Author: ANN WURST, NGS TEACHER CONSULTANTGrade/Audience: grade 6, grade 7, grade 8, high school, ap human geography, post secondary, professional developmentResource type: activitySubject topic(s): cartography, maps, regional geographyRegion: worldStandards: TEXAS TEKS (19) Social studies skills. The student applies critical-thinking skills to organize and use information acquired through established research methodologies from a variety of valid sources, including technology. The student is expected to: (A) analyze information by sequencing, categorizing, identifying cause-and-effect relationships, comparing, contrasting, finding the main idea, summarizing, making generalizations and predictions, and drawing inferences and conclusions; (B) create a product on a contemporary government issue or topic using critical methods of inquiry; (D) analyze and evaluate the validity of information, arguments, and counterarguments from primary and secondary sources for bias, propaganda, point of view, and frame of reference; Objectives: Students will keep a list of the toolkit 'helpers' in their notebook and use the elements to process/apply information in various formats such as short answers responses, tickets out the door, setting up writing samples for world geo, AP Human Geo and other courses involving the study of geographic concepts. Summary: Students can use these 'hooks' in their study of cartography/map making , can be applied in every unit where map skills are needed. Helps further critical thinking skills.

How to set up geophysical grids. Visit https://dataone.org/datasets/doi%3A10.6067%3AXCV8JD4WH0_meta%24v%3D1360955497617 for complete metadata about this dataset.

Use the resources below to understand how smart mapping tools work and how to apply them to enhance your mapping projects.GoalsAccess available smart mapping options to explore and better understand your data.Style maps using cartographically correct symbology for qualitative and quantitative data.Use Arcade expressions to quickly customize data display and map elements.

Two active landslides at and near the retreating front of Barry Glacier at the head of Barry Arm Fjord in southern Alaska could generate tsunamis if they failed rapidly and entered the water of the fjord. Landslide A, at the front of the glacier, is the largest, with a total volume estimated at 455 M m3. Historical photographs from Barry Arm indicate that Landslide A initiated in the mid twentieth century, but there was a large pulse of movement between 2010 and 2017 when Barry Glacier thinned and retreated from about 1/2 of the toe of Landslide A. Interferometric synthetic aperture radar (InSAR) investigations of the area between May and November, 2020, revealed a second, smaller landslide (referred to as Landslide B) on the south-facing slope about 2 km up the glacier from Landslide A. Landslide-generated tsunami modeling in 2020 used a worst-case scenario where the entire mass of Landslide A (about 455 M m3) would rapidly enter the water. The use of multiple landslide volume scenarios in future tsunami modeling efforts would be beneficial in evaluating tsunami risk to communities in the Prince William Sound region. Herein, we present a map of landslide structures and kinematic elements within, and adjacent to, Landslides A and B. This map could form at least a partial basis for discriminating multiple volume scenarios (for example, a separate scenario for each kinematic element). We mapped landslide structures and kinematic elements at scale of 1:1000 using high-resolution lidar data acquired by the Alaska Division of Geological and Geophysical Surveys (DGGS) on June 26, 2020 and high resolution bathymetric data acquired by the National Oceanic and Atmospheric Administration (NOAA) in August, 2020. The predominate structures in both landslides are uphill- and downhill-facing normal fault scarps. Uphill-facing scarps dominate in areas where downslope extension from sliding has been relatively low. Downhill-facing scarps dominate in areas where downlslope extension from sliding has been relatively high. Strike-slip and oblique-slip faults form the boundaries of major kinematic elements. Four major kinematic elements, herein named the Kite, the Prow, the Core, and the Tail, are within, or adjacent to Landslide A. One major kinematic element, herein named the Wedge, forms Landslide B. Kinematic element boundaries are a result of cumulative, differential patterns and amounts of movement that began at inception of the landslides. Elements and/or their boundaries may change location as the landslides continue to evolve. Kinematic elements mapped in 2020 may or may not reflect patterns of historical short-term, episodic movement, or patterns of movement in the future. We were not able to field check our mapping in 2020 because of travel restrictions due to the COVID-19 pandemic. We hope to field check the mapping in the summer of 2021. In this data release, we include GIS files for the structural and kinematic map; metadata files for mapped structural features; and portable document files (PDFs) of a location map, and the structural and kinematic map at a scale of 1:5000. Lidar and bathymetric data used to map landslide structures will be released by DGGS and NOAA in 2021.

