According to a survey conducted in several global markets between September 2021 and April 2022, 84 percent of respondents in Kenya were concerned about personal data collection, while 78 percent of respondents in India felt the same way. In comparison, around six in 10 respondents in the United States and France reported being concerned with personal data collection, respectively. Additionally, around nine in 10 respondents in Australia felt they should be able to opt out of data collection and believed that only data necessary for the product or service should be collected.

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A February 2023 survey in the United Kingdom (UK) found that around four in ten respondents became more aware of their personal data collection after seeing ads tracking them online. Another 31 percent said they developed a better understanding of the matter after significant data breaches. Stories from friends and family impressed about 30 percent of the respondents and motivated them to become more aware of how companies handle their data.

Abstract copyright UK Data Service and data collection copyright owner.

Renaissance was the Museums, Libraries and Archives Council's (MLA) programme to transform England's regional museums. The programme has received over £300 million since 2002 which has been allocated across nine regional museum hubs. Regional museum hubs are a cluster of four-five museums which receive government investment in order to develop as centres of excellence and as leaders of their regional museum communities.

MLA has been gathering data from the nine regional museum hubs from 2002-2003 to 2007-2008. The Renaissance Data Collection is a quarterly return of data from each site participating in the Renaissance in the Regions Programme. The data returns contain information on numbers of: visits; priority group visits; child visits; website visits; school visits; Higher Education visits; adult and child on-site participation; and outreach activity. The data returns support Programme management and monitoring and forms the basis of the Renaissance Museums Performance Indicator statistical series.

From the 30th June 2011, the regional Renaissance hub structure ceases to exist. 2011-12 is a transitional year for Renaissance, in which £37.6 million of grant funding, previously known as museum hub funding, has been made available instead directly to 45 museum services.

Further information about Renaissance can be found on the MLA's Renaissance Data Collection web page.

Main Topics:
Data were recorded by hubs and submitted to the MLA on a quarterly basis, following a financial year cycle e.g. Q1 (April to June).

Data were submitted in an Excel workbook (Data Collection Template) that consists of six worksheets, covering six different areas of museums activity:

• Template 1: number of self-directed visits by children and young people in formal education
• Template 2: number of facilitated visits by children and young people in formal education
• Template 3: number of instances of children, young people and adults participating in museums’ outreach activities
• Template 4: number of instances of teachers in contact with museums
• Template 5: number of instances of children, young people and adults participating in organised activities at museums
• Template 6: visits, child visits, web visits and loan venues

Within each template there are a number of different measures, with hubs reporting on a total of 67 performance indicators. A breakdown of each template measure and accompanying guidelines can be found in the Audience Data Collection Manual 2008.
Data were not collected for templates 2 and 5 and parts of template 3, in 2003-2004 - see individual data files for details.

The research conducted explored both the promise and perils of social economy organisations delivering online services to citizens. An in-depth research case study was conducted; focussing on the development of Freegle (see case study background) - a social economy organisation providing an online waste reduction and prevention service - over a five year period. The research findings highlighted that the promise of such social economy organisations, within a wider ecosystem of eGovernment, to: combine the logics of commerce, social enterprise and the grassroots (i.e. supporting hybrid organisational forms); build social capital; and, deliver services at a national scale. However, research findings also highlighted that such organisations also face considerable challenges in the form of pressures to become more business-like, which in turn erode their distinctive and hybrid characteristics. Furthermore, the process of becoming more business-like is hugely time consuming and can detract from the delivery of online services to citizens.

Data collection: During the project data were collected from two sources: (1) publically available online data relating to the development of Freegle; and (2) interviews with Freegle activists. Only data collected through interview was suitable for archiving with UKDS, as the online data was subject to copyright and other reuse restrictions. Permission to archive interview transcripts was sought after the participants had the opportunity to review the transcript.

The research conducted highlighted both the promise and perils of social economy organisations delivering online services to citizens. The in-depth research case study focussed on the development of Freegle - a social economy organisation providing an online waste reduction and prevention service - over a five year period. This research highlighted that the promise of such social economy organisations, within a wider ecosystem of eGovernment, centres on their ability to: combine the logics of commerce, social enterprise and the grassroots (i.e. supporting hybrid organisational forms); build social capital; and, deliver services at a national scale. Such organisations also face considerable challenges in the form of pressures to become more business-like, which in turn erode their distinctive and hybrid characteristics. Furthermore, the process of becoming more business-like is hugely time consuming and can detract from the delivery of online services to citizens.

