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About 10,000 papers for “geospatial data”

Geospatial Data Portals and Open Geospatial Data

Girgin, Serkan · Zenodo (CERN European Organization for Nuclear Research) · 2023

Data is valuable when used to make better decisions. The true value of data can only be unlocked if it is re-used and shared, not siloed. Data sharing is the process of making the same data resources available to multiple applications, users, or organizations. It improves efficiency within an organization and fosters collaboration with partners and stakeholders, creates new opportunities, contribute to better decisions with positive social impacts. Data sharing includes technologies, practices, legal frameworks, and cultural elements. Governments, NGOs, businesses, and others embrace open data. Open data is free, accessible data that anyone can use for any purpose. It drives increased transparency and accountability, and enables innovative applications and services. Governments and intergovernmental organisations are main providers of open data. Many governments are now mandated to provide open data to their citizens. NGOs have always paid attention to the democratization of data. Many NGOs are producing open data, but they can also be supported by e.g. crowdsourcing. Open research data is a core component of Open Science embraced by academic institutions. Corporations realized that producing open datasets can improve their businesses and public relations. Data portals enable efficient data sharing and management. A data portal is a gateway to data with a core purpose of enabling the rapid discovery and use of data. Along with the essential basic catalog features, modern portals now incorporate an extensive range of functionality for organizing, structuring and presenting data. These include publication workflow and metadata management, interactive exploration, data storage and API for applications and analytics workflows, access control, and data ingestion and transformation. A well implemented data portal goes beyond simple convenience but is a powerful tool in building better overall organisational data governance. Cloud computing platforms facilitate access and use of data. They are accessible through a web browser. Various access interfaces are available (e.g. interactive notebook, remote desktop, terminal). Software is ready to use and up to date. Various computing resources are available on demand (e.g. GPU, large memory, large storage, computing cluster). Shared workspaces allow assets to be shared by groups. Public assets can be shared by all users (e.g. OpenStreetMap, ESA Copernicus, etc.). ITC Geospatial Computing Platform is used by IDEAMAP SUDAN (https://crib.utwente.nl).

Cited by 0Open access

Geospatial Data Portals and Open Geospatial Data

Girgin, Serkan · University of Twente Research Information · 2023

Data is valuable when used to make better decisions. The true value of data can only be unlocked if it is re-used and shared, not siloed. Data sharing is the process of making the same data resources available to multiple applications, users, or organizations. It improves efficiency within an organization and fosters collaboration with partners and stakeholders, creates new opportunities, contribute to better decisions with positive social impacts. Data sharing includes technologies, practices, legal frameworks, and cultural elements. Governments, NGOs, businesses, and others embrace open data. Open data is free, accessible data that anyone can use for any purpose. It drives increased transparency and accountability, and enables innovative applications and services. Governments and intergovernmental organisations are main providers of open data. Many governments are now mandated to provide open data to their citizens. NGOs have always paid attention to the democratization of data. Many NGOs are producing open data, but they can also be supported by e.g. crowdsourcing. Open research data is a core component of Open Science embraced by academic institutions. Corporations realized that producing open datasets can improve their businesses and public relations. Data portals enable efficient data sharing and management. A data portal is a gateway to data with a core purpose of enabling the rapid discovery and use of data. Along with the essential basic catalog features, modern portals now incorporate an extensive range of functionality for organizing, structuring and presenting data. These include publication workflow and metadata management, interactive exploration, data storage and API for applications and analytics workflows, access control, and data ingestion and transformation. A well implemented data portal goes beyond simple convenience but is a powerful tool in building better overall organisational data governance. Cloud computing platforms facilitate access and use of data. They are accessible through a web browser. Various access interfaces are available (e.g. interactive notebook, remote desktop, terminal). Software is ready to use and up to date. Various computing resources are available on demand (e.g. GPU, large memory, large storage, computing cluster). Shared workspaces allow assets to be shared by groups. Public assets can be shared by all users (e.g. OpenStreetMap, ESA Copernicus, etc.). ITC Geospatial Computing Platform is used by IDEAMAP SUDAN (https://crib.utwente.nl).

Cited by 0Open access

Geospatial Data

Jason Williams · OSF Preprints (OSF Preprints) · 2021

Cited by 0

Geospatial Data

Igor Lugo · OSF Preprints (OSF Preprints) · 2017

Cited by 0

Geospatial data.

