All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions and power plant types around the world by country. This information was obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The location of the countries is placed by lat/lon coordinates that was provided by CARMA. The dataset provides the country, region type, plant count, and lat/lon for each individual country. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. % Fossil: The percentage of total electricity that is generated by the combustion of coal, oil, or natural gas. % Hydro: The percentage of total electricity that is generated by hydroelectric power facilities. % Nuclear: The percentage of total electricity that is generated by nuclear power facilities. % Other Renewable: The percentage of total electricity that is generated by the use of wind, solar, biomass, geothermal, captured heat, or hydrogen energy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in China. Power Plant emissions from all power plants in China were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, metro area, lat/lon, and plant id for each individual power plant. Only Power Plants that had a listed longitude and latitude in CARMA's database were mapped. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information http://carma.org/region/detail/47

The Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population density grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken out by age group. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. Data are reported daily, with timestamps indicated in the daily briefings posted at: portal.ct.gov/coronavirus. Data are subject to future revision as reporting changes. Starting in July 2020, this dataset will be updated every weekday. Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020. A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports. Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

Explore detailed subnational population data including total population, % of total, and more on this dataset webpage.

Population, total, % of total, Subnational

World

Follow data.kapsarc.org for timely data to advance energy economics research.

Note: Many of the data come from the country national statistical offices. Other data come from the NASA Socioeconomic Data and Applications Center (SEDAC) managed by the Center for International Earth Science Information Network (CIESIN), Earth Institute, Columbia University. It is the World Bank Group first subnational population database at a global level and there are data limitations. Series metadata includes methodology and the assumptions made.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

This dataset includes worldwide data on a long timespan: - War: HCED Data v2.csv gather data about conflicts since 1468 BC to August 15, 2022. The data includes battle locations and years. This dataset has been created with the intention of producing a worldwide exhaustive catalogue of wars. - Population: population.csv holds records and estimates of world population, by location, since 10000 BCE.

Personally, I intend to use these two in conjunction with the popular Kaggle dataset Countries of the World, since I might need countries areas to estimate population densities.

Check out the output of my Cleaning War and Population Data notebook for a cleaner version of the dataset.

world_battles_and_demographics_master_table is my final version of the dataset, it holds a selected subset of the original information in a single place. Check out the output of my Wrangling War and Population Data if you're interestd in how I combined the tables.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Malawi Integrated Household Panel Survey (IHPS) 2019 and Malawi High-Frequency Phone Survey on COVID-19 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

ALL FILES ARE LOCATED AT MY REPOSITORY: https://github.com/christianio123/TexasAttendance

Context

I was curious about factors affecting school attendance so I gathered data from school districts around Texas to have a better idea.

Topic/Purpose

The purpose of the project is to help determine factors associated with student attendance in the state of Texas. No population is targeted as an audience for the project, however, anyone associated in education may find the dataset used (and other data attained but not used) helpful in any questions they may have regarding student attendance in Texas for the first two months of the 2020-2021 academic school year. This topic was targeted specifically due to the abnormalities in the current academic school year.

Data

Daily Student Attendance

Majority of the data in this project was collected by school districts around the state of Texas, public census information, and public COVID 19 data. To attain student attendance information, an email was sent out to 40 school districts around the state of Texas on November 2nd, 2020 using the Freedom of Information Act (FOIA). Of those districts, 19 responded with the requested data, while other districts required purchase of the data due to the number of hours associated with labor. Due to ambiguity in the original message sent to districts, varying types of data were collected. The major difference between the data received was the “daily” records of student attendance and a “summary” of student attendance records so far, this academic school year. School districts took between 10 to 15 business days to respond, not including the holidays. The focus of this project is “daily student attendance” in order to find relationships or any influences from external or internal factors on any given school day. Therefore, of the 19 school districts that responded, 11 sent the appropriate data.
The 11 school districts that sent data were (1) Conroe ISD, (2) Cypress-Fairbanks ISD, (3) Floydada ISD, (4) Fort Worth ISD, (5) Pasadena ISD, (6) Snook ISD, (7) Socorro ISD, (8) Klein ISD, (9) Garland ISD, (10) Dallas ISD, and (11) Katy ISD. However, even within these datasets, there were discrepancies, that is, three school districts sent daily attendance data including student grade level but one school district did not include any other information. Also, of the 11 school districts, nine school districts included student attendance broken down by school while three other school districts only had student attendance with no other attributes. This information is important to explain certain steps in analysis preparation later. Variables used from school district datasets included (a) dates, (b) weekdays, (c) school name, (d) school type, (e) district, and (f) grade level.

