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.

Percentage of smartphone users by selected smartphone use habits in a typical day.

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.

This datas real-world trends in children's screen time usage. It includes data on educational, recreational, and total screen time for children aged 5 to 15 years, with breakdowns by gender (Male, Female, Other/Prefer not to say) and day type (Weekday, Weekend). The dataset follows expected behavioral patterns:

Screen time increases with age (~1.5 hours/day at age 5 to 6+ hours/day at age 15).

Recreational screen time dominates, making up 65–80% of total screen time.

Weekend screen time is 20–30% higher than weekdays, with a larger increase for teenagers.

Slight gender-based variations in recreational screen time.

The dataset contains natural variability, ensuring realism, and the sample size decreases slightly with age (e.g., 500 respondents at age 5, 300 at age 15).

This dataset is ideal for data analysis, visualization, and machine learning experiments related to children's digital habits. 🚀

In this dataset, we present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

1. HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.
2. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.
3. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.
4. target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

1. YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.
2. H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59.
   The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License
Creative Commons Attribution 4.0 International.

Related datasets

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

Decrease average daily use of potable water.

Related article: Bergroth, C., J��rv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. In this dataset: We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon ��� Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning. Please cite this dataset as: Bergroth, C., J��rv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4 Organization of data The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files: HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area. target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS. Column names YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute. H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as ���Hx���, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period) In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets. License Creative Commons Attribution 4.0 International. Related datasets J��rv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612 Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

This dataset contains the characteristics of rural residents' daily activities in Chengdu, China, extracted from the mobile phone locations produced by China Unicom between April 12 and 18, 2017. The characteristics of daily activities are evaluated by grids of 1000m*1000m. The whole city is divided into 14,856 grids. The characteristics include the number of distinct destination grids visited by the residents of a grid (diversity), the average number of activities/movements conducted by the residents of a grid (number), and the standard distances of work and nonwork activities conducted by the residents of a grid. For privacy, the information of grids where less than ten residents are identified is omitted. We also include the centroid coordinates, distance to the Chengdu city, average slope, and proportion of urban workers of each grid.

Liu, Lun; Gao, Xuesong (2020), “Mobile phone data-extracted daily activities of rural residents in Chengdu, China”, Mendeley Data, v1

http://dx.doi.org/10.17632/w82ygwjy9c.1

A ranking of Office of Hearings Operations (OHO) hearing offices by the average number of hearings dispositions per administrative law judge (ALJ) per day. The average shown will be a combined average for all ALJs working in that hearing office.

Daily average time in hours and proportion of day spent on various activities by age group and sex, 15 years and over, Canada and provinces.

Context

This dataset explores the relationship between digital behavior and mental well-being among 100,000 individuals. It records how much time people spend on screens, use of social media (including TikTok), and how these habits may influence their sleep, stress, and mood levels.

It includes six numerical features, all clean and ready for analysis, making it ideal for machine learning tasks like regression or classification. The data enables researchers and analysts to investigate how modern digital lifestyles may impact mental health indicators in measurable ways.

Dataset Applications

• Quantify how screen‑time, TikTok use, or multi‑platform engagement statistically relate to stress, sleep loss, and mood.
• Train regression or classification models that forecast stress level or mood score from real‑time digital‑usage metrics.
• Feed user‑specific data into recommender systems that suggest screen‑time caps or bedtime routines to improve mental health.
• Provide evidence for guidelines on youth screen‑time limits and platform moderation based on observed stress‑sleep trade‑offs.
• Serve as a teaching dataset for EDA, feature engineering, and model evaluation in data‑science or psychology curricula.
• Evaluate app interventions (e.g., screen‑time nudges) by comparing predicted versus actual post‑intervention stress or mood shifts.
• Cluster individuals into digital‑behavior personas (e.g., “heavy late‑night scrollers”) to tailor mental‑health resources.
• Generate synthetic time‑series scenarios (what‑if reductions in TikTok hours) to estimate downstream impacts on sleep and stress.
• Use engineered features (ratio of TikTok hours to total screen‑time, etc.) in broader wellbeing models that include diet or exercise data.
• Assess whether mental‑health prediction models remain accurate and unbiased across different screen‑time or platform‑use segments. # Column Descriptions
• screen_time_hours – Daily total screen usage in hours across all devices.
• social_media_platforms_used – Number of different social media platforms used per day.
• hours_on_TikTok – Time spent on TikTok daily, in hours.
• sleep_hours – Average number of sleep hours per night.
• stress_level – Stress intensity reported on a scale from 1 (low) to 10 (high).
• mood_score – Self-rated mood on a scale from 2 (poor) to 10 (excell # Inspiration This dataset was inspired by growing concerns about how screen time and social media affect mental health. It enables analysis of the links between digital habits, stress, sleep, and mood—encouraging data-driven solutions for healthier online behavior and emotional well-being. # Ethically Mined Data: This dataset has been ethically mined and synthetically generated without collecting any personally identifiable information. All values are artificial but statistically realistic, allowing safe use in academic, research, and public health projects while fully respecting user privacy and data ethics.

