Related article: Bergroth, C., Järv, 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., Järv, 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

Järv, 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

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

Poland Mobile Phone: Avg Time of Call per One Subscriber: Local data was reported at 1,728.000 min in 2016. This records an increase from the previous number of 1,581.000 min for 2015. Poland Mobile Phone: Avg Time of Call per One Subscriber: Local data is updated yearly, averaging 1,148.500 min from Dec 2003 (Median) to 2016, with 14 observations. The data reached an all-time high of 1,728.000 min in 2016 and a record low of 440.000 min in 2004. Poland Mobile Phone: Avg Time of Call per One Subscriber: Local data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.TB001: Mobile Phone Statistics.

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.

A ranking of the Office of Hearings Operations (OHO) hearing offices by the average number of days until final disposition of the hearing request. The average shown will be a combined average for all cases completed in that hearing office.

Accounts Receivable - Average Call Duration To SSSA

Average time spent being physically active, household population by sex and age group.

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

CESNET-TimeSeries24: The dataset for network traffic forecasting and anomaly detection

The dataset called CESNET-TimeSeries24 was collected by long-term monitoring of selected statistical metrics for 40 weeks for each IP address on the ISP network CESNET3 (Czech Education and Science Network). The dataset encompasses network traffic from more than 275,000 active IP addresses, assigned to a wide variety of devices, including office computers, NATs, servers, WiFi routers, honeypots, and video-game consoles found in dormitories. Moreover, the dataset is also rich in network anomaly types since it contains all types of anomalies, ensuring a comprehensive evaluation of anomaly detection methods.Last but not least, the CESNET-TimeSeries24 dataset provides traffic time series on institutional and IP subnet levels to cover all possible anomaly detection or forecasting scopes. Overall, the time series dataset was created from the 66 billion IP flows that contain 4 trillion packets that carry approximately 3.7 petabytes of data. The CESNET-TimeSeries24 dataset is a complex real-world dataset that will finally bring insights into the evaluation of forecasting models in real-world environments.

Please cite the usage of our dataset as:

Koumar, J., Hynek, K., Čejka, T. et al. CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting. Sci Data 12, 338 (2025). https://doi.org/10.1038/s41597-025-04603-x@Article{cesnettimeseries24, author={Koumar, Josef and Hynek, Karel and {\v{C}}ejka, Tom{\'a}{\v{s}} and {\v{S}}i{\v{s}}ka, Pavel}, title={CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting}, journal={Scientific Data}, year={2025}, month={Feb}, day={26}, volume={12}, number={1}, pages={338}, issn={2052-4463}, doi={10.1038/s41597-025-04603-x}, url={https://doi.org/10.1038/s41597-025-04603-x}}

Time series

We create evenly spaced time series for each IP address by aggregating IP flow records into time series datapoints. The created datapoints represent the behavior of IP addresses within a defined time window of 10 minutes. The vector of time-series metrics v_{ip, i} describes the IP address ip in the i-th time window. Thus, IP flows for vector v_{ip, i} are captured in time windows starting at t_i and ending at t_{i+1}. The time series are built from these datapoints.

Datapoints created by the aggregation of IP flows contain the following time-series metrics:

Simple volumetric metrics: the number of IP flows, the number of packets, and the transmitted data size (i.e. number of bytes)

Unique volumetric metrics: the number of unique destination IP addresses, the number of unique destination Autonomous System Numbers (ASNs), and the number of unique destination transport layer ports. The aggregation of \textit{Unique volumetric metrics} is memory intensive since all unique values must be stored in an array. We used a server with 41 GB of RAM, which was enough for 10-minute aggregation on the ISP network.

Ratios metrics: the ratio of UDP/TCP packets, the ratio of UDP/TCP transmitted data size, the direction ratio of packets, and the direction ratio of transmitted data size

Average metrics: the average flow duration, and the average Time To Live (TTL)

Multiple time aggregation: The original datapoints in the dataset are aggregated by 10 minutes of network traffic. The size of the aggregation interval influences anomaly detection procedures, mainly the training speed of the detection model. However, the 10-minute intervals can be too short for longitudinal anomaly detection methods. Therefore, we added two more aggregation intervals to the datasets--1 hour and 1 day.