FlowMapper.org is a web-based framework for automated production and design of origin-destination flow maps. FlowMapper has four major features that contribute to the advancement of existing flow mapping systems. First, users can upload and process their own data to design and share customized flow maps. The ability to save data, cartographic design and map elements in a project file allows users to easily share their data and/or cartographic design with others. Second, users can generate customized flow symbols to support different flow map reading tasks such as comparing flow magnitudes and directions and identifying flow and location clusters that are strongly connected with each other. Third, FlowMapper supports supplementary layers such as node symbols, choropleth, and base maps to contextualize flow patterns with location references and characteristics. Finally, the web-based architecture of FlowMapper supports server-side computational capabilities to process and normalize large flow data and reveal natural patterns of flows.

When measuring chemical information in biological fluids, challenges of cross-reactivity arise, especially in sensing applications where no biological recognition elements exist. An understanding of the cross-reactions involved in these complex matrices is necessary to guide the design of appropriate sensing systems. This work presents a methodology for investigating cross-reactions in complex fluids. First, a systematic screening of matrix components is demonstrated in buffer-based solutions. Second, to account for the effect of the simultaneous presence of these species in complex samples, the responses of buffer-based simulated mixtures of these species were characterized using an arrayed sensing system. We demonstrate that the sensor array, consisting of electrochemical sensors with varying input parameters, generated differential responses that provide synergistic information of sample. By mapping the sensing array response onto multidimensional heat maps, characteristic signatures were compared across sensors in the array and across different matrices. Lastly, the arrayed sensing system was applied to complex biological samples to discern and match characteristic signatures between the simulated mixtures and the complex sample responses. As an example, this methodology was applied to screen interfering species relevant to the application of schizophrenia management. Specifically, blood serum measurement of antipsychotic clozapine and antioxidant species can provide useful information regarding therapeutic efficacy and psychiatric symptoms. This work proposes an investigational tool that can guide multi-analyte sensor design, chemometric modeling and biomarker discovery.

Abstract The Mineral Potential web service provides access to digital datasets used in the assessment of mineral potential in Australia. The service includes maps showing the potential for carbonatite-related rare earth element mineral systems in Australia. Maps showing the potential for carbonatite-related rare earth element (REE) mineral systems in Australia. Model 1 integrates three components: sources of metals, energy drivers, and lithospheric architecture. Supporting datasets including the input maps used to generate the mineral potential maps, an assessment criteria table that contains information on the map creation, and data uncertainty maps are available here Uncertainty Maps. The data uncertainty values range between 0 and 1, with higher uncertainty values being located in areas where more input maps are missing data or have unknown values. Map images provided in the extended abstract have the same colour ramp and equalised histogram stretch, plus a gamma correction of 0.5 not present in the web map service maps, which was applied using Esri ArcGIS Pro software. The extended abstract is avalable here Alkaline Rocks Atlas Legend

Currency Date modified: 16 August 2023 Next modification date: As Needed Data extent Spatial extent North: -9° South: -44° East: 154° West: 112° Source Information Catalog entry: Carbonatite-related rare earth element mineral potential maps Lineage Statement Product Created 20 April 2023 Product Published 16 August 2023 A large number of published datasets were individually transformed to summarise our current understanding of the spatial extents of key mineral system mappable criteria. These individual layers were integrated using statistically derived importance weightings combined with expert reliability weightings within a mineral system component framework to produce national-scale mineral potential assessments for Australian carbonatite-related rare earth element mineral systems. Contact Geoscience Australia, clientservices@ga.gov.au

The knowledge of the geology of our territory and the processes that determine its evolution is a factor of great importance, always recognised as a priority by the Administration of the Autonomous Province of Trento. The geological map is made by combining different information. The structural element level is part of this information and is used to indicate the different types of traces of axial surfaces of the plicative structures.

