This statistic shows the leading uses of cloud technology for data protection in companies worldwide as of 2019. A total of ** percent of survey respondents stated that, in their organization, cloud technology was used for archiving and long term retention.

This report provides a strategy to ensure that digital scientific data can be reliably preserved for maximum use in catalyzing progress in science and society.Empowered by an array of new digital technologies, science in the 21st century will be conducted in a fully digital world. In this world, the power of digital information to catalyze progress is limited only by the power of the human mind. Data are not consumed by the ideas and innovations they spark but are an endless fuel for creativity. A few bits, well found, can drive a giant leap of creativity. The power of a data set is amplified by ingenuity through applications unimagined by the authors and distant from the original field...

This graph presents the results of a survey, conducted by BARC in 2014/15, into the current and planned use of technology for the analysis of big data. At the beginning of 2015, ** percent of respondents indicated that their company was already using a big data analytical appliance for big data.

The School of Education at the University of Cape Town (UCT) investigated children’s learning through digital play. The aim of the study was to explore the intersection between child play, technology, creativity and learning among children aged between 3 and 11 years. The study also identified skills and dispositions children develop through both digital and non-digital play. The data shared emerged from a survey of parents of children in the stated age group, with particular reference to the parents views on children's play practices, including time parents spent playing with their children, concerns parents had on time children spend playing on various technologies, types of play children in South Africa engaged in and the concerns of parents when children played with some electronic devices. The following data files are shared:SA - Survey - Children, Technology and Play (CTAP) - Google Forms.pdfDescriptive Stats 2020.1.9 -Children Technology and Play SURVEY.xlsxParent Survey RAW PUBLIC DATA 2020.2.29 - Children Technology and Play Project.xlsxParent Survey RAW PUBLIC DATA 2020.2.29 - Children Technology and Play Project.csvParent Survey REPORT DATA 2020.2.29 - Children Technology and Play Project.xlsxParent Survey REPORT DATA 2020.2.29 - Children Technology and Play Project.csvParent Survey RAW and REPORT DATA SYNTAX 2020.2.29 - Children Technology and Play Project.spsNOTE: This survey was adapted from Marsh, J. Stjerne Thomsen, B., Parry, B., Scott, F. Bishop, J.C., Bannister, C., Driscoll, A., Margary, T., Woodgate, A., (2019) Children, Technology and Play. UK Survey Questions. LEGO Foundation.

This dataset captures insights into the use of Artificial Intelligence (AI) among 535 students in Indonesian higher education, focusing on their expectations, challenges, attitudes, perceptions, and motivations regarding AI-based learning tools. Collected through a structured survey, the dataset includes demographic variables such as university type, field of study, and educational level, along with students' self-reported experiences with AI in academic settings. The dataset serves as a valuable resource for understanding AI adoption trends in higher education, identifying barriers to AI integration, and evaluating its impact on student engagement and learning outcomes. It enables comparative analysis across different academic disciplines and institutional contexts, offering opportunities for policymakers and educators to design AI-informed curricula. Additionally, this dataset is structured for reproducibility and reuse, allowing researchers to extend its findings, apply alternative analytical approaches, and conduct cross-regional or longitudinal studies on AI integration in higher education.

Techsalerator’s Business Technographic Data for United States provides a thorough and insightful collection of information essential for businesses, market analysts, and technology vendors. This dataset offers a deep dive into the technological landscape of companies operating in United States, capturing and categorizing data related to their technology stacks, digital tools, and IT infrastructure.

Please reach out to us at info@techsalerator.com or https://www.techsalerator.com/contact-us

