Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

Context: This dataset offers insights into the usage patterns of social media apps for 1,000 users across seven popular platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, LinkedIn, and Pinterest. It tracks various metrics such as daily time spent on the app, number of posts made, likes received, and new followers gained.

Dataset Features:

User_ID: Unique identifier for each user. App: The social media platform being used. Daily_Minutes_Spent: Total time a user spends on the app each day, ranging from 5 to 500 minutes. Posts_Per_Day: Number of posts a user creates per day, ranging from 0 to 20. Likes_Per_Day: Total number of likes a user receives on their posts each day, ranging from 0 to 200. Follows_Per_Day: The number of new followers a user gains daily, ranging from 0 to 50. Context & Use Cases: This dataset could be particularly useful for social media analysts, digital marketers, or researchers interested in understanding user engagement trends across different platforms. It provides insights into how much time users spend, how actively they post, and the level of engagement they receive (in terms of likes and followers).

Conclusion & Outcome: Analyzing this dataset could yield several outcomes:

Engagement Patterns: Identifying which platforms have higher engagement in terms of time spent or likes received. Active Users: Determining which users are the most active across various platforms based on the number of posts and followers gained. User Retention: Studying the correlation between time spent and follower growth, providing insight into user retention strategies for different platforms. Overall, the dataset allows for exploration of social media usage trends and helps drive decision-making for marketing strategies, content creation, and platform engagement.

Data have been collected in Finland in 2017–2019 from basic education teachers.The dataset is fully anonymised to make it suitable for public opening to support the reported results submitted for publication.

Dataset contains the following variables:

Gender (0=female, 1 = male)

Municipality (unique numeric identifier)

School (unique numeric identifier)

Administrative_district (unique numeric identifier)

Usage_of_computers_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_tablets_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_smartphones_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_digital_learning_environments_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_online_learning_materials_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_games_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_internet_for_information_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_videa_sharing_services_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_blogs_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_networking_services_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_digital_assessment_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_mobileapps_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_email_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Usage_of_office_suite_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)

Total_scores_in_ICTskilltest (range 0-30)

Age (years in numbers)

Digital_self_efficacy (perceived level of competence on a scale of 0-100% in relation to one's own work)

In_service_training (perceived adequacy level on a scale of 0 to 100% in relation to one's own work)

STEM_teacher (in the case of a teacher of STEM subjects = 1, otherwise = 0)

Humanities_social_science_teacher (in the case of a teacher of humanities or social science subjects = 1, otherwise = 0)

Arts_skills_teacher (in the case of a teacher of arts and skills subjects = 1, otherwise = 0)

Use_of_devices_for_teaching (the sum variable of the use of digital devices, i.e., the maximum use of any type of device)

Versatility_of_usage (the sum variable for regular (= at least weekly) use of different applications)

Teacher_type (0 = classroom teacher, 1 = subject teacher, missing value = other teaching staff)

Classroom_teacher in the case of a classroom teacher = 1, otherwise = 0

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

A structured, self-report questionnaire designed by our research team was used to develop a customized dataset. The questionnaire was in the form of an online questionnaire comprising 4 main sections: • Demographics: age, gender, and education. • Technology and social media use: Daily hours of screen time, time spent on social media, main platforms used, and preference for technology usage (work or leisure). • Psychological and Cognitive Indicators: Self-rated concentration during the study (1–5), number of interruptions, change in mood following technology use, and perceived difficulty concentrating while using social media. • Self-Awareness and Coping: Perception of being overused, concerns about the use of technology, use of apps to reduce mental fatigue, and use of strategies to reduce duration. The responses were numerical. Physicians left the respondents with missing or invalid responses, which were removed during the pre-processing stage. A new binary response was defined—Brain Rot (Yes/No). A participant was deemed to have brain rot if they demonstrated 3 or more of the 6 brain rot patterns: • Social media use ≥3 hours per day • Screen time ≥ 4 hours per day • Focus level ≤ 2 out of 5 • Reports frequent distraction • Notices mood shift as technology is used • Thinks social media is bad for mental health This was the target variable and the outcome label for classification. However, the dataset was cleaned and pre-processed as follows pre-analysis: • Elimination of incomplete or contradictory records • Conversion of categorical into the numerical form (namely, yes = 1, no = 0). • Normalization of numerical features, if necessary • Treatment of outliers and testing for normality The ultimate dataset was balanced, well-formatted for statistical and machine learning analyses, and presented with well-defined input features and a binary classification output.

📈 Daily Historical Stock Price Data for GigaCloud Technology Inc. (2022–2025)

A clean, ready-to-use dataset containing daily stock prices for GigaCloud Technology Inc. from 2022-08-18 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: GigaCloud Technology Inc. Ticker Symbol: GCT Date Range: 2022-08-18 to 2025-05-28 Frequency: Daily Total Records: 696 rows (one… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-gigacloud-technology-inc-20222025.

It is a qualitative survey on the use of technology in university students with studies in the online modality. The survey seeks to understand the use that university students make of technology in their daily lives and how it influences their learning process.

📈 Daily Historical Stock Price Data for Fun Yours Technology Co.,Ltd. (2014–2025)

A clean, ready-to-use dataset containing daily stock prices for Fun Yours Technology Co.,Ltd. from 2014-11-13 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Fun Yours Technology Co.,Ltd. Ticker Symbol: 6482.TWO Date Range: 2014-11-13 to 2025-05-28 Frequency: Daily Total… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-fun-yours-technology-coltd-20142025.