Time series of institutions: We identify 283 institutions inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution's data.

Time series of institutional subnets: We identify 548 institution subnets inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution subnet's data.

Data Records

The file hierarchy is described below:

cesnet-timeseries24/

 |- institution_subnets/

 |   |- agg_10_minutes/.csv

 |   |- agg_1_hour/.csv

 |   |- agg_1_day/.csv

 |   |- identifiers.csv

 |- institutions/

 |   |- agg_10_minutes/.csv

 |   |- agg_1_hour/.csv

 |   |- agg_1_day/.csv

 |   |- identifiers.csv

 |- ip_addresses_full/

 |   |- agg_10_minutes//.csv

 |   |- agg_1_hour//.csv

 |   |- agg_1_day//.csv

 |   |- identifiers.csv

 |- ip_addresses_sample/

 |   |- agg_10_minutes/.csv

 |   |- agg_1_hour/.csv

 |   |- agg_1_day/.csv

 |   |- identifiers.csv

 |- times/

 |   |- times_10_minutes.csv

 |   |- times_1_hour.csv

 |   |- times_1_day.csv

 |- ids_relationship.csv   |- weekends_and_holidays.csv

The following list describes time series data fields in CSV files:

id_time: Unique identifier for each aggregation interval within the time series, used to segment the dataset into specific time periods for analysis.

n_flows: Total number of flows observed in the aggregation interval, indicating the volume of distinct sessions or connections for the IP address.

n_packets: Total number of packets transmitted during the aggregation interval, reflecting the packet-level traffic volume for the IP address.

n_bytes: Total number of bytes transmitted during the aggregation interval, representing the data volume for the IP address.

n_dest_ip: Number of unique destination IP addresses contacted by the IP address during the aggregation interval, showing the diversity of endpoints reached.

n_dest_asn: Number of unique destination Autonomous System Numbers (ASNs) contacted by the IP address during the aggregation interval, indicating the diversity of networks reached.

n_dest_port: Number of unique destination transport layer ports contacted by the IP address during the aggregation interval, representing the variety of services accessed.

tcp_udp_ratio_packets: Ratio of packets sent using TCP versus UDP by the IP address during the aggregation interval, providing insight into the transport protocol usage pattern. This metric belongs to the interval <0, 1> where 1 is when all packets are sent over TCP, and 0 is when all packets are sent over UDP.

tcp_udp_ratio_bytes: Ratio of bytes sent using TCP versus UDP by the IP address during the aggregation interval, highlighting the data volume distribution between protocols. This metric belongs to the interval <0, 1> with same rule as tcp_udp_ratio_packets.

dir_ratio_packets: Ratio of packet directions (inbound versus outbound) for the IP address during the aggregation interval, indicating the balance of traffic flow directions. This metric belongs to the interval <0, 1>, where 1 is when all packets are sent in the outgoing direction from the monitored IP address, and 0 is when all packets are sent in the incoming direction to the monitored IP address.

dir_ratio_bytes: Ratio of byte directions (inbound versus outbound) for the IP address during the aggregation interval, showing the data volume distribution in traffic flows. This metric belongs to the interval <0, 1> with the same rule as dir_ratio_packets.

avg_duration: Average duration of IP flows for the IP address during the aggregation interval, measuring the typical session length.

avg_ttl: Average Time To Live (TTL) of IP flows for the IP address during the aggregation interval, providing insight into the lifespan of packets.

Moreover, the time series created by re-aggregation contains following time series metrics instead of n_dest_ip, n_dest_asn, and n_dest_port:

sum_n_dest_ip: Sum of numbers of unique destination IP addresses.

avg_n_dest_ip: The average number of unique destination IP addresses.

std_n_dest_ip: Standard deviation of numbers of unique destination IP addresses.

sum_n_dest_asn: Sum of numbers of unique destination ASNs.

avg_n_dest_asn: The average number of unique destination ASNs.

std_n_dest_asn: Standard deviation of numbers of unique destination ASNs)

sum_n_dest_port: Sum of numbers of unique destination transport layer ports.

avg_n_dest_port: The average number of unique destination transport layer ports.

std_n_dest_port: Standard deviation of numbers of unique destination transport layer ports.

