The raster datasets in this data release are maps of soil surface properties that were used in analyzing different approaches for digital soil mapping. They include maps of soil pH, electrical conductivity, soil organic matter, and soil summed fine and very fine sand contents that were created using both 2D and 3D modeling strategies. For each property a map was created using both 2D and 3D approaches to compare the mapped results.

Author: Megan Banaski (mbanaski@esri.com) and Max Ozenberger (mozenberger@esri.com)Last Updated: 1/1/2024Intended Environment: WebPurpose:Exercise B2: Build a Starter 2D Map or Build a Starter 3D Map This lab is part of GitHub repository that contains short labs that step you through the process of developing a web application with ArcGIS API for JavaScript.The labs start from ground-zero and work through the accessing different aspects of the API and how to begin to build an application and add functionality.Requirements: Here are the resources you will use for the labs.ArcGIS for Developers - Account, Documentation, Samples, Apps, DownloadsEsri Open Source Projects - More source codeA simple guide for setting up a local web server (optional)Help with HTML, CSS, and JavaScript

2D Map For T1 distribution vs T2 distribution curve from Halliburton Logging. Measured in unitless.

2D Web Map depicting the Yorkgate Mall (1 York Gate Blvd, North York, ON M3N 3A1) “Parking Area”. Basic Property Condition Assessment (BPCA) and secondary “Crack and Fracture” Inspection conducted by ACCESSiFLY and Gravity Engineering Inc. on Tuesday, May 11, 2021 during a Transport Canada & NAVCanada approved "RPAS Flight" utilizing a DJI Mavic Pro 2 & Draganfly Commander Airframe equipped with a SONY Q100 Optical DSLR & FLIR Vue Pro-R 19mm 30hz, 640x512 Aerial Thermal Imager. Secondary terrestrial imaging conducted via LiDAR/Laser Scan using a Faro Focus.

https://upload.wikimedia.org/wikipedia/commons/7/79/VEGFR2_bound_to_axitinib.gif" alt="image">

Introduction

This dataset is a replication of the dataset described in the paper Generative Modeling for Protein Structures by Namrata Anand and Po-Ssu Huang. The data is used to train a Generative Adversarial Network with the capability of creating protein structures.

Content

The data is stored in a hdf5 file and is structured in the following manner:

{
 "test_16": "16x16 numpy arrays",
 "train_16": "16x16 numpy arrays",
 "test_64": "64x64 numpy arrays",
 "train_64": "64x64 numpy arrays",
 "test_128": "128x128 numpy arrays"
 "train_128": "128x128 numpy arrays"
}

and contains the following number of numpy arrays:

test_16: 69,713

train_16: 1,820,586

test_64: 11,835

train_64: 331,006

test_128: 3,276

train_128: 98,748

Quickstart

Running the following will yeild ```python3 import h5py import matplotlib.pyplot as plt

dataset = h5py.File('dataset.hdf5', 'r') test_64 = dataset['test_64']

plt.imshow(test_64[1], cmap='viridis') plt.colorbar() plt.show() ``` https://i.imgur.com/lb2bOzo.png" alt="image">

Acknowledgements

@incollection{NIPS2018_7978,
title = {Generative modeling for protein structures},
author = {Anand, Namrata and Huang, Possu},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {7494--7505},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7978-generative-modeling-for-protein-structures.pdf}

https://cdn.rcsb.org/rcsb-pdb/v2/common/images/rcsb_logo.png" alt="image"> H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne. (2000) The Protein Data Bank Nucleic Acids Research, 28: 235-242.

The Navigation Electronic Map market is experiencing robust growth, projected to reach a market size of $3021 million in 2025, exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 25.4% from 2019 to 2033. This expansion is fueled by several key factors. The increasing adoption of smartphones and connected vehicles significantly drives demand for accurate and detailed navigation maps, particularly in the rapidly expanding personal use segment. Furthermore, advancements in mapping technologies, including the transition from 2D to higher-resolution 3D mapping, and the integration of advanced features like augmented reality overlays and real-time traffic updates, are enhancing user experience and driving market growth. The commercial and military sectors also contribute significantly, with logistics companies, delivery services, and defense organizations relying heavily on sophisticated navigation systems for efficient operations and strategic planning. Growth is also expected to be fueled by increasing investment in infrastructure development and smart city initiatives, creating a demand for high-precision navigation solutions. However, certain challenges exist. Data privacy concerns and the increasing complexity of map data management could pose challenges. Competition among established players like Google, TomTom, and ESRI, along with the emergence of new entrants, creates a dynamic and competitive landscape. Despite these restraints, the long-term outlook for the Navigation Electronic Map market remains exceptionally positive. The continued integration of navigation systems into various applications, coupled with ongoing technological innovations, will likely sustain high growth rates throughout the forecast period (2025-2033). The market segmentation, encompassing 2D and 3D maps across personal, commercial, and military applications, indicates diverse avenues for growth and caters to specific user requirements. Geographic expansion, particularly in developing regions with rapidly expanding infrastructure projects, also offers significant potential for market expansion.

