The Oxford Internet Survey, 2011 (OxIS 2011) is a representative survey of British internet use in 2011. Data were collected via in-home interviews with respondents and includes both internet users, non-users, and ex-users. The dataset contains over 700 variables measuring internet activities, attitudes, and effects.

Further information about the OxIS, including publications, is available from the Oxford Internet Surveys webpages.

Users should note the data are only available in Stata SE format.

This study is Open Access. It is freely available to download and does not require UK Data Service registration.

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.

The data asset provides a link to all Change of Address Applications filed via the Internet datasets. Each dataset provides monthly volumes at the national level from federal fiscal year 2008 on for Internet Change of Address. The dataset includes only Internet Change of Address transactions. It should be noted that, in addition to using our online Change of Address application, the public might change an address by calling our 800 number, visiting a field office, or mailing us the request. This dataset pertains only to the online alternative.

Automatically describing images using natural sentences is an essential task to visually impaired people's inclusion on the Internet. Although there are many datasets in the literature, most of them contain only English captions, whereas datasets with captions described in other languages are scarce.

PraCegoVer arose on the Internet, stimulating users from social media to publish images, tag #PraCegoVer and add a short description of their content. Inspired by this movement, we have proposed the #PraCegoVer, a multi-modal dataset with Portuguese captions based on posts from Instagram. It is the first large dataset for image captioning in Portuguese with freely annotated images.

PraCegoVer has 533,523 pairs with images and captions described in Portuguese collected from more than 14 thousand different profiles. Also, the average caption length in #PraCegoVer is 39.3 words and the standard deviation is 29.7.

Dataset Structure

PraCegoVer dataset is composed of the main file dataset.json and a collection of compressed files named images.tar.gz.partX

containing the images. The file dataset.json comprehends a list of json objects with the attributes:

user: anonymized user that made the post;

filename: image file name;

raw_caption: raw caption;

caption: clean caption;

date: post date.

Each instance in dataset.json is associated with exactly one image in the images directory whose filename is pointed by the attribute filename. Also, we provide a sample with five instances, so the users can download the sample to get an overview of the dataset before downloading it completely.

Download Instructions

If you just want to have an overview of the dataset structure, you can download sample.tar.gz. But, if you want to use the dataset, or any of its subsets (63k and 173k), you must download all the files and run the following commands to uncompress and join the files:

cat images.tar.gz.part* > images.tar.gz tar -xzvf images.tar.gz

Alternatively, you can download the entire dataset from the terminal using the python script download_dataset.py available in PraCegoVer repository. In this case, first, you have to download the script and create an access token here. Then, you can run the following command to download and uncompress the image files:

python download_dataset.py --access_token=

Graph and download economic data for Internet users for the United States (ITNETUSERP2USA) from 1990 to 2023 about internet, persons, and USA.

When asked about "Attitudes towards the internet", most Japanese respondents pick "I'm concerned that my data is being misused on the internet" as an answer. 35 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

Context

Internet privacy has gained widespread attention in recent years. To measure the degree to which people are concerned about hot-button issues like Internet privacy, social scientists conduct polls in which they interview a large number of people about the topic. In this assignment, we will analyze data from a July 2013 Pew Internet and American Life Project poll on Internet anonymity and privacy, which involved interviews across the United States.

Content

The dataset has the following fields (all Internet use-related fields were only collected from interviewees who either use the Internet or have a smartphone):

