques are illustrated on gene expression data and bilateral trade networks. Supplementary materials are available online.

Wellington 3D buildings modelled from high resolution imagery captured between 29/01/2022 and 03/03/2022. Part of a series of 3D Building data sets covering Wellington City's urban area.We have GDB, DWG & SKP files available for download via S3 Bucket.Intended Purpose: To provide a three dimensional, spatially accurate representation of Wellington's buildings. Refresh Rate: This is a static dataset.Ownership: WCC and AAM Stewardship: Corporate GIS team at WCCSummary of Data Collection:High resolution imagery captured between 29th of Jan 2022 and 3rd of March 2022 with 7.5cm GSD using Vexcel camera system. Further information is available in the 2022 3D building metadata pdf.

This volume contains data from the Imager for Mars Pathfinder 3D Position data set.The data set consists of tables of three-dimensional spatial coordinates of each pixel in an IMP EDR stereo pair. The coordinates are derived from the Ames Stereo Pipeline, an automated machine vision algorithm that correlates features between the left and right images of stereo pairs to determine their disparity, then computes their position using the stereo camera properties

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This three-dimensional visualisation product is intended to accompany the reports from the Great Artesian Basin Water Resource Assessment, specifically "The three-dimensional visualisation of the Great Artesian Basin" report (Nelson et al., 2012). The report describes products, outputs and outcomes of the three-dimensional (3D) visualisation component of the Great Artesian Basin Water Resource Assessment (the Assessment). This report specifically encompasses the following topics associated with the 3D visualisation component: - The requirements and potential benefits - The effective datasets - Methodology used in content creation - The output datasets - Discussions regarding outcomes, limitations and future directions The Assessment is designed to assist water managers in the Great Artesian Basin (GAB) to meet National Water Initiative commitments. The key datasets of the 3D visualisation component include contact surfaces between major aquifers and aquitards with coverage of significant portions of the GAB, well lithostratigraphic and wire-line data and hydrogeochemistry produced by State and National Agencies. These datasets are manipulated within GOCAD® to develop the 3D visualisation component and communication products for use by end users to assist visualisation and conceptualisation of the GAB. While many options have been investigated for distribution of these 3D products, 2D screen captures and content delivery via the Geoscience Australia (GA) World Wind 3D data viewer will be the most efficient and effective products. This 3D visualisation should be viewed in reference to the "Lexicon of the lithostratigraphic and hydrogeological units of the Great Artesian Basin and its Cenozoic cover" report (Radke et al., 2012) also created as part of the Assessment. LINEAGE (continued from Lineage field) REFERENCES 1. Welsh, W.D. 2000. GABFLOW: A steady state groundwater flow model of the Great Artesian Basin, Bureau Rural Sciences. Canberra. 2. Nelson GJ, Carey H, Radke BM and Ransley TR (2012) The three-dimensional visualisation of the Great Artesian Basin. A report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia. 3. Radke BM, Kellett JR, Ransley TR and Bell JG (2012) Lexicon of the lithostratigraphic and hydrogeological units of the Great Artesian Basin and its Cenozoic cover. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia. 4. Ransley TR and Smerdon BD (eds) (2012) Hydrostratigraphy, hydrogeology and system conceptualisation of the Great Artesian Basin. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia. 5. Senior and associates (1997). Geoscience Australia internal data set and contour interpretations by Senior B. Canberra, Groundwater Group, Environmental Geoscience Division, Geoscience Australia. 6. Van der Wielen S, Kirkby A, Britt A, Nicoll M and Skirrow R (in prep.) An integrated, multiuse 3D map for the greater Eromanga Basin, Australia. Geoscience Australia record 2011/XX, Canberra. METHOD The data are visualised in the Geoscience Australia (GA) 3D Data Viewer, a virtual globe application developed at GA using the NASA World Wind Java SDK. A public version of the Viewer is available on the GA website at http://www.ga.gov.au/apps/world-wind, and the source code for the tool is available open-source at http://github.com/ga-m3dv/ga-worldwind-suite.

