Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
213
datasets available to search
ShareScore release 0.9.0
Dataset results
213 results for “Google”
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.
Gastos de IFES com plataformas Google para educação
<p>Dados retirados do diário oficial relativo à contratos de Instituições Federais de Ensino Superior com a empresa Google e Microsoft para serviços associados com plataformas educacionais.</p> <p>Explicações disponíveis no site do <a href="https://educacaovigiada.org.br/">Observatório Educação Vigiada</a>.</p>
Datos de perfiles en Google Scholar en 2017 de Universidades en Centroamérica
<p>Datos de los perfiles de investigadores en Google Scholar de seis universidades en Centroa América identificadas en esa plataforma en 2017. La tabla contiene 13 columnas y 767 filas.</p> <p><strong>Perfiles de Instituciones:</strong></p> <ul> <li>Centro Agronómico Tropical de Investigación y Enseñanza</li> <li>Instituto Tecnológico de Costa Rica</li> <li>Universidad de Costa Rica</li> <li>Universidad del Valle de Guatemala</li> <li>Universidad Nacional Costa Rica</li> <li>Universidad Tecnológica de Panamá</li> </ul> <p><strong>Diccionario de datos:</strong></p> <ul> <li>Sexo: Género del investigador, indicado como "M" para masculino o "F" para femenino.</li> <li>Institucion: Nombre de la institución a la que está afiliado el investigador.</li> <li>Nombre: Nombre completo del investigador.</li> <li>word_key: Áreas de especialización del investigador descritas mediante palabras clave.</li> <li>url_user: Enlace a la página de Google Scholar del investigador.</li> <li>Id_user: Identificador único del usuario en Google Scholar. </li> <li>citaciones: Número total de citas de las publicaciones del investigador en Google Scholar. </li> <li>cita_2011: Número de citas que tenían las publicaciones del investigador hasta el año 2011.</li> <li>hindex: Índice h actual del investigador.</li> <li> hindex_2011: Índice h del investigador hasta el año 2011. </li> <li>index10: Número de artículos del investigador que han sido citados al menos 10 veces. </li> <li>indexi10_2011: Número de artículos del investigador que habían sido citados al menos 10 veces hasta el año 2011.</li> </ul>
Datos de universidades del mundo en Webometrics 2016 y su perfil en Google Scholar
<p>Los datos contiene una hoha llamada gs_mundo con datos extraida de la página de webometrics del 2016 con su url del perfil de google scholar.</p> <p>La estructura de datos contiene:</p> <ul> <li><span>Rank</span>: Indica la posición de la universidad en el ranking basado en el número total de citas académicas registradas en Google Scholar.</li> <li><span>University</span>: Nombre oficial de la universidad. </li> <li><span>url_GS</span>: Dirección URL que lleva a la página de Google Scholar donde se pueden ver las citas académicas de la universid.</li> <li><span>Country</span>: País donde se encuentra la sede principal de la universidad. </li> <li><span>Citations</span>: Total de citas académicas en Google Scholar para las publicaciones asociadas a la universidad.</li> </ul>
Dataset: Direxion Daily GOOGL Bull 2X Shares (GGLL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Direxion Daily GOOGL Bear 1X Shares (GGLS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 2. Screenshot of the Google Earth web-site's prototype on the visualization of heat/cold waves-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology-
<p>A non-anticipative analog method consists of four main steps:<br> 1. Generation of the prediction rules.<br> 2. Analysis of the prediction rules. The rules with time slots, which are not concentrated at<br> the same frame, are excluded.<br> 3. Generation of possible extremes.<br> 4. Analysis of the generated possible extremes. The extremes with time slots, which do not<br> correspond to the time slots of the appropriate rules, are excluded.<br> The results of the heat/cold waves’ prediction from 2011 to 2014 at different locations<br> (places are selected randomly) are presented in Table 2.</p>
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 8. Virtual social space on Google+
<p>For micro-blogging we hashtagged the main topic as #maps_of_time and created keywords related to three ancient technologies specific for the studied contexts (textiles, glass, ceramics) to facilitate a categorization of the topics and their retrieval. To achieve a unified and coherent platform, the personal spaces of the social networks were customized with logos and landing pages, designed by Associate Professor Marina Theodorescu (NUA). </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>
What does Google recommend when you want to compare insurance offerings? – A method and empirical study considering Google's top search results
<p>This dataset is part of a publication and shows Google's search results for German search queries on insurance comparison offerings.</p> <p>Relevant search queries were extracted from a commercial search engine log file consisting of more than 640,000 different search queries. From the log, we extracted a variety of query formulations for the same topic, i.e., queries containing the same word or phrase. The selection was based on pre-defined keywords in the context of insurance comparisons. The queries from the log file were automatically selected by combining the terms "*insurance*" and "*comparison*" (including left as well as right truncation). Examples of such inquiries are "car insurance comparison", "occupational disability insurance comparison", "liability insurance in comparison". This procedure identified a total of 121 different search queries. Scraping of the results took place between 08.05. - 09.05.2018.The adress data were extracted by using a text classification algorithm and a crawler to find the contact data on a website.</p> <p>It is a tab-separated file with the following attributes:</p> <p>ID: Unique row identifier</p> <p>ID Query: Unique search query identifier</p> <p>Query: German search query </p> <p>Position: Result position to the search query </p> <p>URL: URL of the search result </p> <p>Host: Host of the search result </p> <p>Company: Name of the company on the website</p> <p>Street: Street in the address on the website </p> <p>Zipcode: Street in the address on the website </p> <p>Location: Location in the address on the website </p> <p>District: District in the address on the website </p> <p>State: State in the address on the website </p> <p>Country: Country in the address on the website </p>
Data set of the article: Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus
<p>Data of investigation published in the article "Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus".</p> <p>Abstract of the article:</p> <p>Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyse and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyse two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, WoS used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.</p>
Data for study "Direct Answers in Google Search Results"
<p>The goal of this research is to examine <strong>direct answers</strong> in Google web search engine. Dataset was collected using Senuto (<a href="https://www.senuto.com/">https://www.senuto.com/</a>). Senuto is as an online tool, that extracts data on websites visibility from Google search engine.</p> <p>Dataset contains the following elements:</p> <ol> <li>keyword,</li> <li>number of monthly searches,</li> <li>featured domain,</li> <li>featured main domain,</li> <li>featured position,</li> <li>featured type,</li> <li>featured url,</li> <li>content,</li> <li>content length.</li> </ol> <p>Dataset with visibility structure has <strong>743 798 keywords</strong> that were resulting in SERPs with direct answer.</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).
Dataset of the study: "Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard"
<p>This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.</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.