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 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.

Dataset

This dataset was created by Hafiz Amsal

Released under World Bank Dataset Terms of Use

Contents

Capturing variability in use of commercial technologies by children with autism can inform future learning and support technology design. Survey data were collected from parents (n = 388) in the UK, Spain, and Belgium, and includes information about individuals with a range of ages and ability levels. We found a comparable pattern of access and usage across age groups, though higher reading and language ability was linked to use of more devices and interfaces. Reported worries about technology correlated with longer time spent using technology. Autistic people use mainstream technologies for a broad range of recreational uses. The data suggest that technologies developed with therapeutic goals in mind may need to achieve a high standard of design to engage users.

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

2008 European Adult Social Contact and Frequency Networks

Examining Dynamics of Contact in Different Environments

By [source]

About this dataset

This dataset provides valuable insights into the social contact patterns and frequency of contacts between adults in Europe in 2008. It includes a host of features such as age estimates, gender, home life, work, school, transport and leisure activities. The dataset also covers an array of contact frequencies such as regular meetings with family or friends, physical contact with people outside the household and overall duration spent together. Each data point provides an all-encompassing view of social interactions in adult networks between 2008 - all contributing to our understanding of human behaviour across different European contexts!

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How to use the dataset

This dataset aims to measure social contact and the frequency of contact between adults in Europe in 2008. Through this dataset, you can observe how different factors like age, gender, and occupation can predict social interaction. The columns provided in the dataset helps us to analyze how these factors affect the duration and frequency of contacts.

In order to use this dataset effectively, we need to pay close attention to all of the available variables. For example, looking at cnt_age_exact gives us an exact age for each contact person in a particular network or community. Similarly, cnt_age_est_min provides an estimated minimum age while cnt_age_est_max estimates a maximum age range for these contacts. Additionally, both phys_contact and frequency multi tell us about physical contacts that were established with other people as well as their relative durations (duration multi).

Finally, observing the values for cnt home/work/school help uncover how many contacts were made at each associated location on average; furthermore it is possible to see what kind of settings tend to encourage more person-to-person interactions by measuring the number of contacts there are at each site or domain (i.e.: cnt leisure). This data set then takes these observations one step further by delving into other locations such as transport which could potentially hold more meaningful insight into communication rates between groups within society! Thus it is possible not only quantify communication rate but also make connections that may have otherwise been missed without such an expansive source

Research Ideas

• Using the exact and estimated age ranges, gender, contact frequency and duration data, this dataset can be used to analyze differences in social contact patterns between different age groups and genders in Europe.
• The contact data could also be used to study the prevalence of physical contacts between adults in various locations (e.g. home environments, schools or workplaces) as well as to model transmission patterns of infectious diseases through these social networks.
• Additionally, the cnt_home and cnt_work columns could be studied separately to analyze the effect of working from home on people’s social contacts with other family members or peers at work respectively

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 2008_Mossong_POLYMOD_contact_common.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------| | cnt_age_exact | The exact age of the person contacted. (Integer) | | cnt_age_est_min | The estimated lower end age of the person contacted. (Integer) | | cnt_age_est_max | The estimated upper end age of the person contacted. (Integer) | | cnt_gender | The gender of the person contacted. (String) | | cnt_home | The number of contacts made at home. (Integer) | | cnt_work | The number of contacts made at work. (Integer) | | cnt_school | The number of contacts made at school. (Integer) ...

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.

Mobile Broadband penetration is defined as the number of active mobile broadband SIM cards per 100 people.

