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.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

The ESS-DIVE location metadata reporting format provides instructions and templates for reporting a minimum set of metadata for discrete point locations in geographic space represented by x, y, and z coordinates. This format was created based on a need for earth and environmental science researchers to more consistently provide metadata about locations where they conduct studies. To create the format, we incorporated elements from ESS-DIVE’s community reporting formats as well as 12 additional data standards or other data resources (e.g., databases, data systems, or repositories). In the template, we ask researchers to indicate unique locations using Location IDs and indicate hierarchies of locations through parent location IDs. We also provide additional optional fields for researchers to indicate how they measured the point location and the date and time that the location was first used as a research site This dataset contains support documentation for the reporting format (README.md and instructions.md), a terminology guide (guide.md), a crosswalk indicating how this reporting format relates to existing standards and data resources (Location_metadata_crosswalk.csv), a data dictionary (dd.csv), file-level metadata (flmd.csv), and the location metadata templates in both CSV (Location_metadata_template.csv) and Excel formats (Location_metadata_template.xlsx).

The datasets hold information about the cases and deaths from COVID-19 for multiple countries between January 22th 2020, to March 30, 2020. There is a separate excel sheet for every country. The following is the information that the dataset holds.

1. The date for which the observation was made for the country/state.
2. Information regarding the state of the country where the case is reported.
3. The country where the case is reported.
4. Cumulative confirmed cases and cumulative deaths
5. Daily cases reported and daily deaths
6. Latitude and Longitude for the country
7. Average temperature for that day.
8. Minimum and Maximum temperature for that day.
9. Wind speed reported for that day.
10. Precipitation and Fog (1 denotes the presence)
11. Population, Population density and median population for that country.
12. The sex ratio for that country.
13. %of Population above 65 years of age.
14. Hospital Beds and Available Hospital beds/1000 people
15. Confirmed COVID-19 cases/1000 people
16. No. of males and females/1million people suffering from a lung / COPD Disease.
17. Life Expectancy (Males and Females)
18. Total COVID-19 Tests conducted for that country.
19. Outbound | Inbound | Domestic travels for that country.

Separate CSV sheets are made for the country. The datasets would surely be updated on a certain basis to fit with the current COVID-19 values.

Special thanks to - https://www.kaggle.com/koryto/countryinfo for providing the much essential information for building the dataset.

Sentinel2GlobalLULC is a deep learning-ready dataset of RGB images from the Sentinel-2 satellites designed for global land use and land cover (LULC) mapping. Sentinel2GlobalLULC v2.1 contains 194,877 images in GeoTiff and JPEG format corresponding to 29 broad LULC classes. Each image has 224 x 224 pixels at 10 m spatial resolution and was produced by assigning the 25th percentile of all available observations in the Sentinel-2 collection between June 2015 and October 2020 in order to remove atmospheric effects (i.e., clouds, aerosols, shadows, snow, etc.). A spatial purity value was assigned to each image based on the consensus across 15 different global LULC products available in Google Earth Engine (GEE). Our dataset is structured into 3 main zip-compressed folders, an Excel file with a dictionary for class names and descriptive statistics per LULC class, and a python script to convert RGB GeoTiff images into JPEG format. The first folder called "Sentinel2LULC_GeoTiff.zip" contains 29 zip-compressed subfolders where each one corresponds to a specific LULC class with hundreds to thousands of GeoTiff Sentinel-2 RGB images. The second folder called "Sentinel2LULC_JPEG.zip" contains 29 zip-compressed subfolders with a JPEG formatted version of the same images provided in the first main folder. The third folder called "Sentinel2LULC_CSV.zip" includes 29 zip-compressed CSV files with as many rows as provided images and with 12 columns containing the following metadata (this same metadata is provided in the image filenames): Land Cover Class ID: is the identification number of each LULC class Land Cover Class Short Name: is the short name of each LULC class Image ID: is the identification number of each image within its corresponding LULC class Pixel purity Value: is the spatial purity of each pixel for its corresponding LULC class calculated as the spatial consensus across up to 15 land-cover products GHM Value: is the spatial average of the Global Human Modification index (gHM) for each image Latitude: is the latitude of the center point of each image Longitude: is the longitude of the center point of each image Country Code: is the Alpha-2 country code of each image as described in the ISO 3166 international standard. To understand the country codes, we recommend the user to visit the following website where they present the Alpha-2 code for each country as described in the ISO 3166 international standard:https: //www.iban.com/country-codes Administrative Department Level1: is the administrative level 1 name to which each image belongs Administrative Department Level2: is the administrative level 2 name to which each image belongs Locality: is the name of the locality to which each image belongs Number of S2 images : is the number of found instances in the corresponding Sentinel-2 image collection between June 2015 and October 2020, when compositing and exporting its corresponding image tile For seven LULC classes, we could not export from GEE all images that fulfilled a spatial purity of 100% since there were millions of them. In this case, we exported a stratified random sample of 14,000 images and provided an additional CSV file with the images actually contained in our dataset. That is, for these seven LULC classes, we provide these 2 CSV files: A CSV file that contains all exported images for this class A CSV file that contains all images available for this class at spatial purity of 100%, both the ones exported and the ones not exported, in case the user wants to export them. These CSV filenames end with "including_non_downloaded_images". To clearly state the geographical coverage of images available in this dataset, we included in the version v2.1, a compressed folder called "Geographic_Representativeness.zip". This zip-compressed folder contains a csv file for each LULC class that provides the complete list of countries represented in that class. Each csv file has two columns, the first one gives the country code and the second one gives the number of images provided in that country for that LULC class. In addition to these 29 csv files, we provided another csv file that maps each ISO Alpha-2 country code to its original full country name. © Sentinel2GlobalLULC Dataset by Yassir Benhammou, Domingo Alcaraz-Segura, Emilio Guirado, Rohaifa Khaldi, Boujemâa Achchab, Francisco Herrera & Siham Tabik is marked with Attribution 4.0 International (CC-BY 4.0)

