How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are probe requests (PR). They are sent by mobile devices in the unassociated state to search the nearby area for existing wireless networks. The frame part of PRs consists of variable length fields called information elements (IE). IE fields represent the capabilities of a mobile device, such as data rates.
The dataset includes PRs collected in a controlled rural environment and in a semi-controlled indoor environment under different measurement scenarios.
It can be used for various use cases, e.g., analysing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analysing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture Wi-Fi signal traffic in monitoring mode. Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel. The following information about each PR received is collected: MAC address, Supported data rates, extended supported rates, HT capabilities, extended capabilities, data under extended tag and vendor specific tag, interworking, VHT capabilities, RSSI, SSID and timestamp when PR was received. The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package for data collection, preprocessing and transmission.

Data preprocessing

The gateway collects PRs for each consecutive predefined scan interval (10 seconds). During this time interval, the data are preprocessed before being transmitted to the database. For each detected PR in the scan interval, IEs fields are saved in the following JSON structure: PR_IE_data = { 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext}, 'HT_CAP': DATA_htcap, 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap}, 'VHT_CAP': DATA_vhtcap, 'INTERWORKING': DATA_inter, 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...}, 'VENDOR_SPEC': {VENDOR_1:{ 'ID_1': DATA_1_vendor1, 'ID_2': DATA_2_vendor1 ...}, VENDOR_2:{ 'ID_1': DATA_1_vendor2, 'ID_2': DATA_2_vendor2 ...} ...} }

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows: { 'TIME': [ DATA_time ], 'RSSI': [ DATA_rssi ], 'DATA': PR_IE_data }.

This data structure allows storing only TOA and RSSI for all PRs originating from the same MAC address and containing the same PR_IE_data. All SSIDs from the same MAC address are also stored.
The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.
If identical PR's IE data from the same MAC address is already stored, then only data for the keys TIME and RSSI are appended. If no identical PR's IE data has yet been received from the same MAC address, then PR_data structure of the new PR for that MAC address is appended to PROBE_REQs key.
The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png
At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data e.g. wireless gateway serial number and scan start and end timestamps. For an example of a single PR captured, see the ./Single_PR_capture_example.json file.

Environments description

We performed measurements in a controlled rural outdoor environment and in a semi-controlled indoor environment of the Jozef Stefan Institute. See the Excel spreadsheet Measurement_informations.xlsx for a list of mobile devices tested.

Indoor environment

We used 3 RPi's for the acquisition of PRs in the Jozef Stefan Institute. They were placed indoors in the hallways as shown in the ./Figures/RPi_locations_JSI.png. Measurements were performed on weekend to minimize additional uncontrolled traffic from users' mobile devices. While there is some overlap in WiFi coverage between the devices at the location 2 and 3, the device at location 1 has no overlap with the other two devices.

Rural environment outdoors

The three RPi's used to collect PRs were placed at three different locations with non-overlapping WiFi coverage, as shown in ./Figures/RPi_locations_rural_env.png. Before starting the measurement campaign, all measured devices were turned off and the environment was checked for active WiFi devices. We did not detect any unknown active devices sending WiFi packets in the RPi's coverage area, so the deployment can be considered fully controlled. All known WiFi enabled devices that were used to collect and send data to the database used a global MAC address, so they can be easily excluded in the preprocessing phase. MAC addresses of these devices can be found in the ./Measurement_informations.xlsx spreadsheet. Note: The Huawei P20 device with ID 4.3 was not included in the test in this environment.

Scenarios description

We performed three different scenarios of measurements.

Individual device measurements

For each device, we collected PRs for one minute with the screen on, followed by PRs collected for one minute with the screen off. In the indoor environment the WiFi interfaces of the other devices not being tested were disabled. In rural environment other devices were turned off. Start and end timestamps of the recorded data for each device can be found in the ./Measurement_informations.xlsx spreadsheet under the Indoor environment of Jozef Stefan Institute sheet and the Rural environment sheet.

Three groups test

In this measurement scenario, the devices were divided into three groups. The first group contained devices from different manufacturers. The second group contained devices from only one manufacturer (Samsung). Half of the third group consisted of devices from the same manufacturer (Huawei), and the other half of devices from different manufacturers. The distribution of devices among the groups can be found in the ./Measurement_informations.xlsx spreadsheet.