Original source

Electronic communications market indicators collected by Commission services, through National Regulatory Authorities, for the Communications Committee (COCOM) - January and July reports.:

http://ec.europa.eu/digital-agenda/about-fast-and-ultra-fast-internet-access

Parent dataset

This dataset is part of of another dataset:

http://digital-agenda-data.eu/datasets/digital_agenda_scoreboard_key_indicators

LearnPlatform is a unique technology platform in the K-12 market providing the only broadly interoperable platform to the breadth of edtech solutions in the US K12 field. A key component of edtech effectiveness is integrated reporting on tool usage and, where applicable, evidence of efficacy. With COVID closures, LearnPlatform has emerged as an important and singular resource to measure whether students are accessing digital resources within distance learning constraints. This platform provides a unique and needed source of data to understand if students are accessing digital resources, and where resources have disparate usage and impact.In this dataset we are sharing educational technology usage across the 8,000+ tools used in the education field in 2020. We make this dataset available to public so that educators, district leaders, researchers, institutions, policy-makers or anyone interested to learn about digital learning in 2020, can use this dataset to understand student engagement with core learning activities during the COVID-19 pandemic. Some example research questions that this dataset can help stakeholders answer: What is the picture of digital connectivity and engagement in 2020?What is the effect of the COVID-19 pandemic on online and distance learning, and how might this evolve in the future?How does student engagement with different types of education technology change over the course of the pandemic?How does student engagement with online learning platforms relate to different geography? Demographic context (e.g., race/ethnicity, ESL, learning disability)? Learning context? Socioeconomic status?Do certain state interventions, practices or policies (e.g., stimulus, reopening, eviction moratorium) correlate with increases or decreases in online engagement?

📊 Dataset Features This dataset includes 5,000 startups from 10 countries and contains 15 key features: Startup Name: Name of the startup Founded Year: Year the startup was founded Country: Country where the startup is based Industry: Industry category (Tech, FinTech, AI, etc.) Funding Stage: Stage of investment (Seed, Series A, etc.) Total Funding ($M): Total funding received (in million $) Number of Employees: Number of employees in the startup Annual Revenue ($M): Annual revenue in million dollars Valuation ($B): Startup's valuation in billion dollars Success Score: Score from 1 to 10 based on growth Acquired?: Whether the startup was acquired (Yes/No) IPO?: Did the startup go public? (Yes/No) Customer Base (Millions): Number of active customers Tech Stack: Technologies used by the startup Social Media Followers: Total followers on social platforms Analysis Ideas 📈 What Can You Do with This Dataset? Here are some exciting analyses you can perform:

Predict Startup Success: Train a machine learning model to predict the success score. Industry Trends: Analyze which industries get the most funding. **Valuation vs. Funding: **Explore the correlation between funding and valuation. Acquisition Analysis: Investigate the factors that contribute to startups being acquired.

Young people need skills and competencies to make the most of the benefits of the Internet and digital media. This is especially relevant to bridge the socioeconomic and political and cultural participation gap. Public policies should prioritize and support innovation and the acquisition of youth work and development methods in the digital context. This goal should be developed from social education to support youth in their role as active and critical citizens. Thus, the project in which this dataset frames explores how certain uses of the Internet and social media allow young people to position themselves and stand as political actors, actors involved in social, cultural and economic political life and proposes.

we have tested over 6000 people in computational .we use chinese as language and may appear garbled Chinese character.pleasa download and encode with utf-8

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

Object recognition predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset, grounded in a real-world application of teachable object recognizers for people who are blind/low vision. We provide a full, unfiltered dataset of 4,733 videos of 588 objects recorded by 97 people who are blind/low-vision on their mobile phones, and a benchmark dataset of 3,822 videos of 486 objects collected by 77 collectors. The code for loading the dataset, computing all benchmark metrics, and running the baseline models is available at https://github.com/microsoft/ORBIT-DatasetThis version comprises several zip files:- train, validation, test: benchmark dataset, organised by collector, with raw videos split into static individual frames in jpg format at 30FPS- other: data not in the benchmark set, organised by collector, with raw videos split into static individual frames in jpg format at 30FPS (please note that the train, validation, test, and other files make up the unfiltered dataset)- *_224: as for the benchmark, but static individual frames are scaled down to 224 pixels.- *_unfiltered_videos: full unfiltered dataset, organised by collector, in mp4 format.

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).