Moreover, files identifiers.csv in each dataset type contain IDs of time series that are present in the dataset. Furthermore, the ids_relationship.csv file contains a relationship between IP addresses, Institutions, and institution subnets. The weekends_and_holidays.csv contains information about the non-working days in the Czech Republic.

This dataset contains the World Average Degree Days Database for the period 1964-2013. Follow datasource.kapsarc.org for timely data to advance energy economics research.*

Summary_64-13_freq=1D Average Degree Days of various indices for respective countries for the period 1964-2013, converted to a 1 day frequency

Summary_64-13_freq=6hrs Average Degree Days of various indices for respective countries for the period 1964-2013, calculated at 6 hrs frequency

T2m.hdd.18C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=18°C and frequency of 6 hrs

T2m.cdd.18C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=18°C and frequency of 6 hrs

t2m.hdd.15.6C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=15.6°C and frequency of 6 hrs

t2m.hdd.18.3C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=18.3°C and frequency of 6 hrs

t2m.hdd.21.1C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=21.1°C and frequency of 6 hrs

t2m.cdd.15.6C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=15.6°C and frequency of 6 hrs

t2m.cdd.18.3C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=18.3°C and frequency of 6 hrs

t2m.cdd.21.1C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=21.1°C and frequency of 6 hrs

t2m.hdd.60F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=60°F and frequency of 6 hrs

t2m.hdd.65F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=65°F and frequency of 6 hrs

t2m.hdd.70F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=70°F and frequency of 6 hrs

t2m.cdd.60F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=60°F and frequency of 6 hrs

t2m.cdd.65F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=65°F and frequency of 6 hrs

t2m.cdd.70F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=70°F and frequency of 6 hrs

HI.hdd.57.56F Calculation of Heating Degree Days using the Heat Index at Tref=57.56°F and frequency of 6 hrs

HI.hdd.63.08F Calculation of Heating Degree Days using the Heat Index at Tref=63.08°F and frequency of 6 hrs

HI.hdd.68.58F Calculation of Heating Degree Days using the Heat Index at Tref=68.58°F and frequency of 6 hrs

HI.cdd.57.56F Calculation of Cooling Degree Days using the Heat Index at Tref=57.56°F and frequency of 6 hrs

HI.cdd.63.08F Calculation of Cooling Degree Days using the Heat Index at Tref=63.08°F and frequency of 6 hrs

HI.cdd.68.58F Calculation of Cooling Degree Days using the Heat Index at Tref=68.58°F and frequency of 6 hrs

HUM.hdd.13.98C Calculation of Heating Degree Days using the Humidex at Tref=13.98°C and frequency of 6 hrs

HUM.hdd.17.4C Calculation of Heating Degree Days using the Humidex at Tref=17.40°C and frequency of 6 hrs

HUM.hdd.21.09C Calculation of Heating Degree Days using the Humidex at Tref=21.09°C and frequency of 6 hrs

HUM.cdd.13.98C Calculation of Cooling Degree Days using the Humidex at Tref=13.98°C and frequency of 6 hrs

HUM.cdd.17.4C Calculation of Cooling Degree Days using the Humidex at Tref=17.40°C and frequency of 6 hrs

HUM.cdd.21.09C Calculation of Cooling Degree Days using the Humidex at Tref=21.09°C and frequency of 6 hrs

ESI.hdd.12.6C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=12.6°C and frequency of 6 hrs

ESI.hdd.14.9C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=14.9°C and frequency of 6 hrs

ESI.hdd.17.2C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=17.2°C and frequency of 6 hrs

ESI.cdd.12.6C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=12.6°C and frequency of 6 hrs

ESI.cdd.14.9C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=14.9°C and frequency of 6 hrs

ESI.cdd.17.2C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=17.2°C and frequency of 6 hrs

Note:

Divide Degree Days by 4 to convert from 6 hrs to daily frequency

Understanding the mean and variability of the Earth’s radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and CERES, that span nearly 30 years to date.The proposed project utilizes knowledge gained in the last 10 years through CERES data analyses and apply the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.

