This data is collected by me by asking people about various factors that will help to predict mobile usage.

I will try to upload a new and extended version as soon as possible.

feel free to use this dataset in your projects I will be glad

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

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users 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).

The survey charted the late teens' use of mobile phones in Finland. The archived data consist of interviews conducted with late teens (aged 16-18) between 1998 and 1999. Topics covered the use of mobile phones with friends, in everyday life and in school. The dataset comprises 27 interviews. The dataset is only available in Finnish.

The Stata data file "CAP_Demographics_Jumla_Kavre_recoded.dta” and equivalent excel file of the same name comprises data collected by adolescent secondary school students during a "Citizen Science" project in the district of Kavre in the central hills of Nepal during April 2022 and in the district of Jumla in the remote mountains of West Nepal during June 2022. The project was part of a CIFF-funded Children in All Policies 2030 (CAP2030) project.

The data were generated by the students using a mobile device data collection form developed using "Open Data Kit (ODK) Collect" electronic data collection platform by Kathmandu Living Labs (KLL) and University College London (UCL) for the purposes of this study. Researchers from KLL and UCL trained the adolescents to record basic socio-demographic information about themselves and their households including caste/ethnicity, religion, education, water sources, assets, household characteristics, and income sources. The form also asked about their access to mobile phones or other devices and internet and their concerns with respect to climate change. The resulting data describe the participants in the citizen science project, but their names and addresses have been removed. The app and the process of gathering the data are described in a paper entitled "Citizen science for climate change resilience: engaging adolescents to study climate hazards, biodiversity and nutrition in rural Nepal" submitted to Wellcome Open Research in Feb 2023. The data contributed to Tables 2 and 3 of this paper.

Associations of child’s use of mobile devices and parent–child shared reading with the emotional and behavioral problems among children with mothers with a high depression level—results from multiple linear regression models.

The phone survey was conducted to gather data on the socio-economic impacts of COVID-19 crisis, as well as the Hunga Tonga-Hunga Ha'apai volcanic eruption and tsunami in Tonga. Round 2 interviewed 2,503 households both in urban and rural regions of the country from July 2022 to August 2022. Survey topics included employment and income, food security, coping strategies, access to health services, asset ownership, and preparedness. Purpose of Round 2 survey was to continue tracking the impact of the crises after Round 1, which was completed in April, 2022 - May, 2022. Additionally, round 2 survey besides the household information, gathers data on individual level that was not included in Round 1. Two individual datasets explore adult employment and child education. While these findings are not without their caveats due to the lack of baseline data, constraints of the mobile phone survey methodology, and data quality constraints, they represent the best estimates to date and supplement other data on macroeconomic conditions, exports, firm-level information, etc. to develop an initial picture of the impacts of the crises on the population.

Version 01: Cleaned, labelled and anonymized version of the Master file.

• HOUSEHOLD DATASET: Basic Information, Vaccine, Health, Education, Food Insecurity, Employment, Income, Coping Strategies, Assets
• ADULT EMPLOYMENT DATASET: Basic Information, Employment

• CHILD EDUCATION DATASET: Basic Information, Child Education

• Collection start: 2022

• Collection end: 2022

Dataset Description:

This dataset captures the real-world online behavior of teenagers, focusing on e-safety awareness, cybersecurity risks, and device interactions. The data was collected from network activity logs and e-safety monitoring systems across various educational institutions and households in Texas and California. Spanning from January 2017 to October 2024, this dataset includes interactions with social media platforms, educational websites, and other online services, providing an in-depth look at teenage online activities in urban and suburban settings. The dataset is anonymized to protect user privacy and contains real incidents of network threats, security breaches, and cybersecurity behavior patterns observed in teenagers.