The same data collection procedure was used for all three groups. Data for each group were collected in both environments at three different RPis locations, as shown in ./Figures/RPi_locations_JSI.png and ./Figures/RPi_locations_rural_env.png.
At each location, PRs were collected from each group for 10 minutes with the screen on. Then all three groups switched locations and the process was repeated. Thus, the dataset contains measurements from all three RPi locations of all three groups of devices in both measurement environments. The group movements and the timestamps for the start and end of the collection of PRs at each loacation can be found in spreadsheet ./Measurement_informations.xlsx.

One group test

In the last measurement scenario, all devices were grouped together. In rural evironement we first collected PRs for 10 minutes while the screen was on, and then for another 10 minutes while the screen was off. In indoor environment data were collected at first location with screens on for 10 minutes. Then all devices were moved to the location of the next RPi and PRs were collected for 5 minutes with the screen on and then for another 5 minutes with the screen off.

Folder structure

The root directory contains two files in JSON format for each of the environments where the measurements took place (Data_indoor_environment.json and Data_rural_environment.json). Both files contain collected PRs for the entire day that the measurements were taken (12:00 AM to 12:00 PM) to get a sense of the behaviour of the unknown devices in each environment. The spreadsheet ./Measurement_informations.xlsx. contains three sheets. Devices description contains general information about the tested devices, RPis, and the assigned group for each device. The sheets Indoor environment of Jozef Stefan Institute and Rural environment contain the corresponding timestamps for the start and end of each measurement scenario. For the scenario where the devices were divided into groups, additional information about the movements between locations is included. The location names are based on the RPi gateway ID and may differ from those on the figures showing the locations of the RPIs for each environment. The ./Figures folder contains the figures already mentioned above.

Number and percentage of deaths, by month and place of residence, 1991 to most recent year.

Dataset Construction This dataset captures the temporal network of Bitcoin (BTC) flow exchanged between entities at the finest time resolution in UNIX timestamp. Its construction is based on the blockchain covering the period from January, 3rd of 2009 to January the 25th of 2021. The blockchain extraction has been made using bitcoin-etl (https://github.com/blockchain-etl/bitcoin-etl) Python package. The entity-entity network is built by aggregating Bitcoin addresses using the common-input heuristic [1] as well as popular Bitcoin users' addresses provided by https://www.walletexplorer.com/ [1] M. Harrigan and C. Fretter, "The Unreasonable Effectiveness of Address Clustering," 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, 2016, pp. 368-373, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0071.keywords: {Online banking;Merging;Protocols;Upper bound;Bipartite graph;Electronic mail;Size measurement;bitcoin;cryptocurrency;blockchain}, Dataset Description Bitcoin Activity Temporal Coverage: From 03 January 2009 to 25 January 2021 Overview: This dataset provides a comprehensive representation of Bitcoin exchanges between entities over a significant temporal span, spanning from the inception of Bitcoin to recent years. It encompasses various temporal resolutions and representations to facilitate Bitcoin transaction network analysis in the context of temporal graphs. Every dates have been retrieved from bloc UNIX timestamp and GMT timezone. Contents: The dataset is distributed across three compressed archives: All data are stored in the Apache Parquet file format, a columnar storage format optimized for analytical queries. It can be used with pyspark Python package. orbitaal-stream_graph.tar.gz: The root directory is STREAM_GRAPH/ Contains a stream graph representation of Bitcoin exchanges at the finest temporal scale, corresponding to the validation time of each block (averaging approximately 10 minutes). The stream graph is divided into 13 files, one for each year Files format is parquet Name format is orbitaal-stream_graph-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering These files are in the subdirectory STREAM_GRAPH/EDGES/ orbitaal-snapshot-all.tar.gz: The root directory is SNAPSHOT/ Contains the snapshot network representing all transactions aggregated over the whole dataset period (from Jan. 2009 to Jan. 2021). Files format is parquet Name format is orbitaal-snapshot-all.snappy.parquet. These files are in the subdirectory SNAPSHOT/EDGES/ALL/ orbitaal-snapshot-year.tar.gz: The root directory is SNAPSHOT/ Contains the yearly resolution of snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering These files are in the subdirectory SNAPSHOT/EDGES/year/ orbitaal-snapshot-month.tar.gz: The root directory is SNAPSHOT/ Contains the monthly resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-file-id-[ID].snappy.parquet, where [YYYY] and [MM] stands for the corresponding year and month, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year and month ordering These files are in the subdirectory SNAPSHOT/EDGES/month/ orbitaal-snapshot-day.tar.gz: The root directory is SNAPSHOT/ Contains the daily resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-file-id-[ID].snappy.parquet, where [YYYY], [MM], and [DD] stand for the corresponding year, month, and day, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, and day ordering These files are in the subdirectory SNAPSHOT/EDGES/day/ orbitaal-snapshot-hour.tar.gz: The root directory is SNAPSHOT/ Contains the hourly resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-[hh]-file-id-[ID].snappy.parquet, where [YYYY], [MM], [DD], and [hh] stand for the corresponding year, month, day, and hour, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, day and hour ordering These files are in the subdirectory SNAPSHOT/EDGES/hour/ orbitaal-nodetable.tar.gz: The root directory is NODE_TABLE/ Contains two files in parquet format, the first one gives information related to nodes present in stream graphs and snapshots such as period of activity and associated global Bitcoin balance, and the other one contains the list of all associated Bitcoin addresses. Small samples in CSV format orbitaal-stream_graph-2016_07_08.csv and orbitaal-stream_graph-2016_07_09.csv These two CSV files are related to stream graph representations of an halvening happening in 2016.

Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders raw_data and processed_data containing respectively the list of utilised plant species modelled -utilised_plants_species_list.csv-, and their occurrence data -occurrence_data.zip- and predicted distribution -species_proba_per_cell.rds-.

The file utilised_plants_species_list.csv in the raw_data folder contains a list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:

plant_ID: plant identifier number ranging from between 1-35687

binomial_acc_name: binomial accepted name of the plant species

author_acc_name: name of the author(s)

is_hybrid: logical TRUE or FALSE indicating whether the species is an hybrid or not.

AnimalFood: forage and fodder for vertebrate animals only.

EnvironmentalUses: examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.

Fuels: charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.

GeneSources: wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.

HumanFood: food for humans only, including beverages and food additives.

InvertebrateFood: plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.

Materials: woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.

Medicines: both human and veterinary.

Poisons: plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.

SocialsUses: plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.

Totals: total number of uses recorded for a species

The zipfile occurrence_data.zip in the processed_data folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to build species distribution models with the following 14 fields:

Species: the binomial accepted name of the species

Fullname: same as species

decimalLongitude: the geographic longitude of the occurrence records of the species in decimal degrees

decimalLatitude: the geographic latitude of the occurrence records of the species in decimal degrees

countryCode: a three-letter standard abbreviation for the country of the occurrence locality

coordinateUncertaintyinMeters: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location

year: year of the observation of the occurrence record of the species

individualCount: the number of individuals present at the time of the observation

gbifID: unique identifier number for the occurrence from the original database

basisOfRecords: the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen

institutionCode: the name of the institution or organization listed as the data publisher on GBIF

establishmentMeans: statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans

is_cultivated_observation: whether or not an organism is cultivated

sourceID: name of the source database

The file species_proba_per_cell.rds in the processed_data folder is a R Data Serialization (RDS) file containing a data.table object with the following 3 fields:

plant_ID: plant identifier number ranging from between 1-35687

proba: species occurrence probability

cell: raster grid cell number between 1-2251762

This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.

Context

The Heart Attack Risk Prediction Dataset serves as a valuable resource for delving into the intricate dynamics of heart health and its predictors. Heart attacks, or myocardial infarctions, continue to be a significant global health issue, necessitating a deeper comprehension of their precursors and potential mitigating factors. This dataset encapsulates a diverse range of attributes including age, cholesterol levels, blood pressure, smoking habits, exercise patterns, dietary preferences, and more, aiming to elucidate the complex interplay of these variables in determining the likelihood of a heart attack. By employing predictive analytics and machine learning on this dataset, researchers and healthcare professionals can work towards proactive strategies for heart disease prevention and management. The dataset stands as a testament to collective efforts to enhance our understanding of cardiovascular health and pave the way for a healthier future.

Content

This synthetic dataset provides a comprehensive array of features relevant to heart health and lifestyle choices, encompassing patient-specific details such as age, gender, cholesterol levels, blood pressure, heart rate, and indicators like diabetes, family history, smoking habits, obesity, and alcohol consumption. Additionally, lifestyle factors like exercise hours, dietary habits, stress levels, and sedentary hours are included. Medical aspects comprising previous heart problems, medication usage, and triglyceride levels are considered. Socioeconomic aspects such as income and geographical attributes like country, continent, and hemisphere are incorporated. The dataset, consisting of 8763 records from patients around the globe, culminates in a crucial binary classification feature denoting the presence or absence of a heart attack risk, providing a comprehensive resource for predictive analysis and research in cardiovascular health.

