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213 results for “Google”
Google Scholar search record using GSscraper app: 2022-04-16
File generated: Search date, time, timezone: 2022-04-16 13:29:04 (Europe/London) Search parameters: All these words: climate test 3 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=climate+test+3&hl=en&as_vis=0&as_sdt=2007
Google Scholar search record using GSscraper app: 2022-04-16
File generated: Search date, time, timezone: 2022-04-16 13:35:46 (Europe/London) Search parameters: All these words: final test 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=final+test&hl=en&as_vis=0&as_sdt=2007
Google Scholar search record using GSscraper app: 2022-04-16
File generated: Search date, time, timezone: 2022-04-16 13:32:04 (Europe/London) Search parameters: All these words: exciting 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=exciting&hl=en&as_vis=0&as_sdt=2007
Google Scholar search record using GSscraper app: 2022-04-16
File generated: Search date, time, timezone: 2022-04-16 13:39:17 (Europe/London) Search parameters: All these words: spinner 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=spinner&hl=en&as_vis=0&as_sdt=2007
Google Scholar search record using GSscraper app: 2022-04-16
File generated: Search date, time, timezone: 2022-04-16 13:24:45 (Europe/London) Search parameters: All these words: climate test 2 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=climate+test+2&hl=en&as_vis=0&as_sdt=2007
Google Scholar search record using GSscraper app: 2022-04-16
File generated: Search date, time, timezone: 2022-04-16 13:19:28 (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
Google Scholar search record using GSscraper app: 2022-04-18
File generated: Search date, time, timezone: 2022-04-18 16:02:10 (Europe/London) Search parameters: All these words: test 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=test&hl=en&as_vis=0&as_sdt=2007
La recherche avancée dans Google Scholar
<p>Cette vidéo présente la recherche avancée dans Google Scholar. Elle a été réalisée par le groupe de travail "Tutoriel" de ULiège Library et déposée sur Youtube le 2 octobre 2019 : <a href="https://youtu.be/mQpMxXiunuU">https://youtu.be/mQpMxXiunuU</a> </p> <p>Elle s’accompagne de deux fichiers adjuvants qui ont pour but d’expliquer et de partager les méthodes de travail du groupe dans sa création de ressources éducatives libres, à savoir : </p> <ul> <li> <p>le script de la vidéo accompagné d’informations technico-pédagogiques en vis-à-vis (pour l’enregistrement studio de la ressource)</p> </li> <li> <p>la taxonomie des rôles et contributions utilisée pour créditer les différentes personnes ayant participé à la création de cette ressource (adapté de la taxonomie <a href="https://casrai.org/credit/">CRediT</a> (Contributor Roles Taxonomy) pour nos besoins. </p> </li> </ul>
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>
Tutorial to explore riverbank erosion along Jamuna River, Bangladesh, with the Google Earth Engine
<p>This short tutorial shows the different features and data that are available in a tool that allows you to explore riverbank erosion in Bangladesh. The tool has been developed by: Freihardt & Frey (under submission): Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine.</p> <p>The tool can be accessed via this link: <a href="https://code.earthengine.google.com/3ea8f1fd5d771accc621550d744a914e?hideCode=true">https://code.earthengine.google.com/3ea8f1fd5d771accc621550d744a914e?hideCode=true</a></p> <p>To access the video tutorial without downloading it: <a href="https://youtu.be/_b9AAPDw7Wk">youtu.be/_b9AAPDw7Wk</a></p>
DMP Examine the correlation between Twitter Sentiment data and the stock data of the 4 big tech companies Apple, Amazon, Google and Microsoft
<p><span>The purpose of using these specific datasets are, the possibilities they give to analyze the stock data changes, based on the sentiment analysis of the previous day. Including this output information it is possible to analyze our goal of searching for possible correlation between those two. </span></p>
Datos de indicadores bibliométricos en Google Scholar de universidades Centroamericanas y del Caribe - 2018
<p>Datos de indicadores bibliométricos extraidos de Google Scholar en el 2018 de universidades Centroamericanas y del Caribe según ranking de webometrics de ese año. Se listan tambien el perfil en google scholar de las revistas identificada sal extraer los perfiles en GS de las universidades.</p> <p><strong>Diccionario de datos:</strong></p> <ul> <li>pais: nombre del país de la institución</li> <li>nombreU: nombre de la universidad</li> <li>universidades: sigla de la universidad</li> <li>perfiles: número de perfiles extraido de Google Scholar (Datos cuantitativo)</li> <li>publicaciones: número de publicaciones extraidas de Google Scholar (Datos cuantitativo)</li> <li>citaciones : número de citas contabilizadas enGoogle Scholar (Datos cuantitativo)</li> <li>cita_perfil : número de cita por perfil (Datos cuantitativo calculado)</li> <li>cita_publi : número de cita por publicación (Datos cuantitativo calculado)</li> <li>cita_ano : número de cita por año(Datos cuantitativo calculado)</li> <li>hindex: hindex promedio</li> <li>hindex11: hindex i10 promedio</li> <li>anos: año de la universidad</li> </ul> <p> </p> <p> </p>