Use Cases:

Predicting e-safety awareness and online behavior patterns. Detecting malware exposure risk and cybersecurity vulnerabilities. Analyzing online habits related to social media and internet consumption. Evaluating cybersecurity behaviors like password strength, VPN usage, and phishing attempts. Features Overview:

S.No Feature Name Description 1 Device Type The type of device used during the online session (Mobile, Laptop, Tablet, Desktop, etc.) 2 Malware Detection Whether malware was detected on the device during the session (Yes/No) 3 Phishing Attempts Number of phishing attempts experienced during online activity 4 Social Media Usage Frequency of social media usage (Low, Medium, High) 5 VPN Usage Whether a VPN was used during the session (Yes/No) 6 Cyberbullying Reports Number of reported cyberbullying incidents 7 Parental Control Alerts Number of alerts triggered by parental control software 8 Firewall Logs Number of blocked or allowed network connections by the firewall 9 Login Attempts Number of login attempts during the session 10 Download Risk Risk level associated with downloaded files (Low, Medium, High) 11 Password Strength Strength of the passwords used (Weak, Moderate, Strong) 12 Data Breach Notifications Number of alerts regarding compromised personal information 13 Online Purchase Risk Risk level of online purchases made (Low, Medium, High) 14 Education Content Usage Frequency of engagement with educational content (Low, Medium, High) 15 Age Group Age category of the teenager (Under 13, 13-16, 17-19) 16 Geolocation Location of network access (US, EU, etc.) 17 Public Network Usage Whether the online activity occurred over a public network (Yes/No) 18 Network Type Type of network connection (WiFi, Cellular, etc.) 19 Hours Online Total hours spent online during the session 20 Website Visits Number of websites visited per hour during the session 21 Peer Interactions Level of peer-to-peer interactions during online activity 22 Risky Website Visits Whether visits to risky websites occurred (Yes/No) 23 Cloud Service Usage Whether cloud services were accessed during the session (Yes/No) 24 Unencrypted Traffic Whether unencrypted network traffic was accessed during the session (Yes/No) 25 Ad Clicks Whether online advertisements were clicked during the session (Yes/No) 26 Insecure Login Attempts Number of insecure login attempts made (e.g., over unencrypted networks) Potential Research and Machine Learning Applications:

Cybersecurity and anomaly detection models. Predictive modeling for e-safety awareness and risk behaviors. Time-series analysis of internet consumption and security threat trends. Behavioral clustering and pattern recognition in teenage online activity. Data Collection Method: The data was collected through collaboration with local schools and cybersecurity monitoring agencies. Real-time network monitoring systems captured interactions across different online platforms. All personally identifiable information (PII) was anonymized to ensure privacy, making the dataset ideal for public use in research and machine learning tasks.

This dataset provides a rich foundation for studying teenage online behavior patterns and developing predictive models for cybersecurity awareness and risk mitigation. Researchers and data scientists can use this data to create models that better understand online behavior, identify security risks, and design interventions to improve e-safety for teenagers.

Abstract

The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform policy. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques.

The data set contains information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority are randomly selected. The samples cover urban and rural areas and are designed to be representative of the population of Kenya using cell phones. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing.

The data is uploaded in three files. The first is the hh file, which contains household level information. The ‘hhid’, uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the ‘adult_id’. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the ‘child_id’.

The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 4,061 Kenyan households Wave 2: July 16 to September 18, 2020; 4,492 Kenyan households Wave 3: September 28 to December 2, 2020; 4,979 Kenyan households Wave 4: January 15 to March 25, 2021; 4,892 Kenyan households Wave 5: March 29 to June 13, 2021; 5,854 Kenyan households Wave 6: July 14 to November 3, 2021; 5,765 Kenyan households Wave 7: November 15, 2021, to March 31, 2022; 5,633 Kenyan households Wave 8: May 31 to July 8, 2022: 4,550 Kenyan households

The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Household, Individual

Sampling procedure

The COVID-19 RRPS with Kenyan households has two samples. The first sample consists of households that were part of the 2015/16 KIHBS CAPI pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot is representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. The second sample consists of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel and Telkom. An introductory text message was sent to 5,000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they live in. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was administered in English and is provided as a resource in pdf format. Additionally, questionnaires for each wave are also provided in Excel format coded for SCTO. The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Dataset Description This dataset contains information about 8,500+ mobile service customers, including demographic details, device usage, billing patterns, and call behavior. The primary goal of this dataset is to enable analysis and modeling to predict customer churn — i.e., customers who decide to drop their mobile service provider.