School Information, County Description, Metropolitan vs. Non-Metropolitan

  In addition to daily student attendance data, two other datasets were used from the Texas Education Agency with data about each school and school district. In one dataset, “Current Schools”, information about each school in the state of Texas was given such as address, principal, county name, district number and much more as of May 2020. From this dataset, variables selected include (a) school name, (b) school zip, (3) district number, (4) and school type. In the second dataset, “District Type”, attributes of each school district were given such as whether the school district was considered major urban, independent town, or a rural area. From “District Type” dataset, selected variables used were (a) district, district number, Texas Education Agency (TEA) description, and National Center of Education Statistics (NCES). To determine if a county is metropolitan or non-metropolitan, a dataset from the Texas Health and Human Services was used. Selected variables from this dataset include (a) county name and (b) metro area. 

Other Factors: COVID-19

 Student attendance has been noticeably different this academic school year, therefore live COVID-19 data was attained from the New York Times to examine for any relationship. This dataset is updated daily with data being available in three formats (country, state, and county). From this dataset, variables selected were both COVID-19 cases by state, and by county.

Other Factors: Demographics

Each school has a unique student population, therefore census data from 2018 (with best estimate of today’s current population) was used to find the makeup of the population surrounding a school by zip code. From the census data, variables selected were zip code, race/ethnicity, medium income, unemployment rate, and education. These variables were selected to determine differences between school attendance based on the makeup of the population surrounding the school.

Other Factors: Weather

  Weather seems to have an impact on student attendance at schools, so weather data has been included based on county measures.

John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: Though the article says the selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field, the actual career-based ranking list has 204644 names[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.

Severe burns are one of the most complex forms of traumatic injury. People with burn injuries often require long-term rehabilitation. Survivors of a burn injury often have a wide range of physical and psychosocial problems that can affect their quality of life. The Burn Model System (BMS) program began in 1994, with funding from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), in the Administration of Community Living and the U.S. Department of Education. The BMS program seeks to improve, through research, care and outcomes for people with burn injuries. Its research programs are housed in clinical burn centers that provide a coordinated and multidisciplinary system of rehabilitation care, including emergency medical, acute medical, post-acute, and long-term follow-up services. In addition, and with funding from NIDILRR, each BMS center conducts research and contributes follow-up data to the BMS National Data and Statistical Center (BMS NDSC). The four BMS centers are: Boston-Harvard Burn Injury Model System (BH-BIMS) in Boston, Massachusetts North Texas Burn Rehabilitation Model System (NTBRMS) in Dallas, Texas Northwest Regional Burn Model System (NWRBMS) in Seattle, Washington; andSouthern California Burn Model System (SCBMS) in Los Angeles, CaliforniaPast centers include the University of Texas Medical Branch Burn Injury Rehabilitation Model System in Galveston, Texas, the Johns Hopkins University Burn Model System in Baltimore, Maryland, the University of Colorado Denver National Data and Statistical Center, and the University of Colorado Denver Burn Model System Center.The BMS NDSC supports the research teams in the clinical burn centers. It also manages data collected by the BMS centers on more than 7,000 people who have received medical care for burn injuries. The data include a wide range of information—including pre-injury; injury; acute care; rehabilitation; recovery; and outcomes at 6, 12, 24 months, and every five years after the burn injury. To be included in the database, the burn injuries of participants must meet several criteria (as of 2015): ·More than 10% total body surface area (TBSA) burned, 65 years of age and older with burn surgery for wound closure;More than 20% TBSA burned, 0–64 years of age with burn surgery for wound closure; Electrical high voltage/lightning injury with burn surgery for wound closure; or Hand burn and/or face burn and/or feet burn with burn surgery for wound closure.In 2015, the BMS began a major initiative to collect data every five years after the injury and to collect new psychometrically sound, patient-reported outcome measures. On December 31, 2023, the database contained information for 4,913 adults (18 years of age and older at the time of burn) and 2,402 children (17 years of age and younger at the time of burn). The BMS program disseminates evidence-based information to patients, family members, health care providers, educators, policymakers, and the general public. The BMS centers provide information in many ways: peer-reviewed publications, presentations at national professional meetings, fact sheets about different aspects of living with a burn injury, newsletters for patients on BMS research and center events, outreach satellite clinics for patients living in rural areas, and peer-support groups. The BMS program also collaborates with the NIDILRR-funded Model Systems Knowledge Translation Center to promote the adoption of research findings by rehabilitation professionals, policymakers, and persons with burn injuries and their family members. The BMS program establishes partnerships to increase the overall impact of research; information dissemination; and training of clinicians, researchers, and policymakers. Current partners include the American Burn Association (ABA) and the Phoenix Society. Together, these partners help the BMS to ensure that NIDILRR-funded research addresses issues that are relevant to people with burn injuries.

