The centuries-old quest for other worlds like our Earth has been rejuvenated by the intense excitement and popular interest surrounding the discovery of hundreds of planets orbiting other stars. There is now clear evidence for substantial numbers of three types of exoplanets; gas giants, hot-super-Earths in short period orbits, and ice giants. The following websites are tracking the day-by-day increase in new discoveries and are providing information on the characteristics of the planets as well as those of the stars they orbit: The Extrasolar Planets Encyclopedia, NASA Exoplanet Archive, New Worlds Atlas, and Current Planet Count Widget. The challenge now is to find terrestrial planets (i.e., those one half to twice the size of the Earth), especially those in the habitable zone of their stars where liquid water and possibly life might exist. The Kepler Mission, NASA Discovery mission #10, is specifically designed to survey a portion of our region of the Milky Way galaxy to discover dozens of Earth-size planets in or near the habitable zone and determine how many of the billions of stars in our galaxy have such planets. Results from this mission will allow us to place our solar system within the continuum of planetary systems in the Galaxy.

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. This dataset contains transformed stressor data from 17 different human activities that directly or indirectly have an impact on the ecological communities in the ocean's ecosystems. The transformed data contains log[X+1]-transformed and rescaled indices between 0-1 for each activity. This transformation to puts each stressor activity on a single, unitless scale that allows direct comparison.

The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Rational Polynomial Coefficients (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Accuracy <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 3-band natural colour (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Projection UTM WGS84 Accuracy <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and colour-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite. PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided.

Population, female (% of total population) in World was reported at 49.71 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Heliocentric trajectories for Voyager 2 in Heliographic, HG, Heliographic Inertial, HGI, and Solar Ecliptic, SE, Coordinates The original trajectory data are taken from http://ssd.jpl.nasa.gov/horizons.cgi where users can find many more objects. In the case of orbit data for planets, the orbit data can be used as a proxy for spacecraft ephemeris that are in orbit about the planets. On a heliospheric scale, differences between the planet orbital tarjectory and that of the spacecraft are very small. For instance, the heliocentric longitudes differ by only 0.25° for a spacecraft stationed near the L1 Lagrange point at approximately 100 Earth radii upstream of the Earth. The production of the HG, HGI, and SE trajectory data requires a values for the "Equinox Epoch", which is defined as the epoch time when the direction from the Earth to the sun at the time of the vernal equinox when the sun seems to cross equatorial plane of the Earth from below. This direction is called the First Point of Aries, FPA and it is not a fixed direction but drifts by about 1.4° per century or 50.26" per year. In addition, there are tiny irregularities in FPA drift that are on the order of 1" per year or less. The Equinox Epoch can be determined by using a variety of methods for calculating the instantaneous FPA longitudinal direction and whether the tiny irregularities have been smoothed or averaged out. Four methods for determining the Equinox Epoch are in common usage: +---------------------------------------------------------------------+ Method Name FPA Longitude Definition --------------------------------------------------------------------- B1950.0 the actual FPA at 22:09 UT on December 31, 1949 J2000.0 the smoothed FPA at 12:00 UT on January 1, 2000 True of Date the actual FPA at 00:00 UT on the date of interest Mean of Date the smoothed FPA at 00:00 UT on the date of interest +---------------------------------------------------------------------+ The heliocentric trajectory data included in this data product have been calculated by using the Equinox Epoch: defined via the "Mean of Date" method. More precise coordinates, and some planet-centered coordinates, are found in the "traj" subdirectories of spacecraft specific directories at https://spdf.gsfc.nasa.gov/pub/data/ and http://ssd.jpl.nasa.gov/horizons.cgi.

This dataset contains key characteristics about the data described in the Data Descriptor Population Centroids of the World Administrative Units from Nighttime Lights 1992-2013. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format 


      Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

Historical chart and dataset showing total population for the world by year from 1950 to 2025.