This master list of health facilities was developed from a variety of government and non-government sources from 50 countries in sub-Saharan Africa. It uses multiple geocoding methods to provide a comprehensive spatial inventory of 98 745 public health facilities. Each data record represents a health facility and has 8 descriptive variables – Location identifiers including: country, first level administrative division, latitude, longitude and LL source (source of the coordinates). Coordinates are rounded off to four decimal places for uniformity, allowing an accuracy of 5–10 metres in decimal degrees coordinate format. This geocoded master facility list has been made publicly and freely available through both the figshare repository and through the World Health Organization’s Global Malaria Programme in Microsoft Excel format.

Presence records used to calibrate and validate a climatic suitability model for emerald ash borer in the DDRP platform (Degree-Days, Risk, and Phenological event mapping). The first sheet ("Records") of the Excel file provides the range (native or invaded), continent, country, state or province, locality, latitude, and longitude of origin for each record. The year in which the record was collected is provided if known. The second sheet of the Excel file ("References") provides a list of references for each record source.

TimeSpec4LULC is archived in 30 different ZIP files owning the name of the 29 LULC classes (one class is divided into two files since it is too large). Within each ZIP file, there exists a set of seven CSV files, each one corresponding to one of the seven spectral bands. The naming of each file follows this structure: IdOfTheClass_NameOfTheClass_ModisBand.csv For example, for band 1 of the Barren Lands class, the filename is: 01_BarrenLands_MCD09A1b01.csv Inside each CSV file, rows represent the collected pixels for that class. The first 11 columns contain the following metadata: - “IdOfTheClass”: Id of the class. - “NameOfTheClass”: Name of the class. - “IdOfTheLevel0”: Id of the FAO-L0 (i.e., countries). - “IdOfTheLevel1”: Id of the FAO-L1 (i.e., departments, states, or provinces depending on the country). - “IdOfThePixel”: Id of the pixel. - “PurityOfThePixel”: Spatial and inter-annual consensus for this class across multiple land-cover products, i.e., Purity of the pixel. - “DataAvailability”: percentage of non-missing data per band throughout the time series. - “Index_GHM”: average of Global Human Modification index (gHM). - “Lat”: Latitude of the pixel center. - “Lon”: Longitude of the pixel center. - “.geo”: (Longitude, Latitude) of the pixel center with more precision. And, the last 223 columns contain the 223 monthly observations of the time series for one spectral band from 2002-07 to 2021-01. Along with the dataset, an Excel file named 'Countries_Departments_FAO-GAUL' containing the FAO-L0 and the FAO-L1 Ids and names (following the FAO-GAUL standards) is provided.

