The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.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 population share with mobile internet access in countries like Caribbean and Europe.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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 smartphone users in countries like Australia & Oceania and Asia.

Statistics of how many adults access the internet and use different types of technology covering: home internet access how people connect to the web how often people use the web/computers whether people use mobile devices whether people buy goods over the web whether people carried out specified activities over the internet For more information see the ONS website and the UKDS website.

Switzerland is leading the ranking by population share with mobile internet access, recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection. 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).

Internet use and online activities. Topics: internet connection at home; type of internet access in the household: standard telephone line, ISDN, ADSL, special modem using the TV cable, mobile / wireless connection; internet connection via: desktop computer, laptop, TV set-top box, video game console, mobile telephone, handheld / pocket computer; security features of the aforementioned device: antivirus software, smart card reader or other authentication device, encryption software, firewall software, electronic signature software, other security feature; internet use (at home, at work, at school); frequency of internet use; experienced security problems while using the internet: computer virus, fraudulent use of credit card number, unsolicited e-mail (spamming), other security problems, no problems; contact to public administration via the internet: to find administrative information, send e-mail, carry out procedures online, other reasons, no contact; frequency of purchasing products or services via the internet; experienced problems with regard to shopping on the internet: late delivery, product or service not delivered, no possibility to return faulty or unwanted goods, payment problems, unclear pricing, unsatisfactory communication, unauthorized use of personal data, website navigation problems, misleading offer; purchase of goods and services via websites in the following regions: EU, in Europe but outside the EU, North America, Latin America, Africa, Asia, Oceania; online activities: email, banking, read news, seek health-related information, find job ads, take part in forums, improve training or education, seek information on travels, book event tickets; chronic physical or mental health problem; limitation in daily activities due to health problems. Demography: sex; age; current type of education; age at end of education; occupation; professional position; type of community; region; household composition and household size. Additionally coded was: country; questionnaire number; date of interview; time of the beginning of the interview; duration of the interview; weighting factor. Internetnutzung und Online-Aktivitäten. Themen: Internetzugang im Haushalt; Art des Internetzugangs (z.B. ISDN, ADSL, WLAN); Art der Hardware für den Internetzugang (z.B. PC, Laptop, digital TV, Spielkonsole, Mobiltelefon mit WAP, Palm); vorhandene Sicherheitsvorrichtungen an einem Computer mit Internetzugang (Anti-Virus Software, Firewall, elektronische Signatur usw.); Orte der Internetnutzung (zu Hause, Arbeitsplatz, Schule, public access point, Cybercafe usw.); Nutzungshäufigkeit; aufgetretene Sicherheitsprobleme bei der Internetnutzung (Virenbefall, Missbrauch der Kreditkartennummer, Spam); durchgeführte Behördenkontakte via Internet; Häufigkeit privater Einkäufe im Internet; Art der aufgetretenen Probleme bei Interneteinkäufen; Herkunftsregion der Anbieterseiten bei privaten Interneteinkäufen; weitere Nutzungsarten für das Internet: E-Mails, online banking, Nachrichten und Informationsbeschaffung, Gesundheitsinformationen, Jobangebote, Teilnahme an Foren und Chats, Weiterbildung, Reiseinformationen, Ticketbuchungen; Vorhandensein einer chronischen Krankheit oder dauerhaften Behinderung beim Befragten sowie dadurch verursachte Einschränkung des Alltagslebens. Demographie: Geschlecht; Alter; Ausbildungsgrad; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Region; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Fragebogennummer; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Gewichtungsfaktor.