DATASET DESCRIPTION: This Dataset includes the average response time by Call Priority across days of the week and hours of the day. Response Times reflect the same information contained in the APD 911 Calls for Service 2019-2024 dataset. AUSTIN POLICE DEPARTMENT DATA DISCLAIMER 1. The data provided is for informational use only and may differ from official Austin Police Department data. The Austin Police Department’s databases are continuously updated, and changes can be made due to a variety of investigative factors including but not limited to offense reclassification and dates. Reports run at different times may produce different results. Care should be taken when comparing against other reports as different data collection methods and different systems of record may have been used. 4.The Austin Police Department does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided. City of Austin Open Data Terms of Use -https://data.austintexas.gov/stories/s/ranj-cccq

📊 Social Media vs Productivity — Realistic Behavioral Dataset (30,000 Users)

This dataset explores how daily digital habits — including social media usage, screen time, and notification exposure — relate to individual productivity, stress, and well-being.

🔍 What’s Inside?

The dataset contains 30,000 real-world-style records simulating behavioral patterns of people with various jobs, social habits, and lifestyle choices. The goal is to understand how different digital behaviors correlate with perceived and actual productivity.

🧠 Why This Dataset is Valuable

• ✅ Designed for real-world ML workflows
  Includes missing values, noise, and outliers — ideal for practicing data cleaning and preprocessing.

• 🔗 High correlation between target features
  The perceived_productivity_score and actual_productivity_score are strongly correlated, making this dataset suitable for experiments in feature selection and multicollinearity.

• 🛠️ Feature Engineering playground
  Use this dataset to practice feature scaling, encoding, binning, interaction terms, and more.

• 🧪 Perfect for EDA, regression & classification
  You can model productivity, stress, or satisfaction based on behavior patterns and digital exposure.

🧾 Columns & Feature Info

📌 Notes

• Contains NaN values in critical columns (productivity, sleep, stress) for data imputation tasks
• Includes outliers in media usage, coffee intake, and notification count
• Target columns are strongly correlated for multicollinearity testing
• Multi-purpose: regression, classification, clustering, visualization

💡 Use Cases

• Exploratory Data Analysis (EDA)
• Feature engineering pipelines
• Machine learning model benchmarking
• Statistical hypothesis testing
• Burnout and mental health prediction projects

📥 Bonus

👉 Sample notebook coming soon with data cleaning, visualization, and productivity prediction!

Exploring Daily Weather Report Usage

Understanding Regional and Socioeconomic Variations

By FiveThirtyEight [source]

About this dataset

This dataset contains survey responses from people about their daily weather report usage and weather check. It consists of columns such as Do You Typically Check a Daily Weather Report?, How do you Typically Check the Weather?, If You Had a Smartwatch (like the Soon to be Released Apple Watch), How Likely or Unlikely Would You Be to Check the Weather on That Device? Age, What is Your Gender?, and US Region. With this data, we can explore usage patterns in checking for daily weather reports across different regions, genders, ages and preferences for smartwatch devices in doing so. This dataset offers an interesting insight into our current attitudes towards checking for the weather with technology - and by understanding these patterns better, we can create more engaging experiences tailored to individuals’ needs

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To get started, it is helpful to first examine the columns in the dataset. The columns are Do you typically check a daily weather report?, How do you typically check the weather?, If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device?, Age, What is your gender?, US Region. Each row contains data for one survey participant, with their answers for each column included in each row.

The data can be used for exploring correlations between factors such as age, gender, region/location, daily weather checking habits/preferences etc.. Some of these variables are numerical (such as age) and others are categorical (such as gender). You can use this data when creating visualizations showing relationships between these factors. You may also want to create summary tables showing average values for different categories of each factor, allowing for easy comparison across groups or over time periods (depending on how much historical data is available).

Research Ideas

• Analyzing trends in the usage of daily weather reports by age, gender and region.
• Exploring consumer preferences for checking the weather via smartwatches and mobile devices in comparison to other methods (e.g., TV or radio).
• Examining correlations between people's likelihood to check their daily weather report and their demographic characteristics (location, age, gender)

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: weather-check.csv | Column name | Description | |:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------| | Do you typically check a daily weather report? | This column indicates whether or not the respondent typically checks a daily weather report. (Categorical) | | How do you typically check the weather? | This column indicates how the respondent typically checks the weather. (Categorical) | | If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device? | This column indicates how likely or unlikely the respondent would be to check the weather on a smartwatch. (Categorical) | | Age | This column indicates the age of the respondent. (Numerical) ...