Mortality Statistics in US Cities

Deaths by Age and Cause of Death in 2016

By Health [source]

About this dataset

This dataset contains mortality statistics for 122 U.S. cities in 2016, providing detailed information about all deaths that occurred due to any cause, including pneumonia and influenza. The data is voluntarily reported from cities with populations of 100,000 or more, and it includes the place of death and the week during which the death certificate was filed. Data is provided broken down by age group and includes a flag indicating the reliability of each data set to help inform analysis. Each row also provides longitude and latitude information for each reporting area in order to make further analysis easier. These comprehensive mortality statistics are invaluable resources for tracking disease trends as well as making comparisons between different areas across the country in order to identify public health risks quickly and effectively

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How to use the dataset

This dataset contains mortality rates for 122 U.S. cities in 2016, including deaths by age group and cause of death. The data can be used to study various trends in mortality and contribute to the understanding of how different diseases impact different age groups across the country.

In order to use the data, firstly one has to identify which variables they would like to use from this dataset. These include: reporting area; MMWR week; All causes by age greater than 65 years; All causes by age 45-64 years; All causes by age 25-44 years; All causes by age 1-24 years; All causes less than 1 year old; Pneumonia and Influenza total fatalities; Location (1 & 2); flag indicating reliability of data.

Once you have identified the variables that you are interested in,you will need to filter the dataset so that it only includes relevant information for your analysis or research purposes. For example, if you are looking at trends between different ages, then all you would need is information on those 3 specific cause groups (greater than 65, 45-64 and 25-44). You can do this using a selection tool that allows you to pick only certain columns from your data set or an excel filter tool if your data is stored as a csv file type .

Next step is preparing your data - it’s important for efficient analysis also helpful when there are too many variables/columns which can confuse our analysis process – eliminate unnecessary columns, rename column labels where needed etc ... In addition , make sure we clean up any missing values / outliers / incorrect entries before further investigation .Remember , outliers or corrupt entries may lead us into incorrect conclusions upon analyzing our set ! Once we complete the cleaning steps , now its safe enough transit into drawing insights !

The last step involves using statistical methods such as linear regression with multiple predictors or descriptive statistical measures such as mean/median etc ..to draw key insights based on analysis done so far and generate some actionable points !

With these steps taken care off , now its easier for anyone who decides dive into another project involving this particular dataset with added advantage formulated out of existing work done over our previous investigations!

Research Ideas

• Creating population health profiles for cities in the U.S.
• Tracking public health trends across different age groups
• Analyzing correlations between mortality and geographical locations

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: rows.csv | Column name | Description | |:--------------------------------------------|:-----------------------------------...

The Spotted Wing Drosophila (Drosophila suzukii) is native to Southeast Asia. Since its first detection in 2008 in Europe and North America, it has been a pest to the fruit production industry as it feeds and oviposits on ripening fruit. Here we aim to model the potential geographical distribution of D. suzukii. We performed an extensive literature review to map the current records. In total, 517 documented occurrences (96 native and 421 invasive) were identified spanning 52 countries. Next, we constructed three species distribution models (SDMs) based on occurrence records in: 1) the native range (SDMnative), 2) the invasive range in Europe (SDMEurope) and 3) a global model of all records (SDMglobal). The models aimed to investigate, whether this species will be able to occupy additional ecological niches beyond its native range and expand its current geographic distribution both globally and in Europe. The SDMs were generated using Maximum Entropy algorithms (Maxent) based on present ..., Occurrence records of Drosophila suzukii

Microsoft Excel spreadsheet with two worksheets (tabs). Â First sheet: Information about all 517 occurrence records used in the study, as obtained through extensive literature review and through mining online databases, e.g. Taxodros (http://www.taxodros.uzh.ch) and European and Mediterranean Plant Protection Organization (EPPO - https://www.eppo.int). Columns describe: country, state or region (if applicable), latitude, longitude, the year of the first observation at the specific site (if available), status, and references. Regarding the status we distinguish between: widespread, present, restricted distribution, and few occurrences similar to the EPPO and the National Plant Protection Organizations (NPPOs) of the International Plant Protection Convention (IPPC: https://www.ippc.int/en/news/tag/nppo/). In most information sources, GPS coordinates of occurrences were given, but in the cases where direct coordinates could not be obtained, the GP...,

Abstract

General objective: To design and carry out the National Forest Inventory (NFI) of Guatemala and create a system for the periodic gathering of forest information at the national level.

