.GOOGLE Whois Database, discover comprehensive ownership details, registration dates, and more for .GOOGLE TLD with Whois Data Center.

Android is still most popular mobile operating system. That has a potential to analyse users behaviours and trends. Crawl feeds team extracted for research and analysis purposes.

This is dataset is obtained from scraping google play store.

Fields (13): Url,Review id,User name,User image,Content,Score,Likes count,Review created version,Reviewed at,Reply content,Replied at,Uniq id,Scraped at

Format: CSV

Get complete dataset with more than 500K+ reviews records and along with application details from crawl feeds.

This dataset provides processed and normalized/standardized indices for the management practice 'Outsourcing'. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Outsourcing dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "outsourcing" + "outsourcing management". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Outsourcing. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Outsourcing-related keywords ["outsourcing" AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Outsourcing Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Outsourcing (1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014). Note: Not reported after 2014. Processing: Normalization: Original usability percentages normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Outsourcing (1999-2014). Note: Not reported after 2014. Processing: Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Outsourcing dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

This dataset provides processed and normalized/standardized indices for the management activity 'Mergers and Acquisitions' (M&A). Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding M&A dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "mergers and acquisitions" + "mergers and acquisitions corporate". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Mergers and Acquisitions + Mergers & Acquisitions. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching M&A-related keywords [("mergers and acquisitions" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (M&A Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Mergers and Acquisitions (2006, 2008, 2010, 2012, 2014, 2017). Note: Not reported before 2006 or after 2017. Processing: Normalization: Original usability percentages normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Mergers and Acquisitions (2006-2017). Note: Not reported before 2006 or after 2017. Processing: Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding M&A dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

Big Data and Society Abstract & Indexing - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

As of October 2025, Google represented ***** percent of the global online search engine referrals on desktop devices. Despite being much ahead of its competitors, this represents a modest increase from the previous months. Meanwhile, its longtime competitor Bing accounted for ***** percent, as tools like Yahoo and Yandex held shares of over **** percent and **** percent respectively. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of **** trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly ****** billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than ** percent of internet users in Russia used Yandex, whereas Google users represented little over ** percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over ** percent of users in Mexico said they used Yahoo.

This is the second version of the Google Landmarks dataset (GLDv2), which contains images annotated with labels representing human-made and natural landmarks. The dataset can be used for landmark recognition and retrieval experiments. This version of the dataset contains approximately 5 million images, split into 3 sets of images: train, index and test. The dataset was presented in our CVPR'20 paper. In this repository, we present download links for all dataset files and relevant code for metric computation. This dataset was associated to two Kaggle challenges, on landmark recognition and landmark retrieval. Results were discussed as part of a CVPR'19 workshop. In this repository, we also provide scores for the top 10 teams in the challenges, based on the latest ground-truth version. Please visit the challenge and workshop webpages for more details on the data, tasks and technical solutions from top teams.

This dataset provides processed and normalized/standardized indices for the management tool 'Knowledge Management' (KM), including related concepts like Intellectual Capital Management and Knowledge Transfer. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding KM dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "knowledge management" + "knowledge management organizational". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Knowledge Management + Intellectual Capital Management + Knowledge Transfer. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching KM-related keywords [("knowledge management" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (KM Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Knowledge Management (1999, 2000, 2002, 2004, 2006, 2008, 2010). Note: Not reported after 2010. Processing: Normalization: Original usability percentages normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Knowledge Management (1999-2010). Note: Not reported after 2010. Processing: Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding KM dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

This data is used for the Google Data Analytics Professional Certificate program case study: "How Does a Bike-Share Navigate Speedy Success?".

This dataset covers the dates Jan. 2022 to Dec. 2022.

The data license agreement for this dataset is located in this website: https://divvy-tripdata.s3.amazonaws.com/index.html.

This data, and data for future dates are located in this website: https://divvy-tripdata.s3.amazonaws.com/index.html.