The Earth Observing System Data and Information System (EOSDIS) is a major core capability within NASA''s Earth Science Data Systems Program. EOSDIS ingests, processes, archives and distributes data from a large number of Earth observing satellites. EOSDIS consists of a set of processing facilities and Earth Science Data Centers distributed across the United States and serves hundreds of thousands of users around the world, providing hundreds of millions of data files each year covering many Earth science disciplines. In order to serve the needs of a broad and diverse community of users, NASA''s Earth Science Data Systems Program is comprised of both Core and Community data system elements. Core data system elements reflect NASA''s responsibility for managing Earth science satellite mission data characterized by the continuity of research, access, and usability. The core comprises all the hardware, software, physical infrastructure, and intellectual capital NASA recognizes as necessary for performing its tasks in Earth science data system management. Community data system elements are those pieces or capabilities developed and deployed largely outside of NASA core elements and are characterized by their evolvability and innovation. Successful applicable elements can be infused into the core, thereby creating a vibrant and flexible, continuously evolving infrastructure. NASA''s Earth Science program was established to use the advanced technology of NASA to understand and protect our home planet by using our view from space to study the Earth system and improve prediction of Earth system change. To meet this challenge, NASA promotes the full and open sharing of all data with the research and applications communities, private industry, academia, and the general public. NASA was the first agency in the US, and the first space agency in the world, to couple policy and adequate system functionality to provide full and open access in a timely manner - that is, with no period of exclusive access to mission scientists - and at no cost. NASA made this decision after listening to the user community, and with the background of the then newly-formed US Global Change Research Program, and the International Earth Observing System partnerships. Other US agencies and international space agencies have since adopted similar open-access policies and practices. Since the adoption of the Earth Science Data Policy adoption in 1991, NASA''s Earth Science Division has developed policy implementation, practices, and nomenclature that mission science teams use to comply with policy tenets. Data System Standards NASA''s Earth Science Data Systems Groups anticipate that effective adoption of standards will play an increasingly vital role in the success of future science data systems. The Earth Science Data Systems Standards Process Group (SPG), a board composed of Earth Science Data Systems stakeholders, directs the process for both identification of appropriate standards and subsequent adoption for use by the Earth Science Data Systems stakeholders.

The centuries-old quest for other worlds like our Earth has been rejuvenated by the intense excitement and popular interest surrounding the discovery of hundreds of planets orbiting other stars. There is now clear evidence for substantial numbers of three types of exoplanets; gas giants, hot-super-Earths in short period orbits, and ice giants. The following websites are tracking the day-by-day increase in new discoveries and are providing information on the characteristics of the planets as well as those of the stars they orbit: The Extrasolar Planets Encyclopedia, NASA Exoplanet Archive, New Worlds Atlas, and Current Planet Count Widget. The challenge now is to find terrestrial planets (i.e., those one half to twice the size of the Earth), especially those in the habitable zone of their stars where liquid water and possibly life might exist. The Kepler Mission, NASA Discovery mission #10, is specifically designed to survey a portion of our region of the Milky Way galaxy to discover dozens of Earth-size planets in or near the habitable zone and determine how many of the billions of stars in our galaxy have such planets. Results from this mission will allow us to place our solar system within the continuum of planetary systems in the Galaxy.

Open and free data for assessing the human presence on the planet.

The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources.

The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. Global data for marine ecosystems are largely non-existent; here we used available data for several ecosystems, modeled the distribution of many other ecosystems, and assumed a uniform distribution for several intertidal ecosystems for which no data exist. We recognize that differences exist in how people classify ecosystems; for example, estuaries are often considered an ecosystem, but here we focus on the ecosystems (also often labeled ‘habitats’) that occur within estuaries (salt marsh, intertidal mud, beach, soft sediment, mangroves, etc.). All ecosystem data were represented at 1 km2 resolution. This dataset contains maps for 20 distinct marine ecosystems used in the impacts model. More information on data sources can be found in the methods section.

Lets go on an adventure through the mysteries of the universe. The idea is to create a machine learning model that can predict if an observation is a real candidate for an exoplanet or not. The data was collected by the Kepler mission that revealed thousands of planets out of our Solar System.

And how did the Kepler telescope find planets so far from us if no one can take a clear picture of Pluto from Earth? Well, Kepler was able to find planets by looking for small dips in the brightness of a star when a planet transits in front of it. It is possible to measure the size of the planet based on the depth of the transit and the star’s size.

The most recent dataset from the Caltech website. However, if you feel adventurous, you can use NASA’s API and do some web scraping out of the fountain. For now, let’s keep things a little easier and use NASA’s and Caltech dataset. You can find a similar dataset on Kaggle, the problem is that the dataset was uploaded three years ago and it’s not up to date.