About this dataset

Context

The dataset tabulates the New Market population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for New Market. The dataset can be utilized to understand the population distribution of New Market by age. For example, using this dataset, we can identify the largest age group in New Market.

Key observations

The largest age group in New Market, IN was for the group of age 70-74 years with a population of 76 (11.95%), according to the 2021 American Community Survey. At the same time, the smallest age group in New Market, IN was the 65-69 years with a population of 20 (3.14%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the New Market is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of New Market total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Market Population by Age. You can refer the same here

Here we present an open dataset of 33 fibromyalgia female patients and 33 paired healthy controls recruited from a Mexican population. Dataset includes MRI data that consists of: T1w, T2w, resting state fMRI and task-fMRI sequences. The task was an emotion processing and regulation task based on visual stimuli (pictures). The MRI data consists of raw data in BIDS format.

The demographic, clinical, and behavioral data is available at Zenodo: https://doi.org/zenodo.6554870

About this dataset

Context

The dataset tabulates the population of White Earth by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of White Earth across both sexes and to determine which sex constitutes the majority.

Key observations

There is a considerable majority of male population, with 78.57% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the White Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of White Earth total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth Population by Gender. You can refer the same here

This dataset contains information about vehicles (or units as they are identified in crash reports) involved in a traffic crash. This dataset should be used in conjunction with the traffic Crash and People dataset available in the portal. “Vehicle” information includes motor vehicle and non-motor vehicle modes of transportation, such as bicycles and pedestrians. Each mode of transportation involved in a crash is a “unit” and get one entry here. Each vehicle, each pedestrian, each motorcyclist, and each bicyclist is considered an independent unit that can have a trajectory separate from the other units. However, people inside a vehicle including the driver do not have a trajectory separate from the vehicle in which they are travelling and hence only the vehicle they are travelling in get any entry here. This type of identification of “units” is needed to determine how each movement affected the crash. Data for occupants who do not make up an independent unit, typically drivers and passengers, are available in the People table. Many of the fields are coded to denote the type and location of damage on the vehicle. Vehicle information can be linked back to Crash data using the “CRASH_RECORD_ID” field. Since this dataset is a combination of vehicles, pedestrians, and pedal cyclists not all columns are applicable to each record. Look at the Unit Type field to determine what additional data may be available for that record. The Chicago Police Department reports crashes on IL Traffic Crash Reporting form SR1050. The crash data published on the Chicago data portal mostly follows the data elements in SR1050 form. The current version of the SR1050 instructions manual with detailed information on each data elements is available here. Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

Bright Data’s datasets are created by utilizing proprietary technology for retrieving public web data at scale, resulting in fresh, complete, and accurate datasets. CrunchBase datasets provide unique insights into the latest industry trends. They enable the tracking of company growth, identifying key businesses and professionals, tracking employee movement between companies, as well as enabling more efficient competitive intelligence. Easily define your Crunchbase dataset using our smart filter capabilities, enabling you to customize pre-existing datasets, ensuring the data received fits your business needs. Bright Data’s Crunchbase company data includes over 2.8 million company profiles, with subsets available by industry, region, and any other parameters according to your requirements. There are over 70 data points per company, including overview, details, news, financials, investors, products, people, and more. Choose between full coverage or a subset. Get your Crunchbase dataset Today!

