The global number of households with a computer in was forecast to continuously increase between 2024 and 2029 by in total 88.6 million households (+8.6 percent). After the fifteenth consecutive increasing year, the computer households is estimated to reach 1.1 billion households and therefore a new peak in 2029. Notably, the number of households with a computer of was continuously increasing over the past years.Computer households are defined as households possessing at least one computer.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 households with a computer in countries like Caribbean and Africa.

The global household computer penetration in was forecast to continuously increase between 2024 and 2029 by in total 2.4 percentage points. After the eleventh consecutive increasing year, the computer penetration rate is estimated to reach 52.78 percent and therefore a new peak in 2029. Depicted is the estimated share of households owning at least one computer.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 household computer penetration in countries like Australia & Oceania and Caribbean.

Intending to cover the existing gap regarding behavioral datasets modelling interactions of users with individual a multiple devices in Smart Office to later authenticate them continuously, we publish the following collection of datasets, which has been generated after having five users interacting for 60 days with their personal computer and mobile devices. Below you can find a brief description of each dataset. Dataset 1 (2.3 GB). This dataset contains 92975 vectors of features (8096 per vector) that model the interactions of the five users with their personal computers. Each vector contains aggregated data about keyboard and mouse activity, as well as application usage statistics. More info about features meaning can be found in the readme file. Originally, the number of features of this dataset was 24 065 but after filtering the constant features, this number was reduced to 8096. There was a high number of constant features to 0 since each possible digraph (two keys combination) was considered when collecting the data. However, there are many unusual digraphs that the users never introduced in their computers, so these features were deleted in the uploaded dataset. Dataset 2 (8.9 MB). This dataset contains 61918 vectors of features (15 per vector)that model the interactions of the five users with their mobile devices. Each vector contains aggregated data about application usage statistics. More info about features meaning can be found in the readme file.Dataset 3 (28.9 MB). This dataset contains 133590vectors of features (42 per vector)that model the interactions of the five users with their mobile devices. Each vector contains aggregated data about the gyroscope and Accelerometer sensors. More info about features meaning can be found in the readme file.Dataset 4 (162.4 MB). This dataset contains 145465vectors of features (241 per vector)that model the interactions of the five users with both personal computers and mobile devices. Each vector contains the aggregation of the most relevant features of both devices. More info about features meaning can be found in the readme file.Dataset 5 (878.7 KB). This dataset is composed of 7 datasets. Each one of them contains an aggregation of feature vectors generated from the active/inactive intervals of personal computers and mobile devices by considering different time windows ranging from 1h to 24h.1h: 4074 vectors2h: 2149 vectors3h: 1470 vectors4h: 1133 vectors6h: 770 vectors12h: 440 vectors24h: 229 vectors

In the quest for advancing the field of continuous user authentication, we have meticulously crafted two comprehensive datasets: COUNT-OS-I and COUNT-OS-II, each harboring unique characteristics while sharing a common ground in their utility and design principles. These datasets encompass performance counters extracted from the Windows operating system, offering an intricate tapestry of data vital for evaluating and refining authentication models in real-world scenarios.Both datasets have been generated in real-world settings within public organizations in Brazil, ensuring their applicability and relevance to practical scenarios. Volunteers from diverse professional backgrounds participated in the data collection, contributing to the richness and variability of the data. Furthermore, both datasets were collected at a sample rate of every 5 seconds, providing a dense and detailed view of user interactions and system performance. The commitment to preserving user confidentiality is unwavering across both datasets, with pseudonymization applied meticulously to safeguard individual identities while maintaining data integrity and statistical robustness.The COUNT-OS-I dataset was specifically generated in a real-world scenario to evaluate our work on continuous user authentication. This dataset consist of performance counters extracted from the Windows operating system of 26 computers, representing 26 individual users. The data were collected on the computers of the Information Technology Department of a public organization in Brazil.The participants in this study were volunteers, with aged between 20 and 45 years old, consisting of both males and females. The majority of the participants were systems analysts and software developers who performed their routine work activities. There were no specific restrictions imposed on the tasks that the participants were required to perform during the data collection process.The participants used a variety of software applications as part of their regular work activities. This included web browsers such as Firefox, Chrome, and Edge, developer tools like Eclipse and SQL Developer, office programs such as Microsoft Office Word, Excel, and PowerPoint, as well as chat applications like WhatsApp. It's important to note that the list of applications mentioned is not exhaustive, and participants were not limited to using only these applications.For the COUNT-OS-I dataset, the data collected is based on computers with different characteristics and configurations in terms of hardware, operating system versions, and installed software. This diversity ensures a representative sample of real-world scenarios and allows for a comprehensive evaluation of the authentication model.During the data collection process, each sample was recorded at a frequency of every 5 seconds, capturing system data over a period of approximately 26 hours, on average, for each user. This duration provides sufficient data to analyze user behavior and system performance over an extended period. Each sample in the COUNT-OS-I dataset corresponds to a feature vector comprising 159 attributesThe COUNT-OS-II dataset was utilized to evaluate our work in a real-world setting. This dataset comprises performance counters extracted from the Windows operating system installed on 37 computers. These computers possess identical hardware configurations (CPU, memory, network, disk), operating systems, and software installations. The data collection was conducted within various departments of a public organization in Brazil.The participants in this study (37 users) were voluntary administration assistants who performed various administrative tasks as part of their routine work activities. No restrictions were imposed on the specific tasks they were assigned. The participants commonly utilized programs such as the Chrome browser and office applications like Office Word, Excel, and PowerPoint, in addition to the WhatsApp chat application.The data were collected over six days (approximately 48 hours), with sample collected at a 5-second interval. Each sample corresponds to a feature vector composed of 218 attributes. In this dataset, we also apply pseudonymization to hide users' sensitive information.