This layer shows computer ownership and internet access by age and race. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of population age 18 to 64 in households with no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28005, B28003, B28009B, B28009C, B28009D, B28009E, B28009F, B28009G, B28009H, B28009I Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Internet use and online activities. Topics: internet connection at home; type of internet access in the household: standard telephone line, ISDN, ADSL, special modem using the television cable, mobile / wireless connection; internet connection via: desktop computer, laptop, TV set-top box, video game console, mobile telephone, handheld / pocket computer; security features of the aforementioned device: antivirus software, smart card reader or other authentication device, encryption software, firewall software, electronic signature software, other security feature; internet use (at home, at work, at school); frequency of internet use; experienced security problems while using the internet: computer virus, fraudulent use of credit card number, unsolicited e-mail (spamming), other security problems, no problems; contact to public administration via the internet: to find administrative information, send e-mail, carry out procedures online, other reasons, no contact; frequency of purchasing products or services via the internet; experienced problems with regard to shopping on the internet: late delivery, product or service not delivered, no possibility to return faulty or unwanted goods, payment problems, unclear pricing, unsatisfactory communication, unauthorized use of personal data, website navigation problems, misleading offer; purchase of goods and services via websites in the following regions: EU, in Europe but outside the EU, North America, Latin America, Africa, Asia, Oceania; online activities: email, banking, read news, seek health-related information, find job ads, take part in forums, improve training or education, seek information on travels, book event tickets. Demography: sex; age; current type of education; age at end of education; occupation; professional position; region; type of community; household composition and household size. Additionally coded was: country; questionnaire number; weighting factor. Internetnutzung. Themen: Internetzugang im Haushalt; Art des Internetzugangs (z.B. über Telefon, ISDN, ADSL, via Kabel oder WLAN); Art der Hardware für den Internetzugang (z.B. PC, Laptop, TV set-top box, Spielkonsole, Mobiltelefon mit WAP, Palm); vorhandene Sicherheitsvorrichtungen am Computer mit Internetzugang (Anti-Virus Software, smart card reader, Verschlüsselungssoftware, Firewall, elektronische Signatur); Orte der Internetnutzung (zu Hause, Arbeitsplatz, Schule, public access point, Cybercafe usw.); Nutzungshäufigkeit; aufgetretene Sicherheitsprobleme bei der Internetnutzung (Virenbefall, Missbrauch der Kreditkartennummer, Spam); durchgeführte Behördenkontakte via Internet; Häufigkeit privater Einkäufe im Internet und Art dabei aufgetretener Probleme (verspätete Lieferung, fehlgeschlagene Lieferungen, keine Rückgabemöglichkeit, Probleme bei der Bezahlung, unzureichende Preisinformationen, schlechter Kundenservice, Missbrauch der persönlichen Daten z.B. in Form von Spam, Probleme mit der Navigation auf den Webseiten der Anbieter, unlautere/irreführende Angebote); Herkunftsregion der Anbieterseiten bei privaten Interneteinkäufen; weitere Nutzungsarten für das Internet: E-Mails, Online-Banking, Nachrichten und Informationsbeschaffung, Gesundheitsinformationen, Jobangebote, Teilnahme an Foren und Chats, Weiterbildung, Reiseinformationen, Ticketbuchungen. Demographie: Geschlecht; Alter; Studienstatus; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Land; Fragebogennummer; Gewichtungsfaktor.

