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”
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>
GLARE: Google Apps Arabic Reviews Dataset
<p>This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.</p>
FIGURE. Geographical location of the Augusto Ruschi Biological Reserve (ARBR), Santa Teresa, Espírito Santo, Brazil. a the state of Espírito Santo, highlighting the location of the ARBR. b delimitation of the ARBR. c–e ARBR vegetation. (a: prepared by Henrique Lauand Ribeiro, b: adapted from Google Earth Pro, c–e: photos of Gabriel Mendes Marcusso). in Augusto Ruschi Biological Reserve vascular epiphytes: a hotspot in the mountains of the Atlantic Forest of Southeastern Brazil
FIGURE. Geographical location of the Augusto Ruschi Biological Reserve (ARBR), Santa Teresa, Espírito Santo, Brazil. a the state of Espírito Santo, highlighting the location of the ARBR. b delimitation of the ARBR. c–e ARBR vegetation. (a: prepared by Henrique Lauand Ribeiro, b: adapted from Google Earth Pro, c–e: photos of Gabriel Mendes Marcusso).
Interest in insect die-off and intention for action using Google trends
<p><span>1. The publication of "More than 75 percent decline over 27 years in total flying insect biomass in protected areas" by Hallmann et al. in October 2017 gained vast media coverage in Germany. The insect crisis as conservation topic has received little attention among the public before, but since media influences people's awareness, we investigated i) whether the study publication induced </span><span>increased awareness among the German public for insect die-off, and ii) whether it contributed to people's intentions to undertake insect protecting actions. </span></p> <p><span>2. We used Google Trends to examine the people's internet activity in terms of keywords relevant to our research question.</span></p> <p><span>3. A high peak in Google searches for insect die-off (Insektensterben) was indeed visible just after the study publication, and search volume remained significantly higher for the following six months, confirming that the topic gained attention. </span></p> <p><span>4. Searches for the three keywords insect hotel, bee friendly and bee meadow increased significantly over the summers of the years 2017 to 2019. This suggests that intentions to undertake these simple insect protecting actions rose as well. The results propose that media should use the window of opportunity opened by shocking news about a crisis to spread information on feasible counteractions. </span></p> <p><span>5. </span><span>Due to the prevailing topicality in the media and the already increased awareness and willingness to action among the population, conservation organizations can take advantage of the situation by communicating practical conservation measures to the general public in cooperation with media agencies or via own channels such as press releases and social media campaigns.</span></p> <div> <div> <div class="msocomtxt"></div> </div> </div>
Google Trends time series for the term "topic modeling"
<p>Dataset received from Google Trends for the phrase "topic modeling'' on 31 January 2024 using the URL <a href="https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de">https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de</a></p> <p><em>Data obtained from Google LLC, which is the ultimate owner of these data. Published for academic and non-commercial replication purposes only.</em></p>
West Heslerton Anglo-Saxon Settlement -Primary Excavation Archive for interactive viewing in Google Earth Pro
<p>The Landscape Research Centre pioneered digital recording in field archaeology using hand-held computers in the field from the mid 1980s onwards. The excavation of an Early Anglo-Saxon settlement covering nearlly 25Ha, funded by English Heritage from the rescue archaeology commissions budget was one of the largest excavations in Europe conducted between 1986 and 1996 with an analytical program that contuniued into th 2020s. The digital plans provide an interactive interface to the primary excavation archives when this file id loaded inot Google Earth Pro.</p>
Changes Monitoring in Hongjiannao Lake from 1987-2023 using Google Earth Engine and Analysis of Climatic and Anthropogenic Forces
Open the record for dataset details and reuse information.
