Teachers' Use of Educational Technology in U.S. Public Schools, 2009 (FRSS 95), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 95 (https://nces.ed.gov/surveys/frss/) is a sample survey that provides national estimates on the availability and use of educational technology among teachers in public elementary and secondary schools during 2009. 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 using surveys via the web or by mail. Telephone follow-up for survey non-response and data clarification was also used. Questionnaires and cover letters for the teacher survey were mailed to sampled teachers at their schools. Public schools and teachers within those schools were sampled. The weighted response rate for schools providing lists of teachers for sampling was 81 percent, and the weighted response rate for sampled teachers completing questionnaires was 79 percent. Key statistics produced from FRSS 95 were information on the use of computers and internet access in the classroom; availability and use of computing devices, software, and school or district networks (including remote access) by teachers; students' use of educational technology; teachers' preparation to use educational technology for instruction; and technology-related professional development activities.

Percentage of Internet users by selected Internet service and technology, such as; home Internet access, use of smart home devices, use of smartphones, use of social networking accounts, use or purchase of streaming services, use of government services online and online shopping.

Digital technology and Internet use, main benefits of Information and Communication Technology (ICT) use, by North American Industry Classification System (NAICS) and size of enterprise for Canada in 2012.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

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...

Innovation and business strategy, advanced technology use, North American Industry Classification System (NAICS) and enterprise size for Canada and regions 2009 to today.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Technological innovation, often fueled by governments, drives industrial growth and helps raise living standards. Data here aims to shed light on countries technology base: research and development, scientific and technical journal articles, high-technology exports, royalty and license fees, and patents and trademarks. Sources include the UNESCO Institute for Statistics, the U.S. National Science Board, the UN Statistics Division, the International Monetary Fund, and the World Intellectual Property Organization.

This layer shows 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 % With a Desktop or Laptop Computer% With only a Desktop or Laptop% With a Smartphone% With only a Smartphone% With a Tablet% With only a tablet% With other type of computing device% With other type of computing device only% No computerCurrent 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

Innovation and business strategy, advanced technology use, by North American Industry Classification System (NAICS), enterprise size and advanced technology for Canada and selected provinces from 2009 to today.

Information Technology Operations and Maintenance records relate to the activities associated with the operations and maintenance of the basic systems and services used to supply the agency and its staff with access to computers and data telecommunications. Includes the activities associated with IT equipment, IT systems, and storage media, IT system performance testing, asset and configuration management, change management, and maintenance on network infrastructure. Includes records such as:rn- files identifying IT facilities and sitesrn- files concerning implementation of IT facility and site managementrn- equipment support services provided to specific sitesrn- inventories of IT assets, network circuits, and building or circuitry diagramsrn- equipment control systems such as databases of barcodes affixed to IT physical assets, and tracking of approved personally-owned devicesrn- requests for servicern- work ordersrn- service historiesrn- workload schedulesrn- run reportsrn- schedules of maintenance and support activitiesrn- problem reports and related decision documents relating to the software infrastructure of the network or systemrn- reports on operationsrn- website administrationrn- records to allocate charges and track payment for software and services

Electronic commerce and technology, by type of technology being used, present and future intentions and North American Industry Classification System (NAICS) for Canada from 2000 to 2007. (Terminated)

Internet Users Survey - Technology Use for Internet Access by the Internet Users since 2012

A tech stack represents a combination of technologies a company uses in order to build and run an application or project. The most popular technology skill in the database tech stack in 2023 was MySQL, chosen by more than half of respondents. It was followed by PostgreSQL, while NoSQL ranked fifth, chosen by only 4.5 percent of respondents.

In 2023, over 45 percent of surveyed software developers worldwide reported using PostgreSQL, the highest share of any database technology. Other popular database tools among developers included MySQL and SQLite.

Explore our public notebook content!

Meta Kaggle Code is an extension to our popular Meta Kaggle dataset. This extension contains all the raw source code from hundreds of thousands of public, Apache 2.0 licensed Python and R notebooks versions on Kaggle used to analyze Datasets, make submissions to Competitions, and more. This represents nearly a decade of data spanning a period of tremendous evolution in the ways ML work is done.

Why we’re releasing this dataset

By collecting all of this code created by Kaggle’s community in one dataset, we hope to make it easier for the world to research and share insights about trends in our industry. With the growing significance of AI-assisted development, we expect this data can also be used to fine-tune models for ML-specific code generation tasks.

Meta Kaggle for Code is also a continuation of our commitment to open data and research. This new dataset is a companion to Meta Kaggle which we originally released in 2016. On top of Meta Kaggle, our community has shared nearly 1,000 public code examples. Research papers written using Meta Kaggle have examined how data scientists collaboratively solve problems, analyzed overfitting in machine learning competitions, compared discussions between Kaggle and Stack Overflow communities, and more.

The best part is Meta Kaggle enriches Meta Kaggle for Code. By joining the datasets together, you can easily understand which competitions code was run against, the progression tier of the code’s author, how many votes a notebook had, what kinds of comments it received, and much, much more. We hope the new potential for uncovering deep insights into how ML code is written feels just as limitless to you as it does to us!

Sensitive data

While we have made an attempt to filter out notebooks containing potentially sensitive information published by Kaggle users, the dataset may still contain such information. Research, publications, applications, etc. relying on this data should only use or report on publicly available, non-sensitive information.

Joining with Meta Kaggle

The files contained here are a subset of the KernelVersions in Meta Kaggle. The file names match the ids in the KernelVersions csv file. Whereas Meta Kaggle contains data for all interactive and commit sessions, Meta Kaggle Code contains only data for commit sessions.