The data includes 33 features and one binary target column (customer_dropped). This dataset is ideal for exploring churn prediction models, customer segmentation, lifetime value analysis, and marketing strategy development.

Features - customer_id: Unique identifier for each customer - age: Age of the customer - job: Occupation or profession of the customer - urban_rural: Indicates whether the customer resides in an urban or rural area - marital_status: Marital status of the customer - kids: Number of children the customer has - disposable_income: Disposable income of the customer - mobiles_changed: Number of times the customer has changed their mobile device - mobile_age: Age of the current mobile device - own_smartphone: Indicates whether the customer owns a smartphone - current_mobile_price: Price of the customer's current mobile device - credit_card_type: Type of credit card held - own_house: Indicates whether the customer owns a house - own_cr_card: Indicates whether the customer owns a credit card - monthly_bill: Monthly bill for mobile service - call_mins: Total call minutes used - basic_plan_amount: Basic mobile plan amount - extra_mins: Extra minutes used beyond the plan - roam_call_mins: Roaming call minutes - call_mins_delta: Change in call minutes compared to the previous billing period - bill_amount_delta: Change in bill amount compared to the previous billing period - incoming_call_mins: Total incoming call minutes - outgoing_calls: Number of outgoing calls - incoming_calls: Number of incoming calls - day_night_call_ratio: Ratio of call minutes during the day versus night - day_night_call_delta: Change in day vs night call minutes compared to the previous period - calls_dropped: Number of calls dropped - loyalty_months: Customer tenure in months - complaint_calls: Number of complaint calls made - promo_calls_made: Number of promotional calls made - promo_offers_accepted: Number of promotional offers accepted - new_numbers_called: Number of new contacts called - customer_dropped: Target column indicating churn (1 = churned, 0 = retained)

Use Cases - Develop machine learning models for churn prediction - Perform customer segmentation and behavioral profiling - Analyze call usage trends and billing sensitivity - Identify key drivers of customer loyalty or attrition - Design data-driven retention strategies

The ‘Predicting And Preventing Child Neglect In Teen Mothers’ project was designed to assess the impact of varying degrees and types of neglect and poor parenting on children’s development during the first 3 years of life, including changes in intelligence and behavior, language, social and emotional well-being, physical growth, and health status. This study included a broad array of assessments related to the construct of childhood neglect, and can be used to test the developmental associations among parenting characteristics, parenting behaviors and attitudes, and child development in multiple domains. Six hundred and eighty-two expectant mothers were recruited during pregnancy through primary care facilities in the communities of Birmingham, AL, Kansas City, KS, South Bend, IN, and Washington, D.C. Three different groups of first-time mothers were included in the sample: adolescents (n=396), low-ed adults (less than 2 years formal education beyond high school; n=169), and hi-ed adults (at least 2 years of formal education; n=117). The mothers’ ages at child birth ranged from 14.68 to 36.28, with an average of 17.49 for the adolescents, 25.48 for the low-ed adults, and 27.88 for the hi-ed adults. Approximately 65% of the sample were African-American, 19% were White/Non-Hispanic, 15% were Hispanic, 1% were multi-racial, and .5% were of an other race. The adolescent and low-ed adult samples were closely matched on race/ethnicity. Mothers were interviewed in their last trimester of pregnancy as well as when their children were 4, 6, 8, 12, 18, 24, 30, and 36-months old. Interviews at the prenatal, 6, 12, 24, and 36-month visits primarily focused on risks for poor parenting, such as maternal depression (Beck II), parenting stress (Parenting Stress Index – Short Form), and lack of social support; parenting beliefs and practices; as well as other demographic information. The 4, 8, 18, and 30-month visits occurred in the home and included both interviews and observations of parenting practices (Home Observation for Measurement of the Environment, Supplement to the HOME for Impoverished Families, and Landry Naturalistic Observation). After each of the home visits, mothers were given a cellular phone and interviewed multiple times concerning their daily parenting practices (Parent-Child Activities Interview). At the 12, 24, and 36-months visits, the children were also tested for intellectual (Bayley II) and language abilities (Pre-School Language Scales – IV), rated on their behavior by both their mother (Infant Toddler Social and Emotional Assessment) and child tester (Bayely Behavioral Rating Scale II), and their height and weight were measured. Upon completing each assessment after the child’s birth, the interviewers also rated the child’s environment for risks of physical neglect. This study represents one of the first-ever prospective broad-based, multi-site investigations of child neglect among a diverse sample of adolescent mothers and will help to establish a foundation for future preventive interventions to reduce the incidence and impact of neglect and abuse on child development. This data set provides a broad range of risk and protective factors to better map the multiple and fluctuating social ecologies and life circumstances of teen mothers and their young children. This dataset contains data from pre-natal to 36-months. Please note: attachment codes, Parent-Child Activity interviews, short cell phone interviews are NOT included in this data collection.