The "Basic Geological Map of Sardinia on a scale of 1:25,000" project aimed at creating a homogeneous geological map covering the whole island, suitable for the planning objectives of the Regional Landscape Plan (PPR) and compliant with the indications of the Geological Service of 'Italy. The geology was represented at 1:25,000, a compromise scale between the lack of homogeneity of the basic data and the need to have a single and homogeneous cartography for the entire island (58 Sheets at 1:50,000 scale, including 197 Sections at 1 :25,000).

Overview

3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

Our corresponding paper (published at ITSC 2022) is available here. Further, we have applied 3DHD CityScenes to map deviation detection here.

Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

Python tools to read, generate, and visualize the dataset,

3DHDNet deep learning pipeline (training, inference, evaluation) for map deviation detection and 3D object detection.

The DevKit is available here:

https://github.com/volkswagen/3DHD_devkit.

The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

When using our dataset, you are welcome to cite:

@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}

Acknowledgements

We thank the following interns for their exceptional contributions to our work.

Benjamin Sertolli: Major contributions to our DevKit during his master thesis

Niels Maier: Measurement campaign for data collection and data preparation

The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

The Dataset

After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

1. Dataset

This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

import json

json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)

1. HD_Map

Map items are stored as lists of items in JSON format. In particular, we provide:

traffic signs,

traffic lights,

pole-like objects,

construction site locations,

construction site obstacles (point-like such as cones, and line-like such as fences),

line-shaped markings (solid, dashed, etc.),

polygon-shaped markings (arrows, stop lines, symbols, etc.),

lanes (ordinary and temporary),

relations between elements (only for construction sites, e.g., sign to lane association).

1. HD_Map_MetaData

Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

1. HD_PointCloud_Tiles

The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

x-coordinates: 4 byte integer

y-coordinates: 4 byte integer

z-coordinates: 4 byte integer

intensity of reflected beams: 2 byte unsigned integer

ground classification flag: 1 byte unsigned integer

After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

import numpy as np import pptk

file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['

The quantitative estimation of elemental concentrations at the spatial resolution of hyperspectral near-infrared (NIR) images of the lunar surface is an important tool for understanding the processes relevant for the origin and evolution of the Moon. The NIR reflectance of the lunar regolith is an integrated response to the presence of refractory elements and soil alteration processes. Our approach was to define a combination of spectral parameters that are robust with respect to the effects of soil maturity. We calibrated the spectral parameters with respect to elemental abundances measured by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) and the Kaguya GRS (KGRS). For this purpose, we compared a classical multivariate linear regression (MLR) approach and the machine learning based support vector regression (SVR) technique applied to M3 global observations. The M 3 -based global elemental maps are consistent in distribution and range with the LP GRS and KGRS elemental maps and do not show artifacts in immature areas such as small fresh craters. The results derived using MLR and SVR are compared to sample-based ground truth data of the Apollo and Luna sample-return sites, where the root-mean-square deviations obtained by the two regression models are similar. The main advantage of the proposed new algorithm is its ability to minimize artifacts due to space-weathering effects. The elemental maps of Mg and Ca provide additional information and reveal structures not always visible in the Fe map. The global elemental abundance maps derived for the fully calibrated M 3 observations might thus serve as important tools to investigate the lunar geology and evolution.

A series of element maps collected using the Maia 384 detector array on the XFM beamline at the Australian Synchrotron. Maps are RGB composites (indicated by file name) or a combination of black and white intensity with RGB. Lower resolution versions of these files are used in a paper on the application of microanalysis techniques in ore geology and are made available here to allow the full resolution images (1:1 binning of pixels) to be accessed. Lineage: X-ray spectra were collected using the Maia 384 detector on the XFM beamline at the Australian Synchrotron. The spectral data were collected with an incident beam energy of 18.5 keV, a pixel size of 4 µm and dwell times per pixel of 0.97 msec. The Maia XFM full spectral data were analysed using the GeoPIXE software suite which uses a fundamental parameters approach, with spectral deconvolution and imaging using the Dynamic Analysis method based on fitting a representative total spectrum, and a detailed model of Maia detector array efficiency. Spectra are fitted using a X-ray line relative intensities that reflect integration of yields and X-ray self-absorption effects for the given matrix or mineral phase and the contrasting efficiency characteristics across the detector array. The result is a matrix transformation that allows projection of the full-spectral data into element maps in this collection.