Internetnutzung und Online-Aktivitäten. Themen: Internetzugang im Haushalt; Art des Internetzugangs (z.B. ISDN, ADSL, WLAN); Art der Hardware für den Internetzugang (z.B. PC, Laptop, digital TV, Spielkonsole, Mobiltelefon mit WAP, Palm); vorhandene Sicherheitsvorrichtungen an einem Computer mit Internetzugang (Anti-Virus Software, Firewall, elektronische Signatur usw.); Orte der Internetnutzung (zu Hause, Arbeitsplatz, Schule, public access point, Cybercafe usw.); Nutzungshäufigkeit; aufgetretene Sicherheitsprobleme bei der Internetnutzung (Virenbefall, Missbrauch der Kreditkartennummer, Spam); durchgeführte Behördenkontakte via Internet; Häufigkeit privater Einkäufe im Internet; Art der aufgetretenen Probleme bei Interneteinkäufen; Herkunftsregion der Anbieterseiten bei privaten Interneteinkäufen; weitere Nutzungsarten für das Internet: E-Mails, online banking, Nachrichten und Informationsbeschaffung, Gesundheitsinformationen, Jobangebote, Teilnahme an Foren und Chats, Weiterbildung, Reiseinformationen, Ticketbuchungen; Vorhandensein einer chronischen Krankheit oder dauerhaften Behinderung beim Befragten sowie dadurch verursachte Einschränkung des Alltagslebens. Demographie: Geschlecht; Alter; Ausbildungsgrad; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Region; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Fragebogennummer; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Gewichtungsfaktor. Internet use and online activities. Topics: internet connection at home; type of internet access in the household: standard telephone line, ISDN, ADSL, special modem using the TV cable, mobile / wireless connection; internet connection via: desktop computer, laptop, TV set-top box, video game console, mobile telephone, handheld / pocket computer; security features of the aforementioned device: antivirus software, smart card reader or other authentication device, encryption software, firewall software, electronic signature software, other security feature; internet use (at home, at work, at school); frequency of internet use; experienced security problems while using the internet: computer virus, fraudulent use of credit card number, unsolicited e-mail (spamming), other security problems, no problems; contact to public administration via the internet: to find administrative information, send e-mail, carry out procedures online, other reasons, no contact; frequency of purchasing products or services via the internet; experienced problems with regard to shopping on the internet: late delivery, product or service not delivered, no possibility to return faulty or unwanted goods, payment problems, unclear pricing, unsatisfactory communication, unauthorized use of personal data, website navigation problems, misleading offer; purchase of goods and services via websites in the following regions: EU, in Europe but outside the EU, North America, Latin America, Africa, Asia, Oceania; online activities: email, banking, read news, seek health-related information, find job ads, take part in forums, improve training or education, seek information on travels, book event tickets; chronic physical or mental health problem; limitation in daily activities due to health problems. Demography: sex; age; current type of education; age at end of education; occupation; professional position; type of community; region; household composition and household size. Additionally coded was: country; questionnaire number; date of interview; time of the beginning of the interview; duration of the interview; weighting factor.

English(North America) Scripted Monologue Smartphone and PC speech dataset, collected from monologue based on given scripts, covering common expressions. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(302 North American), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Datasys Gamer Audiences dataset tracks 10M+ gaming consumers, including platform usage, time spent, and title engagement.

Internetnutzung. Themen: Internetzugang im Haushalt; Art des Internetzugangs (z.B. über Telefon, ISDN, ADSL, via Kabel oder WLAN); Art der Hardware für den Internetzugang (z.B. PC, Laptop, TV set-top box, Spielkonsole, Mobiltelefon mit WAP, Palm); vorhandene Sicherheitsvorrichtungen am Computer mit Internetzugang (Anti-Virus Software, smart card reader, Verschlüsselungssoftware, Firewall, elektronische Signatur); Orte der Internetnutzung (zu Hause, Arbeitsplatz, Schule, public access point, Cybercafe usw.); Nutzungshäufigkeit; aufgetretene Sicherheitsprobleme bei der Internetnutzung (Virenbefall, Missbrauch der Kreditkartennummer, Spam); durchgeführte Behördenkontakte via Internet; Häufigkeit privater Einkäufe im Internet und Art dabei aufgetretener Probleme (verspätete Lieferung, fehlgeschlagene Lieferungen, keine Rückgabemöglichkeit, Probleme bei der Bezahlung, unzureichende Preisinformationen, schlechter Kundenservice, Missbrauch der persönlichen Daten z.B. in Form von Spam, Probleme mit der Navigation auf den Webseiten der Anbieter, unlautere/irreführende Angebote); Herkunftsregion der Anbieterseiten bei privaten Interneteinkäufen; weitere Nutzungsarten für das Internet: E-Mails, Online-Banking, Nachrichten und Informationsbeschaffung, Gesundheitsinformationen, Jobangebote, Teilnahme an Foren und Chats, Weiterbildung, Reiseinformationen, Ticketbuchungen. Demographie: Geschlecht; Alter; Studienstatus; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Land; Fragebogennummer; Gewichtungsfaktor. Internet use and online activities. Topics: internet connection at home; type of internet access in the household: standard telephone line, ISDN, ADSL, special modem using the television cable, mobile / wireless connection; internet connection via: desktop computer, laptop, TV set-top box, video game console, mobile telephone, handheld / pocket computer; security features of the aforementioned device: antivirus software, smart card reader or other authentication device, encryption software, firewall software, electronic signature software, other security feature; internet use (at home, at work, at school); frequency of internet use; experienced security problems while using the internet: computer virus, fraudulent use of credit card number, unsolicited e-mail (spamming), other security problems, no problems; contact to public administration via the internet: to find administrative information, send e-mail, carry out procedures online, other reasons, no contact; frequency of purchasing products or services via the internet; experienced problems with regard to shopping on the internet: late delivery, product or service not delivered, no possibility to return faulty or unwanted goods, payment problems, unclear pricing, unsatisfactory communication, unauthorized use of personal data, website navigation problems, misleading offer; purchase of goods and services via websites in the following regions: EU, in Europe but outside the EU, North America, Latin America, Africa, Asia, Oceania; online activities: email, banking, read news, seek health-related information, find job ads, take part in forums, improve training or education, seek information on travels, book event tickets. Demography: sex; age; current type of education; age at end of education; occupation; professional position; region; type of community; household composition and household size. Additionally coded was: country; questionnaire number; weighting factor. Telephone interview Bevölkerung im Alter von 15 Jahren und älter