The oceanographic time series data collected by U.S. Geological Survey scientists and collaborators are served in an online database at http://stellwagen.er.usgs.gov/index.html. These data were collected as part of research experiments investigating circulation and sediment transport in the coastal ocean. The experiments (projects, research programs) are typically one month to several years long and have been carried out since 1975. New experiments will be conducted, and the data from them will be added to the collection. As of 2016, all but one of the experiments were conducted in waters abutting the U.S. coast; the exception was conducted in the Adriatic Sea. Measurements acquired vary by site and experiment; they usually include current velocity, wave statistics, water temperature, salinity, pressure, turbidity, and light transmission from one or more depths over a time period. The measurements are concentrated near the sea floor but may also include data from the water column. The user interface provides an interactive map, a tabular summary of the experiments, and a separate page for each experiment. Each experiment page has documentation and maps that provide details of what data were collected at each site. Links to related publications with additional information about the research are also provided. The data are stored in Network Common Data Format (netCDF) files using the Equatorial Pacific Information Collection (EPIC) conventions defined by the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory. NetCDF is a general, self-documenting, machine-independent, open source data format created and supported by the University Corporation for Atmospheric Research (UCAR). EPIC is an early set of standards designed to allow researchers from different organizations to share oceanographic data. The files may be downloaded or accessed online using the Open-source Project for a Network Data Access Protocol (OPeNDAP). The OPeNDAP framework allows users to access data from anywhere on the Internet using a variety of Web services including Thematic Realtime Environmental Distributed Data Services (THREDDS). A subset of the data compliant with the Climate and Forecast convention (CF, currently version 1.6) is also available.

The RBDC Metadata contains information related to the fields in each of the datasets. It includes a unique identifier for each field, field name and plain English descriptions. Additional metadata is also provided in the portal’s ArcGIS Online description (where the RBDC data is hosted) including Open Data License and terms of use. Fields in each dataset may vary, therefore the metadata is provided per table in a downloadable Excel Spreadsheet. Each tab on this document corresponds to the RBDC open dataset table unique identifier.

Graph and download economic data for Real Gross Domestic Product: Private Industries: Information: Data Processing, Internet Publishing, and Other Information Services for United States Metropolitan Portion (RGMPDATAWWWUSMP) from 2001 to 2016 about metropolitan portion, internet, printing, information, private industries, services, private, real, industry, GDP, and USA.

This is a national data collection of data resources managed by the Bureau of Ocean Energy Management (BOEM) for the Outer Continental Shelf (OCS). The data collection is designated as a National Geospatial Data Asset (NGDA) and includes: OCS BOEM Offshore Boundary Lines (Submerged Lands Act Boundary, OCSLA Limit of “8(g) Zone,” and Continental Shelf Boundary), OCS Protraction Polygons - 1st Division, OCS Gulf of Mexico NAD27 Protraction Polygons - 1st Division, OCS Block Polygons - 2nd Division, OCS Gulf of Mexico NAD27 Block Polygons - 2nd Division, and Aliquot 16ths Polygons - 3rd Division.All polygons are clipped to the Submerged Land Act Boundary and Continental Shelf Boundaries reflecting federal jurisdiction. The NAD27 Gulf of Mexico Protractions and Blocks have a different protraction and block configuration when compared to the OCS Protraction Polygons - 1st Division and OCS Block Polygons - 2nd Division. The NAD27 Gulf of Mexico data is used for Oil and Gas leasing.These data were created in the applicable NAD83 UTM or NAD27 UTM/SPCS Projection and re-projected to GCS WGS84 (EPSG 4326) for management in BOEM"s enterprise GIS. However, the services in this collection have been published in WGS 1984 Web Mercator Auxiliary Sphere (EPSG 3857). Because GIS projection and topology functions can change or generalize coordinates,these data are NOT an OFFICIAL record for the exact boundaries. These data are to be used for Cartographic purposes only and should not be used to calculate area.Layers MetadataOCS BOEM Offshore Boundary LinesOCS Protraction Polygons - 1st DivisionOCS Gulf of Mexico NAD27 Protraction Polygons - 1st DivisionOCS Block Polygons - 2nd DivisionOCS Gulf of Mexico NAD27 Block Polygons - 2nd DivisionAliquot 16ths Polygons - 3rd Division