Stephen M. Chignell (6376643), Madeline E. Myers (11648787), Adrian Howkins (11648790) et al. · Figshare · 2021

<p>Annual GIS layers of camp locations; study site locations with associated publication counts; randomly generated point locations; geospatial layers resulting from analyses of study sites (Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0257950#pone.0257950.g002" target="_blank">2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0257950#pone.0257950.g004" target="_blank">4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0257950#pone.0257950.s005" target="_blank">S1 Video</a>).</p> <p>(ZIP)</p>

Cited by 0Open access

Geospatial Data

Igor Lugo · OSF Preprints (OSF Preprints) · 2017

Cited by 0

geospatial data

Jianbin Zhou, Jin Ben · Figshare · 2022

2017 global Earth observation population data released by the Oak Ridge National Laboratory in the United State;vector data of the continental United States;shared bicycles in Beijing, China and taxis in New York<br>

Cited by 0Open access

geospatial data

Jianbin Zhou, Jin Ben · Figshare · 2022

2017 global Earth observation population data released by the Oak Ridge National Laboratory in the United State;vector data of the continental United States;shared bicycles in Beijing, China and taxis in New York<br>

Cited by 0Open access

Geospatial Data

Computación Y Sistemas, Moreno Ibarra, Marco Antonio et al. · 2007

Cómo citar el artículo Número completo Más información del artículo Página de la revista en redalyc.org

Cited by 0

Geospatial Data

Watkinson, Ian · Figshare · 2019

Digital data repository for Watkinson, I. M. and Hall, R., Impact of communal irrigation on the 2018 Palu earthquake-triggered landslides<br><br>Nature Geoscience Manuscript NGS-2018-12-02760<br>Submitted 21st December 2018; revised version submitted 11th June 2019; accepted 18th July 2019.<br><br>Contents include vector files for ArcGIS (shapefiles).<br>All files use the projected coordinate system UTM zone 50S, WGS1984.<br>All shapefiles are also provided as KMZ files for use in Google Earth.<br>Files are provided as-is and without guarantees in terms of accuracy, reliability or significance. We accept no liability for use of these files under any circumstances. See main manuscript, Methods section and supplementary materials for full description and uncertainties. <br><br>Contents:<br><br>*** Landslide displacement geospatial data ***<br>---------------------------------------------<br>- Observed_displacement_lines_MAIN.shp/kmz (main high-density object tracking lines)<br>- Observed_displacement_points_START.shp/kmz (main high-density object tracking lines start points)<br>- Observed_displacement_points_END.shp/kmz (main high-density object tracking lines end points)<br>- Observed_displacement_lines_WIDER.shp/kmz (main high-density object tracking lines plus lower density lines in wider study area for PCA)<br><br>*** Landslide features geospatial data ***<br>----------------------------------------<br>- Landslide_domains.shp/kmz<br>- Landslide_faults_fractures.shp/kmz<br>- Mottling.shp/kmz<br>- Sand_blows.shp/kmz<br>- Water_ponding.shp/kmz<br><br>*** Irrigation/water geospatial data ***<br>-----------------------------------------------<br>- Conveyance_canal.shp/kmz<br>- Irrigation_nodes.shp/kmz<br>- Other_channels.shp/kmz<br>- Natural_rivers.shp/kmz<br><br>*** Spreadsheets ***<br>--------------------------<br>- Observed_displacement_data.xlsx<br>- RESULT_PCA_FULL and 1-10_formatted for publication.xlsx

Cited by 0Open access

Geospatial Data

Igor Lugo · OSF Preprints (OSF Preprints) · 2017

Cited by 0

Geospatial Data

2022

This chapter introduces unofficial package loading. One method for installing unofficial packages is with the ‘remotes’ package. The chapter exposes the reader to the basics of obtaining data, visualizing and hopefully extracting meaningful player insights. It serves as a foundation for thinking systemically when presented with relatively large data sets where periodicity and other factors can impact outcomes. The chapter helps the readers to create and interpret a box plot of pitch speed by type and time and to create a 2D density plot of pitch type. The ‘Pitching ’ data table contains the ‘playerID’ column but different data specific to pitchers. The chapter examines the geospatial pitch and swing location. It presents a brief example from the ‘CalledStrike’ package vignette demonstrating the ease of player-to-player assessments for two batters.

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