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

This dataset contains two significant mobile gestures for brain-mobile phone interfaces (BMPIs: (i) motor imagery of tapping on the screen of a mobile device and (ii) motor imagery of swiping down with a thumb on the screen of a mobile device. The raw EEG signals were recorded using the Emotiv EPOC Flex (Model 1.0) headset with saline-based sensors and Emotiv Pro (2.5.1.227) software. The sampling rate is 128 Hz. Each epoch contains 3.5 s signals. The first 1 s signal is recorded before the MI task starts (5 s to 6 s interval in the timing plan), and the next 2.5 s signal is recorded during the MI execution (6 s to 8.5 s interval in the timing plan). Please refer to the reference study below for details.

The file names are constructed as follows. For example, taking "D01_s1" and "D01" in the file name refers to subject "01", and "s1" refers to session 1 ("s2" refers to session 2). The label data is given in a separate folder in Matlab format.

The data is provided in two different forms for use (the desired is preferable):

The set_files folder contains the data prepared for import in EEGLAB. EEGLAB must be installed, and the set files must be imported to access the data. The data is in epoched format in 3D (channels, sample_points, trials). With the EEGLAB interface, all the data can be accessed, and EEGLAB functions can be executed. Also, the EEG variable, which is built after importing the *.set file, contains all the information about the experiment. With the EEG.data variable, epoched data in the dimensions (channels, sample_points, trials) can be accessed.

The mat_files folder contains data in mat file format. In these files, epoched data is stored in a 3-D array of size (channels, sample_points, trials). You can access the data as follows. For example, all data from the first session of subject D01 can be retrieved as follows. Load the mat file with the load('D01_s1.mat') code, and access the data using the EEG variable in the workspace. For instance, 13x448 x101 sized epoched data (channels, sample_points, trials) can be retrieved with the command EEG.data. Other information about the experiments and subjects is also included in the fields of the EEG variable.

This research was supported by the Turkish Scientific and Research Council (TUBITAK) under project number 119E397.

The following article can be cited in academic studies as follow.

Yilmaz, C.M., Yilmaz, B.H. & Kose, C. MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM. Neural Comput & Applic 37, 4679–4696 (2025). https://doi.org/10.1007/s00521-024-10917-5

Permission must be obtained for use in commercial studies.