Internet.Use: A binary variable indicating if the interviewee uses the Internet, at least occasionally (equals 1 if the interviewee uses the Internet, and equals 0 if the interviewee does not use the Internet). Smartphone: A binary variable indicating if the interviewee has a smartphone (equals 1 if they do have a smartphone, and equals 0 if they don't have a smartphone). Sex: Male or Female. Age: Age in years. State: State of residence of the interviewee. Region: Census region of the interviewee (Midwest, Northeast, South, or West). Conservativeness: Self-described level of conservativeness of interviewee, from 1 (very liberal) to 5 (very conservative). Info.On.Internet: Number of the following items this interviewee believes to be available on the Internet for others to see: (1) Their email address; (2) Their home address; (3) Their home phone number; (4) Their cell phone number; (5) The employer/company they work for; (6) Their political party or political affiliation; (7) Things they've written that have their name on it; (8) A photo of them; (9) A video of them; (10) Which groups or organizations they belong to; and (11) Their birth date. Worry.About.Info: A binary variable indicating if the interviewee worries about how much information is available about them on the Internet (equals 1 if they worry, and equals 0 if they don't worry). Privacy.Importance: A score from 0 (privacy is not too important) to 100 (privacy is very important), which combines the degree to which they find privacy important in the following: (1) The websites they browse; (2) Knowledge of the place they are located when they use the Internet; (3) The content and files they download; (4) The times of day they are online; (5) The applications or programs they use; (6) The searches they perform; (7) The content of their email; (8) The people they exchange email with; and (9) The content of their online chats or hangouts with others. Anonymity.Possible: A binary variable indicating if the interviewee thinks it's possible to use the Internet anonymously, meaning in such a way that online activities can't be traced back to them (equals 1 if he/she believes you can, and equals 0 if he/she believes you can't). Tried.Masking.Identity: A binary variable indicating if the interviewee has ever tried to mask his/her identity when using the Internet (equals 1 if he/she has tried to mask his/her identity, and equals 0 if he/she has not tried to mask his/her identity). Privacy.Laws.Effective: A binary variable indicating if the interviewee believes United States law provides reasonable privacy protection for Internet users (equals 1 if he/she believes it does, and equals 0 if he/she believes it doesn't).

Acknowledgements

MITx ANALYTIX

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad,

Czech Republic Enterprises: Internet Access: Mobile Only: 10+ E data was reported at 4.077 % in 2023. Czech Republic Enterprises: Internet Access: Mobile Only: 10+ E data is updated yearly, averaging 4.077 % from Dec 2023 (Median) to 2023, with 1 observations. The data reached an all-time high of 4.077 % in 2023 and a record low of 4.077 % in 2023. Czech Republic Enterprises: Internet Access: Mobile Only: 10+ E data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.TB002: Information and Communicaton Technology Usage: Enterprises.

Czech Republic Enterprises: Internet Access: Mobile Only: 10+ E: Accommodation data was reported at 2.098 % in 2023. Czech Republic Enterprises: Internet Access: Mobile Only: 10+ E: Accommodation data is updated yearly, averaging 2.098 % from Dec 2023 (Median) to 2023, with 1 observations. The data reached an all-time high of 2.098 % in 2023 and a record low of 2.098 % in 2023. Czech Republic Enterprises: Internet Access: Mobile Only: 10+ E: Accommodation data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.TB002: Information and Communicaton Technology Usage: Enterprises.

The Oxford Internet Survey, 2007 (OxIS 2007) is a representative survey of British internet use in 2007. Data were collected via in-home interviews with respondents and includes both internet users and non-users. The dataset contains 411 variables measuring internet activities, attitudes and effects.

Further information about the OxIS, including publications, is available from the Oxford Internet Surveys webpages.

Users should note the data are only available in Stata SE format.

This study is Open Access. It is freely available to download and does not require UK Data Service registration.

This table provides data about access and use of the internet by the population of the Caribbean Netherlands aged 15 years and older in private households. The following topics are presented: access to the internet at home, type of internet connections and when persons have last used the internet. For persons who have used the internet in the last three months, the table provides information about the frequency of the use of internet in the last three months, in which location the persons last went online and the kind of internet activities. In 2017/2018, questions about the use of the internet were only asked to people who have internet access at home, whereas in 2013 these questions were asked to people who have internet access at home and/or elsewhere. The figures on the use of the internet are therefore not comparable between 2013 and 2017/2018. Breakdowns by sex, age and level of education are presented. These aspects are shown for the Caribbean Netherlands and also for the islands Bonaire, St Eustatius and Saba separately. The research is a sample survey. This means that the figures shown are estimates for which reliability margins apply. These margins are also included in the table. The Omnibus survey was carried out for the first time on Bonaire, Saba and St. Eustatius in 2013 during the month of June and the first week of July. For the second time the Omnibus survey was carried out on Bonaire during the months of October and November 2017, and on Saba and St. Eustatius in the period January to March 2018. Data available from: 2013 Status of the figures: The figures in this table are final. Changes as of 4 April 2019 None, this is a new table. When will new figures be published? New data will be published every four years.