In Pittsburgh, Autonomous Vehicle (AV) companies have been testing autonomous vehicles since September 2016. However, the tech is new, and there have been some high-profile behavior that we believe warrants a larger conversation. So in early 2017, we set out to design a survey to see both how BikePGH donor-members, and Pittsburgh residents at large, feel about about sharing the road with AVs as a bicyclist and/or as a pedestrian. Our survey asked participants how they feel about being a fellow road user with AVs, either walking or biking. We also wanted to collect stories about people’s experiences interacting with this nascent technology. We are unaware of any public surveys about people’s feelings or understanding of this new technology. We hope that our results will help add to the body of data and help the public and politicians understand the complexity of possible futures that different economic models AV technology can bring to our cities and towncenters.

We conducted our 2017 survey in two parts. First, we launched the survey exclusively to donor-members, yielding 321 responses (out of 2,900) via email. Once we closed the survey, we launched it again, but allowed the general public to take it. Through promoting it on our website, social media channels, and a few news articles, we yielded 798 responses (mostly from people in the Pittsburgh region), for a combined total of 1,119 responses.

Regarding the 2019 survey: In total, 795 people responded. BikePGH solicited responses from their blog, website, and email list. There were also a few local news articles about the survey. While many questions were kept similar to the 2017 survey, BikePGH wanted to dig a bit deeper into regulations as well as demographics this time around.

The 2019 follow up survey also aims to see how the landscape has changed, and how specifically, Pittsburghers on bike and on foot feel about sharing the road with AVs so that we’re all better prepared to deal with this new reality and help make sure that it is introduced as safely as humanly possible.

Recent advancements in sign language recognition technology have significantly improved communication for individuals who are deaf or hard of hearing. Despite these advancements, many people who use sign language still face challenges in everyday interactions due to widespread unfamiliarity with sign language. However, new technologies have greatly enhanced our ability to recognize and interpret sign language, making communication more accessible and inclusive.

This dataset includes images of common phrases in both Indian Sign Language (ISL) and American Sign Language (ASL). The images were captured using a standard laptop webcam with a resolution of 680x480 pixels and a bit depth of 24 pixels. The dataset covers 44 different phrases, each represented by 40 images. All images are stored in PNG format. Note that this dataset includes static signs only and does not contain any dynamic sign language gestures.

Students' Academic Performance Dataset (xAPI-Edu-Data)

Data Set Characteristics: Multivariate

Number of Instances: 480

Area: E-learning, Education, Predictive models, Educational Data Mining

Attribute Characteristics: Integer/Categorical

Number of Attributes: 16

Date: 2016-11-8

Associated Tasks: Classification

Missing Values? No

File formats: xAPI-Edu-Data.csv

Source:

Elaf Abu Amrieh, Thair Hamtini, and Ibrahim Aljarah, The University of Jordan, Amman, Jordan, http://www.Ibrahimaljarah.com www.ju.edu.jo

Dataset Information:

This is an educational data set which is collected from learning management system (LMS) called Kalboard 360. Kalboard 360 is a multi-agent LMS, which has been designed to facilitate learning through the use of leading-edge technology. Such system provides users with a synchronous access to educational resources from any device with Internet connection.

The data is collected using a learner activity tracker tool, which called experience API (xAPI). The xAPI is a component of the training and learning architecture (TLA) that enables to monitor learning progress and learner’s actions like reading an article or watching a training video. The experience API helps the learning activity providers to determine the learner, activity and objects that describe a learning experience. The dataset consists of 480 student records and 16 features. The features are classified into three major categories: (1) Demographic features such as gender and nationality. (2) Academic background features such as educational stage, grade Level and section. (3) Behavioral features such as raised hand on class, opening resources, answering survey by parents, and school satisfaction.

The dataset consists of 305 males and 175 females. The students come from different origins such as 179 students are from Kuwait, 172 students are from Jordan, 28 students from Palestine, 22 students are from Iraq, 17 students from Lebanon, 12 students from Tunis, 11 students from Saudi Arabia, 9 students from Egypt, 7 students from Syria, 6 students from USA, Iran and Libya, 4 students from Morocco and one student from Venezuela.