This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

Ziel dieser Studie war es, den Einfluss verschiedener Anreizsysteme auf die Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung unter deutschen Smartphone-Besitzern experimentell zu messen. Die Daten stammen aus einer Webumfrage unter deutschen Smartphone-Nutzern ab 18 Jahren, die aus einem deutschen, nicht wahrscheinlichen Online-Panel rekrutiert wurden. Im Dezember 2017 beantworteten 1.214 Teilnehmer einen Fragebogen zu den Themen Smartphone-Nutzung und -Fähigkeiten, Datenschutz und Sicherheit, allgemeine Einstellungen gegenüber der Umfrageforschung und Forschungseinrichtungen. Darüber hinaus enthielt der Fragebogen ein Experiment zur Bereitschaft, an der mobilen Datenerhebung unter verschiedenen Anreizbedingungen teilzunehmen. Themen: Besitz von Smartphone, Handy, Desktop- oder Laptop-Computer, Tablet-Computer und/oder E-Book-Reader; Art des Smartphones; Bereitschaft zur Teilnahme an der mobilen Datenerfassung unter verschiedenen Anreizbedingungen; Wahrscheinlichkeit des Herunterladens der App zur Teilnahme an dieser Forschungsstudie; Befragter möchte lieber an der Studie teilnehmen, wenn er 100 Euro erhalten könnte; Gesamtbetrag, den der Befragte für die Teilnahme an der Studie verdienen müsste (offene Antwort); Grund, warum der Befragte nicht an der Forschungsstudie teilnehmen würde; Bereitschaft zur Teilnahme an der Studie für einen Anreiz von insgesamt 60 Euro; Bereitschaft zur Aktivierung verschiedener Funktionen beim Herunterladen der App (Interaktionshistorie, Smartphone-Nutzung, Merkmale des sozialen Netzwerks, Netzqualitäts- und Standortinformationen, Aktivitätsdaten); vorherige Einladung zum Herunterladen der Forschungs-App; Herunterladen der Forschungs-App; Häufigkeit der Nutzung des Smartphones; Smartphone-Aktivitäten (Browsen, E-Mails, Fotografieren, Anzeigen/Post-Social-Media-Inhalte, Einkaufen, Online-Banking, Installieren von Apps, Verwenden von GPS-fähigen Apps, Verbinden über Bluethooth, Spielen, Streaming von Musik/Videos); Selbsteinschätzung der Kompetenz im Umgang mit dem Smartphone; Einstellung zu Umfragen und Teilnahme an Forschungsstudien (persönliches Interesse, Zeitverlust, Verkaufsgespräch, interessante Erfahrung, nützlich); Vertrauen in Institutionen zum Datenschutz (Marktforschungsunternehmen, Universitätsforscher, Regierungsbehörden wie das Statistische Bundesamt, Mobilfunkanbieter, App-Unternehmen, Kreditkartenunternehmen, Online-Händler und Social-Media-Plattformen); allgemeine Datenschutzbedenken; Gefühl der Datenschutzverletzung durch Banken und Kreditkartenunternehmen, Steuerbehörden, Regierungsbehörden, Marktforschung, soziale Netzwerke, Apps und Internetbrowser; Bedenken zur Datensicherheit bei Smartphone-Aktivitäten für Forschungszwecke (Online-Umfrage, Umfrage-Apps, Forschungs-Apps, SMS-Umfrage, Kamera, Aktivitätsdaten, GPS-Ortung, Bluetooth). Demographie: Geschlecht, Alter; Bundesland; höchster Schulabschluss; höchstes berufliches Bildungsniveau. Zusätzlich verkodet wurden: laufende Nummer; Dauer (Reaktionszeit in Sekunden); Gerätetyp, mit dem der Fragebogen ausgefüllt wurde. 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.

The dataset contains information about the contents of 100 Terms of Service (ToS) of online platforms. The documents were analyzed and evaluated from the point of view of the European Union consumer law. The main results have been presented in the table titled "Terms of Service Analysis and Evaluation_RESULTS." This table is accompanied by the instruction followed by the annotators, titled "Variables Definitions," allowing for the interpretation of the assigned values. In addition, we provide the raw data (analyzed ToS, in the folder "Clear ToS") and the annotated documents (in the folder "Annotated ToS," further subdivided).

SAMPLE: The sample contains 100 contracts of digital platforms operating in sixteen market sectors: Cloud storage, Communication, Dating, Finance, Food, Gaming, Health, Music, Shopping, Social, Sports, Transportation, Travel, Video, Work, and Various. The selected companies' main headquarters span four legal surroundings: the US, the EU, Poland specifically, and Other jurisdictions. The chosen platforms are both privately held and publicly listed and offer both fee-based and free services. Although the sample cannot be treated as representative of all online platforms, it nevertheless accounts for the most popular consumer services in the analyzed sectors and contains a diverse and heterogeneous set.