Access to image-based resources is fundamental to research and the transmission of cultural knowledge. Digital access offers the potential for scholars to employ heritage collections internationally via the internet. However, most of the internet’s image-based resources have been locked up in silos, and access at resolutions useful for research has been restricted to bespoke, locally-built applications. The International Image Interoperability Framework (IIIF) can solve this problem using Application Program Interfaces (APIs) which allow images and metadata held in different digital collections to be accessed in a standardised format.

In this EU-funded MUYA-IIIF project 694612 Proof of Concept project (the MUYA-IIIF PoC) the School of African and Oriental Studies (SOAS) has addressed the problem of tools and infrastructure to realize the potential of IIIF in practical workflows for researchers in the social sciences and humanities. Heretofore the large number of organizations worldwide now providing access to their image-based resources via IIIF have only supported viewing, and not the routine use of research processes such as standards-based scientific annotation. The MUYA-IIIF PoC has worked with the British Library (BL) and the hasdai Partnership of CERN and Data Futures GmbH to build and employ a general-purpose IIIF annotation workflow, extending existing research conducted under earlier The Multimedia Yasna ERC advanced grant. Significantly, not only have problems of annotation workflows been addressed by the MUYA-IIIF PoC, but also the creation of new, reusable primary data resources from research employing annotation, which can be preserved using the W3C's Web Annotation Data Model (WADM) standards and state-of-the art InvenioRDM repository technology.

Specifically, MUYA-IIIF has annotated textual structure in key Avestan manuscripts from multiple collections, including from the British Library, to connect the digitized manuscript imagery with structured transcriptions of the text it bears, enabling analysis and searching.

While many institutions internationally now provide IIIF data resources based on their manuscript collections, very few of these are yet compatible with standards-based annotation. The Oxford MA in digital scholarship found, as recently as Fall 2022, that it needed to convert libraries' IIIF resources before being able to annotate them. In contrast, MUYA-IIIF has produced a new WADM-compliant IIIF service, which can now be freely annotated by scholars, and it has also created annotations of all of the stanzas of the Zoroastrian Yasna ceremony. In turn, this has permitted reuse of existing research investment using Text Encoding Initiative (TEI) analysis of the Yasna. As a result a significant speed-up has been achieved in developing comprehensive interactive transcription of the Yasna manuscripts.

The second part of the MUYA-IIIF project has addressed sustainability and reuse of this new digital collection: in contrast, many data resources in the Humanities and in cultural heritage become vulnerable to technology obsolescence. In particular, WADM annotations are stand-off in nature—they are stored separately from the digitized manuscript imagery and demand new approaches for effective preservation and accessibility for the wider research community. MUYA-IIIF has therefore worked with the hasdai partnership to gain access to new repository technology developed in the InvenioRDM consortium, which supports annotation. InvenioRDM is the software platform on which the upgrade of the European Commission's OpenAIRE trusted Zenodo repository is based.

To support such long-term access and reuse, the project's outputs comprise four components, which together form a sustainable data resource on which not only SOAS but also the external research community can build.

• the MUYA InvenioRDM corpus repository provides metadata linked to the British Library manuscript record, and a IIIF viewer for the BL Arundel Yasna manuscript imagery.
• annotations produced in the MUYA-IIIF project are available for download as a WADM. dataset—these annotations are linked to the manuscript imagery via Persistent IDentifiers (PIDs).
• the IIIF service for the manuscript is available for use by external IIIF applications and researchers' tools.
• a Zenodo record, linked to the corpus repository, provides the Yasna annotation collection dataset, and this is discoverable by international libraries via the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH).

The new MUYA InvenioRDM corpus repository is supported in the long-term through the hasdai Partnership, and the Zenodo record is supported by the EC's OpenAIRE program. In this way MUYA-IIIF has created a new sustainability benchmark for digital research investments using scientific annotation, by assembling standards-based infrastructures and making very long-term costs of operation of complex data resources forecastable in concrete terms.

