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213 results for “google”
Auswirkungen von Google, Apple, Facebook, Amazon und Microsoft auf Schule und Unterricht in Deutschland
<p>Google, Apple, Facebook, Amazon und Microsoft (GAFAM) sind einige der größten Unternehmen der IT-Welt. Viele ihrer Produkte werden von Millionen von Personen nahezu täglich genutzt. Ihre Produkte werden unter Anderem auch in Schulen und im Unterricht eingesetzt. Um zu untersuchen, welche Auswirkungen GAFAM auf Schule und Unterricht aus Sicht der Lehrkräften haben, wurde eine Online-Befragung mit Lehrer*innen durchgeführt.</p> <p> </p>
Relação de artigos científicos sobre Humanidades Digitais obtidos pelo software Google Scholar Crawler
<p>Resultado do processamento do Google Scholar Crawler para o termo "Humanidades Digitais". Neste arquivo já foram retirados os itens duplicados e os falsos positivos (artigos que não consideramos como sendo de Humanidades Digitais.</p>
List of articles resulting from the Google Scholar search "graph based author name disambiguation" published after 1/1/2021
<p>This dataset contains the list of articles resulting from the Google Scholar search “graph based author name disambiguation” published after 1/1/2021. The list is provided for reproducibility of the survey article “Graph-based Methods for Author Name Disambiguation: A Survey” and it was obtained using the following Python script available at <a href="https://github.com/WittmannF/sort-google-scholar">https://github.com/WittmannF/sort-google-scholar</a>:</p> <blockquote> <p>$ python sortgs.py --kw “graph based author name disambiguation” --startyear 2021</p> </blockquote> <p>The command returned the CSV file that contains the first 94 publications matching the query (articles with corrupted metadata have been excluded), each with metadata about Title, Number of Citations, and Rank. The CSV contains a column that specified which articles have been eventually selected for the survey.</p>
Screen captures illustrating molecular 3D model sharing through Sketchfab, Google Poly and NIH Print Exchange
<p>Sharing 3D models illustrated by 6 screen captures. </p> <p> </p> <p>1: cardboard stereo view with Sketchfab of example 1 (ACE-spike coronavirus complex)</p> <p> </p> <p>2: tuning of VR/AR settings on the Sketchfab platform (example 1)</p> <p> </p> <p>3: Sketchfab web view of example 1</p> <p> </p> <p>4: Sketchfab 3D Model inspector applied to example 1 model</p> <p> </p> <p>4: Google Poly web view of example 1</p>
Data set of the article: Language Bias in the Google Scholar Ranking Algorithm
<p>Data of investigation published in the article Cristòfol Rovira; Lluís Codina; Carlos Lopezosa Language Bias in the Google Scholar Ranking Algorithm. Future Internet, 2021, 13.</p> <p><strong>Abstract: </strong>The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.</p>
News of CanalUGR tracked on Google News, Yahoo! News and Bing News
Dataset contains 613 news of CanalUGR (University of Granada Communication Office) tracked on the main online news aggregators (Google News, Yahoo! News and Bing News). We include: number in CanalUGR, media, country, type.
