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.

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.

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

This data release contains a geospatial database related to a digital 3D geologic framework of the Rio San Jose watershed, New Mexico. The geospatial database contains two main data elements: (1) input data to the 3D framework model; (2) interpolated elevations and thicknesses of stratigraphic units as a cellular array. Input surface and subsurface data for 18 stratigraphic units have been condensed to points that define the elevation of the top of each stratigraphic unit; these point data sets serve as the digital input to the framework model. The point data are derived from geologic maps, cross sections, oil and gas wells, water wells, structure contour maps, and thickness maps. Additional input geologic features that either cut or overlay the stratigraphic units in the model are provided as separate features classes, including the location of faults, volcanic dikes, and volcanic vents. The interpolated elevations and thickness of stratigraphic units are presented as a cellular array: essentially a “flattened”, two-dimensional representation of the digital 3D geologic framework, which defines the elevation and thickness of 18 geologic units within the geologic framework model. The elevation and thickness of the geologic units are contained within a single polygon feature class ModelCells, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each geologic unit. The elevation and thickness of the geologic units are also provided as individual raster layers in geoTIFF format. The 3D model output is described in a file as an ascii array of points: the 3D model was sampled within a 3D modeling program with a 3D array of nodes with 500-m spacing in the X and Y directions and 50-m in the Z direction. The 3D model was sampled at each node and the model unit intersected at the node saved as a coded value. This array of X,Y,Z coordinates and the coded formation values is presented as a CSV ascii file. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units, and a Data Dictionary that duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles.

This data set has been generated using dried vertebrae with an image-based algorithm. It contains 86 models of lumbar vertebrae. For more details please look at the paper enclosed with this data set. It will be published in Data Science Journal. To open the files, please use MeshLab or any other 3D viewer. The files are OBJ extension.

50% and 75% respectively

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.

Although modern fluorescence microscopy produces detailed three-dimensional (3D) datasets, colocalization analysis and region of interest (ROI) selection is most commonly performed two-dimensionally (2D) using maximum intensity projections (MIP). However, these 2D projections exclude much of the available data. Furthermore, 2D ROI selections cannot adequately select complex 3D structures which may inadvertently lead to either the exclusion of relevant or the inclusion of irrelevant data points, consequently affecting the accuracy of the colocalization analysis. Using a virtual reality (VR) enabled system, we demonstrate that 3D visualization, sample interrogation and analysis can be achieved in a highly controlled and precise manner. We calculate several key colocalization metrics using both 2D and 3D derived super-resolved structured illumination-based data sets. Using a neuronal injury model, we investigate the change in colocalization between Tau and acetylated α-tubulin at control conditions, after 6 hours and again after 24 hours. We demonstrate that performing colocalization analysis in 3D enhances its sensitivity, leading to a greater number of statistically significant differences than could be established when using 2D methods. Moreover, by carefully delimiting the 3D structures under analysis using the 3D VR system, we were able to reveal a time dependent loss in colocalization between the Tau and microtubule network as an early event in neuronal injury. This behavior could not be reliably detected using a 2D based projection. We conclude that, using 3D colocalization analysis, biologically relevant samples can be interrogated and assessed with greater precision, thereby better exploiting the potential of fluorescence-based image analysis in biomedical research.

The TROPESS Chemical Reanalysis O3 Increment Monthly 3-dimensional Product contains the ozone increment by data assimilation. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.

Three-dimensional (3D) visualization has opened up a Universe of possible scientific data representations. 3D printing has the potential to make seemingly abstract and esoteric data sets accessible, particularly through the lens of translating data into forms that can be explored in the tactile modality for people who are blind or visually impaired. This article will briefly review 3D modeling in astrophysics, astronomy, and planetary science, before discussing 3D printed astrophysical and planetary geophysical data sets and their current and potential applications with non-expert audiences. The article will also explore the prospective pipeline and benefits of other 3D data outputs in accessible scientific research and communications, including extended reality and data sonification.

The TROPESS Chemical Reanalysis NO 6-Hourly 3-dimensional Product contains vertical concentrations of nitric oxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.

The TROPESS Chemical Reanalysis O3 Increment 6-Hourly 3-dimensional Product contains the ozone increment by data assimilation. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.

This data set is used in evaluating small three-dimensional region forgery detection of videos

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.

The TROPESS Chemical Reanalysis Surface Aerosol NO3 Monthly 3-dimensional Product contains the volume mixing rations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.

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 11 April 2016, http://data.bioregionalassessments.gov.au/dataset/98e49f7c-28a6-4ad1-83c7-fdf606954fbc.

The ARPA-E funded TERRA-REF project generated open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active fluorescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets. Data were collected at the University of Arizona Maricopa Agricultural Center in Maricopa, Arizona. This site hosts a large field scanner with fifteen sensors, many of which are capable of capturing mm-scale images and point clouds at daily to weekly intervals. These data are intended to be reused and are accessible as a combination of files and databases linked by spatial, temporal, and genomic information. In addition to providing open access data, the entire computational pipeline is open source, and we enable users to access high-performance computing environments. The study has evaluated a sorghum diversity panel, biparental cross populations, and elite lines and hybrids from structured sorghum breeding populations. In addition, a durum wheat diversity panel was grown and evaluated over three winter seasons. The initial release includes derived data from two seasons in which the sorghum diversity panel was evaluated. Future releases will include data from additional seasons and locations. The TERRA-REF reference dataset can be used to characterize phenotype-to-genotype associations, on a genomic scale, that will enable knowledge-driven breeding and the development of higher-yielding cultivars of sorghum and wheat. The data is also being used to develop new algorithms for machine learning, image analysis, genomics, and optical sensor engineering. Resources in this dataset:Resource Title: Link to dataset at Datadryad.org. File Name: Web Page, url: https://datadryad.org/stash/dataset/doi:10.5061/dryad.4b8gtht99 The ARPA-E funded TERRA-REF project is generating open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active flourescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets.

No description is available. Visit https://dataone.org/datasets/e253a09fc8cb774bf504dae528ec5cbf for complete metadata about this dataset.

SOLIDWORKS & MATLAB Data Files

Supplementary data to reproduce figures for: Three-dimensional Configuration of Induced Magnetic Fields around Mars