The WMTS implementation standard provides a standards-based solution for serviing digital maps using predefined image tiles. Through the constructs of the specification, a WMTS service advertises imagery layers (e.g. imagery product) and defines the coordinate reference system, scale, and tiling grid available for access.

cryoEM map (Fig. 2d) and model (Fig. 3h) of Rad51 mini filament in complex with dsDNA and formation of the 8-base-pair initial D-loop

GeoJunxion uses a combination of methods to make this service very fast and efficient. The map service comes with on-demand tile rendering, often with smart-tiling, and custom styling. With smart tiling, all populated areas are pre-rendered to provide super-fast response to map requests.

KEY FEATURES

• 3 databases: GeoJunxion Maps, OSM Maps, Aerial/Satellite Imagery. • 4 custom map styles: GeoJunxion MapStyle, OSM Generic/Default, OSM Bright, OSM Bright with house numbers • Map tiles are delivered following the Slippy Maps convention.

TYPICAL USE CASES

The OSM Map Tile Server will help to display business locations on a map within a company website, it will also show moving objects on a map within a track & trace application. And furthermore it will also Provide an overview to a company’s assets on a map, as well as include geospatial analysis results within a GIS solution

BENEFITS

OSM Map Tile Server enables you to view online maps within websites or alternatively to view those maps hosted on premise through GIS software

DELIVERY FORMATS API

COVERAGE GeoJunxion, OSM: World Aerial/Satellite Imagery: The Netherlands, Flanders (Belgium)

The GeoJunxion Tile Server is the easiest way to receive map tiles to use within your own organization, application and with your preferred map viewer. The GeoJunxion Tile Server installation is Quick & Easy.

Security: On your own server or in the cloud Smart: Intelligent Map Tiling Quick & Easy: Seamless set-up of map tiles Legal: GeoJuxnion as an European contract party Helpdesk: Support from GeoJunxion with SLA LBS: Additional APIs available

On your own server or in the cloud: With the GeoJunxion Tile Server you can host your own map tiles in your own secure environment. You control your own data and connections. Alternatively, GeoJunxion can host the map tiles in the cloud for you.

OSM for Professional use: GeoJunxion offers enhanced services on top of OpenStreetMap for Professional use. The GeoJunxion Tile Server is part of the OSM for Professionals product portfolio: GeoJunxion will your contract party GeoJunxion can offer support on OSM services based on an agreed SLAControlled QA/QC reports on OpenStreetMap

Slippy Map

The provided map tiles can be used in a modern slippy map web map application which let you zoom and pan around. With a slippy map, basically, the map slips around when you drag the mouse. More info regarding this kind of map, can be found here: https://wiki.openstreetmap.org/wiki/Slippy_Map. Slippy Map - OpenStreetMap Wiki

2D integrated dust extinction maps in the K band, computed using the NICEST algorithm. We also provide the configuration files used to produce the maps. These maps are provided at their native resolution, but were smoothed to match the projected 3D dust maps in the analysis in Zucker et al. 2021. When computing cloud masses AK = 0.15 mag was subtracted from the Pipe extinction map, to account for unassociated background extinction. We caution that for applications other than those discussed in the paper, some of the extinction far beyond the cloud (at the edges of the map) can go slightly negative. This is due to the fact that the dust "zero" point (defined using a low extinction region as close to the cloud as possible) only applies locally near the cloud.

This repository contains fit output details as well as the data files necessary to recreate the results of paper "Eclipse Mapping with MIRI: 2D Map of HD 189733b from 8μm JWST MIRI LRS Observations" by Lally et al., 2025, 10.3847/2041-8213/adc096.