Author: J Cain, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 6Resource type: lessonSubject topic(s): mapsRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1: People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context. Objectives: Students will be able to:

1. Create a sketch map
2. Explore a variety of maps
3. Analyze maps by observing, thinking, and questioning
4. Recognize and identify elements (TODALS) of maps
5. Compare and contrast elements (TODALS) of given mapsSummary: Students will recognize, identify, compare and contrast TODALS elements on maps.

The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.

Concept Map resource is a statement of relationships from one set of concepts to one or more other concepts - either concepts in code systems, or data element/data element concepts, or classes in class models.

The projection mapping market, valued at $4.58 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 21.87% from 2025 to 2033. This surge is driven by increasing adoption across diverse sectors like entertainment (large venues, events, festivals), advertising, and architectural installations. Technological advancements, particularly in higher resolution projectors, improved software capabilities for complex mapping, and the emergence of 4D projection mapping (incorporating elements like wind, mist, or scent), are fueling market expansion. The market is segmented by offering (hardware and software), dimension (2D, 3D, 4D), throw distance (standard, short), and application. Hardware currently dominates the market share, but software solutions are witnessing significant growth due to increasing demand for sophisticated content creation and control systems. The short-throw projector segment is gaining traction, driven by its flexibility and ease of installation in various spaces. Geographically, North America and Europe currently hold significant market shares, but the Asia-Pacific region is anticipated to demonstrate the fastest growth due to rising disposable incomes and increasing adoption in entertainment and advertising sectors. While the market presents significant opportunities, challenges remain. High initial investment costs for advanced systems can be a barrier to entry for smaller businesses. Competition among established players and the emergence of new entrants necessitates continuous innovation and cost optimization strategies for market survival. Furthermore, the dependence on skilled professionals for designing and implementing projection mapping projects limits widespread adoption. Despite these challenges, the projection mapping market's potential is vast, fueled by ongoing technological advancements and a growing demand for immersive and engaging experiences across diverse sectors. The forecast period of 2025-2033 promises continued expansion, making it an attractive market for both established players and new entrants. This comprehensive report provides a detailed analysis of the global projection mapping market, offering valuable insights for businesses and investors seeking to understand this dynamic industry. The report covers the period from 2019 to 2033, with 2025 serving as the base and estimated year. We project market growth, analyze key trends, and identify leading players shaping the future of projection mapping technology. This in-depth study delves into various aspects, including market segmentation, technological advancements, and competitive landscape, utilizing data from the historical period (2019-2024) to forecast market performance from 2025 to 2033. The report's findings provide crucial information for strategic decision-making in this rapidly evolving sector. The market is valued in millions of US dollars. Recent developments include: January 2024 - Epson's latest 4K ultra-lightweight 3LCD projector range, unveiled at ISE 2023, has garnered substantial orders from its rental partner, AED Display. This move solidifies Epson's position as the go-to manufacturer in the high-lumens projector segment., January 2024 - BenQ, an internationally renowned visual display and collaboration solutions provider, announced its new "Teach Your Way" Projector Program. Committed to partnering with schools to enable greater positive outcomes for all students by transforming classrooms, BenQ's program offers planning, pricing, and customer support benefits for BenQ's latest lineup of maintenance-free LED and laser projectors and InstaShow Wireless Presentation System (WPS).. Key drivers for this market are: Increasing Demand for Projection Size, and High Brightness of the Projectors, Rapid Growth of Smart Cities. Potential restraints include: Shorter Operating Range of WiGig Products. Notable trends are: Event Segment to Witness Major Growth.

Map file package containing shaded relief base with Hot Pot project area, major roads, railroads, and rivers. The inset map shows regional Paleozoic structural elements.

BioMap is the result of an ongoing collaboration between MassWildlife and the Massachusetts Chapter of The Nature Conservancy (TNC). Since its inception in 2001, this comprehensive tool has become a trusted source of information to guide conservation that is used by a wide spectrum of conservation practitioners. Today’s BioMap builds on previous iterations with the continuing goal of protecting the diversity of species and natural ecosystems within the Commonwealth. BioMap is an important tool to guide strategic protection and stewardship of lands and waters that are most important for conserving biological diversity in Massachusetts.More details...