General social survey (GSS), average time spent with various social contacts for the population aged 15 years and over, by population cohorts.

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million 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 Mexico and Canada.

This data set is supplement to this Scientific Reports article.

The data set provides estimates of country-level daily mobility metrics (uncertainty included) for 17 countries from March 11, 2020 to present. Estimates are based on more than 3.8 million smartphone trajectories.

Metrics:

Estimated daily average travelled distance by people.

Estimated percentage of people who did not move during the 24 hours of the day.

Countries: Argentina (ARG), Chile (CHL), Colombia (COL), Costa Rica (CRI), Ecuador (ECU), Greece (GRC), Guatemala (GTM), Italy (ITA), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Peru (PER), Philippines (PHL), Slovenia (SVN), Turkey (TUR), United States (USA) and Venezuela (VEN).

Covered period: from March 11, 2020 to present.

Temporal resolution: daily.

Temporal smoothing:

No smoothing.

7-day moving average.

14-day moving average.

21-day moving average.

28-day moving average.

Uncertainty: 95% bootstrap confidence interval.

Data ownership

Anonymized data on smartphone trajectories are collected, owned and managed by Futura Innovation SRL. Smartphone trajectories are stored and analyzed on servers owned by Futura Innovation SRL and not shared with third parties, including the author of this repository and his organization (University of Bergamo).

Contribution

Ilaria Cremonesi of Futura Innovation SRL is the data owner and data manager.

Francesco Finazzi of University of Bergamo developed the statistical methodology for the data analysis and the algorithms implemented on Futura Innovation SRL servers.

Repository update

CSV files of this repository are regularly produced by Futura Innovation SRL and published by the repository's author after validation.

In 2023, Android users in Singapore spent an average of 4.51 hours per day using their mobile devices. This represents an increase from the 4.17 hours that users in the country spent on their devices in 2020.

This dataset shows the number of hospital admissions for influenza-like illness, pneumonia, or include ICD-10-CM code (U07.1) for 2019 novel coronavirus. Influenza-like illness is defined as a mention of either: fever and cough, fever and sore throat, fever and shortness of breath or difficulty breathing, or influenza. Patients whose ICD-10-CM code was subsequently assigned with only an ICD-10-CM code for influenza are excluded. Pneumonia is defined as mention or diagnosis of pneumonia. Baseline data represents the average number of people with COVID-19-like illness who are admitted to the hospital during this time of year based on historical counts. The average is based on the daily avg from the rolling same week (same day +/- 3 days) from the prior 3 years. Percent change data represents the change in count of people admitted compared to the previous day. Data sources include all hospital admissions from emergency department visits in NYC. Data are collected electronically and transmitted to the NYC Health Department hourly. This dataset is updated daily. All identifying health information is excluded from the dataset.

Overview:

This is the raw data of a current consumption measurement campaign of an end-device implementing the novel LoRaWAN LR-FHSS mechanism. The measurements have been made implementing a complete network, which includes a gateway, end-device and network server all implementing the LoRaWAN LR-FHSS technology. We used the following equipment:

Gateway: Kerlink iBTS Compact

End-Device: LR1121DVK1TBKS

Network Server: ChirpStack

Power Analyzer: Keysight 14585A

The provided files are for uplink LR-FHSS transmission measurements with and without confirmation with different LR-FHSS DR configurations. The current consumption exclusively accounts for the radio interface.

The configuration of the end-device is the following:

FRM Payload Size: 4 bytes

Transmission Power: +14 dBm

This dataset is part of a published journal article: R. Sanchez-Vital, L. Casals, B. Heer-Salva, R. Vidal, C. Gomez, E. Garcia-Villegas, "Energy Performance of LR-FHSS: Analysis and Evaluation", Sensors 24, no. 17: 5770, Sep. 2024. https://doi.org/10.3390/s24175770

The manuscript provides current consumption measurements, an analytical model of the average current consumption, battery lifetime, and energy efficiency of data transmission, and the evaluation of several parameters.

Journal Article Abstract:

Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network’s capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery.

Data structure:

Filenames:

ACK and noACK state the use (or not) of confirmation. DR8 to DR11 state the use of each of the LR-FHSS DR configurations.

CSV file structure:

The first three rows refer to metadata (Power Analyzer and End-Device models, utilization of ACK, DR configuration, Sampling Period and Measurement Date).

Then, the labels are in the fourth row (Time, Current).

The other rows refer to the actual measurements. Time instants are measured in seconds and current in Amperes.