Full Metadata available from: http://www.ga.gov.au/metadata-gateway/metadata/record/77777/

Dataset History

SOURCE DATA: **HYDROSTRATIGRAPHY Formation bases [available from http://www.ga.gov.au using catalogue numbers listed below ] 01 3-second Digital Elevation Model surface [catalogue #75990] 02 Base of Cenozoic surface [catalogue #75991] 03 Base of Mackunda Formation and equivalents surface [catalogue #76021] 04 Base of Rolling Downs Group surface [catalogue #76022] 05 Base of Hooray Sandstone and equivalents surface [catalogue #76023] 06 Base of Injune Creek Group surface [catalogue #76024] 07 Base of Hutton Sandstone surface [catalogue #76025] 05-07 Base of Algebuckina Sandstone surface [catalogue #76952] 08A Base of Evergreen and Marburg formations [catalogue #76026] 08B Base of Poolowanna Formation [catalogue #76953] 09 Base of Precipice Sandstone and equivalents surface [catalogue #76027] 10 Base of Jurassic-Cretaceous surface [catalogue #76028] Other Formation Bases 1. Beautified surfaces (with minimised crossovers) for formation bases 02 to 10 Constraints 1. Well constraints for formation bases 02 to 10. These are positions in 3D space where the surface is known to intersect. See Nelson et al (2012) for more information. **GROUNDWATER 1. Water table elevation of the Great Artesian Basin [available from http://www.ga.gov.au , catalogue #75830] 2. Modelled potentiometric surface of the Cadna-owie - Hooray aquifer (Welsh, 2000) **BOUNDARIES 1. Revised Great Artesian Basin Jurassic-Cretaceous boundary [available from http://www.ga.gov.au , catalogue # 75904] 2. GABWRA reporting region boundaries for the Carpentaria, Central Eromanga, Surat and Western Eromanga basins (Ransley TR & Smerdon BD (eds), 2012) **GEOLOGY 1. Geoscience Australia - Fault surfaces over 40km Senior and associates (1997). 2. Known structural faults in the Eromanga Basin Vertical fault traces from Van der Wielen et. al. (2011/in press) **GEOSCIENCE AUSTRALIA DATA SETS Additional objects that are optional packages to the Assessment 3D visualisation are sourced from the GA Common Earth Model datasets which include: - 1:1,000,000 scale surface geology maps of Australia - Radiometric map of Australia, 2nd Edition, 2010 - Gravity anomaly map of the Australian Region, 3rd Edition, 2008 - Magnetic anomaly map of Australia, 5th Edition, 2009 - Australia's Dynamic Land Cover Map, 1st Edition, 2010 *** References and method listed in Abstract due to space constraints in Lineage field ***

Dataset Citation

Geoscience Australia (2013) Three-dimensional visualisation of the Great Artesian Basin - GABWRA. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/98e49f7c-28a6-4ad1-83c7-fdf606954fbc.

Wellington 3D buildings modelled from high resolution imagery captured between 29/01/2022 and 03/03/2022. Part of a series of 3D Building data sets covering Wellington City's urban area.We have GDB, DWG & SKP files available for download via S3 Bucket.Intended Purpose: To provide a three dimensional, spatially accurate representation of Wellington's buildings. Refresh Rate: This is a static dataset.Ownership: WCC and AAM Stewardship: Corporate GIS team at WCCSummary of Data Collection:High resolution imagery captured between 29th of Jan 2022 and 3rd of March 2022 with 7.5cm GSD using Vexcel camera system. Further information is available in the 2022 3D building metadata pdf.

Wellington 3D buildings modelled from high resolution imagery captured between 29/01/2022 and 03/03/2022. Part of a series of 3D Building data sets covering Wellington City's urban area.We have GDB, DWG & SKP files available for download via S3 Bucket.Intended Purpose: To provide a three dimensional, spatially accurate representation of Wellington's buildings. Refresh Rate: This is a static dataset.Ownership: WCC and AAM Stewardship: Corporate GIS team at WCCSummary of Data Collection:High resolution imagery captured between 29th of Jan 2022 and 3rd of March 2022 with 7.5cm GSD using Vexcel camera system. Further information is available in the 2022 3D building metadata pdf.

Three-dimensional model of up-to-date mapping buildings. The extrusion process to generate the geometry in 3D is carried out from the restitution of all the roof lines of the building layer of the municipal base cartography at 1:1000 scale. It obtains a level of detail LOD2 (level of definition) of great geometric precision in cover. Next to the download links in I3S/SLPK format of the 3D objects is attached the 2D layer of polygon type of the data model, 01_EDIFICATIONS_P, which provides a unique identifier (ID_3D, also existing in 3D geometries) with the main objective of transferring and representing thematic information in the 3D objects themselves. In the Downloads tab you can get the following formats: - Shapefile Multipatch grouped by districts - OBJ of each building grouped by districts. The free Windows 3D Viewer is suggested for viewing. - FBX of each building grouped by districts. The free Windows 3D Viewer is suggested for viewing. NOTICE: In the geoportal of the Madrid City Council, the information and complete services of this data set are available through the following link: Three-dimensional model of buildings of the municipal base cartography Source: Geoportal City Council of Madrid.

