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213 results for “Google”
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>
Stereotype elicitation in Google, DuckDuckGo and Yahoo! autcompletion
<p>Dataset released as supplementary material to the following publication:</p> <p>Alina Leidinger and Richard Rogers. 2023. Which Stereotypes Are Moderated and Under-Moderated in Search Engine Autocompletion?. In 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23), June 12–15, 2023, Chicago, IL, USA. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3593013.3594062</p>
Search Behavior can Affect Financial Decision Results: A Behavior Study of Google Trends Data and Linguistic Scale
Open the record for dataset details and reuse information.
Nimble code and dataset for: Estimating true density in large, alpine herbivores using Google Earth imagery
Open the record for dataset details and reuse information.
Índice H en Google Scholar de los Titulares y Catedráticos de comunicación (2000-19)
<p>En el presente listado se identifica el índice H y las citas de los profesores (titulares y catedráticos) de Comunicación de España contratados en 2000-19.</p>
Interesse de pesquisa sobre podcast no Google Trends
<p>Tivemos a comprovação do aumento pelo interesse em <em>podcasts </em>ao realizarmos uma pesquisa no Google Trends, ferramenta amplamente utilizada por jornalistas e profissionais que atuam no mercado de <em>Search Engine Optimization</em> (SEO), a qual nos permitiu constatar, através da busca pela palavra ‘<em>podcast</em>’ no Brasil, considerando o recorte temporal dos últimos três anos (de janeiro de 2017 a dezembro de 2019), que a ascensão de pesquisa por ‘<em>podcast’</em> no Brasil deu-se entre os dias 25 e 31 de agosto de 2019, período que coincidiu com o lançamento do <em>podcast</em> O Assunto, mediado pela jornalista Renata Lo Prete, do portal de notícias G1, cujo primeiro episódio (denominação atribuída, por padrão, a cada áudio do <em>podcast</em>) foi publicado no dia 26 de agosto.</p> <p>Ainda com base em pesquisa no Google Trends, em janeiro de 2020, constatamos que o Ceará ocupava a 10ª posição em interesse de pesquisa por <em>podcast</em>. Além desses dados, o Google Trends nos apresentou os assuntos e as consultas relacionadas, as quais indicaram que os usuários que pesquisaram por ‘<em>podcast’</em> também pesquisaram por outros temas, dentre eles, o significado da palavra e como instalar o Spotify.</p>
Developing an English course for Beginners with the topic Part of Speech using Google Classroom
<p>Self-paced learning means that you can study on your own time and schedule. You don't have to complete the same assignment or study at the same time as others. You can proceed from one topic or segment to the next at your own pace. Why we chose Google Classroom Media is because it's so easy to use. For people who are using Google classrooms for the first time, they will definitely have no trouble operating them.</p>
Data from: Association between stock market gains and losses and Google searches
Experimental studies in the area of Psychology and Behavioral Economics have suggested that people change their search pattern in response to positive and negative events. Using Internet search data provided by Google, we investigated the relationship between stock-specific events and related Google searches. We studied daily data from 13 stocks from the Dow-Jones and NASDAQ100 indices, over a period of 4 trading years. Focusing on periods in which stocks were extensively searched (Intensive Search Periods), we found a correlation between the magnitude of stock returns at the beginning of the period and the volume, peak, and duration of search generated during the period. This relation between magnitudes of stock returns and subsequent searches was considerably magnified in periods following negative stock returns. Yet, we did not find that intensive search periods following losses were associated with more Google searches than periods following gains. Thus, rather than increasing search, losses improved the fit between people's search behavior and the extent of real-world events triggering the search. The findings demonstrate the robustness of the attentional effect of losses.
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.
Data from: Adaptive nowcasting of influenza outbreaks using Google searches
Seasonal influenza outbreaks and pandemics of new strains of the influenza virus affect humans around the globe. However, traditional systems for measuring the spread of flu infections deliver results with one or two weeks delay. Recent research suggests that data on queries made to the search engine Google can be used to address this problem, providing real-time estimates of levels of influenza-like illness in a population. Others have however argued that equally good estimates of current flu levels can be forecast using historic flu measurements. Here, we build dynamic 'nowcasting' models; in other words, forecasting models that estimate current levels of influenza, before the release of official data one week later. We find that when using Google Flu Trends data in combination with historic flu levels, the mean absolute error (MAE) of in-sample 'nowcasts' can be significantly reduced by 14.4%, compared with a baseline model that uses historic data on flu levels only. We further demonstrate that the MAE of out-of-sample nowcasts can also be significantly reduced by between 16.0% and 52.7%, depending on the length of the sliding training interval. We conclude that, using adaptive models, Google Flu Trends data can indeed be used to improve real-time influenza monitoring, even when official reports of flu infections are available with only one week's delay.