Dataset

This dataset was created by MoMasoud

Contents

Graph and download economic data for CBOE Equity VIX on Google (VXGOGCLS) from 2010-06-01 to 2025-11-10 about VIX, volatility, equity, stock market, and USA.

a Beta-coefficients, p-values and R2 were calculated by using a simple time-lagged regression model SPYt+1 = β0t + β1t 〈Δn〉(t,Δt)+ β2tVIXt to investigate the correlation of the S&P 100 index development in the next period (SPYt+1) with the current change over all (and sectoral) company search queries 〈Δn〉(t,Δt) and, as a basic control variable, with the volatility index of the S&P 500 (VIXt), respectively.Influence of company search queries on the S&P 100 index development.

This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).

The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.

Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.

An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8

This dataset contains trace data describing user interactions with the Inter-university Consortium for Political and Social Research website (ICPSR). We gathered site usage data from Google Analytics. We focused our analysis on user sessions, which are groups of interactions with resources (e.g., website pages) and events initiated by users. ICPSR tracks a subset of user interactions (i.e., other than page views) through event triggers. We analyzed sequences of interactions with resources, including the ICPSR data catalog, variable index, data citations collected in the ICPSR Bibliography of Data-related Literature, and topical information about project archives. As part of our analysis, we calculated the total number of unique sessions and page views in the study period. Data in our study period fell between September 1, 2012, and 2016. ICPSR's website was updated and relaunched in September 2012 with new search functionality, including a Social Science Variables Database (SSVD) tool. ICPSR then reorganized its website and changed its analytics collection procedures in 2016, marking this as the cutoff date for our analysis. Data are relevant for two reasons. First, updates to the ICPSR website during the study period focused only on front-end design rather than the website's search functionality. Second, the core features of the website over the period we examined (e.g., faceted and variable search, standardized metadata, the use of controlled vocabularies, and restricted data applications) are shared with other major data archives, making it likely that the trends in user behavior we report are generalizable.

This dataset contains raw, unprocessed data files pertaining to the management tool group focused on 'Activity-Based Costing' (ABC) and 'Activity-Based Management' (ABM). The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "activity based costing" + "activity based management" + "activity based costing management" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Activity Based Management + Activity Based Costing Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("activity based costing" OR "activity based management") AND ("management" OR "accounting" OR "cost control" OR "financial" OR "analysis" OR "system") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Activity-Based Costing (1993); Activity-Based Management (1999, 2000, 2002, 2004). (Note: Some sources use Activity Based Management). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 1994, 2001, 2003, 2005). Note: Tool potentially not surveyed or reported after 2004 under these specific names. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1999/475; 2000/214; 2002/708; 2004/960. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Activity-Based Costing (1993); Activity-Based Management (1999, 2000, 2002, 2004). (Note: Some sources use Activity Based Management). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 1994, 2001, 2003, 2005). Note: Tool potentially not surveyed or reported after 2004 under these specific names. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1999/475; 2000/214; 2002/708; 2004/960. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

United States - CBOE Equity VIX on Google was 37.25000 Index in November of 2025, according to the United States Federal Reserve. Historically, United States - CBOE Equity VIX on Google reached a record high of 78.07000 in March of 2020 and a record low of 9.21000 in March of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - CBOE Equity VIX on Google - last updated from the United States Federal Reserve on November of 2025.