The dataset is collected through two educational semesters: 245 student records are collected during the first semester and 235 student records are collected during the second semester.

The data set includes also the school attendance feature such as the students are classified into two categories based on their absence days: 191 students exceed 7 absence days and 289 students their absence days under 7.

This dataset includes also a new category of features; this feature is parent parturition in the educational process. Parent participation feature have two sub features: Parent Answering Survey and Parent School Satisfaction. There are 270 of the parents answered survey and 210 are not, 292 of the parents are satisfied from the school and 188 are not.

(See the related papers for more details).

Attributes

1 Gender - student's gender (nominal: 'Male' or 'Female’)

2 Nationality- student's nationality (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

3 Place of birth- student's Place of birth (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

4 Educational Stages- educational level student belongs (nominal: ‘lowerlevel’,’MiddleSchool’,’HighSchool’)

5 Grade Levels- grade student belongs (nominal: ‘G-01’, ‘G-02’, ‘G-03’, ‘G-04’, ‘G-05’, ‘G-06’, ‘G-07’, ‘G-08’, ‘G-09’, ‘G-10’, ‘G-11’, ‘G-12 ‘)

6 Section ID- classroom student belongs (nominal:’A’,’B’,’C’)

7 Topic- course topic (nominal:’ English’,’ Spanish’, ‘French’,’ Arabic’,’ IT’,’ Math’,’ Chemistry’, ‘Biology’, ‘Science’,’ History’,’ Quran’,’ Geology’)

8 Semester- school year semester (nominal:’ First’,’ Second’)

9 Parent responsible for student (nominal:’mom’,’father’)

10 Raised hand- how many times the student raises his/her hand on classroom (numeric:0-100)

11- Visited resources- how many times the student visits a course content(numeric:0-100)

12 Viewing announcements-how many times the student checks the new announcements(numeric:0-100)

13 Discussion groups- how many times the student participate on discussion groups (numeric:0-100)

14 Parent Answering Survey- parent answered the surveys which are provided from school or not (nominal:’Yes’,’No’)

15 Parent School Satisfaction- the Degree of parent satisfaction from school(nominal:’Yes’,’No’)

16 Student Absence Days-the number of absence days for each student (nominal: above-7, under-7)

The students are classified into three numerical intervals based on their total grade/mark:

Low-Level: i...

Context

The data was extracted to be used for my submission to the 2020 kaggle online survey. You can find the related submission here. I used it to analyze the searches related to Automated Machine learning.

Content

The data consists of google trends for AutoML /Automated machine learning. This data is Aggregated, **Anonymised **, Indexed and Normalized. The dataset consists of four files:

• Timeline Data(2015-2020) Timeline data shows interest over time. The Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means that there was not enough data for this term. I have used the search data from the year 2015 to 2020.

• GeoMap data for the year 2020 GeoMap data shows Interest by sub-region. It essentially highlights locations where the search term was most popular during the specified time frame. Values are calculated on a scale from 0 to 100, where 100 is the location with the most popularity as a fraction of total searches in that location, a value of 50 indicates a location that is half as popular. A value of 0 indicates a location where there was not enough data for this term.

• Related Entities data for the year 2020 Related entities mean users searching for your term also searched for these topics. Scoring is on a relative scale where a value of 100 is the most commonly searched topic and a value of 50 is a topic searched half as often as the most popular term, and so on

• Related Queries data Related Queries mean users searching for your term also searched for these queries. Scoring is on a relative scale where a value of 100 is the most commonly searched query, 50 is a query searched half as often as the most popular query, and so on.

The explanations have been taken from the Google trend official site.

Acknowledgements

• Banner Image: People vector created by pch.vector - www.freepik.com
• Google Trends

Useful links: - Google Trends - Google News Lab - @GoogleTrends