Dataset Glossary (Column-wise)

• Patient ID - Unique identifier for each patient
• Age - Age of the patient
• Sex - Gender of the patient (Male/Female)
• Cholesterol - Cholesterol levels of the patient
• Blood Pressure - Blood pressure of the patient (systolic/diastolic)
• Heart Rate - Heart rate of the patient
• Diabetes - Whether the patient has diabetes (Yes/No)
• Family History - Family history of heart-related problems (1: Yes, 0: No)
• Smoking - Smoking status of the patient (1: Smoker, 0: Non-smoker)
• Obesity - Obesity status of the patient (1: Obese, 0: Not obese)
• Alcohol Consumption - Level of alcohol consumption by the patient (None/Light/Moderate/Heavy)
• Exercise Hours Per Week - Number of exercise hours per week
• Diet - Dietary habits of the patient (Healthy/Average/Unhealthy)
• Previous Heart Problems - Previous heart problems of the patient (1: Yes, 0: No)
• Medication Use - Medication usage by the patient (1: Yes, 0: No)
• Stress Level - Stress level reported by the patient (1-10)
• Sedentary Hours Per Day - Hours of sedentary activity per day
• Income - Income level of the patient
• BMI - Body Mass Index (BMI) of the patient
• Triglycerides - Triglyceride levels of the patient
• Physical Activity Days Per Week - Days of physical activity per week
• Sleep Hours Per Day - Hours of sleep per day
• Country - Country of the patient
• Continent - Continent where the patient resides
• Hemisphere - Hemisphere where the patient resides
• Heart Attack Risk - Presence of heart attack risk (1: Yes, 0: No)

Structure of the Dataset

https://i.imgur.com/5cTusqA.png" alt="">

Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world scenarios. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation.

Cover Photo by: brgfx on Freepik

Thumbnail by: vectorjuice on Freepik

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.

Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.

Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.html

We propose Safe Human dataset consisting of 17 different objects referred to as SH17 dataset. We scrapped images from the Pexels website, which offers "https://www.pexels.com/license/">clear usage rights for all its images, showcasing a range of human activities across diverse industrial operations.

To extract relevant images, we used multiple queries such as manufacturing worker, industrial worker, human worker, labor, etc. The tags associated with Pexels images proved reasonably accurate. After removing duplicate samples, we obtained a dataset of 8,099 images. The dataset exhibits significant diversity, representing manufacturing environments globally, thus minimizing potential regional or racial biases. Samples of the dataset are shown below.

Paper available at Arxiv Link.

GitHub link: https://github.com/ahmadmughees/SH17dataset

Key features

• Collected from diverse industrial environments globally
• High quality images (max resolution 8192x5462, min 1920x1002)
• Average of 9.38 instances per image
• Includes small objects like ears and earmuffs (39,764 annotations < 1% image area, 59,025 annotations < 5% area)

Classes

1. Person
2. Head
3. Face
4. Glasses
5. Face-mask-medical
6. Face-guard
7. Ear
8. Earmuffs
9. Hands
10. Gloves
11. Foot
12. Shoes
13. Safety-vest
14. Tools
15. Helmet
16. Medical-suit
17. Safety-suit

The data consists of three folders, - images contains all images - labels contains labels in YOLO format for all images - voc_labels contains labels in VOC format for all images - train_files.txt contains list of all images we used for training - val_files.txt contains list of all images we used for validation

Disclaimer and Responsible Use:

This dataset, scrapped through the Pexels website, is intended for educational, research, and analysis purposes only. You may be able to use the data for training of the Machine learning models only. Users are urged to use this data responsibly, ethically, and within the bounds of legal stipulations.

Users should adhere to Copyright Notice of Pexels when utilizing this dataset.

Legal Simplicity: All photos and videos on Pexels can be downloaded and used for free.