Wellington 3D buildings modelled from high resolution imagery captured between 29/01/2022 and 03/03/2022. Part of a series of 3D Building data sets covering Wellington City's urban area.We have GDB, DWG & SKP files available for download via S3 Bucket.Intended Purpose: To provide a three dimensional, spatially accurate representation of Wellington's buildings. Refresh Rate: This is a static dataset.Ownership: WCC and AAM Stewardship: Corporate GIS team at WCCSummary of Data Collection:High resolution imagery captured between 29th of Jan 2022 and 3rd of March 2022 with 7.5cm GSD using Vexcel camera system. Further information is available in the 2022 3D building metadata pdf.

A detailed description of this dataset can be found in https://doi.org/10.1016/j.dib.2023.108903.

This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.

The dataset contains two sets of images:

• “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)

• smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm3 constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see 10.1016/j.cemconcomp.2022.104798 for details.

Note that the same dataset stored in *raw format is available from https://doi.org/10.5281/zenodo.7193819

This data-set contains raw and processed videos of two-photon lithography additive manufacturing processes for various parameters such as light dosages, photo-curable resins, and structures

Wellington 3D buildings modelled from high resolution imagery captured between 29/01/2022 and 03/03/2022. Part of a series of 3D Building data sets covering Wellington City's urban area.We have GDB, DWG & SKP files available for download via S3 Bucket.Intended Purpose: To provide a three dimensional, spatially accurate representation of Wellington's buildings. Refresh Rate: This is a static dataset.Ownership: WCC and AAM Stewardship: Corporate GIS team at WCCSummary of Data Collection:High resolution imagery captured between 29th of Jan 2022 and 3rd of March 2022 with 7.5cm GSD using Vexcel camera system. Further information is available in the 2022 3D building metadata pdf.

This report includes the previously unpublished primary and derivative data sets that underpin the construction of the three dimensional (3D) geologic map of the upper part of the Earth's crust beneath the Sacramento-San Joaquin River Delta, California. The primary data is X,Y,Z locations of stratigraphic horizons and, to a much lesser extent, geologic structures where penetrated by oil and gas wells in the subsurface as recorded in well logs and well records. The derivative data sets were constructed to more closely constrain the principal stratigraphic horizons and geologic structures that were incorporated into the 3D model. The derivative data sets are extracted from the principal data set or a combination of the principal data set with other previously published data. This Data Release is not intended to be a stand-alone publication, but is only intended to release the digital data sets that undergird the related Scientific Investigations Report. The information in this Data Release mainly describes the details of the contents and formats of the digital files. The user is encouraged to refer to the main report for more discussion of sources and methods.

This three-dimensional visualisation product is intended to accompany the reports from the Great Artesian Basin Water Resource Assessment, specifically "The three-dimensional visualisation of the Great Artesian Basin" report (Nelson et al., 2012). The report describes products, outputs and outcomes of the three-dimensional (3D) visualisation component of the Great Artesian Basin Water Resource Assessment (the Assessment). This report specifically encompasses the following topics associated with the 3D visualisation component: - The requirements and potential benefits - The effective datasets - Methodology used in content creation - The output datasets - Discussions regarding outcomes, limitations and future directions The Assessment is designed to assist water managers in the Great Artesian Basin (GAB) to meet National Water Initiative commitments. The key datasets of the 3D visualisation component include contact surfaces between major aquifers and aquitards with coverage of significant portions of the GAB, well lithostratigraphic and wire-line data and hydrogeochemistry produced by State and National Agencies. These datasets are manipulated within GOCAD® to develop the 3D visualisation component and communication products for use by end users to assist visualisation and conceptualisation of the GAB. While many options have been investigated for distribution of these 3D products, 2D screen captures and content delivery via the Geoscience Australia (GA) World Wind 3D data viewer will be the most efficient and effective products. This 3D visualisation should be viewed in reference to the "Lexicon of the lithostratigraphic and hydrogeological units of the Great Artesian Basin and its Cenozoic cover" report (Radke et al., 2012) also created as part of the Assessment. LINEAGE (continued from Lineage field) REFERENCES 1. Welsh, W.D. 2000. GABFLOW: A steady state groundwater flow model of the Great Artesian Basin, Bureau Rural Sciences. Canberra. 2. Nelson GJ, Carey H, Radke BM and Ransley TR (2012) The three-dimensional visualisation of the Great Artesian Basin. A report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia. 3. Radke BM, Kellett JR, Ransley TR and Bell JG (2012) Lexicon of the lithostratigraphic and hydrogeological units of the Great Artesian Basin and its Cenozoic cover. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia. 4. Ransley TR and Smerdon BD (eds) (2012) Hydrostratigraphy, hydrogeology and system conceptualisation of the Great Artesian Basin. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia. 5. Senior and associates (1997). Geoscience Australia internal data set and contour interpretations by Senior B. Canberra, Groundwater Group, Environmental Geoscience Division, Geoscience Australia. 6. Van der Wielen S, Kirkby A, Britt A, Nicoll M and Skirrow R (in prep.) An integrated, multiuse 3D map for the greater Eromanga Basin, Australia. Geoscience Australia record 2011/XX, Canberra. METHOD The data are visualised in the Geoscience Australia (GA) 3D Data Viewer, a virtual globe application developed at GA using the NASA World Wind Java SDK. A public version of the Viewer is available on the GA website at http://www.ga.gov.au/apps/world-wind, and the source code for the tool is available open-source at http://github.com/ga-m3dv/ga-worldwind-suite.