Investigators: John G. BorkowskiUniversity of Notre Dame Notre Dame, INJudy CartaUniversity of Kansas Kansas City, KSSteven F. WarrenUniversity of Kansas Lawrence, KSSharon L. RameyGeorgetown University NW Washington, DCCraig RameyGeorgetown University NW Washington, DCKristi GuestUniversity of Alabama - Birmingham Birmingham, ALBette KeltnerGeorgetown University NW Washington, DCRobin G. LanziGeorgetown University NW Washington, DCLorraine KlermanBrandeis University Wa

The Chinese Kids Speech database (Lower Grade) contains the total recordings of 184 Chinese Kids speakers (98 males and 86 females), from 6 to 10 years’ old recorded in quiet rooms using smartphone. This database may be combined with the Chinese Kids Speech database (Upper Grade) also available in the ELRA Catalogue under reference ELRA-S0497.Number of speakers, utterances, duration and age are as follows :Number of speakers (Male/Female): 184 (98/86)Number of utterances (average): 237 utt/spkrTotal number of utterances: 43,667Age: from 6 to 10Total hours of data: 871,426 sentences were used. Recordings were made through smartphones and audio data stored in .wav files as sequences of 16KHz Mono, 16 bits, Linear PCM.Database・Audio data: WAV format, 16KHz, 16bit, mono (recorded with smartphone)・Transcription data: TSV format(tab-delimited), UTF-8 (without BOM) ), Line ending: LF・Size: 9.4GBAgeMaleFemaleTotal611617711819818294794736831011718Structure of database :├─ readme.txt├─ Chinese Kids Speech Database (Lower grade).pdfDescription document of the database├─ transcription(Lower).tsvTranscription└─ Low/directory of audio data └─ (1st/2nd/3rd)directory of version ID └─(0/1)directory of gender (0: male, 1: female) └─(audio_file)audio file (WAV format, 16KHz, 16bit, mono)Field information of “transcription(Lower).tsv” are as follows:Field numberContents0Script ID1Speaker ID2Audio file name3Transcription (in Chinese)File naming conventions of audio files are as follows:Field numberContentsDescriptionRemarks0Script IDFour digitsXXXX: four digits1Speaker IDThree digitsXXX: three digits2AgeTwo digitsFrom 06 to 103Gender0: male, 1: female4Utterance No.Three digitsSequential numbering starting from 001 within each speaker5Recording dateYYYYMMDDHHMM6Recording device nameRecording device nameEx. NTH-AN007OSOperating System info of recording deviceEx. android-118Durationduration in msecDuration of the actual spoken utteranceFiled separation character is “_”.For example, if the audio file name is “1318_373_09_1_010_202205041857_NTH-AN00_android-11_5480.wav “, this file has the following meaning:1318: script ID373: speaker ID09: age (nine years old)1: gender (female)010: utterance number202205041857: recording date (May 4, 2022, at 6:57 PM)NTH-AN00: recording device nameandroid-11: operating system info of recording device5480: duration of the actual spoken utterance (5,480 msec)

A child data set of --Mobile Food Facility Permit-- includes day of week, start / end time, location and a description of type of food sold by vendor. Mobile Food Facility Permit data is here: https://data.sfgov.org/d/rqzj-sfat

The phone survey was conducted to gather data on the socio-economic impact of COVID-19 crisis in Vanuatu. Community transmission of COVID-19 in Vanuatu started only in March 2022 followed by the nation-wide lockdown and other restrictions. Round 1 HFPS survey was a timely process to observe the effect of the crisis on the country. Round 1 interviewed 2,515 households both in urban and rural regions of the country from July 2022 to September 2022.