Top 5 Most Utilized Data Fields Company Name: This field lists the name of the company being analyzed. Understanding the companies helps technology vendors target their solutions and enables market analysts to evaluate technology adoption trends within specific businesses. Technology Stack: This field details the technologies and software solutions a company utilizes, such as CRM systems, ERP software, and cloud services. Knowledge of a company’s technology stack is vital for understanding its operational capabilities and technology needs. Deployment Status: This field indicates whether the technology is currently in use, planned for deployment, or under evaluation. This status helps vendors gauge the level of interest and current adoption among businesses. Industry Sector: This field identifies the industry sector in which the company operates, such as finance, manufacturing, or retail. Segmenting by industry sector helps vendors tailor their offerings to specific market needs and trends. Geographic Location: This field provides the geographic location of the company's headquarters or primary operations within United States. This information is useful for regional market analysis and understanding local technology adoption patterns. Top 5 Technology Trends in the United States Artificial Intelligence and Machine Learning: AI and ML continue to drive innovation across various sectors, from autonomous vehicles and healthcare to finance and customer service. Key advancements include natural language processing, computer vision, and reinforcement learning. Cloud Computing and Edge Computing: The shift towards cloud computing remains strong, with major providers like AWS, Azure, and Google Cloud leading the way. Edge computing is also gaining traction, enabling faster processing and data analysis closer to the source, which is crucial for IoT applications. 5G Technology: The rollout of 5G networks is transforming connectivity, enabling faster data speeds, lower latency, and new applications in IoT, smart cities, and augmented reality (AR). Major telecom companies and technology providers are heavily invested in this technology. Cybersecurity and Privacy: As digital threats become more sophisticated, there is an increased focus on cybersecurity solutions, including threat detection, data encryption, and privacy protection. Innovations in this space aim to combat ransomware, data breaches, and other cyber risks. Blockchain and Decentralized Finance (DeFi): Blockchain technology is expanding beyond cryptocurrencies, with applications in supply chain management, digital identity, and smart contracts. DeFi is a growing sector within blockchain, offering decentralized financial services and products. Top 5 Companies with Notable Technographic Data in the United States Microsoft: A leading technology company known for its software, cloud computing services (Azure), and AI research. Microsoft's diverse portfolio includes operating systems, enterprise solutions, and gaming (Xbox). Google (Alphabet Inc.): A major player in search engines, cloud computing, AI, and consumer electronics. Google is at the forefront of innovations in machine learning, autonomous driving (Waymo), and digital advertising. Amazon: Known for its e-commerce platform, Amazon is also a significant force in cloud computing (AWS), AI, and logistics. AWS is a leading cloud service provider, and Amazon's technology initiatives span various industries. Apple Inc.: Renowned for its consumer electronics, including iPhones, iPads, and Macs. Apple is also investing in emerging technologies such as AR, wearable technology (Apple Watch), and health tech. IBM: A historic leader in technology and consulting services, IBM focuses on enterprise solutions, cloud computing, AI (IBM Watson), and quantum computing. The company is known for its research and development in cutting-edge technologies. Accessing Techsalerator’s Business Technographic Data If you’re interested in obtaining Techsalerator’s Business Technographic Data for United States, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide a customized quote based on the number of data fields and records you need, with the dataset available for delivery within 24 hours. Ongoing access options can also be discussed as needed.

Included Data Fields Company Name Technology Stack Depl...

Originally published by Harte-Hanks, the CiTDS dataset is now produced by Aberdeen Group, a subsidiary of Spiceworks Ziff Davis (SWZD). It is also referred to as CiTDB (Computer Intelligence Technology Database). CiTDS provides data on digital investments of businesses across the globe. It includes two types of technology datasets: (i) hardware expenditures and (ii) product installs. Hardware expenditure data is constructed through a combination of surveys and modeling. A survey is administered to a number of companies and the data from surveys is used to develop a prediction model of expenditures as a function of firm characteristics. CiTDS uses this model to predict the expenditures of non-surveyed firms and reports them in the dataset. In contrast, CiTDS does not do any imputation for product install data, which comes entirely from web scraping and surveys. A confidence score between 1-3 is assigned to indicate how much the source of information can be trusted. A 3 corresponds to 90-100 percent install likelihood, 2 corresponds to 75-90 percent install likelihood and 1 corresponds to 65-75 percent install likelihood. CiTDS reports technology adoption at the site level with a unique DUNS identifier. One of these sites is identified as an “enterprise,” corresponding to the firm that owns the sites. Therefore, it is possible to analyze technology adoption both at the site (establishment) and enterprise (firm) levels. CiTDS sources the site population from Dun and Bradstreet every year and drops sites that are not relevant to their clients. Due to this sample selection, there is quite a bit of variation in the number of sites from year to year, where on average, 10-15 percent of sites enter and exit every year in the US data. This number is higher in the EU data. We observe similar turnover year-to-year in the products included in the dataset. Some products have become absolute, and some new products are added every year. There are two versions of the data: (i) version 3, which covers 2016-2020, and (ii) version 4, which covers 2020-2021. The quality of version 4 is significantly better regarding the information included about the technology products. In version 3, product categories have missing values, and they are abbreviated in a way that are sometimes difficult to interpret. Version 4 does not have any major issues. Since both versions of the data are available in 2020, CiTDS provides a crosswalk between the versions. This makes it possible to use information about products in Version 4 for the products in Version 3, with the caveats that there will be no crosswalk for the products that exist in 2016-2019 but not in 2020. Finally, special attention should be paid to data from 2016, where the coverage is significantly different from 2017. From 2017 onwards, coverage is more consistent. Years of Coverage: APac: 2019 - 2021 Canada: 2015 - 2021 EMEA: 2019 - 2021 Europe: 2015 - 2018 Latin America: 2015, 2019- 2021 United States: 2015 - 2021

For questions about this data please contact ITOpenData@minneapolismn.gov2014 Minneapolis Community Technology Survey Data

Thanks to the 3,015 residents for their participation, the third year's results are in on a survey the City of Minneapolis conducted to understand how Minneapolis residents use computers, mobile devices and the Internet. Access to computers and the Internet, along with the skills to use these tools is critical as technology becomes more and more a part of our daily lives and is integrated in our economic, educational, health, and workforce systems. The results will inform priorities for the City’s digital inclusion initiatives, and help engage businesses, neighborhood and community groups, public sector partners, and funders to more effectively address community technology and economic development needs. In addition, the survey provides data to measure changes in the community over time.