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.

Nanoscale industrial robots have potential as manufacturing platforms, capable of automatically performing repetitive tasks to handle and produce nanomaterials with consistent precision and accuracy. We demonstrate a DNA industrial nanorobot, that fabricates a three-dimensional (3D) optically active, chiral structure from optically inactive parts. By making use of externally controlled temperature and ultraviolet (UV) light, our programmable robot, ~ 100 nanometers in size, grabs different parts, positions and aligns them so that they can be "welded", releases the construct and returns to its original configuration ready for its next operation. Our robot can also self-replicate its 3D structure and functions, surpassing single-step templating (restricted to two-dimensional (2D)) by using folding to access the third dimension and more degrees of freedom. Our introduction of multiple-axis precise folding and positioning as a tool/technology for nanomanufacturing will open the door to more...

This dataset accompanies the research presented in the paper:

Esteban, D.A., Wang, D., Kadu, A., Olluyn, N., Iglesias, A.S., Perez, A.G., Casablanca, J.G., Nicolopoulos, S., Liz-Marzán, L.M. and Bals, S., 2023. Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies. arXiv preprint arXiv:2311.05309. [link]

It provides a comprehensive collection of three-dimensional reconstructions and quantitative descriptors for small colloidal particles. These gold nanoparticles are arranged in tetrahedral and other intricate geometries under both dry and liquid conditions. The dataset contains 3D reconstructions and quantitative indicators such as centroids, volumes, surface areas, solidity measures, and principal axis lengths for assemblies with 4, 5, and 6 particles.

The dataset includes: N4_dry_dart.rec and N4_liquid_dart.rec for the 3D reconstructions of an assembly with 4 particles in dry and liquid conditions respectively; N4_quant_descriptors_dry.mat and N4_quant_descriptors_liquid.mat providing quantitative descriptors for these conditions. Similar files are provided for assemblies with 5 and 6 particles, such as N5_dry_dart.rec, N5_liquid_dart.rec, N5_quant_descriptors_dry.mat, N5_quant_descriptors_liquid.mat, and the corresponding files for N6.

This dataset can be used to study the structural dynamics of nanoparticle assemblies and studies in colloidal chemistry, materials science, and nanotechnology. The .rec files can be visualized using volume rendering software (e.g. Amira or Avizo), while the .mat files contain structured data for analysis in MATLAB. The supporting code and scripts for this dataset are available on the GitHub repository: https://github.com/ajinkyakadu/LiquidET_NatComm2024.

Wellington 3D buildings modelled from high resolution imagery captured between 29/01/2022 and 03/03/2022. Part of a series of 3D Building data sets covering Wellington City's urban area.We have GDB, DWG & SKP files available for download via S3 Bucket.Intended Purpose: To provide a three dimensional, spatially accurate representation of Wellington's buildings. Refresh Rate: This is a static dataset.Ownership: WCC and AAM Stewardship: Corporate GIS team at WCCSummary of Data Collection:High resolution imagery captured between 29th of Jan 2022 and 3rd of March 2022 with 7.5cm GSD using Vexcel camera system. Further information is available in the 2022 3D building metadata pdf.