The file "fuels.txt" includes daily data for Brent futures (BrentF) and spot (BrentS) prices obtained from nasdaq.com database and three NASDAQ indices: 1) NASDAQ OMX Bio/Clean Fuels Index (GRNBIO). Source: {https://indexes.nasdaqomx.com/Index/Overview/GRNBIO} 2) NASDAQ OMX Fuel Cell Index (GRNFUEL). Source:{https://indexes.nasdaqomx.com/Index/Overview/GRNFUEL} 3) NASDAQ OMX Transportation Index (GRNTRN). Source: {https://indexes.nasdaqomx.com/Index/Overview/GRNTRN} The file "fundamentals.txt" includes monthly data for the following variables: 1) WIP: world industrial production index collected from:{https://sites.google.com/site/cjsbaumeister/datasets?authuser=0} 2) COMM: real commodity price factor - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 3) GECON: global economic condition indicator (standardised) - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 4) S.SH: oil supply shock - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 5) OCDSH: oil consumption demand - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 6) OIDSH: oil inventory demand- obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 7) EASH: oil demand shocks driven by global economic activity - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 8) GEPU: global economic policy uncertainty index - , a normalised index of the volume of news articles discussing economic policy uncertainty; due to the nonstationarity of the data, obtained from: {https://www.policyuncertainty.com/global_monthly.html} 9) EXPT: Brent spot prices expectations formulated by the U.S. Energy Information Association; 10) SPX - end-of-month data of S&P500 11) SPECUL1: Net position of Money Managers (long-short) for Brent contract - based on the ICE Futures Europe Commitments of Traders Reports ({www.ice.com/marketdata/reports/122}); 12) SPECUL2: Speculation measure analogous to Working's (1960) index, which measures the speculative activity of non-commercial traders in the crude oil market.

Leaf Area Index (LAI) is a fundamental vegetation structural variable that drives energy and mass exchanges between the plant and the atmosphere. Moderate-resolution (300m – 7km) global LAI data products have been widely applied to track global vegetation changes, drive Earth system models, monitor crop growth and productivity, etc. Yet, cutting-edge applications in climate adaptation, hydrology, and sustainable agriculture require LAI information at higher spatial resolution (< 100m) to model and understand heterogeneous landscapes. This dataset was built to assist a machine-learning-based approach for mapping LAI from 30m-resolution Landsat images across the contiguous US (CONUS). The data was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Version 6 LAI/FPAR, Landsat Collection 1 surface reflectance, and NLCD Land Cover datasets over 2006 – 2018 using Google Earth Engine. Each record/sample/row includes a MODIS LAI value, corresponding Landsat surface reflectance in green, red, NIR, SWIR1 bands, a land cover (biome) type, geographic location, and other auxiliary information. Each sample represents a MODIS LAI pixel (500m) within which a single biome type dominates 90% of the area. The spatial homogeneity of the samples was further controlled by a screening process based on the coefficient of variation of the Landsat surface reflectance. In total, there are approximately 1.6 million samples, stratified by biome, Landsat sensor, and saturation status from the MODIS LAI algorithm. This dataset can be used to train machine learning models and generate LAI maps for Landsat 5, 7, 8 surface reflectance images within CONUS. Detailed information on the sample generation and quality control can be found in the related journal article. Resources in this dataset:Resource Title: README. File Name: LAI_train_samples_CONUS_README.txtResource Description: Description and metadata of the main datasetResource Software Recommended: Notepad,url: https://www.microsoft.com/en-us/p/windows-notepad/9msmlrh6lzf3?activetab=pivot:overviewtab Resource Title: LAI_training_samples_CONUS. File Name: LAI_train_samples_CONUS_v0.1.1.csvResource Description: This CSV file consists of the training samples for estimating Leaf Area Index based on Landsat surface reflectance images (Collection 1 Tire 1). Each sample has a MODIS LAI value and corresponding surface reflectance derived from Landsat pixels within the MODIS pixel. Contact: Yanghui Kang (kangyanghui@gmail.com)
Column description

