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Dataset results
23 results for “Google maps”
A 30-meter terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine
<p>This dataset contains the China terrace map at 30 m resolution in 2018. The map values and their corresponding classes are as follows:</p> <p><em>0: Non-terrace 1: Terrace 255: No data</em></p> <p>The 30 m China terrace map can also be viewed online at <a href="https://cbw.users.earthengine.app/view/chinaterracemap">https://cbw.users.earthengine.app/view/chinaterracemap</a></p> <p><strong>Citations:</strong></p> <p>When using this dataset, please cite both the dataset and the following data description article:</p> <p><em>Cao, B., Yu, L., Naipal, V., Ciais, P., Li, W., Zhao, Y., Wei, W., Chen, D., Liu, Z., and Gong, P.: A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine, Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, 2021.</em></p> <p> </p>
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth.
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 9.The educational blog on Google+ Time Maps page–the weaving techniques
<p>For this subject two video films were posted on Google+ (a performance and a 3D reconstruction), slightly different from those available on the Time Maps web site, but containing the same information. The children had to make a little effort to relate this information with the one presented on the site, to make a connection between the questions, the fragments from videos at which the answers referred to and the information from the site. The set of questionnaires lead the school children through the majority of data offered by the web site regarding to the two historical periods (Figures 9, 10).</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 14. The survey as a public posting on Google+ Time Maps page
<p>During this experimentation phase a symposium was organized at Vădastra School with the purpose to present our learning experiment to a group of 30 teachers from the Olt County. An open history lesson on the Time Maps web site was held by a history teacher, and a school girl described the Facebook page of the Vădastra School (Figure13), maintained by both teachers and children. The invited teachers gave a feedback on the effectiveness and utility of the Time Maps learning system by responding to a questionnaire-based survey, which was posted on the Google+ page (Figure 14). </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 10. The educational blog on Google+ Time Maps page – the glass manufacturing techniques
<p>For this subject two video films were posted on Google+ (a performance and a 3D<br> reconstruction), slightly different from those available on the Time Maps web site, but containing<br> the same information. The children had to make a little effort to relate this information with the one<br> presented on the site, to make a connection between the questions, the fragments from videos at<br> which the answers referred to and the information from the site.<br> The set of questionnaires lead the school children through the majority of data offered by the<br> web site regarding to the two historical periods (Figures 9, 10).</p>
Google Location Criteria ID (Geotargets) to Geonames mapping
<p>This deposit contains the RDF version of the Google Location Criteria ID dataset (named Geotargets, available at https://goo.gl/cZXkiJ - last access: 2018-06-29) and a subset of the Geonames XML dump.</p> <p>All datasets contain only locations located in Spain or Germany.</p> <p>There are also mapping files that contain <em>sameAs</em> links between instances described in these datasets. Links have been created with a custom SILK pipeline.</p>
Figure. Map of the study area, the province of Ordu in the Black Sea region of Turkey (from Google Maps). in New records of springtail fauna (Hexapoda: Collembola: Entomobryomorpha) from Ordu Province in Turkey
Figure. Map of the study area, the province of Ordu in the Black Sea region of Turkey (from Google Maps).
Text-fig. 2. Fossiliferous localities in the Ashawq Formation north and north-east of Thaytiniti, Dhofar Governorate, Oman (for latitude, longitude and altitude, see App. 1). The large white area either side of the north-south road is the village of Aydim (map modified from Google Earth). in Large Mammals From The Rupelian Of Oman - Recent Finds
Text-fig. 2. Fossiliferous localities in the Ashawq Formation north and north-east of Thaytiniti, Dhofar Governorate, Oman (for latitude, longitude and altitude, see App. 1). The large white area either side of the north-south road is the village of Aydim (map modified from Google Earth).
Text-fig. 1. Location of Gánovce-Hrádok Neanderthal site in northern Slovakia and the rest of the travertine mound (map source: https://commons.wikimedia.org and Google Earth; 2017). in Revised Floral And Faunal Assemblages From Late Pleistocene Deposits Of The Gánovce-Hrádok Neanderthal Site -Biostratigraphic And Palaeoecological Implications
Text-fig. 1. Location of Gánovce-Hrádok Neanderthal site in northern Slovakia and the rest of the travertine mound (map source: https://commons.wikimedia.org and Google Earth; 2017).