Specific objectives: A. To adapt the methodology provided by FRA to carry out the National Forest Inventory, adequate to the needs of the country. The methodology shall be statistically reliable and allow periodic surveys of information related to forest resources. B. To carry out the first data collection of the variables that respond to the needs of the country's forestry sector, with emphasis on: forest cover, total and commercial volume of timber species, biomass based on stem volume, non-timber products, biophysical data, and socioeconomic data on the use and management of forest products and services. C. To design a database to archive and manage the field inventory information, which may be part of the National Forest Information System.

Geographic coverage

National Coverage

Analysis unit

Forest types and land use classes

Universe

Tree and stump population > 10 cm diameter at breast height across the nation, in and out of forest. The socioeconomic surveys focused on users of forest products across the nation.

Kind of data

Sample survey data [ssd]

Sampling procedure

The design of the NFI was based on the aforementioned objectives and the methodological design proposed by FAO. It had a low sample intensity, but was statistically reliable. It was designed to cover the total area of the country (108,889 km2). The sampling did not only contemplate forest areas, because it was aiming to carry out periodic surveys in the same plots to know land use dynamics throughout the country. In addition, it aimed to evaluate forest resources outside of forest areas, to expand the forest information towards other land uses where these resources are also managed.

The sampling design is systematic stratified. It has three defined strata based on the map of natural divisions of Guatemala ("Mapa de Divisiones Naturales de Guatemala" in the original document), because it was sought that the strata are stable over time to ensure that the area they occupied was the same in future measurements). The strata are named: Zona Norte, Centro and Sur (North, Central and South), according to the geographical area of the country they represent. The systematic design is predetermined by a grid of geographic coordinates (latitude-longitude).

The sampling intensity is relatively low, compared to larger-scale inventories, such as those carried out on farms where forest harvesting or forest concessions. This low intensity only affects the sampling error, but the data are statistically valid, since they will be developed under a strict statistical design and must be interpreted on a national scale. The number of sampling units (SUs) vary according to the defined strata. The largest number of SUs was collected in the Central Zone (70 SUs - with 15min x 15min grid, approximately 26.8 x 26.8 km) because it is the area with the greatest diversity of ecosystems and socioeconomic activities. In the North and South Zones, 30 and 8 SUs were built, respectively (with SUs every 15 minutes in latitude and 30 minutes in longitude - approximately every 26.8 x 53.6 km).

A specific land use and forest type classification was developed, based on the global FAO classes (Forest, Other Forest Lands, Other Lands and Inland Waters) and the classes used in the country's forest cover map. The global classes are located in the upper hierarchical level and in the next levels the national categories are specified. The definitions of each class are described in the adjunct document "Inventario Forestal Nacional de Guatemala: Manual de Campo". Plots were positioned around the selected center point of the point grid. The SU consists of a square conglomerate, with 4 rectangular plots, whose starting point is located at each corner of the square (Figure 2 in "Inventario Forestal Nacional de Guatemala: Manual de Campo"). The first plot was located in the southwest corner of the square and had a northward direction, the second plot was located in the northwest corner and had an eastward direction, the third one was in the northeast corner with a southward direction and the fourth one in the southeast corner facing west.

The plots, following FAO's NFMA design, had a rectangular shape and a size of 250 x 20 m (0.5 ha). They had a nested structure, according to the size and type of resources measured. There were also measurement points for the soil and topographic variables. Each plot has three groups of nested plots and three measurement points, systematically distributed. The nested structure is described below: - The SU is a cluster of 500 x 500 m composed by four rectangular plots, depicted below. - At plot level (250 x 20 m - 0.5 ha) all trees with diameter at breast height DBH=20 cm were measured. - 3 nested plots below (PAN1, 20 x 10 m - 0.02 ha), all trees 10=DBH<20 cm were measured. - One PAN2 plot nested per PAN1 plot (3 per main plot). Circle 3.99 m radius (0.005 ha), enumerating all trees DBH<10 cm and height=1.3 m plus regeneration abundance by species. - One PAN3 plot per PAN1 plot (3 per main plot). 10 x 10 m (0.01 ha) square measuring presence and abundance of bayal and mimbre. - One PAN4 plot per PAN2 plot (3 per main plot). Northwest quadrant from PAN2 circle (0.00125 ha) measuring presence and abundance of xate. - Finally in the center of each PAN2, topographic and soil characteristics were recorded.