In this work, we provide predicted yearly performance improvement rates for nearly all definable technologies for the first time. We do this by creating a correspondence of all patents within the US patent system to a set of 1757 technology domains. A technology domain is a body of patented inventions achieving the same technological function using the same knowledge and scientific principles. These domains contain 97.2% of all patents within the entire US patent system. From the identified patent sets, we calculated the average centrality of the patents in each domain to predict their improvement rates, following a patent network-based methodology tested in prior work. They vary from a low of 2% per year for the Mechanical Skin treatment- Hair Removal and wrinkles domain to a high of 216% per year for the Dynamic information exchange and support systems integrating multiple channels domain, but more that 80% of technologies improve at less than 25% per year. Fast improving domains are concentrated in a few technological areas. The domains that show improvement rates greater than the predicted rate for integrated chips, from Moore’s law, are predominantly based upon software and algorithms. In addition, the rates of improvement were not a strong function of the patent set size.

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.

About the Dataset

The Historical Adoption of TeCHnologies (HATCH) dataset provides yearly data of technology adoption levels at both the global and country levels. The dataset includes a heterogeneous set of technologies that differ in their size, use, and spatial diffusion. The following files are available.

Citation

The paper can be cited as: Nemet, G., Greene, J., Müller-Hansen, F. et al. Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures. Commun Earth Environ 4, 397 (2023). https://doi.org/10.1038/s43247-023-01056-1

Paper describing the data is available here: https://www.nature.com/articles/s43247-023-01056-1

File Descriptions

HATCH1.0.csv

Description: Dataset used in the analysis for "Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures," which includes a limited number of country-level datapoints.

HATCH_v1.5_Clean.csv

Description: Expanded dataset that includes country-level adoption of many technologies used in v1.0, with the addition of more global technology time series.

Tech_Growth_V1.5_variabledescriptions_Clean.yaml

Description: Description of each technology, including description, metric, and technology category.

HATCH_v1.5_DataSources.csv

Description: Lists the data sources and corresponding citations for each technology in the HATCH dataset.

Code for calculating growth rates available at: DOI: 10.5281/zenodo.8327347

Table of growth rate calculations for each technology available at: https://zenodo.org/records/10056128

Link to Scenario Explorer hosted by IIASA: https://cdr.apps.ece.iiasa.ac.at/story/hatch/

This dataset captures insights from a survey on social media usage across diverse age groups and genders. It includes data on the most used platforms, daily screen time, reasons for usage, preferred content types, and how social media influences buying decisions. Additionally, it reflects users' concerns about privacy and their willingness to reduce usage. The dataset is useful for analyzing digital behavior, content preferences, and the social impact of online platforms. It can support research in marketing, psychology, and digital well-being, offering a snapshot of how people interact with and perceive social media in their daily lives.

Data Set

The data set contains the qualitative analysis of fourteen design concepts aiming to support positive activities as Active Design in consumer technology.

Codebook

The codebook specifies two classification schemes for a) design mechanisms and b) drivers of behavior that were used to analyze the data set.

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 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.

📈 Daily Historical Stock Price Data for accesso Technology Group plc (2002–2025)

A clean, ready-to-use dataset containing daily stock prices for accesso Technology Group plc from 2002-04-24 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: accesso Technology Group plc Ticker Symbol: ACSO.L Date Range: 2002-04-24 to 2025-05-28 Frequency: Daily Total Records:… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-accesso-technology-group-plc-20022025.

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

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).

This dataset contains Axivity AX3 wrist-worn activity tracker data that were collected from 151 participants in 2014-2016 around the Oxfordshire area. Participants were asked to wear the device in daily living for a period of roughly 24 hours, amounting to a total of almost 4,000 hours. Vicon Autograph wearable cameras and Whitehall II sleep diaries were used to obtain the ground truth activities performed during the period (e.g. sitting watching TV, walking the dog, washing dishes, sleeping), resulting in more than 2,500 hours of labelled data. Accompanying code to analyse this data is available at https://github.com/activityMonitoring/capture24. The following papers describe the data collection protocol in full: i.) Gershuny J, Harms T, Doherty A, Thomas E, Milton K, Kelly P, Foster C (2020) Testing self-report time-use diaries against objective instruments in real time. Sociological Methodology doi: 10.1177/0081175019884591; ii.) Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. (2018) Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Scientific Reports. 8(1):7961. Regarding Data Protection, the Clinical Data Set will not include any direct subject identifiers. However, it is possible that the Data Set may contain certain information that could be used in combination with other information to identify a specific individual, such as a combination of activities specific to that individual ("Personal Data"). Accordingly, in the conduct of the Analysis, users will comply with all applicable laws and regulations relating to information privacy. Further, the user agrees to preserve the confidentiality of, and not attempt to identify, individuals in the Data Set.

Dataset for my (German) Python Data Science Tutorial on YouTube.

Playlist: https://www.youtube.com/playlist?list=PLW4WJMmOF9juA1Ebs1vNwTBuF7ck6YCT7

My version of: 'Bike Share Daily Data' (https://www.kaggle.com/contactprad/bike-share-daily-data)

Data used in this competition: https://www.kaggle.com/c/bike-sharing-demand

Use of this dataset in publications must be cited to the following publication:

[1] Fanaee-T, Hadi, and Gama, Joao, "Event labeling combining ensemble detectors and background knowledge", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007/s13748-013-0040-3.

@article{ year={2013}, issn={2192-6352}, journal={Progress in Artificial Intelligence}, doi={10.1007/s13748-013-0040-3}, title={Event labeling combining ensemble detectors and background knowledge}, url={http://dx.doi.org/10.1007/s13748-013-0040-3}, publisher={Springer Berlin Heidelberg}, keywords={Event labeling; Event detection; Ensemble learning; Background knowledge}, author={Fanaee-T, Hadi and Gama, Joao}, pages={1-15} }