This dataset is about countries. It has 194 rows. It features 3 columns: land area, and death rate. It is 100% filled with non-null values.

The World Health Organization reported 6932591 Coronavirus Deaths since the epidemic began. In addition, countries reported 766440796 Coronavirus Cases. This dataset provides - World Coronavirus Deaths- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The world population has grown rapidly, particularly over the past century: in 1900, there were fewer than 2 billion people on the planet. The world population is around 8045311488 in 2023.

Two metrics determine the change in the world population: the number of babies born and the number of people dying. How many babies are born each year?

There were 133.99 million births in 2022, compared to 92.08 million births in 1950

The WHO Mortality Database is the leading data source for comparative epidemiological studies of mortality by cause. The visualization portal gives the WHO Mortality database unprecedented impact, accessibility and relevance and provides export facilities for cause-of-death data from 1950 to date .

All CSV have been cleaned to be used immediately.

It should be noted that these data are transmitted on the understanding that no use will be made of them for commercial purposes and that no such permission or right to use may be implied thereby. WHO requests data users to adhere to the guidelines outlined

Tree mortality maps derived from aerial photos (NAIP) of 2020 in California for the paper titled Scattered tree death contributes to substantial forest loss in California. The dataset includes dead tree count per ha (100 m), median dead crown size per ha (100 m), percentage of dead canopy area per ha (100 m), percentage of brown-stage mortality per ha (100 m), eccentricity map (500 m), and percentage of tree mortality (240 m). The spatial coverage is the vegetated area (woody vegetation) in California. The projection system is EPSG:5072.Publication: Cheng, Y. et al. Scattered tree death contributes to substantial forest loss in California. Nat. Commun. (2024). https://doi.org/10.1038/s41467-024-44991-z

This dataset is about countries in Europe. It has 44 rows. It features 3 columns: urban land area, and death rate.

Heart failure

The Heart failure dataset from Kaggle. Predict patient death from earth failure given some personal medical data .

  Configurations and tasks

Configuration Task Description

death Binary classification Did the patient die?

  Usage

from datasets import load_dataset

dataset = load_dataset("mstz/heart_failure", "death")["train"]

  Features

Feature Type

age int8

has_anaemia int8… See the full description on the dataset page: https://huggingface.co/datasets/mstz/heart_failure.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

This dataset is about countries per year in Israel. It has 64 rows. It features 4 columns: country, land area, and death rate.

Famines are still a major global problem. From 2020 to 2023 alone, they caused over a million deaths.

Yet the long-term trend shows significant progress. In the late 1800s and the first half of the 1900s, it was common for famines to kill over 10 million people per decade. This was true as recently as the 1960s, when China’s Great Leap Forward became the deadliest famine in history.

But as you can see in the chart, that number has dropped sharply, to about one to two million per decade.

This improvement is even more striking given that the world’s population has grown substantially. Despite many more people living on Earth, far fewer die from famines than before.

The model on which this dataset is based contain the following nodes, divided as follow: • Personal info, containing the node: Age range and Gen- der; • Probability to contract the disease/infection described by the node, containing the nodes: Respiratory infection, Malignancy, Cardiovascular disease, Poisoning, Nature force, Fall; • Probability to take damage from something in between described in the previous point, containing the nodes: Other injuries, Sickness, Transport incident, Self-harm vi- olence, Other sickness and Unintentional injuries • Probability of dying, containing the node Death. All of the nodes are discrete random variable with range true, false. The only differences are represented by Age range and Gender that have range respectively in young, adult, old and male, female. Primarily, in order to build our model, we followed the causal order trying to figure out which nodes had directly influence on which others.

This dataset is about countries in Oceania. It has 14 rows. It features 3 columns: urban land area, and death rate.

This dataset contains estimates of the number of persons per square kilometer consistent with national censuses and population registers. There is one image for each modeled year. General Documentation The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution of global human population for the years 2000, 2005, 2010, 2015, and 2020 on 30 arc-second (approximately 1 km) grid cells. Population is distributed to cells using proportional allocation of population from census and administrative units. Population input data are collected at the most detailed spatial resolution available from the results of the 2010 round of censuses, which occurred between 2005 and 2014. The input data are extrapolated to produce population estimates for each modeled year.