Implementation of the project

The project was divided into work-packages, reflecting three main activities:

• creating a IIIF service for the British Library Arundel manuscript of the Yasna ceremony and building an annotation workflow
• annotating the chapters and stanzas of the Yasna and connecting them with transcriptions and analyses already accomplished in other research activities
• developing a corpus repository for Zoroastrian manuscripts to support both accessibility for the research community and also preservation of the research investment in digitization and annotation, as well as connecting other existing research outcomes

The annotation workflow for the project employed the freizo anəstor platform developed by Data Futures GmbH and employed by institutions in Europe and the U.S. including CERN, Heidelberg, Oxford and Notre Dame, and this was configured for work on the Yasna manuscript. Anəstor can generate multiple versions of Open Annotation Data Model and Web Annotation Data Model (WADM) annotations, to address differences between existing, current and future standards-based WADM research environments, and it is being integrated with the Zenodo global catch-all repository of OpenAIRE.

The annotation workflow provided security for SOAS scholars through ORCID authentication, so that their work was protected from unauthorized modification, and also allowed their contributions to be tracked and credited for citation. In addition the workflow exported annotation collections in a preservable form for efficient access by the research community and for preservation.

Developing an InvenioRDM corpus repository for the MUYA-IIIF project enabled the digital version of the manuscript, together with the British Library metadata, to be presented without restrictions on the internet, and for the annotation collections to form a foundation for future research via down-loadable JSON datasets (JSON is technology-agnostic and can be employed by a wide range of current and future research software applications).

SOAS now plans to extend the MUYA-IIIF repository with additional manuscripts based on fieldwork in India and Iran and through collaborations with other institutions worldwide. Long-term hosting of this data resource by the hasdai Partnership is already organized for 10 years and new developments such as the Oxford Common File Layout (OCFL) are enabling both very long-term preservation using LTO tape libraries and also cross repository interoperability for resilience as technologies continue to evolve.

The European questionnaire on Information and Communication Technologies Data reveals that there exists a disparity between the frequency in which people with a low (At most lower secondary education), medium (Upper secondary and post-secondary non-tertiary education), and high (Tertiary education) formal education level use the internet. This disparity although present in most countries, differs widely in its severity. In 2020, Croatia had one of the most severe rifts in internet usage between different educational levels. While 96 percent of people with higher formal education used the internet daily, only 33 percent with low formal education did so. Data for Portugal shows a similar trend in internet usage, with 96 percent of people with high formal education using the internet daily but only 45 percent with low formal education do so. Scandinavian countries like Norway and Sweden have only small differences in daily internet usage between groups with different formal education.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

INFO ABOUT THE BOT-IOT DATASET, NOTE: only the csv files stated in the description are used

The BoT-IoT dataset can be downloaded from HERE. You can also use our new datasets: the TON_IoT and UNSW-NB15.

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The BoT-IoT dataset was created by designing a realistic network environment in the Cyber Range Lab of UNSW Canberra. The network environment incorporated a combination of normal and botnet traffic. The dataset’s source files are provided in different formats, including the original pcap files, the generated argus files and csv files. The files were separated, based on attack category and subcategory, to better assist in labeling process.

The captured pcap files are 69.3 GB in size, with more than 72.000.000 records. The extracted flow traffic, in csv format is 16.7 GB in size. The dataset includes DDoS, DoS, OS and Service Scan, Keylogging and Data exfiltration attacks, with the DDoS and DoS attacks further organized, based on the protocol used.

To ease the handling of the dataset, we extracted 5% of the original dataset via the use of select MySQL queries. The extracted 5%, is comprised of 4 files of approximately 1.07 GB total size, and about 3 million records.

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Free use of the Bot-IoT dataset for academic research purposes is hereby granted in perpetuity. Use for commercial purposes should be agreed by the authors. The authors have asserted their rights under the Copyright. To whom intent the use of the Bot-IoT dataset, the authors have to cite the following papers that has the dataset’s details: .

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Benjamin Turnbull. "Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset." Future Generation Computer Systems 100 (2019): 779-796. Public Access Here.

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Jill Slay. "Towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques." In International Conference on Mobile Networks and Management, pp. 30-44. Springer, Cham, 2017.

Koroniotis, Nickolaos, Nour Moustafa, and Elena Sitnikova. "A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework." Future Generation Computer Systems 110 (2020): 91-106.

Koroniotis, Nickolaos, and Nour Moustafa. "Enhancing network forensics with particle swarm and deep learning: The particle deep framework." arXiv preprint arXiv:2005.00722 (2020).

Koroniotis, Nickolaos, Nour Moustafa, Francesco Schiliro, Praveen Gauravaram, and Helge Janicke. "A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports." IEEE Access (2020).