File organization

The files are organized into a two-level directory structure. Each top level folder contains up to 1 million files, e.g. - folder 123 contains all versions from 123,000,000 to 123,999,999. Each sub folder contains up to 1 thousand files, e.g. - 123/456 contains all versions from 123,456,000 to 123,456,999. In practice, each folder will have many fewer than 1 thousand files due to private and interactive sessions.

The ipynb files in this dataset hosted on Kaggle do not contain the output cells. If the outputs are required, the full set of ipynbs with the outputs embedded can be obtained from this public GCS bucket: kaggle-meta-kaggle-code-downloads. Note that this is a "requester pays" bucket. This means you will need a GCP account with billing enabled to download. Learn more here: https://cloud.google.com/storage/docs/requester-pays

Questions / Comments

We love feedback! Let us know in the Discussion tab.

Happy Kaggling!

We start by identifying U.S.-based software organizations in the computer programming and data processing industry (SIC 737), as a knowledge-intensive high-growth setting. We integrate two main data sources. First, to collect the knowledge-based measures, we use publicly available data provided by the U.S. Patent and Trademark Office (USPTO). Using the General Architecture for Text Engineering (GATE) software, we design queries that retrieve the complete class and subclass information for each patent, as well as citations, inventors, and total patents granted between 1998 and 2011 inclusive. We aggregate the data by organization-year observation at the class and subclass levels and use these aggregated measures to compute the knowledge-based predictors and covariates. To compute moving averages for some variables, we collect five years of additional USPTO data which makes our knowledge dataset span between 1993 and 2011. Second, we use Compustat to collect organization-level control variables such as assets, number of employees, market valuation, R&D expenditures, intangibles, solvency, and slack. The integration of the two datasets yields a final sample panel of 100 organizations with 3.2 years of observations on average per organization from 1998 to 2011.

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.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Technological innovation, often fueled by governments, drives industrial growth and helps raise living standards. Data here aims to shed light on countries technology base: research and development, scientific and technical journal articles, high-technology exports, royalty and license fees, and patents and trademarks. Sources include the UNESCO Institute for Statistics, the U.S. National Science Board, the UN Statistics Division, the International Monetary Fund, and the World Intellectual Property Organization.

Abstract (en): The Global Digital Activism Data Set (GDADS), released February 2013 by the Digital Activism Research Project (DARP) at the University of Washington in Seattle, features coded cases of online digital activism from 151 countries and dependent territories. Several features from each case of digital activism were documented, including the year that online action commenced, the country of origin of the initiator(s), the geographic scope of their campaign, and whether the action was online only, or also featured offline activities. Researchers were interested in the number and types of software applications that were used by digital activists. Specifically, information was collected on whether software applications were used to circumvent censorship or evade government surveillance, to transfer money or resources, to aid in co-creation by a collaborative group, or for purposes of networking, mobilization, information sharing, or technical violence (destructive/disruptive hacking). The collection illustrates the overall focus of each case of digital activism by defining the cause advanced or defended by the action, the initiator's diagnosis of the problem and its perceived origin, the identification of the targeted audience that the campaign sought to mobilize, as well as the target whose actions the initiators aimed to influence. Finally, each case of digital activism was evaluated in terms of its success or failure in achieving the initiator's objectives, and whether any other positive outcomes were apparent. Through GDADS and associated works, DARP aims to study the effect of digital technology on civic engagement, nonviolent protest, and political change around the world. The GDADS contains three sets of data: (1) Coded Cases, (2) Case Sources, and (3) Coded Cases 2.0. The Coded Cases dataset contains 1179 coded cases of digital activism from 1982 through 2012. The Case Sources dataset is an original deposited Excel document that contains source listings from all cases documented by researchers, including those that were ultimately excluded from the original Coded Cases dataset. Coded Cases 2.0 contains 426 additional cases from 2010 through 2012; these cases were treated with a revised coding scheme and an extended review process. GDADS was assembled with the following inclusion criteria: cases needed to exhibit either (1) an activism campaign with at least one digital tactic, or (2) an instance of online discourse aimed at achieving social or political change, and (3) needed to be described by a reliable third party source. In addition to these inclusion criteria, researchers required that the digital activism be initiated by a traditional civil society organization, such as a nongovernmental organization or a nonprofit, or by the collaborative effort of one or more citizens. Digital activism cases initiated by governments or for-profit entities were not included in the collection. The data were assembled by a team of volunteers searching Web sites that are known to document global digital activism; researchers also collected data from peer reviewed journal articles that included digital activism case studies. This data collection does not feature a weighting scheme. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Global occurrences of online digital activism and journal article case studies of digital activism from 1982 through 2012. Smallest Geographic Unit: country Dataset 1: Coded Cases, contains the entire collection of coded cases, according to the inclusion criteria, for 1982-2009, but is incomplete for 2010-2012. Dataset 2: Case Sources, is an original deposited Excel document that contains links and citations used to code dataset 1 cases, plus 166 cases collected but not included in dataset 1. Dataset 3: Coded Cases 2.0, contains additional cases using purposive, multi-source, multilingual, sampling. For more information on sampling, please refer to the Methodology section in the ICPSR Codebooks. 2014-06-12 The collection has been updated with file set 3, Coded Cases 2.0, which contains additional cases that use an updat...

At King County, we believe increasing access to technology and the internet improves the quality of life. We want to better understand how residents use technology, as well as barriers preventing residents from getting connected. The findings from the study will help us better serve our community and ensure all residents have the resources to succeed.