Allowed 👌

• All photos and videos on Pexels are free to use.
• Attribution is not required. Giving credit to the photographer or Pexels is not necessary but always appreciated.
• You can modify the photos and videos from Pexels. Be creative and edit them as you like. #### Not allowed 👎
• Identifiable people may not appear in a bad light or in a way that is offensive.
• Don't sell unaltered copies of a photo or video, e.g. as a poster, print or on a physical product without modifying it first.
• Don't imply endorsement of your product by people or brands on the imagery.
• Don't redistribute or sell the photos and videos on other stock photo or wallpaper platforms.
• Don't use the photos or videos as part of your trade-mark, design-mark, trade-name, business name or service mark.

No Warranty Disclaimer:

The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for its use by others.

Ethical Use:

Users are encouraged to consider the ethical implications of their analyses and the potential impact on broader community.

GitHub Page:

https://github.com/ahmadmughees/SH17dataset

Citation:

@misc{ahmad2024sh17datasethumansafety,
   title={SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry}, 
   author={Hafiz Mughees Ahmad and Afshin Rahimi},
   year={2024},
   eprint={2407.04590},
   archivePrefix={arXiv},
   primaryClass={cs.CV},
   url={https://arxiv.org/abs/2407.04590}, 
}

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2806979%2F0a24bd8b9a3f281cf924a5171db28a40%2Fpexels-photo-3862627.jpeg?generation=1720104820503689&alt=media" alt="">

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Cassava (Manihot esculenta) also known as manioc in South America, is grown world-wide in tropical and sub-tropical regions providing an important staple for the diet of over half a billion people. It is drought tolerant and grows well in marginal soils. More than half of the world"s cassava production is from Africa and Nigeria is the world"s largest producer. In Ghana, cassava accounts for roughly 30% of the calories eaten. The root of the cassava plant must be prepared to remove harmful compounds prior to eating. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofcassava harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of casava as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Groundnut (Peanut) Maize (Corn) Millet Potato Rice Sorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics.

Number, rate and percentage changes in rates of homicide victims, Canada, provinces and territories, 1961 to 2023.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 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 two 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 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

Zoonoses are naturally transmissible between humans and animals. Globally, they account for more than 60% of human infections, 75% of emerging infections, 2.7 million human deaths, and 10% of the total DALYs lost yearly in Africa. In the last three decades, Kenya has had sporadic outbreaks of zoonoses. To increase the speed of reporting and efficiencies in detection and control, a multi-sectoral collaboration in zoonotic disease surveillance (MZDS) between human and animal health workers is essential. In an effort, Zoonotic disease unit (ZDU) in Kenya has been established at national and county levels. A cross sectional study was carried out to determine the level of utilization of multisectoral collaboration and its associated determinants in zoonotic disease surveillance among animal and human healthcare workers in Nakuru County. Quantitative data was gathered from 102 participants and quantitative data from 5 key informants. To test for significant differences, Chi-square and independent t-test were used. MZDS utilization level was 16% and the factors associated with higher utilization include; knowing what MZDS entails, education level, sector affiliation, trainings, supportive infrastructure and data storage. Lack of financing and poor coordination are hindrances to MZDS. In conclusion, there is need to finance MZDS activities, strengthen coordination mechanisms, carry out more sensitization and trainings among animal and human healthcare. Methods Type and Period of Study Analytical cross-sectional study design was used and the study covered the period between August 20, 2023 and October 15, 2023. Setting of the Study The study was conducted in Nakuru County, a third most popular county and located in Rift valley region of Kenya. It is bordered by; Baringo, Laikipia, Nyandarua, Kajiado, Narok, Bomet and Kericho counties. Has an area of 7,496.5km² and a population of 1,603,325. It has physical features like; L. Naivasha a home of millions of flamingos, sanctuary to protect Rothschild giraffe and black rhinos, and Nakuru national park. The site is a hotspot for Anthrax, Brucellosis and Rabies and this formed the basis for purposive selection of the study area. Inclusion Criteria: Human and Animal Healthcare workers who consented. Exclusion Criteria: Healthcare workers that were not on duty over the period of the study. Sampling: A census was conducted. Data Collection and Tools A semi-structured pretested interviewer-administered questionnaire was used in face-to-face interviews to collect quantitative data from 102 participants who serve as frontline workers on zoonotic disease surveillance activities at the sub-county levels. Key informant interview guide was used to collect data on institutional factors (Funding, space, Priorities, Staffing, MZDS plans, political will) from county head of veterinary services, county head of public health, County director of medical services, Data analyst and county emergency and operations center officer. Audio tape recording was used to maintain and capture their exact words. The sessions lasted 45-60 minutes. Saturation marked the end of interview sessions. Data Processing and Analysis Quantitative data was entered to excel file, cleaned and exported to R 4.3.1 Software for descriptive and inferential statistical analysis. The descriptive statistics were presented on tables. Hearing about MZDS and the regularity of carrying out joint data collection, analysis, interpretation and sharing with other sectors formed the basis for inferential statistics. Chi-square and independent t-test were used to measure association at a p value < 0.05 and 95% confidence interval. Qualitative data was analyzed manually using MS Excel spreadsheets. Ethical Considerations Ethical approval and a research permit were attained from: the ethics review board for Jomo Kenyatta University of Agriculture and Technology (JKUAT), Ref: JKU/ISERC/023/0842, JKUAT Board of postgraduate studies, Ref: JKU/2/11/HSH315R-0088/2022, National Council for Science, Technology and Innovation, Ref: NACOSTI/P/23/25534, and Nakuru county. Informed consent was acquired from the participants. Completed questionnaires were kept under lock and key and accessed by only authorized people. Soft copies and all analyzed data were passworded.