The online survey software market is experiencing robust growth, projected to reach a value of $7.22 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 13.6% from 2025 to 2033. This expansion is driven by several key factors. The increasing need for efficient data collection across diverse sectors, including retail, financial services, healthcare, and manufacturing, fuels demand for user-friendly and scalable survey platforms. The rise of digital transformation initiatives within both SMEs and large enterprises is further propelling market growth, as businesses seek to understand customer preferences, employee satisfaction, and market trends through sophisticated data analytics provided by these platforms. Additionally, the continuous innovation in survey methodologies, including the integration of advanced analytics and AI-powered features, enhances the value proposition of these tools, attracting a wider user base. The competitive landscape is characterized by a mix of established players like Qualtrics and SurveyMonkey and emerging innovative solutions, leading to ongoing product improvements and price optimization. However, market growth is not without challenges. Data privacy concerns and the rising costs associated with implementing and maintaining advanced survey platforms can act as restraints. Furthermore, the market’s reliance on internet penetration and digital literacy levels can hinder adoption in certain regions. To address these challenges, vendors are focusing on developing robust data security features, offering flexible pricing models, and providing comprehensive training and support to enhance user adoption. Geographic expansion, particularly in developing economies with growing internet access, presents significant opportunities for future market growth. The segmentation by end-user (Retail, Financial Services, Healthcare, Manufacturing, Others) and application (SMEs, Large Enterprises) highlights the market's broad appeal and diversified application across numerous industries. This segmentation allows vendors to tailor their offerings and marketing strategies to specific industry needs, optimizing market penetration and profitability.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. The objective of the project was to develop a light time budget instrument suitable for use as an add-on component to other surveys, without adding unduly to respondent burden. In the course of the activity, a development programme was undertaken, involving workshops, field-testing of alternative experimental instruments, evaluation and redesign of these, and a full-scale pilot study. The instrument is designed to be used in both self-response and interview completion modes. Some 2005 Omnibus Survey respondents were asked to provide a retrospective diary-type account on a designated day. The pilot study has thus yielded useful statistical information, sufficient to make broad national estimates of adult time use patterns in the early summer of 1995. The sample is sufficient to make reliable contrasts between broad time use aggregates for subgroups at, for example, a full-time employed woman vs part-time employed woman level. It is too small to make reliable estimates for smaller time use categories and for smaller classificatory categories. Despite the presence of geographic classificatory variables (Standard Regions), the sample size is not sufficiently large to make reliable sub-national estimates of any of the time use categories. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: Time use (module 117): Each case records data for each of the 2005 people surveyed. There are around 100 classificatory variables which have SPSS data labels which are largely self-explanatory. These data were derived by interviewer or self-completion of a questionnaire. The remaining 96 variables record activities in each of the 96 quarter hour periods throughout the designated day being measured. These data were derived from a self-completion diary, and again the data variables in the SPSS datasets are largely self-explanatory. Respondents were asked to code their major activity in each of the quarter hour periods, according to a coding frame specifying 30 separate activity codes. Standard Measures: Prevailing Government Standard Socio-Economic Classificatory Variables were used. Multi-stage stratified random sample Self-completion Diaries Face-to-face interview

Abstract copyright UK Data Service and data collection copyright owner.The Community Life Survey (CLS) is a household survey conducted in England, tracking the latest trends and developments across areas key to encouraging social action and empowering communities, including: volunteering and charitable giving; views about the local area; community cohesion and belonging; community empowerment and participation; influencing local decisions and affairs; and subjective well-being and loneliness. The CLS was first commissioned by the Cabinet Office in 2012. From 2016-17, the Department for Digital, Culture, Media and Sport (DCMS) took over responsibility for publishing results. During 2020, the DCMS also commissioned the Community Life COVID-19 Re-contact Survey (CLRS) (SN 8781) to provide data on how the COVID-19 pandemic has affected volunteering, charitable giving, social cohesion, wellbeing and loneliness in England. BackgroundUp to 2015-16, the survey used a face-to-face methodology. Following thorough testing (experimental online versions of the survey were released for 2013-14, 2014-15 and 2015-16), the CLS moved online from 2016-17 onwards, with an end to the previous face-to-face method. The survey uses a push-to-web methodology (with paper mode for those who are not digitally engaged). The survey informs and directs policy and action in these areas; to provide data of value to all users, including public bodies, external stakeholders and the public; and underpin further research and debate on building stronger communities. The Community Life Survey incorporates a small number of priority measures from the Citizenship Survey, which ran from 2001-2011, conducted by the then Department for Communities and Local Government. These measures were incorporated in the Community Life Survey so that trends in these issues could continue to be tracked over time. (The full Citizenship Survey series is held at the UK Data Archive under GNs 33347 and 33474.) Further information may be found on the GOV.UK Community Life Survey webpage. The Community Life Survey Experimental Online Data, 2013-2014 includes the data from a project testing the viability of an online alternative to the face-to-face survey. This dataset covers the 2013-2014 online survey, with a sample size of 10,215 adults (aged 16 years and over) in England, which ran from June 2013 to March 2014. Data from a postal version of the questionnaire, which was available on request, is also included in the dataset. This questionnaire covered the same topics as the online survey but was reduced in length. Full details can be found in the Web Survey Technical Report which is available in the Documentation section below. End User Licence and Special Licence data Users should note that there are two versions of each Community Life Survey Experimental Online Data experimental online dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains more detailed variables relating to: social class; ethnicity; religion; sexual identity and lower level geographical classifications. The SL data have more restrictive access conditions than those made available under the standard EUL. Prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. Therefore, users are strongly advised to order the standard version of the data. The SL version of the Community Life Survey Experimental Online Data, 2013-2014 is held under SN 7738.