Geo-referenced vector-type database, containing geomorphological and anthropogenic elements in linear form, collected as part of the national geological mapping project (CARG) at the 1: 25.000 acquisition scale and reviewed at regional level. The geographical area covered comprises the sheets on a scale of 1: 50.000 in which the regional territory falls.

This dataset contains a collection of sources that mark the geographical areas of regions/elements/biomes of the Biosphere and the Cryosphere identified as tipping elements on various temporal and spatial scales by the Global Tipping Points Report 2023 (https://global-tipping-points.org) based on:- Armstrong McKay, Sakschewski, Roman-Cuesta et al., In prep.- Winkelmann, Steinert, Armstrong McKay et al., In prep. Below you find the data sources for the collection of this dataset. Note that the biosphere data sources provide more biomes than listed here, as only those identified as tipping element are selected here. For the full datasets, including non-tipping biomes, please refer to the original sources listed below. BIOSPHERE: 1. Ecoregions 2017 biomes: https://ecoregions.appspot.com Note that only the following biomes were selected: "Tundra", "Tropical & Subtropical Moist Broadleaf Forests","Tropical & Subtropical Coniferous Forests", "Tropical & Subtropical Dry Broadleaf Forests", "Boreal Forests/Taiga", "Temperate Broadleaf & Mixed Forests","Temperate Conifer Forests", "Temperate Grasslands, Savannas & Shrublands","Tropical & Subtropical Grasslands, Savannas & Shrublands","Flooded Grasslands & Savannas","Montane Grasslands & Shrublands", "Deserts & Xeric Shrublands","Mediterranean Forests, Woodlands & Scrub", "Mangroves". 2. Keith, David A., Ferrer-Paris, Jose R., Nicholson, Emily, ..., Kingsford, Richard T. (2020). Indicative distribution maps for Ecological Functional Groups - Level 3 of IUCN Global Ecosystem Typology [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3546513; and Keith et al. 2022: https://www.nature.com/articles/s41586-022-05318-4 Note that only the following biomes were selected: "F2_1_Large_perm_lakes", "F2_2_Small_perm_lakes", "F2_4_Freeze-thaw_lakes", "F2_6_Perm_salt_lakes", "M1_1_Seagrass_meadows", "M1_2_Kelp_forests", "M1_3_Photic_coral_reefs", "M4_2_Marine_aquafarms", "M1_5_Marine_animal_forests". CRYOSPHERE: 1. Land permafrost: Obu, Jaroslav; Westermann, Sebastian; Kääb, Andreas; Bartsch, Annett (2018): Ground Temperature Map, 2000-2016, Northern Hemisphere Permafrost. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.888600 2. Subsea permafrost: Overduin, Pier Paul; Schneider von Deimling, Thomas; Miesner, Frederieke; Grigoriev, Mikhail N; Ruppel, Carolyn D; Vasiliev, Alexander; Lantuit, Hugues; Juhls, Bennet; Westermann, Sebastian; Laboor, Sebastian (2020): Submarine Permafrost Map (SuPerMAP), modeled with CryoGrid 2, Circum-Arctic. PANGAEA, https://doi.org/10.1594/PANGAEA.910540 3. Sea Ice: Keith, David A., Ferrer-Paris, Jose R., Nicholson, Emily, ..., Kingsford, Richard T. (2020). Indicative distribution maps for Ecological Functional Groups - Level 3 of IUCN Global Ecosystem Typology [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3546513; and Keith et al. 2022: https://www.nature.com/articles/s41586-022-05318-4 4. Antarctica: Medium resolution vector polygons of the Antarctic coastline (2014) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/ed0a7b70-5adc-4c1e-8d8a-0bb5ee659d18 5. Greenland: Morlighem M. et al., (2017), BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multi-beam echo sounding combined with mass conservation, Geophys. Res. Lett., 44, doi:10.1002/2017GL074954 6. Glaciers: RGI Consortium, . (2012). Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 2 [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center. https://doi.org/10.7265/cc6e-zp12. For any questions regarding the dataset, please free feel to contact Norman J. Steinert (nste@norceresearch.no, normanst@ucm.es)