FREE-ROAMING DOGS DETECTED USING GOOGLE STREET VIEW
<p>Datasets to count free-roaming dogs using Google Street View, and compare with population of free-roaming dog from surveys in Arequipa, Peru.</p>
Content Hosting & Services Agreement between Google Arts & Culture and The University of Texas at Austin
<p>This document was obtained through a public records request filed under the <a href="https://www.texasattorneygeneral.gov/open-government/members-public/overview-public-information-act" target="_blank" rel="noopener">Texas Public Information Act</a>. It details the terms of a collaboration between the University of Texas at Austin and the Google Cultural Institute (now Google Arts & Culture). The document was requested as part of a data collection process for my PhD dissertation at the University of Texas at Austin, which, among other topics, examined the platform's use by cultural institutions. Contracts, alongside terms of service and content guidelines, are a critical aspect of a platform’s governance mechanisms.</p> <p>The document was released in response to request <strong>R005413-103123</strong> and made available on <strong>November 17, 2023</strong>, without redactions. For this version, I redacted email addresses and other personally identifiable information.</p> <p>You can find my dissertation, which references this document, on <a href="https://zenodo.org/records/13994044" target="_blank" rel="noopener">Zenodo</a> or through the <a href="https://doi.org/10.26153/tsw/55818" target="_blank" rel="noopener">University</a>.</p>
Latin American and Caribbean journals indexed in Google Scholar Metrics
<p>Dataset from a study aiming to analyze the coverage of Latin American and Caribbean journals in Google Scholar Metrics (GSM). Data from 8,205 journals from 24 countries of the region were downloaded from Latindex database. A Python script was used for automated title search and data extraction (titles, h5-index, h5-median, URLs) in GSM. For the journals not found, a manual search was carried out, with attempts by variations of the title. It was found 3,070 journals indexed in GSM, which corresponds to 37.42% of the Latindex list. The search was performed on the 2021 edition of GSM, which considers articles published between 2016 and 2020 and citations registered until July 2021. The number of all types of documents published (productivity) in the h5-index period (2016-2020) in Scopus, Journal Citation Reports, and SciELO of 1,314 journals was also identified. </p> <p>The present dataset is the result of this study, which is under peer-review in a scientific journal. </p> <p>The dataset comprises titles, h5-index; h5-median, URLs of 3,070 publications from Latin America and the Caribbean identified in Google Scholar Metrics, and the respective editorial information of the publications was extracted from Latindex</p> <p>The original language of the content was kept, mainly Spanish in the case of editorial data from Latindex. The columns descriptors are also shown in English.</p> <p>The productivity data refer to the number of all types of documents published by the journals in the period 2016-2020. Data were extracted from the InCities Journal Citation Reports, Scopus, and SciELO Citation Index (Web of Science database).</p> <p>In this version 2, only the productivity data were changed, covering a larger number of journals (1,314) and including all types of documents. Other data are the same as in the first version (https://doi.org/10.5281/zenodo.5572873).</p> <p> </p> <p> </p> <p> </p> <pre> </pre> <p> </p>
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>
Google Scholar search record: ?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
File generated: Search date, time, timezone: 2022-04-15 17:50:45 (Europe/London) Search parameters: All these words: crayfish None of these words: This exact word or phrase: "water chemistry" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
Google Scholar search record: ?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
File generated: Search date, time, timezone: 2022-04-15 17:34:33 (Europe/London) Search parameters: All these words: crayfish None of these words: This exact word or phrase: "water chemistry" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
Google Scholar search record using GSscraper app: 2022-04-19
File generated: Search date, time, timezone: 2022-04-19 16:54:44 (Europe/London) Search parameters: All these words: crayfish None of these words: This exact word or phrase: "" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=crayfish&hl=en&as_vis=0&as_sdt=2007
FABIAN: a daily product of Fractional Austral-summer Blue Ice over ANtarctica during 2000-2021 based on MODIS imagery using Google Earth Engine
<p>This is a supplementary data set for FABIAN: a daily product of Fractional Austral-summer Blue Ice over ANtarctica during<br> 2000{2021 based on MODIS imagery using Google Earth Engine</p> <p>The files include:</p> <p>(1) Snow spectra modelled by TARTES, a two-stream radiative transfer model for light in snow (Libois et al., 2013).</p> <p>(2) Spectra extracted from MODIS, using AUTO-EM.</p> <p>(3) Endmember selection results, including ESS, EMC, and AMUSES.</p> <p>The other hyperspectral data from field measurements can be acquired by contact the original authors.</p> <p>Libois, Q., Picard, G., France, J., Arnaud, L., Dumont, M., Carmagnola, C., King, M., 2013. Influence of grain shape on light penetration in snow. The Cryosphere 7, 1803-1818.</p>
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Task resource usage of Google Cluster Usage Trace dataset
<p>The dataset contains csv files. All csv files named by its cluster number and machine id. There are 2 features , time stamp and mean CPU usage. This data set is prepared from task resource usage (TRU) table of GCT dataset for the research purpose. Mainly TRU contains task information. Sum Average algorithm employed and calculate the machine information for every 5 minute time stamp.</p>
A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store
<p>This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the <a href="https://github.com/mooselab/Sports-Apps-Analysis">replication package</a> to direct you to help replicate it for your dataset too. </p> <p>Note: The dataset given are protected with password, and the password is available in our published paper</p>
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. 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.
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.