Data from: Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine
<p><strong>Paper Abstract:</strong></p> <p>The 2019–2020 Kangaroo Island bushfires in South Australia burned almost half of the island. To understand how to avoid future severe ‘mega-fires’ and how vegetation may recover from 2019–2020, we can utilize information from the bulk of historical fires in an area. Landsat time-series of vegetation change provide this opportunity, but there has been little analysis of large numbers of fires to build a landscape-level understanding and quantify drivers in an Australian context. In this study, we built a yearly cloud-free surface reflectance normalized burn ratio (NBR) time-series (1988–2020) using all available summer Landsat images over Kangaroo Island. Data were collected in Google Earth Engine and fitted with LandTrendr. Burn severity and post-fire recovery were quantified for 47 fires, with a new recovery metric facilitating comparison where fire frequency is high. Variables representing the current burn, fire history, vegetation structure, and topography were related to severity and yearly recovery with random forest and bivariate analysis. Results show that the 2019–2020 bushfires were the most widespread and severe, followed by 2007–2008. Vegetation recovers quickly, with NBR stabilizing ten years post-fire on average. Severity is most influenced by fire frequency, vegetation capacity and land use with more severe burns in nature conservation areas with dense vegetation and a history of frequent fires. Influence on recovery varied with time since fire, with initial (year 1–3) faster recovery observed in areas with less surviving vegetation. Later (year 6–10) recovery was most influenced by a variable representing burn year and further investigation indicates that precipitation increases in later post-fire years likely facilitated faster recovery. The relative abundance of eucalypt woodlands also has a positive influence on recovery in middle and later years. These results provide valuable information to land managers on Kangaroo Island and in similar environments, who should consider adjusting practices to limit future mega-fire risk and potential ecosystem shifts if severe fires become more frequent with climate change.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper: <a href="https://www.mdpi.com/2072-4292/12/23/3942">Remote Sensing | Free Full-Text | Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine (mdpi.com)</a></p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/KangarooIslandFireHistory_1988to2020">ZZMitch/KangarooIslandFireHistory_1988to2020: Code from "Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine" (RS, 2020) (github.com)</a></p> <p> </p> <p><strong>If you use these data, please reference: </strong></p> <p>Bonney, M.T., He, Y., Myint, S.W., 2020. Contextualizing the 2019–2020 Kangaroo Island bushfires: Quantifying landscape-level influences on past severity and recovery with Landsat and Google Earth Engine. Remote Sensing 12(23), <a href="https://doi.org/10.3390/rs12233942" rel="nofollow">https://doi.org/10.3390/rs12233942</a>.</p>
Workflow Trace Archive Google trace
<p>This workload contains the popular Google cluster trace (2014) in the workflow trace archive format.</p>
Melbourne Google Street View imagery dataset
<p>The data presented in this article is related to the research article entitled "Urban design using generative adversarial networks: optimising citizen health and wellbeing" (Wijnands et al 2018). The data consists of Google Street View (Google Maps, 2017) imagery (4,473,991 images, 8-bit JPEG at 256x256 resolution) from four headings (0, 90, 180, and 270 degrees) at 1,118,534 locations in the greater metropolitan area of Melbourne, Australia. Locations were determined using the nodes of the vector lines in the PSMA Street Network dataset (PSMA 2018) and data was post-processed by removing indoor images. Please cite this paper if you use the dataset.</p> <p>The data is broken up into four archives, 000.zip, 090.zip, 180.zip, and 270.zip, containing the imagery from each compass heading. A csv file (contained in MelbourneStreetViewImagesData.zip) provides a mapping between the filenames, location names, direction, latitude, and longitude.</p>
sample google map (EOL v3 test): raw eol google map
Open the record for dataset details and reuse information.