Context

As a community of responsible people can we focus to predict these disasters to save lives around. People are already going through worse as if that was not enough if a sudden earthquake comes up it becomes hell for them.

The United States Geological Survey (USGS) determines the location and size of all significant earthquakes that occur in US.The USGS provides science about the natural hazards that threaten lives and livelihoods; the water, energy, minerals, and other natural resources we rely on; the health of our ecosystems and environment; and the impacts of climate and land-use change. Established: 1879 Location: Reston, United St

Content

time latitude longitude depth mag magType nst gap dmin rms net id updated place type horizontalError depthError magError magNst status locationSource magSource

1.)time Data Type-Long Integer The time when the event occurred. Times are reported in milliseconds since the epoch ( 1970-01-01T00:00:00.000Z), and do not include leap seconds. In certain output formats, the date is formatted for readability.(We provide time in UTC (Coordinated Universal Time). Seismologists use UTC to avoid confusion caused by local time zones and daylight savings time.) Additional Information

2.)latitude Data Type-Decimal Typical Values-[-90.0, 90.0] Decimal degrees latitude. Negative values for southern latitudes. Additional Information An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world.

3.)longitude Data Type-Decimal Typical Values-[-180.0, 180.0] Description-Decimal degrees longitude. Negative values for western longitudes. Additional Information An earthquake begins to rupture at a hypocenter which is defined by a position on the surface of the earth (epicenter) and a depth below this point (focal depth). We provide the coordinates of the epicenter in units of latitude and longitude. The latitude is the number of degrees north (N) or south (S) of the equator and varies from 0 at the equator to 90 at the poles. The longitude is the number of degrees east (E) or west (W) of the prime meridian which runs through Greenwich, England. The longitude varies from 0 at Greenwich to 180 and the E or W shows the direction from Greenwich. Coordinates are given in the WGS84 reference frame. The position uncertainty of the hypocenter location varies from about 100 m horizontally and 300 meters vertically for the best located events, those in the middle of densely spaced seismograph networks, to 10s of kilometers for global events in many parts of the world.

4.)depth Data Type-Decimal Typical Values-[0, 1000] Depth of the event in kilometers. Additional Information Sometimes when depth is poorly constrained by available seismic data, the location program will set the depth at a fixed value. For example, 33 km is often used as a default depth for earthquakes determined to be shallow, but whose depth is not satisfactorily determined by the data, whereas default depths of 5 or 10 km are often used in mid-continental areas and on mid-ocean ridges since earthquakes in these areas are usually shallower than 33 km.

5.)mag Data Type-Decimal Typical Values-[-1.0, 10.0] Description-The magnitude for the event. See also magType. Additional Info

6.)magType Data Type-String Typical Values-“Md”, “Ml”, “Ms”, “Mw”, “Me”, “Mi”, “Mb”, “MLg” The method or algorithm used to calculate the preferred magnitude for the event. Additional Information See Magnitude Types Table.

7.)nst Data Type-Integer The total number of seismic stations used to determine earthquake location. Additional Information Number of seismic stations which reported P- and S-arrival times for this earthquake. This number may be larger than the Number of Phases Used if arrival times are rejected because the distance to a seismic station exceeds the maximum allowable distance or because the arrival-time observation is inconsistent with the solution.

8.)gap Data Type-Decimal Typical Values-[0.0, 180...

Number and percentage of live births, by month of birth, 1991 to most recent year.