UID: Unique identifier. Format: LATITUDE_LONGITUDE_SENSOR_PATHROW_DATE
Landsat_ID: Landsat image ID Date: Landsat image date in "YYYYMMDD" Latitude: Latitude (WGS84) of the MODIS LAI pixel center Longitude: Longitude (WGS84) of the MODIS LAI pixel center MODIS_LAI: MODIS LAI value in "m2/m2" MODIS_LAI_std: MODIS LAI standard deviation in "m2/m2" MODIS_LAI_sat: 0 - MODIS Main (RT) method used no saturation; 1 - MODIS Main (RT) method with saturation NLCD_class: Majority class code from the National Land Cover Dataset (NLCD) NLCD_frequency: Percentage of the area cover by the majority class from NLCD Biome: Biome type code mapped from NLCD (see below for more information) Blue: Landsat surface reflectance in the blue band Green: Landsat surface reflectance in the green band Red: Landsat surface reflectance in the red band Nir: Landsat surface reflectance in the near infrared band Swir1: Landsat surface reflectance in the shortwave infrared 1 band Swir2: Landsat surface reflectance in the shortwave infrared 2 band Sun_zenith: Solar zenith angle from the Landsat image metadata. This is a scene-level value. Sun_azimuth: Solar azimuth angle from the Landsat image metadata. This is a scene-level value. NDVI: Normalized Difference Vegetation Index computed from Landsat surface reflectance EVI: Enhanced Vegetation Index computed from Landsat surface reflectance NDWI: Normalized Difference Water Index computed from Landsat surface reflectance GCI: Green Chlorophyll Index = Nir/Green - 1

Biome code

1 - Deciduous Forest
2 - Evergreen Forest
3 - Mixed Forest
4 - Shrubland
5 - Grassland/Pasture
6 - Cropland
7 - Woody Wetland
8 - Herbaceous Wetland

Reference Dataset: All data was accessed through Google Earth Engine Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. MODIS Version 6 Leaf Area Index/FPAR 4-day L5 Global 500m Myneni, R., Y. Knyazikhin, T. Park. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.006 Landsat 5/7/8 Collection 1 Surface Reflectance Landsat Level-2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey. Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008. National Land Cover Dataset (NLCD) Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case, Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C., Rigge, Matthew B., Xian, George, A new generation of the United States National Land Cover Database—Requirements, research priorities, design, and implementation strategies: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108–123, at https://doi.org/10.1016/j.isprsjprs.2018.09.006 Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

Advances in Human Computer Interaction Abstract & Indexing - ResearchHelpDesk - Advances in Human-Computer Interaction is an interdisciplinary journal that publishes theoretical and applied papers covering the broad spectrum of interactive systems. The journal is inherently interdisciplinary, publishing original research in the fields of computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization, as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Subject areas covered by the journal include (but are not limited to): Human-computer interaction Interface design and universal design/access Predictive models and theories of interaction Adaptive and intelligent systems Speech, graphic, haptic, and multimodal interaction Natural language systems and methods Mobile, wearable, and ubiquitous computing systems Computer-mediated communication methods and systems Virtual, mixed and augmented reality interfaces and systems Agent-based interfaces and systems System and user evaluation studies Full list of databases and services Academic OneFile Academic Search Research and Development ACM Digital Library Advanced Technologies Database with Aerospace Aerospace and High Technology Database Airiti Library Applied Science and Technology Source Biotechnology and BioEngineering Abstracts Biotechnology Research Abstracts Cabell’s Directories CNKI Scholar Computer and Information Systems Abstracts Computer Database Computer Science Index Computers and Applied Sciences Complete Current Abstracts DBLP Computer Science Bibliography Directory of Open Access Journals (DOAJ) EBSCO Discovery Service EBSCO MegaFILE EBSCOhost Connection EBSCOhost Research Databases Emerging Sources Citation Index Engineering Research Database Google Scholar Health and Safety Science Abstracts InfoTrac Custom journals INSPEC J-Gate Portal Neurosciences Abstracts Open Access Journals Integrated Service System Project (GoOA) Primo Central Index ProQuest Advanced Technologies and Aerospace Collection ProQuest Computer Science Journals ProQuest SciTech Premium Collection ProQuest Technology Collection PsycINFO SafetyLit Science Resource Center Scopus Technology and Management (TEMA Database) Technology Research Database The Index of Information Systems Journals The Summon Service TOC Premier WorldCat Discovery Services