Alevin notebook for Google Collab & backup Alevin output
<p>A Jupyter notebook containing the workflow presented in the GTN tutorial <a href="https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/alevin-commandline/tutorial.html">Generating a single cell matrix using Alevin and combining datasets (bash + R)</a> that can be used in Google Collab. <br><br>Also, a folder of Alevin outputs resulting from running salmon alevin as shown in the tutorial.</p>
AppSet: Most Popular Google Play Apps in December 2023
<p>APK files obtained from AndroZoo and metadata retrieved from Google Play for the most popular apps for all app categories from Google Play in December 2023. Used in our ACSAC'24 paper "Manifest Problems: Analyzing Code Transparency for Android Application Bundles".</p>
Google Trend Enhanced Deep Learning Dataset for Renewable Energy Asset Price Prediction
<h3>Overview</h3> <p>This dataset accompanies the research paper titled <strong>“<a href="https://doi.org/10.1016/j.knosys.2024.112733">A Google Trend Enhanced Deep Learning Model for the Prediction of Renewable Energy Asset Price</a>”</strong> by Dr. Nachiketa Mishra, Dr. Lalatendu Mishra, Balaji Dinesh, P M Kavyassree . The study investigates the predictive efficiency of various forecasting models using oil prices and investor sentiment for renewable energy assets, specifically focusing on renewable energy ETFs such as ICLN, PBD, and QCLN.</p> <p>The dataset contains the processed inputs and raw data used in the analysis, including sentiment indices derived from Google Trends and traditional financial indices.</p> <h3>Citation :</h3> <p>Please cite this dataset as:</p> <ul> <li>Mishra, L., Dinesh, B., Kavyassree, P.M. and Mishra, N., 2024. A Google Trend enhanced deep learning model for the prediction of renewable energy asset price. <em>Knowledge-Based Systems</em>, p.112733.</li> </ul> <pre><code>@bibtex<br><br>@article{MISHRA2025112733,<br>title = {A Google Trend enhanced deep learning model for the prediction of renewable energy asset price},<br>journal = {Knowledge-Based Systems},<br>volume = {308},<br>pages = {112733},<br>year = {2025},<br>issn = {0950-7051},<br>doi = {https://doi.org/10.1016/j.knosys.2024.112733},<br>url = {https://www.sciencedirect.com/science/article/pii/S0950705124013674},<br>author = {Lalatendu Mishra and Balaji Dinesh and P.M. Kavyassree and Nachiketa Mishra},<br>}</code><code><br></code></pre> <h2>Code : </h2> <p>Refer Repository URL provided</p> <h2>Directory Structure and Description</h2> <pre><code>📦 data ├── 📂 etf-data │ ├── 📜 ICLN_INPUT.csv # Input data for ICLN │ ├── 📜 PBD_INPUT.csv # Input data for PBD │ ├── 📜 QCLN_INPUT.csv # Input data for QCLN │ └── 📂 raw-data # Original unprocessed data │ ├── 📂 market-data # ETF market prices and oil volatility (OVX) │ ├── 📂 navs # Net Asset Value (NAV) data │ └── 📂 volatility # Volatility data (GARCH and Moving Average models) ├── 📂 google-trends │ ├── 📜 keys.txt # Keywords for Google Trends search │ ├── 📂 trends │ ├── 📂 first-principal-components # Final Google Trend Index (PCA) │ ├── 📂 formatted-trends # Cleaned trends data │ └── 📂 raw-google-trends # Raw fetched Google Trends data</code></pre> <pre>Key Files</pre> <ul> <li><strong>ICLN_INPUT.csv</strong>, <strong>PBD_INPUT.csv</strong>, <strong>QCLN_INPUT.csv</strong>: Processed inputs for the prediction models of each ETF.</li> <li><strong>raw-data</strong>: Contains original data for market prices, NAVs, and volatility measures (GARCH, Moving Average).</li> <li><strong>google-trends</strong>: Data related to Google search trends, including raw, formatted, and the final index derived using Principal Component Analysis (PCA).</li> </ul> <h3>Usage Notes</h3> <ol> <li><strong>Google Trends Data</strong>: The Google Trend Index constructed from the keywords can be found in the <code>first-principal-components</code> folder. This index was a key input in the predictive models and used to construct modified indices in data>*_INPUT.csv’s.</li> <li><strong>Reproducibility</strong>: For reproducing the results from the study, you can directly use the inputs provided under <code>/data</code> to build predictive models.</li> <li><strong>Modifications</strong>: If you aim to modify or extend the dataset, be cautious of the index construction process, particularly around Principal Component Analysis (PCA) in the Google Trends data.</li> </ol> <h2>License</h2> <p>This dataset is released under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. You are free to share and adapt the data, provided appropriate credit is given.</p> <h2>Contact Information</h2> <p>For any questions or further information, please contact:</p> <ul> <li><strong>Dr. Nachiketa Mishra</strong>: Department of Mathematics, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India</li> <li><strong>Dr. Lalatendu Mishra</strong>: Department of Management Sciences, Indian Institute of Technology Kanpur, India</li> <li><strong>Balaji Dinesh</strong>: Department of Computer Science, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India. email : <a href="mailto:balajidinesh918@gmail.com">balajidinesh918@gmail.com</a></li> </ul>