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. Part of the DEEPEN project involved developing and testing a methodology for a 3D play fairway analysis (PFA) for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). This was tested using new and existing geoscientific exploration datasets at Newberry Volcano. This GDR submission includes images, data, and models related to the 3D favorability and uncertainty models and the 2D favorability and uncertainty maps. The DEEPEN PFA Methodology is based on the method proposed by Poux et al. (2020), which uses the Leapfrog Geothermal software with the Edge extension to conduct PFA in 3D. This method uses all available data to build a 3D geodata model which can be broken down into smaller blocks and analyzed with advanced geostatistical methods. Each data set is imported into a 3D model in Leapfrog and divided into smaller blocks. Conditional queries can then be used to assign each block an index value which conditionally ranks each block's favorability, from 0-5 with 5 being most favorable, for each model (e.g., lithologic, seismic, magnetic, structural). The values between 0-5 assigned to each block are referred to as index values. The final step of the process is to combine all the index models to create a favorability index. This involves multiplying each index model by a given weight and then summing the resulting values. The DEEPEN PFA Methodology follows this approach, but split up by the specific geologic components of each play type. These components are defined as follows for each magmatic play type: 1. Conventional hydrothermal plays in magmatic environments: Heat, fluid, and permeability 2. Superhot EGS plays: Heat, thermal insulation, and producibility (the ability to create and sustain fractures suitable for and EGS reservoir) 3. Supercritical plays: Heat, supercritical fluid, pressure seal, and producibility (the proper permeability and pressure conditions to allow production of supercritical fluid) More information on these components and their development can be found in Kolker et al., 2022. For the purposes of subsurface imaging, it is easier to detect a permeable fluid-filled reservoir than it is to detect separate fluid and permeability components. Therefore, in this analysis, we combine fluid and permeability for conventional hydrothermal plays, and supercritical fluid and producibility for supercritical plays. More information on this process is described in the following sections. We also project the 3D favorability volumes onto 2D surfaces for simplified joint interpretation, and we incorporate an uncertainty component. Uncertainty was modeled using the best approach for the dataset in question, for the datasets where we had enough information to do so. Identifying which subsurface parameters are the least resolved can help qualify current PFA results and focus future efforts in data collection. Where possible, the resulting uncertainty models/indices were weighted using the same weights applied to the respective datasets, and summed, following the PFA methodology above, but for uncertainty. There are two different versions of the Leapfrog model and associated favorability models: - v1.0: The first release in June 2023 - v2.1: The second release, with improvements made to the earthquake catalog (included additional identified events, removed duplicate events), to the temperature model (fixed a deep BHT), and to the index models (updated the seismicity-heat source index models for supercritical and EGS, and the resistivity-insulation index models for all three play types). Also uses the jet color map rather than the magma color map for improved interpretability. - v2.1.1: Updated to include v2.0 uncertainty results (see below for uncertainty model versions) There are two different versions of the associated uncertainty models: - v1.0: The first release in June 2023 - v2.0: The second release, with improvements made to the temperature and fault uncertainty models. ** Note that this submission is deprecated and that a newer submission, linked below and titled "DEEPEN Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano" contains the final versions of these resources. **

In chemography, grid-based maps sample molecular descriptor space by injecting a set of nodes, and then linking them to some regular 2D grid representing the map. They include self-organizing maps (SOMs) and generative topographic maps (GTMs). Grid-based maps are predictive because any compound thereupon projected can “inherit” the properties of its residence node(s)node properties themselves “inherited” from node-neighboring training set compounds. This Article proposes a formalism to define the trustworthiness of these nodes as “providers” of structure–activity information captured from training compounds. An empirical four-parameter node trustworthiness (NT) function of density (sparsely populated nodes are less trustworthy) and coherence (nodes with training set residents of divergent properties are less trustworthy) is proposed. Based upon it, a trustworthiness score T is used to delimit the applicability domain (AD) by means of a trustworthiness threshold TT. For each parameter setup, success of ensuing inside-AD predictions is monitored. It is seen that setup-specific success levels (averaged over large pools of prediction challenges) are highly covariant, irrespectively of the targets of prediction challenges, of the (classification or regression) type of problems, of the specific parametrization, and even of the nature (GTM or SOM) of underlying maps. Thus, success levels determined on the basis of regression problems (445 target-specific affinity QSAR sets) on GTMs and levels returned by completely unrelated classification problems (319 target-specific active-/inactive-labeled sets) on SOMs were seen to correlate to a degree of 70%. Therefore, a common, general-purpose setup of the herein proposed parametric AD definition was shown to generally apply to grid-based map-driven property prediction problems.