Koroniotis, Nickolaos. "Designing an effective network forensic framework for the investigation of botnets in the Internet of Things." PhD diss., The University of New South Wales Australia, 2020.

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This map shows the percent and count of households who have only a cellular plan as their only internet subscription. This covers all households that have a cellular data plan and no other type of internet subscription. These households do not have broadband (high speed) such as Cable, Fiber Optic or DSL; satellite internet service; dial-up; or other wireless subscription.This map is multi-scale, with data for states, counties, and tracts. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. For more information about the layer used in this map, along with the vintage and data source, visit this Living Atlas item.Zoom or pan the map to see the pattern in your area and explore where the digital divide exists in cities and rural areas across the US.

Digital Distress Metric:Four variables from the U.S. Census American Community Survey were used: The percent of homes with no internet access, Using only cellular data, as well as The percent of homes relying on mobile devices only, or Having no computing devices. Data was obtained for all U.S. census tracts and categorized into low, moderate, and high digital distress.The Digital Divide Index or DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. It is composed of two scores, also ranging from 0 to 100: the infrastructure/adoption (INFA) score and the socioeconomic (SE) score.The INFA score groups five variables related to broadband infrastructure and adoption: Percentage of total 2020 population without access to fixed broadband of at least 100 Mbps download and 20 Mbps upload as of 2020 based on Ookla Speedtest® open dataset; Percent of homes without a computing device (desktops, laptops, smartphones, tablets, etc.); Percent of homes with no internet access (have no internet subscription, including cellular data plans or dial-up); Median maximum advertised download speeds; and Median maximum advertised upload speeds.The SE score groups five variables known to impact technology adoption: Percent population ages 65 and over; Percent population 25 and over with less than high school; Individual poverty rate; Percent of noninstitutionalized civilian population with a disability: and A brand new digital inequality or internet income ratio measure (IIR). In other words, these variables indirectly measure adoption since they are potential predictors of lagging technology adoption or reinforcing existing inequalities that also affect adoption.These two scores are combined to calculate the overall DDI score. If a particular county or census tract has a higher INFA score versus a SE score, efforts should be made to improve broadband infrastructure. If on the other hand, a particular geography has a higher SE score versus an INFA score, efforts should be made to increase digital literacy and exposure to the technology’s benefits.The DDI measures primarily physical access/adoption and socioeconomic characteristics that may limit motivation, skills, and usage. Due to data limitations it was designed as a descriptive and pragmatic tool and is not intended to be comprehensive. Rather it should help initiate important discussions among community leaders and residents.

Abstract copyright UK Data Service and data collection copyright owner.

The Oxford Internet Surveys (OxIS) is the longest-running academic survey of internet use in Britain, describing how internet use has evolved from 2003 to the present day. Run by the Oxford Internet Institute, a Social Sciences department at the University of Oxford, this survey provides unrivalled data, rigorous analysis and policy-relevant insights into key aspects of life online.

OxIS is a multi-stage national probability sample of 2,000 people in Britain, enabling researchers to project estimates to the nation as a whole. Undertaken every two years since 2003, it surveys users, non-users, and ex-users, covering internet and ICT access and use, attitudes to technology, and supporting demographic and geographic information.

The Oxford Internet Survey, 2013 (OxIS 2013) is a representative survey of British internet use in 2013. Data were collected via in-home interviews with respondents. It includes internet users, ex-users and non-users. The dataset contains almost 800 variables measuring internet activities, attitudes and effects. This wave included an oversample of people living in rural areas.

Further information about the OxIS, including publications, is available from the Oxford Internet Surveys webpages.

Users should note the data are only available in Stata format.

This study is Open Access. It is freely available to download and does not require UK Data Service registration.

Main Topics:

The data include a wide variety of items measuring issues related to internet use, including:

• internet activity
• social media use
• privacy
• mobile phone use
• trust
• internet use
• internet search
• political activity on the internet.

This dataset provides monthly volumes at the national level for federal fiscal years 2016 on for Internet Direct Deposit applications. The dataset includes only Internet Direct Deposit transactions. It should be noted that, in addition to using our online Direct Deposit application, the public might also call our 800 number, visit a field office, or request a change of direct deposit by mail. This data set pertains only to the online alternative.