Changes Monitoring in Hongjiannao Lake from 1987-2023 using Google Earth Engine and Analysis of Climatic and Anthropogenic Forces (Climatic Data)
<p>This dataset presents temporal (1987 to 2023) climatic data for the weather station near Hongjiannao Lake.</p>
Forest Fire Dataset for Peninsular Malaysia (2001-2023) Extracted from Multiple-Source Remote Sensing Data using Google Earth Engine
<ul> <li>Dataset: Forest Fire data</li> <li>Time Period: 2001 to 2023</li> <li>Location: Peninsular Malaysia</li> <li>Historical Fire Source: MCD64A1 and FIRMS Hotspots</li> <li>Fire Factors Extracted: Global Remote Sensing Data from GEE</li> </ul> <p>The framework extraction process can be reffered from the following publication:</p> <ul> <li>Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis</li> <li>Journal: <em>Forests</em> <strong>2024</strong>, <em>15</em>(6), 923;</li> <li><a href="https://doi.org/10.3390/f15060923">https://doi.org/10.3390/f15060923</a></li> <li>The variables name such as AET (actual evapotranspiration) can be found from the article.</li> </ul> <p>Access the framework code from: </p> <ul> <li><a href="https://github.com/chewyeejian/GEE_FrameworkForestFireDataset">https://github.com/chewyeejian/GEE_FrameworkForestFireDataset</a></li> </ul> <p>The time sequence in the variable indicate whether it's a monthly data / yearly accumulated data / seasonal data, example:</p> <ul> <li>200101_aet (Year 2001, Month 01, value for aet (actual evapotranspiration)</li> <li>2001_aet_DJF (Average of December, January, February)</li> <li>2001_aet_MAM (Seasonal Average of March, April, May)</li> <li>2001_aet_JJA (Seasonal Average of June, July, August)</li> <li>2001_aet_SON (Seasonal Average of September, October, November)</li> <li>2001_aet_annual (Annual average of 2001)</li> </ul>
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>
Twitter and Google Trend data about heat waves in India 2010-2017
<p>The dataset contains:</p> <p>1) The list of tweets corresponding to the keywords "heat wave india" and "heatwave india" between 2010 and 2017.</p> <p>2) The daily count of the same tweets</p> <p>3) The monthly Google Trends data corresponding to the keywords "heat wave", "heatwave", "heat wave india", and "heatwave india" limited to the searches from India in the period 2010-2017</p> <p>The Twitter data has been obtained wth the Python package Get-Old-Tweets (https://github.com/Jefferson-Henrique/GetOldTweets-python); the Google Trends data are obtained from the Google Trends webpage (https://trends.google.com/trends/?geo=US).</p>
Benchmark results for Vellamo and AnTuTu from Google Play in Xiaomi Redmi Note 4
<p>The dataset contains the print screens of the execution of following benchmark: Vellamo Browser, Vellamo Metal, Vellamo Multicore, and AnTuTu General. The benchmark was repeated until the smartphone reaches a battery level below 20%. For each benchmark, we recorded the numerical results for each microbenchmark. Each benchmark was evaluated using different approaches for CPU frequency scaling: Ondemand, Performance, Interactive, HS, ZT, OUR-G-0.1, OUR-G-0.5 and OUR-G-0.9.</p> <p>Ondemand, Performance, and Interactive are native from Android OS.</p> <p>OUR-G-0.1, OUR-G-0.5 and OUR-G-0.9 referrers to our proposed method to save energy, executed in smartphone Xiaomi Redmi Note 4.</p> <p>The approaches HS is from:<br> @article{hshen2013,<br> author = "H. Shen and Y. Tan and J. Lu and Q. Wu and Q. Qiu",<br> title = "Achieving Autonomous Power Management Using Reinforcement Learning",<br> journal = "ACM Transactions on Design Automation of Electronic Systems (TODAES)",<br> volume = "18",<br> month = "",<br> number = "2",<br> year = "2013",<br> pages = "24-32"<br> }</p> <p>and ZT in from:<br> @inproceedings{ztian2018,<br> author = "Zhongyuan Tian and Zhe Wang and Haoran Li and Peng Yang and Rafael Kioji Vivas Maeda and Jiang Xu",<br> title = "Multi-device collaborative management through knowledge sharing",<br> booktitle = "Proc. 23rd Asia and South Pacific Design Automation Conference (ASP-DAC)",<br> month = "" ,<br> year = "2018",<br> pages = "22-27"<br> }</p> <p>The energy.txt file contains the acquisition of voltage and current from smartphone fuel gauge.</p> <p>The other files are self-explained.</p> <p> </p> <p> </p>
Interview with Noble Ackerson - Google Glass Explorer
<p>A short interview conducted by the researcher, Alexander Hayes, PhD Candidate at the University of Wollongong with Noble Ackerson, Google Glass Explorer regarding the phenomenon of Google Glass, a head-worn computer enabling an augmented field-of-view. This interview was conducted by the researcher whilst in the role as Professional Associate at the University of Canberra, ACT Australia. Read more about this research at <a href="https://www.alexanderhayes.com/journal/glassmeetup-symposium">https://www.alexanderhayes.com/journal/glassmeetup-symposium</a></p>
Interview with Cecilia Abadie - Google Glass Explorer
<p>A short interview conducted by the researcher Alexander Hayes, PhD candidate with Cecilia Abadie, Google Glass Explorer exploring the many and varied social, ethical and cultural implications of this head-worn computing innovation. Cecilia was speaking from San Francisco and Alexander Hayes was located in Australia during the interview conducted using Google + Hangouts.</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.