CONTENT: Each ToS has been assigned the following information: 1. Metadata: 1.1. the name of the service; 1.2. the URL; 1.3. the effective date; 1.4. the language of ToS; 1.5. the sector; 1.6. the number of words in ToS; 1.7–1.8. the jurisdiction of the main headquarters; 1.9. if the company is public or private; 1.10. if the service is paid or free. 2. Evaluative Variables: remedy clauses (2.1– 2.5); dispute resolution clauses (2.6–2.10); unilateral alteration clauses (2.11–2.15); rights to police the behavior of users (2.16–2.17); regulatory requirements (2.18–2.20); and various (2.21–2.25). 3. Count Variables: the number of clauses seen as unclear (3.1) and the number of other documents referred to by the ToS (3.2). 4. Pull-out Text Variables: rights and obligations of the parties (4.1) and descriptions of the service (4.2)

ACKNOWLEDGEMENT: The research leading to these results has received funding from the Norwegian Financial Mechanism 2014-2021, project no. 2020/37/K/HS5/02769, titled “Private Law of Data: Concepts, Practices, Principles & Politics.”

Ivory Coast CI: Internet Users: Individuals: % of Population data was reported at 26.527 % in 2016. This records an increase from the previous number of 21.885 % for 2015. Ivory Coast CI: Internet Users: Individuals: % of Population data is updated yearly, averaging 1.039 % from Dec 1990 (Median) to 2016, with 23 observations. The data reached an all-time high of 26.527 % in 2016 and a record low of 0.000 % in 1990. Ivory Coast CI: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.

ABSTRACT: With the popularization of low-cost mobile and wearable sensors, many prior studies used such sensors to track and analyze people's mental well-being, productivity, and behavioral patterns. However, there is a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention. This limits the advances in affective computing and human-computer interaction research. In this work, we present K-EmoPhone, an in-the-wild naturalistic dataset (n=80, 1-week) of smartphone use, wearable sensing, and self-reported affect states from college students. The dataset contains continuous probing of peripheral physiological signals and mobility data measured by off-the-shelf commercial devices in addition to context and interaction data by users' smartphones. Moreover, the dataset includes self-reports of in-situ affect states (n=5,753) such as emotion, stress level, attention level, and disturbance level, acquired by the experience sampling method. The resulting K-EmoPhone dataset helps to advance the research and development of affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

Login Data Set for Risk-Based Authentication

Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

Overview

The data set contains the following features related to each login attempt on the SSO:

    Feature
    Data Type
    Description
    Range or Example




    IP Address
    String
    IP address belonging to the login attempt
    0.0.0.0 - 255.255.255.255


    Country
    String
    Country derived from the IP address
    US


    Region
    String
    Region derived from the IP address
    New York


    City
    String
    City derived from the IP address
    Rochester


    ASN
    Integer
    Autonomous system number derived from the IP address
    0 - 600000


    User Agent String
    String
    User agent string submitted by the client
    Mozilla/5.0 (Windows NT 10.0; Win64; ...


    OS Name and Version
    String
    Operating system name and version derived from the user agent string
    Windows 10


    Browser Name and Version
    String
    Browser name and version derived from the user agent string
    Chrome 70.0.3538


    Device Type
    String
    Device type derived from the user agent string
    (mobile, desktop, tablet, bot, unknown)1


    User ID
    Integer
    Idenfication number related to the affected user account
    [Random pseudonym]


    Login Timestamp
    Integer
    Timestamp related to the login attempt
    [64 Bit timestamp]


    Round-Trip Time (RTT) [ms]
    Integer
    Server-side measured latency between client and server
    1 - 8600000


    Login Successful
    Boolean
    True: Login was successful, False: Login failed
    (true, false)


    Is Attack IP
    Boolean
    IP address was found in known attacker data set
    (true, false)


    Is Account Takeover
    Boolean
    Login attempt was identified as account takeover by incident response team of the online service
    (true, false)

Data Creation

As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

Regarding the Data Values

Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

You can recognize them by the following values:

ASNs with values >= 500.000

IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

Study Reproduction

Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

See RESULTS.md for more details.