In 2023, customers used on average *** GB per month of mobile data per active SIM card in Belgium. This is the highest mobile data per active SIM card in the given time period and an increase of about ** percent compared to the previous year.

This dataset contains time series estimates of GB earnings growth sourced from the Monthly Wages and Salaries Survey. The dataset is overwritten every month and it therefore always contains the latest published data. The Time Series dataset facility is a tabulation tool primarily designed for users who wish to customise their own datasets.

Previously named Employment & Earnings

🇺🇸 미국

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

ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries is the Earth Radiation Budget Experiment (ERBE) through Earth Radiation Budget Satellite (ERBS) area average time series through Wide-field-of-view nonscanner abroad Earth Radiation Budget Satellite Edition 4.1 data product. Understanding the mean and variability of the Earth’s radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and Clouds and the Earth's Radiant Energy System (CERES), that span nearly 30 years to date.The ERBE MEaSUREs project uses knowledge gained in the last 10 years through CERES data analyses and applies the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.

ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries is the Earth Radiation Budget Experiment (ERBE) through Earth Radiation Budget Satellite (ERBS) area average time series through Wide-field-of-view nonscanner abroad Earth Radiation Budget Satellite Edition 4.1 data product. Understanding the mean and variability of the Earth’s radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and Clouds and the Earth's Radiant Energy System (CERES), that span nearly 30 years to date. The ERBE MEaSUREs project uses knowledge gained in the last 10 years through CERES data analyses and applies the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.

Thailand Average Monthly Revenue: Per Mobile Phone Number: Prepaid data was reported at 151.000 THB in Sep 2019. This records a decrease from the previous number of 152.000 THB for Jun 2019. Thailand Average Monthly Revenue: Per Mobile Phone Number: Prepaid data is updated quarterly, averaging 152.000 THB from Mar 2014 (Median) to Sep 2019, with 23 observations. The data reached an all-time high of 165.000 THB in Mar 2016 and a record low of 134.000 THB in Sep 2014. Thailand Average Monthly Revenue: Per Mobile Phone Number: Prepaid data remains active status in CEIC and is reported by Office of The National Broadcasting and Telecommunications Commission. The data is categorized under Global Database’s Thailand – Table TH.TB006: Telecommunication Statistics: Office of The National Broadcasting and Telecommunications Commission .

Surface ocean velocities estimated from HF-Radar are representative of the upper 0.3 meters of the ocean. The main objective of near-real time processing is to produce the best product from available data at the time of processing. Radial velocity measurements are obtained from individual radar sites through the U.S. HF-Radar Network. Hourly radial data are processed by unweighted least squares on a 500m resolution grid of the U.S. West Coast to produce hourly near real-time surface current maps. The year average is computed from all available hourly near real-time surface current maps for the given year.

Statewide Intake serves as the “front door to the front line” for all DFPS programs. As the central point of contact for reports of abuse, neglect and exploitation of vulnerable Texans, SWI staff are available 24 hours a day, 7 days per week, 365 days per year.

SWI is the Centralized point of intake for child abuse and neglect, abuse, neglect or exploitation of people age 65 or older or adults with disabilities, clients served by DSHS or DADS employees in State Hospitals or State Supported Living Centers, and children in licensed child-care facilities or treatment centers for the entire State of Texas.

SWI provides daily reports on call volume per application; hold times per application, etc. and integrates hardware and software upgrades to phone and computer systems to reduce hold times and improve efficiency.

NOTE: Past Printed Data Books also included EBC, Re-Entry and Support Staff in all queues total.

An abandoned call is a call that disconnects after completing navigation of the recorded message, but prior to being answered by an intake specialist.

Legislative Budget Board (LBB) Performance Measure Targets are set every two years during Legislative Sessions.

LBB Average Hold Time Targets for English Queue: 2010 11.4 minutes 2011 11.4 minutes 2012 8.7 minutes 2013 8.7 minutes 2014 8.7 minutes 2015 8.7 minutes 2016 7.2 minutes 2017 10.5 minutes 2018 12.0 minutes 2019 9.8 minutes

Visit dfps.state.tx.us for information on all DFPS programs