This EnviroAtlas dataset describes the total counts and percentage of population, land area, and impervious surface in the 1% Annual Chance Flood Hazard area or 0.2% Annual Chance Flood Hazard area of each block group. The flood hazard area is defined by the National Flood Hazard Layer (NFHL) produced by the Federal Emergency Management Agency (FEMA, www.fema.gov). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

About this dataset

Context

The dataset tabulates the White Earth population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of White Earth across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of White Earth was 93, a 0% decrease year-by-year from 2022. Previously, in 2022, White Earth population was 93, a decline of 4.12% compared to a population of 97 in 2021. Over the last 20 plus years, between 2000 and 2023, population of White Earth increased by 28. In this period, the peak population was 99 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the White Earth is shown in this column.
• Year on Year Change: This column displays the change in White Earth population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Year. You can refer the same here

PI: James Dolan, University of Southern California. The project area consists of two rectangular polygons covering an area of 38 square kilometers. Data were collected along the Furnace Creek and Fish Lake Valley Fault Zones in Death Valley National Park. The data were collected February 28, 2005. Bare-earth extraction was not performed on this dataset due to the scarceness of vegetation in the interest area.Publications associated with this dataset can be found at NCALM's Data Tracking Center

Years of Life Lost (YLL) as a result of death from land transport accidents. Directly age-Standardised Rates (DSR) per 100,000 population Source: Office for National Statistics (ONS) Publisher: Information Centre (IC) - Clinical and Health Outcomes Knowledge Base Geographies: Local Authority District (LAD), Government Office Region (GOR), National, Strategic Health Authority (SHA) Geographic coverage: England Time coverage: 2005-07, 2007 Type of data: Administrative data

PI: Noah Snyder, Boston College. The survey area is a 38 square kilometer polygon near the Furnace Creek Inn and Ranch in Death Valley National Park, California. This area was flown on February 27, 2005. The data were collected to investigate transient response of a desert river to forced diversion. Bare-earth extraction was not performed on this dataset due to the scarceness of vegetation in the interest area.Publications associated with this dataset can be found at NCALM's Data Tracking Center

This dataset "Global hotspots of climate related disasters" shows the number of people impacted by climate-related disasters recorded in the EM-DAT database between 2000 and 2020. This dataset was used to prepare the maps and the analysis of the paper Donatti C.I., Nicholas K., Fedele G., Delforge D., Speybroeck N., Moraga P., Blatter J., Below R., Zvoleff A. 2024. Global hotspots of climate-related disasters. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2024.104488. This dataset includes information on people impacted by Drought, tropical cyclones, flash flood, riverine flood, forest fire, land fire, heat wave, landslide and mudslide. Data on coastal flood was not included because the database only had recordings until 2013. Data on disaster sub-types “landslides” and “mudslides” as presented in the EM-DAT were further combined as one single climate-related disaster (“land and mudslides”) for the analyses. Likewise, data on disaster sub-types “forest fire” and “land fire” were further combined as one climate-related disaster (“wildfire”). The data was accessed directly from the EM-DAT database and then summarized as show in the dataset. We used this database, downloaded on June 2nd 2021, to access data on “total affected” people and the “total deaths” per disaster event impacting a country (i.e., an entry in the EM-DAT), which were combined in this study to create the variable “total people impacted”. In the EM-DAT database, “total affected” represents the sum of people “injured,” “affected,” and “homeless” resulting from a particular event. “Injured” were considered those that have suffered from physical injuries, trauma, or an illness requiring immediate medical assistance, including people hospitalized, as a direct result of a disaster, “affected” were considered people requiring immediate assistance during an emergency and “homeless” were considered those whose homes were destroyed or heavily damaged and therefore needed shelter after an event. “Total deaths” include people that have died or were considered missing, those whose whereabouts since the disaster were unknown and presumed dead based on official figures. More details can be found under “documentation, data structure and content description” at emdat.be. In the dataset, "ADM-CODE" refers to the code used to identify each administrative area, which refers to the code of FAO's Global Administrative Unit Layer, GAUL.

Dataset

This dataset was created by Stepan Dupliak

Contents