Abstract copyright UK Data Service and data collection copyright owner.

The Opinions and Lifestyle Survey (OPN) is an omnibus survey that collects data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).

Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules.

The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain.

From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers.

In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, 2019-2023: Secure Access. Other Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093).

From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable.

The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.

ONS Opinions and Lifestyle Survey, 2019-2023: Secure Access

The aim of the COVID-19 Module within this study was to help understand the impact of the coronavirus (COVID-19) pandemic on people, households and communities in Great Britain. It was a weekly survey initiated in March 2020, and since August 2021, as COVID-19 restrictions were lifted, the survey has moved to fortnightly data collection, sampling around 5,000 households in each survey wave. The study allows the breakdown of impacts by at-risk age, gender and underlying health condition. The samples are randomly selected from those that had previously completed other ONS surveys (e.g., Labour Market Survey, Annual Population Survey). From each household, one adult is randomly selected but with unequal probability: younger people are given a higher selection probability than older people because of under-estimation in the samples available for the survey.

The study also includes data for the Internet Access Module from 2019 onwards. Data from this module for previous years are available as End User Licence studies within GN 33441. Also included are data from the Winter Lifestyle Survey for January and February 2023.

Latest edition information

For the eleventh edition (March 2024), data and documentation for the main OPN survey for waves DN (June 2023) to EB (December 2023) have been added. Data and documentation for the Winter Lifestyle Survey for January-February 2023 have also been added.

Main Topics:
Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month.

The questions and topics covered by the main OPN survey have changed over time. Topics covered have included:

• health conditions
• concerns about work
• working from home
  
• understanding information about COVID-19
• actions undertaken to prevent the spread of the virus
• coping whilst staying at home and community support networks
• indicators of well-being and loneliness
• ways pandemic has effected life, work and finances
  
• caring responsibilities
• returning to school or college
  
• leaving home
• modes of transport used
• cost of living
  
• problems with companies
• vaccinations
  
• individual characteristics (e.g., age, gender, health conditions, qualifications, ethnicity, employment,...

This dataset comes from the Annual Community Survey question related to satisfaction with the quality of the city’s online services. Respondents are asked to provide their level of satisfaction related to “Tempe's online services (registration, payment, etc.)” on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (without "don't know" as an option).The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This page provides data for the Online Service Satisfaction performance measure. The performance measure dashboard is available at 2.05 Online Services Satisfaction Rate.Additional Information Source: Community Attitude Survey ( Vendor: ETC Institute)Contact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: Excel and PDFPreparation Method: Extracted from Annual Community Survey results  Publish Frequency: Annual  Publish Method: Manual  Data Dictionary

This dataset represents an inventory of research data services at 120 US colleges and universities. The data was collected using a systematic web content analysis process in late 2019. This dataset underlies the following report: Jane Radecki and Rebecca Springer, "Research Data Services in US Higher Education: A Web-Based Inventory," Ithaka S+R, Nov. 2020, https://doi.org/10.18665/sr.314397.

We defined research data services as any concrete, programmatic offering intended to support researchers (including faculty, postdoctoral researchers, and graduate students) in working with data, and identified services within the following campus units: library, IT department/research computing, independent research centers and facilities, academic departments, medical school, business school, and other professional schools. We also recorded whether the institution offered local high performance computing facilities. For detailed definitions, exclusions, and data collection procedures, please see the report referenced above.