Survey topics included employment and income, food security, coping strategies, access to health services, and asset ownership - all on household level. Additionally, two individual-level datasets explore adult employment and child education. The former selects a randomly chosen adult in the household - could be the respondent of a household-level data, head of the household or another individual - and inquires about their employment status. For the latter, the respondent is being asked about education of a randomly chosen child in the household with more than one child.

While these findings are not without their caveats due to the lack of baseline data, constraints of the mobile phone survey methodology, and data quality constraints, they represent the best estimates to date and supplement other data on macroeconomic conditions, exports, firm-level information, etc. to develop an initial picture of the impacts of the crises on the population.

Version 01: Cleaned, labelled and anonymized version of the Master file

-HOUSEHOLD DATASET: Basic Information, Vaccine, Health, Education, Food Insecurity, Employment, Income, Coping Strategies, Assets
-ADULT EMPLOYMENT DATASET: Basic Information, Employment
-CHILD EDUCATION DATASET: Basic Information, Child Education

• Collection start: 2022
• Collection end: 2022

Comprehensive YouTube channel statistics for SMART CHILD, featuring 207,000 subscribers and 54,414,420 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in RO. Track 1,159 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

This dataset contains de-identified data with an accompanying data dictionary and the R script for de-identification procedures., Objective(s): To demonstrate application of a risk based de-identification framework using the Smart Triage dataset as a clinical example. Data Description: This dataset contains the de-identified version of the Smart Triage Jinja dataset with the accompanying data dictionary and R script for de-identification procedures. Limitations: Utility of the de-identified dataset has only been evaluated with regard to use for the development of prediction models based on a need for hospital admission. Abbreviations: NA Ethics Declaration: The study was reviewed by the instituational review boards at the University of British Columbia in Canada (ID: H19-02398; H20-00484), The Makerere University School of Public Health in Uganda and the Uganda National Council for Science and Technology

Standardized factor loadings from an exploratory factor analysis (n = 580).

Clinical dataset #1 from the Smart Discharges 6-60m observational study (6-60m - Phase 1). March 2017 to March 2019., NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

Dataset Description: This dataset contains materials from a parent study within the Smart Discharges program of research. Materials include the parent study protocol and associated documents. See the Metadata section below for links to related publications and datasets. Background: Substantial mortality occurs after hospital discharge in children under 5 years old with suspected sepsis. A better understanding of risk and ability to mitigate risk for those who are most vulnerable is needed to reduce child mortality in resource limited settings. Methods: This is a prospective before-after study with staggered implementation at six Ugandan hospitals. Phase I is a prospective observational cohort study, while Phase II is a stepped-wedge intervention. The study also includes a long-term follow-up phase. The ultimate outcome to be studied is post-discharge mortality for children < 5 years old by 6 months after admission. The study has two objectives, each corresponding to a phase: Phase I: To refine and externally validate the existing post-discharge mortality prediction model. Phase II: To determine the effectiveness of a Smart Discharge program on post-discharge health seeking behaviour and mortality. We also seek to lay the groundwork to study the long-term effects of sepsis on morbidity over a 10 year period. Data Collection Methods: All data were collected at the point of care using encrypted study tablets. These data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). At admission, trained study nurses systematically collected data on clinical, social and demographic variables. Following discharge, field officers contacted caregivers at 2 and 4 months by phone, and in-person at 6 months, to determine vital status, post-discharge health-seeking, and readmission details. Verbal autopsies were conducted for children who had died following discharge. Ethics Declaration: This study was approved by the Mbarara University of Science and Technology Research Ethics Committee (No. 15/10-16; No. 07/01-21), the Uganda National Institute of Science and Technology (HS 2207), and the University of British Columbia / Children & Women’s Health Centre of British Columbia Research Ethics Board (H16-02679). Associated datasets: Post-discharge mortality among children under 5 years admitted with suspected sepsis in Uganda: a prospective multi-site study NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."