The City of Minneapolis Information Technology Department contracted with National Research Center, Inc. (NRC) to conduct a survey of residents to inform the City’s efforts to overcome the digital equity gap between individuals and groups in their access to and use and knowledge of information and communication technologies. This is the third iteration of the Minneapolis Community Technology Survey; the first was conducted in 2012 and the second in 2013.Summary of Data Fields:Field 1 – Overall percentage of respondents who have lived in Minneapolis for 5 years or less by community and user levelField 2 – Overall percentage of foreign-born respondents by community and user levelField 3 – Overall percentage of respondents who rent their homes by community and user levelField 4 – Overall percentage of respondents who live in attached homes by community and user levelField 5 – Overall percentage of respondents living in households with three or more people by community and user levelField 6 – Overall percentage of respondents living in households with children under the age of 18 by community and user levelField 7 – Overall percentage of female respondents by community and user levelField 8 – Overall percentage of respondents aged 55 years or older by community and user levelField 9 – Overall percentage of respondents who are hispanic and/or any race other than white by community and user levelField 10 – Overall percentage of respondents who prefer to speak a language other than English at home by community and user levelField 11 – Overall percentage of respondents having annual household incomes of less than $50,000 by community and user levelField 12 – Overall percentage of respondents who do not work full- or part-time by community and user level

Field 13 – Overall percentage of respondents who do not have a 4-year degree by community and user level

Full data set (Raw data and data dictionary in Excel format)

The workbook has two tabs, the first is the data dictionary that is needed to translate the data; the second is the raw data.

See data summarized in a variety of formats at: http://www.minneapolismn.gov/it/inclusion/WCMS1P-118865

For additional details about the survey, the survey questionnaire, methodology and more, see: http://www.minneapolismn.gov/it/inclusion/WCMS1P-118865 or contact: Elise Ebhardt, 612-673-2026, City of Minneapolis IT Department

See also: 2012 and 2013 survey results

The City's IT Vision includes a component for addressing the digital divide in Minneapolis: All City residents, institutions and businesses will have the tools, skills and motivation to gain value from the digital society. Our residents and businesses need to be equipped to effectively compete with others around the world—to be smarter, more creative, more knowledgeable, and more innovative. Leveraging technology is a necessary ingredient of success.

Educational Technology in Public School Districts, 2008 (FRSS 93), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 93 (https://nces.ed.gov/surveys/frss/) is a sample survey that provides national estimates on the availability and use of educational technology in public school districts during Fall 2008. This is one of a set of three surveys (at the district, school, and teacher levels) that collected data on a range of educational technology resources. The study was conducted by having school superintendents fill out surveys via the web or by mail. Public school districts were sampled. The study's weighted response rate was 90 percent. Key statistics produced from FRSS 93 were information on networks and internet capacity, technology policies, district-provided resources, teacher professional development, and district-level leadership for technology. Respondents reported the number of schools in the district with a local area network and the number of schools with each type of district network connection. The survey collected information on written district policies on acceptable student use of various technologies. Other survey topics included employment of staff responsible for educational technology leadership and the type of teacher professional development offered or required by districts for educational technology. Respondents gave their opinions on statements related to the use of educational technology in the instructional programs in their districts.

The dataset consists of 277 valid records of Vietnamese students’ acceptance of using VCTs in distance learning in COVID-19 pandemic through an extended Technology Acceptance Mode. There are eight factors: Output quality, Computer playfulness, Subjective norm, Perceived usefulness, Perceived ease of use, Attitude towards to use, Behavioral intention to use, Actual system to use.

Information Technology Usage and Penetration - Table 720-90006 : Persons aged 10 and over who had a mobile phone (including smartphone and non-smartphone) by sex and age group

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

In a 2019 survey, ** percent of talent acquisition professionals mentioned that data is useful to improve outreach when recruiting. Email is the main outreach channel for recruiters to reach out to candidates.

Dataset Info:

MyDigitalFootprint (MDF) is a novel large-scale dataset composed of smartphone embedded sensors data, physical proximity information, and Online Social Networks interactions aimed at supporting multimodal context-recognition and social relationships modelling in mobile environments. The dataset includes two months of measurements and information collected from the personal mobile devices of 31 volunteer users by following the in-the-wild data collection approach: the data has been collected in the users' natural environment, without limiting their usual behaviour. Existing public datasets generally consist of a limited set of context data, aimed at optimising specific application domains (human activity recognition is the most common example). On the contrary, the dataset contains a comprehensive set of information describing the user context in the mobile environment.