Envestnet®| Yodlee®'s Online Purchase Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

PromptCloud emerges as a pivotal player in the realm of AI and ML training, offering bespoke web data extraction services. Our expertise lies in delivering custom datasets specifically tailored for AI and ML applications, ensuring that businesses and researchers have access to the most relevant and high-quality data for their unique needs.

Our services extend beyond mere data collection. We provide a comprehensive suite of web data extraction solutions, ranging from scraping e-commerce sites for product data, prices, and customer reviews, to extracting complex datasets from a multitude of web sources. This is particularly crucial for training sophisticated AI and ML algorithms, where the quality and specificity of data can significantly influence the outcome.

In the rapidly evolving landscape of AI and ML, the need for custom-tailored data is paramount. PromptCloud recognizes this necessity and offers customizable web data solutions. Clients can specify their data requirements, including source websites, data collection frequencies, and specific data points, making our service highly adaptable to diverse industry needs.

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Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (OPN) is an omnibus survey that collects data from respondents in Great Britain. Information is gathered on a range of subjects, commissioned both internally by the Office for National Statistics (ONS) and by external clients (other government departments, charities, non-profit organisations and academia).One individual respondent, aged 16 or over, is selected from each sampled private household to answer questions. Data are gathered on the respondent, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. Each regular OPN survey consists of two elements. Core questions, covering demographic information, are asked together with non-core questions that vary depending on the module(s) fielded.The OPN collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living. The OPN has expanded to include questions on other topics of national importance, such as health and the cost of living.For more information about the survey and its methodology, see the gov.uk OPN Quality and Methodology Information (QMI) webpage.Changes over timeUp to March 2018, the OPN was conducted as a face-to-face survey. From April 2018 to November 2019, the OPN changed to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for module customers.In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held under Secure Access conditions in SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. (See below for information on other Secure Access OPN modules.)From August 2021, as coronavirus (COVID-19) restrictions were lifted across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remained sustainable. Secure Access OPN modulesBesides SN 8635 (the COVID-19 Module), other Secure Access OPN data includes sensitive modules run at various points from 1997-2019, including Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See the individual studies for further details and information on how to apply to use them. Main Topics: The non-core questions for these months were: Internet access (Module MAZ): this module was asked on behalf of ONS. The module contains computer and technology related terminology and interviewer instructions were added to the questionnaire throughout to assist the interview. Multi-stage stratified random sample Face-to-face interview

The MODIS Web Service provides data access capabilities for Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 land products. The web service provides data access functions for users to execute on their local computing resources. The MODIS Web Service is built upon the ORNL DAAC's MODIS Global Subsetting and Visualization Tool that provides customized subsets and visualization of MODIS land products for any land location on earth. With the web service users can retrieve land product subsets through command line operations; download and integrate subsets directly into client side workflows; download and visualize subsets with customized code; and download subsets and write custom code for data reformatting.

The goal of this study was to experimentally measure the influence of different incentive schemes on the willingness to participate in passive mobile data collection among German smartphone owners. The data come from a web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2017, 1,214 respondents completed a questionnaire on smartphone use and skills, privacy and security concerns, general attitudes towards survey research and research institutions. In addition, the questionnaire included an experiment on the willingness to participate in mobile data collection under different incentive conditions.

Topics: Ownership of smartphone, cell phone, desktop or laptop computer, tablet computer, and/or e-book reader; type of smartphone; willingness to participate in mobile data collection under different incentive conditions; likelihood of downloading the app to particiapte in this research study; respondent would rather participate in the study if he could receive 100 euros; total amount to be earned for the respondent ot participate in the study (open answer); reason why the respondent wouldn´t participate in the research study; willlingness to participate in the study for an incentive of 60 euros in total; willingness to activate different functions when downloading the app (interaction history, smartphone usage, charateristics of the social network, network quality and location information, activity data); previous invitation for research app download; research app download; frequency of smartphone use; smartphone activities (browsing, e-mails, taking pictures, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, playing games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, government authorities such as the Federal Statistical Office, mobile service provider, app companies, credit card companies, online retailer, and social media platforms); general privacy concern; feeling of privacy violation by banks and credit card companies, tax authorities, government agencies, market research, social networks, apps, and internet browsers; concern regarding data security with smartphone activities for research purposes (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth).

Demography: sex, age; federal state; highest level of school education; highest level of vocational education.

Additionally coded was: running number; duration (response time in seconds); device type used to fill out the questionnaire.