Columns:

COLUMN kepid: KepID,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN kepoi_name: KOI Name,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN kepler_name: Kepler Name,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_disposition: Exoplanet Archive Disposition,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_pdisposition: Disposition Using Kepler Data,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_score: Disposition Score,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_fpflag_nt: Not Transit-Like False Positive Flag,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_fpflag_ss: Stellar Eclipse False Positive Flag,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_fpflag_co: Centroid Offset False Positive Flag,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_fpflag_ec: Ephemeris Match Indicates Contamination False Positive Flag,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_period: Orbital Period [days],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_period_err1: Orbital Period Upper Unc. [days],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_period_err2: Orbital Period Lower Unc. [days],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_time0bk: Transit Epoch [BKJD],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_time0bk_err1: Transit Epoch Upper Unc. [BKJD],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_time0bk_err2: Transit Epoch Lower Unc. [BKJD],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_impact: Impact Parameter,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_impact_err1: Impact Parameter Upper Unc.,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_impact_err2: Impact Parameter Lower Unc.,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_duration: Transit Duration [hrs],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_duration_err1: Transit Duration Upper Unc. [hrs],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_duration_err2: Transit Duration Lower Unc. [hrs],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_depth: Transit Depth [ppm],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_depth_err1: Transit Depth Upper Unc. [ppm],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_depth_err2: Transit Depth Lower Unc. [ppm],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_prad: Planetary Radius [Earth radii],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_prad_err1: Planetary Radius Upper Unc. [Earth radii],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_prad_err2: Planetary Radius Lower Unc. [Earth radii],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_teq: Equilibrium Temperature [K],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_teq_err1: Equilibrium Temperature Upper Unc. [K],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_teq_err2: Equilibrium Temperature Lower Unc. [K],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_insol: Insolation Flux [Earth flux],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_insol_err1: Insolation Flux Upper Unc. [Earth flux],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_insol_err2: Insolation Flux Lower Unc. [Earth flux],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_model_snr: Transit Signal-to-Noise,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_tce_plnt_num: TCE Planet Number,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_tce_delivname: TCE Delivery,,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_steff: Stellar Effective Temperature [K],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_steff_err1: Stellar Effective Temperature Upper Unc. [K],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_steff_err2: Stellar Effective Temperature Lower Unc. [K],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_slogg: Stellar Surface Gravity [log10(cm/s**2)],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_slogg_err1: Stellar Surface Gravity Upper Unc. [log10(cm/s**2)],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_slogg_err2: Stellar Surface Gravity Lower Unc. [log10(cm/s**2)],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_srad: Stellar Radius [Solar radii],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_srad_err1: Stellar Radius Upper Unc. [Solar radii],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN koi_srad_err2: Stellar Radius Lower Unc. [Solar radii],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN ra: RA [decimal degrees],,,,,,,,,,,,,,,,,,,,,,,,,,, COLUMN dec: Dec [d...

What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. This dataset contains raw stressor data from 17 different human activities that directly or indirectly have an impact on the ecological communities in the ocean's ecosystems. For more information on specific dataset, see the methods section. All data are projected in WGS 1984 Mollweide.

The Meteoritical Society collects data on meteorites that have fallen to Earth from outer space. This dataset includes the location, mass, composition, and fall year for over 45,000 meteorites that have struck our planet.

Notes on missing or incorrect data points:

• a few entries here contain date information that was incorrectly parsed into the NASA database. As a spot check: any date that is before 860 CE or after 2016 are incorrect; these should actually be BCE years. There may be other errors and we are looking for a way to identify them.
• a few entries have latitude and longitude of 0N/0E (off the western coast of Africa, where it would be quite difficult to recover meteorites). Many of these were actually discovered in Antarctica, but exact coordinates were not given. 0N/0E locations should probably be treated as NA.

The starter kernel for this dataset has a quick way to filter out these observations using dplyr in R, provided here for convenience:

meteorites.geo <- meteorites.all %>%
filter(year>=860 & year<=2016) %>% # filter out weird years
filter(reclong<=180 & reclong>=-180 & (reclat!=0 | reclong!=0)) # filter out weird locations

The Data

Note that a few column names start with "rec" (e.g., recclass, reclat, reclon). These are the recommended values of these variables, according to The Meteoritical Society. In some cases, there were historical reclassification of a meteorite, or small changes in the data on where it was recovered; this dataset gives the currently recommended values.