Mongolia Number of Personal Computers per 1000 Inhabitants data was reported at 208.000 Unit in 2019. This records an increase from the previous number of 202.000 Unit for 2018. Mongolia Number of Personal Computers per 1000 Inhabitants data is updated yearly, averaging 136.000 Unit from Dec 2001 (Median) to 2019, with 19 observations. The data reached an all-time high of 208.000 Unit in 2019 and a record low of 15.000 Unit in 2001. Mongolia Number of Personal Computers per 1000 Inhabitants data remains active status in CEIC and is reported by National Statistics Office of Mongolia. The data is categorized under Global Database’s Mongolia – Table MN.TB006: Number of Internet Users and Computer.

Netherlands F3 Interpretation Dataset

Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. Such a fact is arguably due to three main factors: powerful computers, new techniques to train deeper networks and more massive datasets. Although the first two are readily available in modern computers and ML libraries, the last one remains a challenge for many domains. It is a fact that big data is a reality in almost all fields today, and geosciences are not an exception. However, to achieve the success of general-purpose applications such as ImageNet - for which there are +14 million labeled images for 1000 target classes - we not only need more data, we need more high-quality labeled data. Such demand is even more difficult when it comes to the Oil & Gas industry, in which confidentiality and commercial interests often hinder the sharing of datasets to others. In this letter, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation. The Netherlands F3 dataset was acquired in the North Sea, offshore Netherlands. The data is publicly available and comprises pos-stack data, eight horizons and well logs of 4 wells. However, for the dataset to be of practical use for our tasks, we had to reinterpret the seismic, generating nine horizons separating different seismic facies intervals. The interpreted horizons were used to create 651 labeled masks for inlines and 951 for crosslines. We present the results of two experiments to demonstrate the utility of our dataset.

Dataset contents

• Crosslines:
  • Classes: 10
  • Number of slices: 651
  • Records per class: 9,440
  • Total of records: 94,400
• Inlines:
  • Classes: 10
  • Number of slices: 951
  • Records per class: 9,720
    • Total of records: 94,720
• Configuration:
  • Crop: [0, 0, 0, 0]
  • Gray levels: 256
  • Noise: 0.3
  • Percentile: 5.0
  • Strides: [20, 48]
  • Tile shape: [25, 64, 1]

As of 2024, the estimated number of internet users worldwide was 5.5 billion, up from 5.3 billion in the previous year. This share represents 68 percent of the global population. Internet access around the world Easier access to computers, the modernization of countries worldwide, and increased utilization of smartphones have allowed people to use the internet more frequently and conveniently. However, internet penetration often pertains to the current state of development regarding communications networks. As of January 2023, there were approximately 1.05 billion total internet users in China and 692 million total internet users in the United States. Online activities Social networking is one of the most popular online activities worldwide, and Facebook is the most popular online network based on active usage. As of the fourth quarter of 2023, there were over 3.07 billion monthly active Facebook users, accounting for well more than half of the internet users worldwide. Connecting with family and friends, expressing opinions, entertainment, and online shopping are amongst the most popular reasons for internet usage.

As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

By 2025, forecasts suggest that there will be more than 75 billion Internet of Things (IoT) connected devices in use. This would be a nearly threefold increase from the IoT installed base in 2019.

What is the Internet of Things?

The IoT refers to a network of devices that are connected to the internet and can “communicate” with each other. Such devices include daily tech gadgets such as the smartphones and the wearables, smart home devices such as smart meters, as well as industrial devices like smart machines. These smart connected devices are able to gather, share, and analyze information and create actions accordingly. By 2023, global spending on IoT will reach 1.1 trillion U.S. dollars.

How does Internet of Things work?

IoT devices make use of sensors and processors to collect and analyze data acquired from their environments. The data collected from the sensors will be shared by being sent to a gateway or to other IoT devices. It will then be either sent to and analyzed in the cloud or analyzed locally. By 2025, the data volume created by IoT connections is projected to reach a massive total of 79.4 zettabytes.

Privacy and security concerns 

Given the amount of data generated by IoT devices, it is no wonder that data privacy and security are among the major concerns with regard to IoT adoption. Once devices are connected to the Internet, they become vulnerable to possible security breaches in the form of hacking, phishing, etc. Frequent data leaks from social media raise earnest concerns about information security standards in today’s world; were the IoT to become the next new reality, serious efforts to create strict security stands need to be prioritized.

In 2024, spending on devices amounted to around734 billion U.S. dollars globally, an increase of around 5.1 percent from the previous year. Global IT spending is expected to reach approximately 5.6 trillion U.S. dollars in 2025, increasing by about four percent compared to 2024. Around 810 billion U.S. dollars are forecast to be spent on devices.

As of the third quarter of 2024, almost 57 percent of the total daily time spent online by internet users was via mobile devices, including smartphones and feature phones. The remaining 43.2 percent of the time, they used computers. Five years before that, the picture was quite different, as smartphones comprised around 47 percent of the daily internet usage time.

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.