Ethics

By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

Publication

You can find more details on our conducted study in the following journal article:

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022) Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono. ACM Transactions on Privacy and Security

Bibtex

@article{Wiefling_Pump_2022, author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi}, title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}}, journal = {{ACM} {Transactions} on {Privacy} and {Security}}, doi = {10.1145/3546069}, publisher = {ACM}, year = {2022} }

License

This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

This layer shows Computers and Internet Use. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show Percentage of Households with a Broadband Internet Subscription. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2017-2021ACS Table(s): DP02, S2801Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 16, 2023National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Attitudes of parents towards safer internet use for children. Topics: number of children in household between the age of 6 and 17; sex of child whose birthday is closest to date of interview; age of the child; frequency of personal internet use; internet use of the child: from personal computer at home, from family’s computer at home, at school, in internet café, at friends’ homes, in public places, somewhere else; frequency of the following measures with regard to child’s internet use at home: stay nearby, sit with the child, ask child about online activities, check computer later, check child’s messages, check whether child has a profile on a social network; restrictions regarding the child’s internet use; allowed activities: spend a lot of time online, talk to people unknown in real life, use email and instant messaging tools, use chat rooms, create profile in online community, access certain websites, download content, buy online, give out personal information; child’s use of mobile phone with internet access; concern about the child’s internet use via mobile phone with regard to the following activities: give out personal information online, see sexually or violently explicit images on the internet, be bullied by other children online, see sexually or violently explicit images via mobile phone, be bullied by other children via mobile phone, get information about self-harm, become isolated if spending too much time online, be victim of online grooming; use of filtering or monitoring software at computer at home; reasons for not using filtering or monitoring software; child ever been in need of help concerning unclear situation on the internet; kind of situation; most effective measures with regard to safer and more effective internet use for the child; most important sources of information on safety tools and safe internet usage; first point to turn to in case of encountering illegal content. Demography: sex; age; age at end of education; occupation; professional position; type of community. Additionally coded was: respondent ID; interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; region; weighting factor. Internetnutzung durch Kinder. Sicherheitsmaßnahmen der Eltern. Themen: Anzahl der Kinder zwischen 6-17 Jahren im Haushalt; Auswahl des Kindes, über das die Eltern berichten durch die Geburtstagsmethode; Geschlecht; Alter des Kindes; Häufigkeit der Internetnutzung; Nutzung des Internets außerhalb der Wohnung; Orte des Internetzugangs des Kindes; Kontrollmaßnahme bei der Internetnutzung durch das Kind (Skala); Beschränkungen der Internetnutzung für das Kind; weitere Einschränkungen der Internetnutzung: viel Zeit online verbringen, Unterhaltungen mit Unbekannten (Chatten), Nutzung von Kontaktmedien (instant messaging), Nutzung von Chaträumen, Erstellen eines Online-Profils, Besuch bestimmter Web-Seiten, Download von Musik und Filmen, Onlineshopping, Herausgabe persönlicher Daten; Handy-Besitz des Kindes; Besorgnis über die Internetnutzung bzw. die Handy-Nutzung durch das Kind (Skala); installierte Filter- oder Kontrollsoftware; Gründe für eine Entscheidung gegen Kontrollsoftware; Hilfesuchen seitens des Kindes bei der Internetnutzung; Art der Hilfeleistung: bei technischen Problemen, bei Online-Belästigung, bei der Informationssuche, bei Online-Schikane, bei Online-Kontaktaufnahme durch Fremde, bei dem Fund von sexuellen oder gewalttätigen Bildern; Maßnahmen, die zu einer erhöhten Sicherheit des Internets für Kinder führen würden: Aufklärung in Schulen, Information der Eltern, Kurse für Eltern, bessere Kontrollsoftware, strengere Regulierungen bei Providern von Internetseiten, Kampagnen zur Aufklärung über Onlinerisiken, Kontaktstellen zur sicheren Internetnutzung; Informationsquelle über Sicherheitstools; Kontaktstelle bei einem Fund illegaler Inhalte: Polizei, Hotlines, gemeinnützige Organisationen. Demographie: Geschlecht; Alter; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad. Zusätzlich verkodet wurde: Befragten-ID; Interviewer-ID; Interviewsprache; Land; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Interviewmodus (Mobiltelefon oder Festnetz); Region; Gewichtungsfaktor.