Predicting Heart Disease Risk Using Clinical Variables

A 270-Patient Dataset with 13 Features

By Robert Hoyt MD [source]

About this dataset

The Heart Disease Prediction dataset provides vital insight in the relationship between risk factors and cardiac health. This dataset contains 270 case studies of individuals classified as either having or not having heart disease based on results from cardiac catheterizations - the gold standard in heart health assessment. Each patient is identified by 13 independent predictive variables revealing their age, sex, chest pain type, blood pressure measurements, cholesterol levels, electrocardiogram results, exercise-induced angina symptoms, and the number of vessels seen on fluoroscopy showing narrowing of their coronary arteries. Provided with this data set is an opportunity to evaluate how these characteristics interact with each other in order to determine an individual’s level of risk for developing cardiovascular problems that lead to heart failure or stroke. With this knowledge we can create preventative strategies beyond what traditional medical treatment can do by identifying those at risk earlier and aid our healthcare professionals in treating them better. By analyzing a combination of clinical variables explained here, we have a powerful tool at our fingertips to try and combat cardiovascular illness before it even has the chance to take root!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides the information necessary to predict whether a patient has heart disease or not, using clinical variables. The dataset contains information from 270 patients and 13 independent predictive variables.

To use this dataset effectively, it is best to start by understanding the column attributes and their importance in predicting heart disease risk.

The attributes are Age, Sex, Chest Pain Type, BP (blood pressure), Cholesterol level, FBS over 120 (fasting blood sugar), EKG Results (electrocardiogram results), Max HR (maximum heart rate), Exercise Angina status, ST Depression ( depression of ST segment on ECG) , Slope of ST(slope of the ST segment on the ECG), Number of Vessels Fluroscopy (number of vessels seen on fluoroscopy) and Thallium Stress test results.

Understanding how each variable relates to heart disease risk will help you make better predictions based on this data set. For example age is one variable that affects the odds of someone having a heart attack or stroke as it relates to arterial blockages- so it is important to note if age could be an independent factor or if other factors could be enhancing the odds in an individual patient’s case . It should also be noted that although cholesterol levels are included in this data set , other laboratory parameters such as hdl cholesterol , triglycerides and ldl need to also be considered when assessing overall cardiovascular risk . Knowing gender can also play an important role when analyzing possible trends for diagnosing new cases with suspected cardiac symptoms . Finally , understanding exercise angina status can provide critical information about a patient’s history with exercise -induced chest pain which could result from myocardial ischemia .

Once you have understood all these factors along with other pertinent medical history including family medical background and lifestyle habits like smoking/ vaping , consuming alcohol etc., , you can use machine learning techniques along with logistic regression models such as k-nearest neighbours algorithms & decision trees to predict whether someone has a higher risk for developing cardiovascular problems like coronary artery disease [CAD]. This can help physicians plan out preventive measures for reducing chances for future myocardial infarctions [MIs] among such patients

Research Ideas

• Developing predictive models to predict a patient's risk of having heart disease based on demographic and medical data.
• Creating an algorithm to classify patients with and without heart disease using all the available clinical characteristics including age, sex, chest pain type, BP, cholesterol, FBS over 120 and EKG results.
• Identifying patterns in the data that could help identify which factors have the biggest influence on a person's risk for developing heart disease. This could lead to better preventive health care measures for heart disease in general or for specific groups of people at higher risk than others (e.g., due to certain age or gender)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Heart_Disease_Prediction.csv | Column name | Description | |:----------------------------|:--------------------------------------------------------------------------------| | Age | The age of the patient. (Numeric) | | Sex | The gender of the patient. (Categorical) | | Chest pain type | The type of chest pain experienced by the patient. (Categorical) | | BP | The blood pressure level of the patient. (Numeric) | | Cholesterol | The cholesterol level of the patient. (Numeric) | | FBS over 120 | The fasting blood sugar test results over 120 mg/dl. (Numeric) | | EKG results | The electrocardiogram results of the patient. (Categorical) | | Max HR | The maximum heart rate levels achieved during exercise testing. (Numeric) | | Exercise angina | The angina experienced during exercise testing. (Categorical) | | ST depression | The ST depression on an Electrocardiogram. (Numeric) | | Slope of ST | The slope of ST segment electrocardiogram readings. (Categorical) | | Number of vessels fluro | The amount vessels seen in Fluoroscopy images. (Numeric) | | Thallium | The Thallium Stress test findings. (Categorical) | | Heart Disease | Whether or not the patient has been diagnosed with Heart Disease. (Categorical) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Robert Hoyt MD.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in Germany. Power Plant emissions from all power plants in Germany were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, metro area, lat/lon, and plant id for each individual power plant. Only Power Plants that had a listed longitude and latitude in CARMA's database were mapped. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information http://carma.org/region/detail/78