Flood Hazard Maps using Google Earth Engine: Thrace and Thessaly River Basin Districts (Greece)
<p>This dataset contains three raster files with a spatial resolution of 10 m, derived by the Google Earth Engine:</p> <p> </p> <p>1) DynamicWorld_Floods_2015_2023.tif: Number of days flooded for the River Basin District of Thrace (Greece) starting from 2015 until 2023</p> <p>2) Thessaly_2015_August2023.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until August 2023</p> <p>3) Thessaly_2015_now.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until January 2024</p> <p> </p>
Google Scholar as a Data Source for Research Assessment in the Social Sciences
<p>Column 1</p> <p>Source</p> <p>Data sources that the publications retrieved. Values for this column are “Google Scholar”, “Scopus”, and “Web of Science”.</p> <p>Column 2</p> <p>Authors</p> <p>The authors of the publications. This column is kept as additional information for verification of data. Not used in the analysis, it has not been standardized.</p> <p>Column 3</p> <p>Title</p> <p>Titles of the publications. For non-English publications, English titles, if available, are kept in this column. Otherwise, the original titles have been entered. The headings were checked and errors and omissions were corrected. Corrected titles are marked in red.</p> <p>Column 4</p> <p>Title translated with Google Translate</p> <p>In this Column, the English translated titles of the publications that do not have English titles are kept. Google Translate is used for detecting the language and translation. For publications with an English title, the expression [Title in English] has been entered. The translations of the original titles kept in this field were used in the analysis made through VOSviewer. It is marked in red as it is newly added data.</p> <p>Column 5</p> <p>Language</p> <p>Language of the publications. The languages of all publications were checked, missing data were completed and errors were corrected. If the language of the publication could not be determined, the value is [Not found]. The cells with addition or correction are marked in red.</p> <p>Column 6</p> <p>Document type</p> <p>Types of the documents. For all publications, publication type information was checked, missing ones were completed and corrections were made. All intervened cells are marked in red. Article and Review types are referred to as “Article” in the text.</p> <p>Column 7</p> <p>Full-text available</p> <p>Values for this column are “Yes” and “No”. The values for this column are Yes and No. If there is access to the full text of the publication via the web, "Yes", otherwise the "No" value has been entered.</p> <p>Column 8</p> <p>On research evaluation</p> <p>Values for this column are “Yes” and “No”. Using the title and/or abstract information, it was tried to determine whether the publications were related to the research evaluation. “Yes”, if found relevant, and “No” if not. It is marked in red as it is newly added data.</p> <p>Column 9</p> <p>Publication year</p> <p>The publication years of the documents. If the publication years are missing, they have been completed. The current publication years have been checked and corrected if necessary. If the year of publication could not be found, it is indicated as [Not found].</p> <p>Column 10</p> <p>English abstract</p> <p>Abstracts of the publications. If there is an accessible/available English abstract for the publication, it is kept in this column. [Not found/Not available] for missing values. Abstracts that were added, changed, corrected, or completed are marked in red.</p>
Google Earth trace and GPS coordinates of Mongolian Great Wall
<p>Google Earth trace and GPS coordinates of Mongolian Great Wall.</p>
GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform
<p>A novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform.</p>
Google Map JSON data file
<p>A data file used by several JSON engine.</p>
Data for study "Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data"
<p>Data for the study "Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data".</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.