Could Google Trends be used to predict methamphetamine-related crime? An analysis of search volume data in Switzerland, Germany, and Austria
<p>Data for paper submitted to PLoS One on 2016-07-08. Title: Could Google Trends be used to predict methamphetamine-related crime? An analysis of search volume data in Switzerland, Germany, and Austria</p> <p>Authors: Alex Gamma, Roman Schleifer, Wolfgang Weinmann, Anna Buadze, Michael Liebrenz</p> <p>Format: ZIP-file</p> <p>Contains:<br /> - Two datafiles, each as .csv and .dta (Stata version 11) file.<br /> - README file with instructions</p> <p> </p> <p> </p>
FIGURE 4. Google Earth 2015 in Checklist of Recent thecideoid brachiopods from the Indian Ocean and Red Sea, with a description of a new species of Thecidellina from Europa Island and a re-description of T. blochmanni Dall from Christmas Island
FIGURE 4. Google Earth 2015 Digital Globe map of Christmas Island, showing collecting areas for Thecidellina blochmanni and T. cf. blochmanni at Flying Fish Cove and West White Beach (Bay), respectively. Flying Fish Cove coordinates are lat. 10° 25'S, long. 105° 40'E.
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.
Análise de contratos de secretarias estaduais de educação com a empresa Google - USP 2023
<p>Atualização dos pedidos de Lei de Acesso à informação a partir do trabalho de AMIEL, T. et al. Os modos de adesão e a abrangência do capitalismo de vigilância na educação brasileira. Zenodo, 14 set. 2021. Disponível em: <https://zenodo.org/record/5507265>. Acesso em: 14 set. 2021, selecionando-se os que tratem sobre contratos de Secretarias Estaduais de Educação e contratos com a empresa Google. A análise dos novos resultados de pedidos de LAI em 2022 são comparados aos documentos de 2020 trazendo mais informações sobre as contratações de tecnologia pelas secretarias. As conclusões estão expostas na tese produzida por Stephane Hilda Barbosa Lima na Universidade de São Paulo, em 2023. </p>
Fact checks versus problematic content in search rankings: SEO effects and the question of Google's content moderation
<p>Dataset released as supplementary material to the following publication:</p> <p>Kamila Koronska and Richard Rogers.2024. <span>Fact-checks versus problematic content in search rankings: SEO </span><span>effects and the question of Google’s content moderation.Kamila Koronska and Richard Rogers. 2024. In ACM Web Science Conference (Websci ’24), May 21–24, 2024, Stuttgart, Germany. ACM, New York, NY, USA, 11 pages, https://<span>doi</span>.org/10.1145/3614419.3644017</span></p>
Google Play Store Permission Analysis
<p>A tool designed to evaluate the risk of Android apps based on permissions, size anomalies, and other factors. It fetches datasets from Kaggle, processes the data, calculates risk scores using a weighted system, and generates insightful visualizations to understand risk distribution across app categories.</p> <h3>Key Features:</h3> <ul> <li><strong>Data Handling</strong>: Downloads, cleans, and preprocesses datasets for analysis.</li> <li><strong>Risk Scoring</strong>: Analyzes permissions, app size anomalies, and other patterns to assign risk scores.</li> <li><strong>Visual Insights</strong>: Creates heatmaps, bar charts, and scatter plots to visualize correlations and risk distributions.</li> </ul> <h3>Project Outputs:</h3> <ul> <li>Cleaned data, analysis reports, and visualizations.</li> </ul> <p>Licensed under Creative Commons Attribution 3.0.</p>
Artifacts for the Paper: Analyzing the Feasibility of Adopting Google's Nonce-Based CSP Solutions on Websites
<p>This is the artifact for the Paper "Analyzing the Feasibility of Adopting Google's Nonce-Based CSP Solutions on Websites" in Proceedings of the IEEE/ACM International Conference on Software Engineering (ICSE), 2025. </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.