The dataset contains the following variables:

• name: the name of the meteorite (typically a location, often modified with a number, year, composition, etc)
• id: a unique identifier for the meteorite
• nametype: one of:
  -- valid: a typical meteorite
  -- relict: a meteorite that has been highly degraded by weather on Earth
• recclass: the class of the meteorite; one of a large number of classes based on physical, chemical, and other characteristics (see the Wikipedia article on meteorite classification for a primer)
• mass: the mass of the meteorite, in grams
• fall: whether the meteorite was seen falling, or was discovered after its impact; one of:
  -- Fell: the meteorite's fall was observed
  -- Found: the meteorite's fall was not observed
• year: the year the meteorite fell, or the year it was found (depending on the value of fell)
• reclat: the latitude of the meteorite's landing
• reclong: the longitude of the meteorite's landing
• GeoLocation: a parentheses-enclose, comma-separated tuple that combines reclat and reclong

What can we do with this data?

Here are a couple of thoughts on questions to ask and ways to look at this data:

• how does the geographical distribution of observed falls differ from that of found meteorites? -- this would be great overlaid on a cartogram or alongside a high-resolution population density map
• are there any geographical differences or differences over time in the class of meteorites that have fallen to Earth?

Acknowledgements

This dataset was downloaded from NASA's Data Portal, and is based on The Meteoritical Society's Meteoritical Bulletin Database (this latter database provides additional information such as meteorite images, links to primary sources, etc.).

Population, male in World was reported at 4054352036 Persons in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, male - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Population, female in World was reported at 4007523965 Persons in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

The SUCCESS_UTAH_PDL data set contains ground-based measurements made by the University of Utah Polarization Diversity LIDAR at the CART site during the April-May 1996 SUCCESS Mission.SUbsonic aircraft: Contrail & Clouds Effects Special Study (SUCCESS) is a NASA field program using scientifically instrumented aircraft and ground based measurements to investigate the effects of subsonic aircraft on contrails, cirrus clouds and atmospheric chemistry. The experiment is cosponsored by NASA's Subsonic Assessment Program and the Radiation Sciences Program which are part of the overall Aeronautics and Mission to Planet Earth Programs, respectively. SUCCESS has well over a hundred direct participants from several NASA Centers, other agencies, universities and private research companies.

As an inescapable concomitant with the traditional route of economic development, Pakistan has been facing natural resource degradation and pollution problems. The unsavory spectacle of air pollution, water contamination and other macro environmental impacts such as water logging, land degradation and desertification, are on rise. All this, in conjunction with rapid growth in population, have been instrumental to the expanding tentacles of poverty. In order to assess the environmental problems as a prelude to arrest the pace of degeneration and provide for sustainable course of economic development, the availability of adequate data is imperative. This publication is an attempt to provide relevant statistics compiled through secondary sources collected from different departments. The task of environmental data collection does not consist just in determining the frame and approaching the selected sources of information because environmental statistics per se do not exist as a ready-to-compile/pick category as generally perceived about data and statistics. The information on environment has generated through deliberate scientific observations and measurements in a consistent way, under the aegis of specialized agencies. Since it is skill and resource intensive pursuit and generally undertaken in public sector, the overall budgetary/financial constraints do take the toll of the canvas and continuity of environmental data generation down the time lane. Consequently, availability of the statistics falls short of desired level. Further, the studies pertaining to normal over a period of time are repeated after long time intervals, which may not conform with the quinquennial periodicity of this document. Similarly, many variables antecedental, associated with and, consequential to, environment are derived from population census, which is yet to be carried out even though the stipulated decennial time frame has long been overstepped. Nevertheless, the latest update of the compendium is a good attempt to mirror quite a few environmental factors as a means to raise awareness and help stay focus on the pivotality of environmental concerns for instituting sustainable development paradigm-the only way forward to ensuring the continuity of human race on the face of planet earth.

Summary:

There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.

The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.

How to cite this dataset:

Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. Earth System Science Data, 14(9), pp.4397-4418.

Update history:

I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com)

Version 1.03: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same.

Version 1.02: New data summary, add Netcdf data format

Version 1: Initial dataset for peer-review, CSV format only

Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.

SPAM2010:

Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.

GAEZv4:

FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021

The dataset of Ricciardi et al.'s:

Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.

The global dominant field size dataset:

Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.

GLC-Share:

Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.

CAAS-IFPRI cropland extent map:

Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.