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12 results for “Google Images”
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth.
Text-fig. 9. Fossil wood localities 4 and 5 close to the palaeopan on the north flank of the Muaredzi Gorge. Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 9. Fossil wood localities 4 and 5 close to the palaeopan on the north flank of the Muaredzi Gorge. Image modified from Google Earth.
Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth.
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>
SCALE-WIN22 cruise track, stations, sea-ice edge, and SAR images on Google Earth
<p>This Google Earth KMZ file contains the cruise track of the SCALE-WIN22 expedition, the description of activities on each station, the daily ice-edge location, and the quick-view SAR images. This is an interactive description of the cruise report:</p> <p>Vichi, M. (2023). SCALE-WIN22 Cruise Report. Zenodo. <a href="https://doi.org/10.5281/zenodo.7901529">https://doi.org/10.5281/zenodo.7901529</a></p> <p>The layers are separated in single folders that can be activated or deactivated. All data have a time stamp and a time span. By moving the time slider on the upper left (and adjusting the time interval), it is possible to follow the ship and display the corresponding SAR images and sea-ice edge. All the images are displayed for a time duration of one day. The exact time and name of the image is found in the object description.</p> <p>Legend:</p> <ul> <li>White line: ship track</li> <li>Red pin: station (with the main activities: refer to the report for more informations)</li> <li>Yellow pin: ship location at the time of the COSMO-SkyMed image</li> <li>Green pin: ship location at the time of the Terrasar-X image</li> <li>Red thick line: sea-ice edge from AMSR2 (green shading is the uncertainty)</li> </ul> <p>Quick-view SAR images are courtesy of DLR and ASI. Sea-ice edge location was produced by SAWS.</p> <p>The code to generate the KML file is available on github: <a href="https://github.com/mvichi/SCALE">https://github.com/mvichi/SCALE</a></p>
Data from: Just Google it: assessing the use of Google Images to describe geographical variation in visible traits of organisms
Describing spatial patterns of phenotypic traits can be important for evolutionary and ecological studies. However, traditional approaches, such as fieldwork, can be time-consuming and expensive. Information technologies, such as Internet search engines, could facilitate the collection of these data. Google Images is one such technology that might offer an opportunity to rapidly collect information on spatial patterns of phenotypic traits. We investigated the use of Google Images in extracting data on geographical variation in phenotypic traits visible from photographs. We compared the distribution of visual traits obtained from Google Images with four previous studies: colour morphs of black bear (Ursus americanus); colouration and spottiness in barn owl (Tyto alba); colour morphs of black sparrowhawk (Accipiter melanoleucus) and the distribution of hooded (Corvus corone) and carrion crows (Corvus cornix) across their European hybrid zone. Additionally, we develop and present a web application (Morphic), which facilitates the human data capture process of this method. We found good agreement between fieldwork data and Google Images data across all studies. Indeed, there was strong agreement between the data obtained from the original study and from the Google Images method for the colour morphs of black bear (R2 = 80%) and for two barn owl plumage traits (R2 = 64% and 53%). Our approach also successfully matched the clinal variation of black sparrowhawks morphs across South Africa. Our method also gave a good agreement between the distribution of hooded and carrion crows (with 86% placed on the correct side of the hybrid zone line). Our results suggest that this method can work well for visible traits of common and widespread species that are objective, binary, and easy to see irrespective of angle. The Google Images method is cost-effective and rapid and can be used with some confidence when investigating patterns of geographical variation, as well as a range of other applications. In many cases, it could therefore supplement or replace fieldwork.
Global Landside Clustering of Aquaculture Ponds Distribution Acquired from Dense Time-Series Sentinel-2 Images by Google Earth Engine
<p>This dataset reveals the global distribution pattern of landside clustering aquaculture ponds (LCAP) from a spatial perspective for the first time. It was derived from 4,015,054 tiles of the 10-m Sentinel-2 time-series images collected throughout 2020. The total area of global LCAP was estimated at 55,337.03 km2. Accuracy verification revealed that the Omission Error and Commission Error of the data is 7.51% and 16.69% respectively. We provide this dataset in <em>ESRI</em> <em>shapefile </em>format (.zip), which can be opened by <em>ArcGIS. </em>We invite you to download and utilize this dataset and recommend citing the following two references.</p>
A global data set of realized treelines sampled from Google Earth aerial images
<div> <span>We </span><span>sampled</span><span> Google Earth aerial images</span><span> to get a representative and globally distributed dataset of treeline locations</span><span>. </span><span>Google Earth images</span><span> are available to everyone, but may not be automatically downloaded and processed according to Google's license terms. Since we only wanted to detect tree individuals, we evaluated the aerial images manually by hand.</span> </div> <div> </div> <div> <span>Doing so, we scaled Google Earth's GUI interface to a buffer size of approximately 6000 m from a perspective of 100 m (+/- 20 m) above Earth's surface. Within this buffer zone, we took coordinates and elevation of the highest </span><span>realized </span><span>treeline locations. In some remote areas of Russia and Canada, individual trees were not identifiable due to insufficient image resolution. If this was the case, no treeline was sampled, unless we detected another visible treeline within the 6,000 m buffer and took this next highest treeline</span><span>. We did not ap</span><span>p</span><span>ly an automated image processing approach. </span><span>We calculated mass elevation effect as the distance to the nearest mountain chain limits. Continentality was assessed by the distance to the nearest coastline. Isolation was calculated by the nearest distance of a mountain chain to another mountain chain within a comparable elevational band. </span> </div>
Data from: Just Google it: assessing the use of Google Images to describe geographical variation in visible traits of organisms
Open the record for dataset details and reuse information.
A global data set of realized treelines sampled from Google Earth aerial images
Open the record for dataset details and reuse information.
Text-fig. 8. Mhengere Hill fossiliferous localities (1–3). Silicified tree trunks and fragments of wood are abundant in the poorly indurated basal deposits (marly sand and conglomerate) as well as in the silicified lime-rich sandstones that form the hill, which is interpreted to be the remains of a palaeopan. Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 8. Mhengere Hill fossiliferous localities (1–3). Silicified tree trunks and fragments of wood are abundant in the poorly indurated basal deposits (marly sand and conglomerate) as well as in the silicified lime-rich sandstones that form the hill, which is interpreted to be the remains of a palaeopan. Image modified from Google Earth.
Petra Treasury - Made with random google images.
This scan was reconstructed using random images found online. More info here: https://packet39.com/blog/2019/01/09/how-i-3d-scanned-the-treasury-at-petra-without-leaving-home/ Source: Objaverse 1.0 / Sketchfab
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.