The complete analysis of the data contained in MDF has been presented in the following publication:

https://www.sciencedirect.com/science/article/abs/pii/S1574119220301383?via%3Dihub

The full anonymised dataset is contained in the folder MDF. Moreover, in order to demonstrate the efficacy of MDF, there are three proof of concept context-aware applications based on different machine learning tasks:

1. A social link prediction algorithm based on physical proximity data,
2. The recognition of daily-life activities based on smartphone-embedded sensors data,
3. A pervasive context-aware recommender system.

For the sake of reproducibility, the data used to evaluate the proof-of-concept applications are contained in the folders link-prediction, context-recognition, and cars, respectively.

This layer contains information on technology access by Household. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer represents the underlying data for several data visualizations on the Tempe Equity Map.Data visualized as a percent of total households in given census tract.Layer includes:Key demographicsTotal Households % Less than $20,000: With dial-up Internet subscription alone% Less than $20,000: With a broadband Internet subscription% Less than $20,000: Without an Internet subscription% $20,000 to $74,999: With dial-up Internet% $20,000 to $74,999: With a broadband Internet subscription% $20,000 to $74,999: Without an Internet subscription% $75,000 or more: With dial-up Internet subscription alone% $75,000 or more: With a broadband Internet subscription% $75,000 or more: Without an Internet subscriptionCurrent Vintage: 2017-2021ACS Table(s): S2801 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: Dec 8, 2022Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

Colombia Use of Internet: Male: 5 to 11 Years data was reported at 1,646.650 Person th in 2017. This records a decrease from the previous number of 1,779.339 Person th for 2016. Colombia Use of Internet: Male: 5 to 11 Years data is updated yearly, averaging 1,792.595 Person th from Dec 2012 (Median) to 2017, with 6 observations. The data reached an all-time high of 1,925.154 Person th in 2015 and a record low of 1,646.650 Person th in 2017. Colombia Use of Internet: Male: 5 to 11 Years data remains active status in CEIC and is reported by National Statistics Administrative Department. The data is categorized under Global Database’s Colombia – Table CO.TB003: Technology and Communication Usage.

Business Software Alliance is a trade association that represents the world's leading software companies, including Autodesk, IBM, and Symantec. The organization's members are committed to promoting the use of legitimate software and ensuring the integrity of their intellectual property.

As a result, the data housed on BSA's website is rich in information related to the software industry, including software licensing, anti-piracy efforts, and digital piracy statistics. The data includes information on software usage, software development, and the impact of piracy on the technology industry. With its focus on promoting legitimate software use, the data on BSA's website provides valuable insights into the global software industry.

Survey of advanced technology, enterprises that use advanced technology, by technology domain, North American Industry Classification System (NAICS) and enterprise size for Canada and certain provinces, in 2014.

This project leveraged existing datasets to ground policy for children in the digital age for the first time. The project provided evidence to policy-makers, parents, teachers, and GPs on the impact of digital technologies in the lives of British children, highlighting key risk and resilience factors for future interventions. Using existing data, advanced statistical techniques, and robust open science methodologies, we addressed three main research questions: 1. What risk and resilience factors influence the effect of digital technology on adolescents' psychological well-being? 2. How does digital technology use relate to psychological well-being, and do identified risk factors mediate this relationship? 3. What are the causal pathways between risk factors, digital technology use, and psychological well-being that could inform future interventions? This helped develop profiles to explore long-term technology use and effects, distinguishing between over-hyped concerns, like social isolation, and those warranting further scrutiny, such as poor sleep. While the data cannot be shared or underlaying code is made available open access under Related Resources.

Before using this data set, refer to instructions below! Since 2000, the City's Information Technology Indicators project has been collecting extensive and statistically valid data on residential use of cable tv, broadband adoption and uses (including health, work, education, finance and civic engagement), barriers to broadband adoption, and customer service needs. This project is managed by our Community Technology Program with technical advice from our Citizens Telecommunications and Technology Advisory Board (CTTAB). INSTRUCTIONS FOR USING DATA SET Do not pull down the data set and use it without using the code book. When you run the data set, be sure to refer to the Code Book for invalid or missing values for each question item (variables). You must recognize that the data here is unweighted; to replicate the figures in the City’s Information Technology Access and Adoption Report and get values that are representative of Seattle population, you should use the weights. There are 2 weighting variables, one that allows you to use the phone and online survey together (labeled "weightfin") and one that is for only the phone survey (labeled "wgtphoneonly", which excludes or zeros out everyone in the online survey).