2d adaptive resolution extinction maps for the VVV footprint

Extinction maps associated with the publication Sanders et al. (2022, MNRAS)

Three maps are provided: E(J-Ks), E(H-Ks) and E(H-[4.5]). The E(J-Ks) and E(H-Ks) maps have been computed over the entire VVV footprint whilst the E(H-[4.5]) map is only computed for the inner -1.5

The files *.csv.gz give the colour excess (e*) and its spread (sigma_e*) at a set of Healpix labelled by their unique index. A series of Healpix resolutions have been used to provide higher resolution where needed (except for the E(H-[4.5]) map that is only at level=13). The indices are using the nested scheme given the Galactic coordinates (l,b). In this way, it is simple to handle the varying resolution (see https://ivoa.net/documents/MOC/).

The colour excesses have been found from Gaussian fits to the red clump colours for (J-Ks) and (H-Ks) and an average over all giant stars for (H-[4.5]) (accounting for the weak gradient of the giant branch in the (H-[4.5]) vs. Ks colour-magnitude space). The spreads in extinction come from the width of the Gaussian peak for (J-Ks) and (H-Ks) and the width of the full distribution for (H-[4.5]) after subtracting the average photometric uncertainties and accounting for a (0.05,0.02,0.00) intrinsic colour width for (J-Ks, H-Ks, H-[4.5]) respectively.

The provided file extinction_maps.py provides a class for reading in all extinction maps (version=JK,HK,H45 allows one to pick the required map) and querying the colour excess and its spread for large numbers of Galactic coordinates. Also there is functionality for finding the resolution of the map at a given location.

The queries will throw a warning but return a value if the coordinate is outside the reliable footprint (the entire VVV footprint for JK and HK and the inner -1.5

The example.ipynb notebook shows an example of querying the extinction map and plotting the result.

$\Delta \chi^2$ map of phase-I+II data calculated by the two-dimensional method. To be used in combination with the $\Delta \chi^2_{\text{crit},x}$...

Create 2D and 3D maps to analyze flooding in Venice, Italy.

The high-precision real-time map market is experiencing robust growth, driven by the increasing demand for autonomous driving, advanced driver-assistance systems (ADAS), and precise location-based services. The market's expansion is fueled by technological advancements in sensor technologies (LiDAR, radar, cameras), improved mapping techniques, and the proliferation of connected vehicles. Key applications include automotive driving, tracking & positioning, and mobile phones, with the automotive sector currently dominating due to the surge in autonomous vehicle development. The 3D segment is projected to witness significant growth, exceeding the 2D and 2.5D segments in the coming years, owing to its ability to provide more detailed and accurate representations of the environment, crucial for autonomous navigation and precise location services. Geographic regions like North America and Europe are currently leading the market, driven by early adoption of autonomous vehicle technologies and well-established infrastructure for data collection and processing. However, rapid technological advancements and government initiatives supporting autonomous driving are driving market expansion in the Asia-Pacific region, with China and India emerging as key growth markets. While data security and privacy concerns present potential restraints, the overall market outlook remains positive, with a projected compound annual growth rate (CAGR) indicating substantial market expansion through 2033. Competition among major players like TomTom, Google, and Baidu is intensifying, leading to continuous innovation and the development of more sophisticated and accurate mapping solutions. The market segmentation by type (2D, 2.5D, 3D) reveals a clear shift towards higher-dimensionality maps. While 2D maps still hold a significant share, 3D mapping technology is rapidly gaining traction due to its enhanced capabilities for autonomous navigation and detailed environmental modeling. The application-based segmentation underscores the importance of the automotive sector, particularly autonomous vehicles, as the primary driver of market growth. However, other sectors like mobile phones and tracking & positioning are also contributing significantly, fostering a diversified market landscape. The ongoing development of 5G and edge computing infrastructure further accelerates the market's growth by facilitating real-time data processing and transmission, enhancing the accuracy and responsiveness of high-precision real-time maps. The competitive landscape is characterized by both established mapping companies and emerging technology providers, driving innovation and potentially leading to further market consolidation in the coming years.

STEREO median $\Delta\chi^2$ map (2D Feldman-Cousins method) obtained from $10^4$ no-oscillation toys. $\Delta\chi^2$ is defined as $\Delta\chi^2 = \chi^2_\text{H0} -...

STEREO $\Delta\chi^2$ map (2D Feldman-Cousins method) obtained from STEREO data. $\Delta\chi^2$ is defined as $\Delta\chi^2 = \chi^2_\text{H0} - \chi^2_\text{min}$ with...