How to Snap the Sneed Prairie Map to Google Earth — BlueStems™
<p>A video tutorial showing how to snap a transparent PNG image to Google Earth.</p><p>The video is available on YouTube here: <a href="https://youtu.be/6BRdA6yj57Q">https://youtu.be/6BRdA6yj57Q</a></p>
Dataset for image segmentation of tree trunks from depth maps captured with a an Android app using Google ARCore
<p>This dataset consists of pairs of depth maps created with a custom-built Android app using Google's ARCore for depth estimates, and tree trunk segments obtained from depth maps captured by Huawei''s AREngine and processed with a tree diameter estimation algorithm. This dataset was used for a machine learning segmentation task that aimed to improve the inputs from ARCore tree trunk depths to the tree diameter estimation algorithm. For each pair of depth maps and segments an RGB image of the scene where samples were captured is also included.</p>
The ScaleMaster: The decompostion of Pan-Scalar, Interactive Map (OSM,Google Maps,IGN scan)
<p>The ScaleMaster diagram of Brewer and Buttenfield, "where the scaleLine replaces the timeLine", is a formal tool (Excel sheets) designed to formalize the rules for manual map design and "emphasize changes to the map display" . Inspired by Brewer and Buttenfield, we use ScaleMaster to standardize and formalize changes while zooming and exploring each of pan-scalar map (OSM,Google Maps,Scan IGN). In our methodology, however, we go a step further. The timeline of exploration is also examined in addition to the scaleline of zooming. We focus on map design practices that account for pan-scalar map exploration. For example, we account for generalization changes between scales based on empirically or theoretically justifiable reasons.</p> <p>we use ScaleMaster to analyze particular and common geographic entities in the maps (including rivers, urban areas, bus stations, and administrative borders) representing but a fraction of all map ontologies (e.g., water, roads, transportation networks, relief, points-of-interest, vegetation, administrative districts). We constructed a ScaleMaster for each of the three pan-scalar maps (OSM, Google Map, Scan IGN). </p> <p>Our hope is that this first analysis, and the resulting categories below, will lead to critique, comment, and iterative improvement in the future. In other words, our initial findings are just that – outcomes that further exploration on pan-scalar maps can add to, revise, and improve upon. </p>
Google Categories (ProductsServices) to DMOZ mapping
<p>This deposit contains the RDF version of a subset of the Google Categories dataset (named ProductsServices, available at https://developers.google.com/adwords/api/docs/appendix/productsservices.csv - last access: 2018-06-29) and the RDF version of a subset of the DMOZ categories dump.</p> <p>Categories have been modeled as SKOS Concept in both datasets.</p> <p>There is also a mapping file that contains links between instances described in these datasets. Links have been created manually (we adopted the SKOS vocabulary).</p>
Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform
<p>An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.</p>
FIGURE 3 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 3. (A) Specimen of the bromeliad Alcantarea imperialis with inflorescence photographed about 1 km from the type locality of Scinax v-signatus in the Municipality of Teresópolis, State of Rio de Janeiro. (B) A granitic outcrop with several individuals of A. imperialis, photographed in the Municipality of Miguel Pereira, State of Rio de Janeiro. (C) The same outcrop as it appears on Google Earth images. Yellow arrows indicate small whitish dots that represent individual bromeliads.
FIGURE 4 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 4. Relief map of the State of Rio de Janeiro (Modified from http://www.agritempo.gov.br/altimetria/RJ.html) with the potential distribution of Alcantarea imperialis produced based on images of Google Earth (yellow circles), occurrence of Scinax v-signatus based on voucher specimens (blue stars), localities for Scinax insperatus (gray triangle), localities we identified A. imperialis on the field but were unable to detect using Google Earth (white square), and localities Based on A. imperialis from the Herbarium on the Jardim Botânico do Rio de Janeiro (Square) – http://www.jbrj.gov.br/.
FIGURE 2 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 2. Map from GAA depicting the distribution of Scinax v-signatus. Note that the type locality (Black circle) was not included in distribution area.
FIGURE 1 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 1. Map of southeastern Brazil highlighting the area of the States of Minas Gerais, Rio de Janeiro, and Espírito Santo. Symbols indicate the type localities for Scinax v-signatus (white circle), the type locality for S. arduous (white square) and S. belloni (black square). The white hexagons indicate three new localities for S. arduous. The star indicates two localities where an undescribed species was collected in the south of the State of the Espírito Santo, and the black circle indicates the locality for another undescribed species collected in the north of the State of Rio de Janeiro.
sample google map (EOL v3 test): raw eol google map
Open the record for dataset details and reuse information.
Flood Hazard Maps using Google Earth Engine: Thrace and Thessaly River Basin Districts (Greece)
<p>This dataset contains three raster files with a spatial resolution of 10 m, derived by the Google Earth Engine:</p> <p> </p> <p>1) DynamicWorld_Floods_2015_2023.tif: Number of days flooded for the River Basin District of Thrace (Greece) starting from 2015 until 2023</p> <p>2) Thessaly_2015_August2023.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until August 2023</p> <p>3) Thessaly_2015_now.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until January 2024</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.