Besides, data were collected about the villages, which benefitted from the area occupied by the SU. These had to be obtained in the municipalities or auxiliary townships.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

The databases of each sampling unit were entered into the general NFI database by the field crews, after the approval of the reports and field forms. Subsequently the last control filter was performed by the technical unit, based on a protocol of review of the database: scientific names of species were normalized, development of outlier analysis, data gaps, discussions and decisions of data management. The review criteria for each registered attribute were reported. The data processing rout map was performed for the estimation and reporting with the support of national and international specialist.

The processing and analysis was carried out in Microsoft Excel. This program has certain advantages, although it is not the most suitable for all processing, however, since it was the most accessible tool at the beginning of the project, it was decided to use it. However, the importance of building a more adequate database was discussed, and that is how FAO-FRA created a Microsoft Access Data Management System for all the projects they have worldwide, so the data was migrated from Excel. Certain adaptations were made to each of the countries, according to the information requirements.

The structure of the Excel and Access databases are quite compatible, since from the design of the forms, easy links were sought between all the information of the NFI. In the documentation (“Evaluación Nacional Forestal: Inventario Nacional Forestal de Guatemala 2002-2003”) the field forms can be found. For each field form, there is an Excel sheet and an Access form.

Response rate

98% of projected primary sampling units were finally enumerated. Hence, 2% were inaccessible, mostly due to topography and denied access permissions.

Sampling error estimates

All the estimates were made with the estimation error, which is the limit of the estimator with a confidence level of 95% (alpha/2) expressed as a percentage of the mean.

The NFI 2002-03 design has a multidimensional approach, that is, it includes information on various topics related to forest resources and areas outside the forest. That is why there are several target populations from which various measurements were obtained according to the variables that were initially proposed. On the other hand, a design was sought that is practical and economical that provides information at the strategic level for the country, and not at the specific planning level of management units. Under these considerations, it is necessary to interpret the results of the estimates obtained and their respective sampling errors, where each user decides their use depending on the level of risk that this error can determine. There is no scientific way to decide which error is acceptable, because it is an administrative, pragmatic and even political decision. The estimation error is a function of the variability of the data for each variable. In addition, they are also affected by the number of samples that we have of each variable in the sample. The greater the number of samples, the more precise and potentially more accurate the data.

Forest inventories are designed depending on the geographical distribution of the elements to be measured. The largest elements of IFN 2002-03 are forests and the smallest were the leaves, roots and stems of non-timber forest products. Thus, the design tried to focus on the range of intermediate elements, obviously the trees being the most important according to the objectives and information needs. Currently a stratified systematic design was used, which had a direct effect on the size of the units to be measured, which is why in general better precision was achieved in the elements that occupy more area than in those scarce. However, high errors should not totally disqualify the data, since they only indicate that the probability of not being

The Sri Lanka Weather Dataset is a comprehensive collection of weather data for 30 prominent cities in Sri Lanka, covering the period from January 1, 2010, to January 1, 2023. The dataset offers a wide range of meteorological parameters, enabling detailed analysis and insights into the climate patterns of different regions in Sri Lanka.

The dataset includes information such as: - Time: The timestamp of each weather observation. - Weather Code: A numerical code representing the weather conditions at the given time. - Temperature: Maximum, minimum, and mean values of 2-meter temperature. - Apparent Temperature: Maximum, minimum, and mean values of apparent temperature, which takes into account factors like wind chill or heat index. - Sunrise and Sunset: The times of sunrise and sunset for each day. - Shortwave Radiation: Sum of shortwave radiation received during the observation period. - Precipitation: Total sum of precipitation, including rainfall and snowfall. - Precipitation Hours: The duration of time with measurable precipitation. - Wind Speed and Gusts: Maximum values of wind speed and wind gusts at 10 meters above ground level. - Wind Direction: Dominant wind direction at 10 meters above ground level. - Evapotranspiration: Reference evapotranspiration (ET0) based on the FAO Penman-Monteith equation. - Latitude, Longitude, and Elevation: Geographic coordinates and elevation of each city. - Country and City: Names of the country and city corresponding to each weather observation.

This dataset was sourced from Open-Meteo and simplemaps, and the data was collected using a basic Python script. The collected data was pre-processed to ensure cleanliness and readability before being stored in CSV format.