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.

GeoPolHist is a dataset that focuses on the questions “what is a country?” and “how many countries are there in the world?” Created from the lists of states and dependencies built by the Correlates of War project, GeoPolHist provides a dataset and visual documentation that identifies the political status of each of the geopolitical entities that existed in the world since 1816. It allows for an approach of the political history of the world based on the dichotomy between sovereign and non-sovereign entities.

This work was funded by the Fondation Del Duca.

Historical chart and dataset showing World population growth rate by year from 1961 to 2023.

(Abstract from 'The ducks of Macquarie Island') Early reference to waterfowl on Macquarie Island and observations made by ANARE expeditioners between 1949 and 1985 are reviewed and discussed. Apart from a unique (perhaps erroneous) record of a mute swan Cygnus olor, information is restricted to the Pacific black duck Anas superciliosa, the grey teal A. gibberifrons and the alien mallard A. platyrhynchos and its hybrids.

Black duck and grey teal were seen by early visitors to the Island, but despite the infrequent potential for escapes of domestic ducks, mallards were not recorded until 1949. Occasional teal and mallards were seen in the years following the establishment of the permanent scientific station (1948) but mallards (and hybrids) have become more numerous in recent years. Though grey teal may disperse to Macquarie Island in times of drought on the Australian mainland, the source of mallards may be New Zealand or the less distant Campbell and Auckland Islands.

The few available records of breeding (eggs, ducklings and nests) for black duck suggest that laying begins in September and extends at least into January. Zooplankton is most abundant in spring and summer, but ducks may obtain high protein foods from the littoral and sublittoral areas and may also take seeds of terrestrial plants.

Available information does not allow separation of habitats used by black duck or mallards. However, most observations are around coastal areas. There is some indication that records have increased along the south-western and eastern sides of the Island, but generally there are few observations of either species on the higher, central plateau.

The intrusion of mallards onto the Island and the resultant hybridisation with black duck poses a threat for the future integrity of the latter native species.

This dataset contains a review of the data available for Pacific black duck (Anas superciliosa), mallard (Anas platyrhynchos) and grey teal (Anas gibberifrons) and hybrids on Macquarie Island, collected from mid-1949 to January 1985. There is a period of relatively continuous and extensive data between February 1963 and January 1985. Discussion and references about habitat, breeding, feeding and hybrids is provided in the dataset (see the reference), as well as distribution maps.

The fields in this dataset are: Year Month Presence/Absence

The Digital Bedrock Geologic-GIS Map of Weir Farm National Historical Park and Vicinity, Connecticut is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (wefa_bedrock_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (wefa_bedrock_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (wefa_bedrock_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (wefa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (wefa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (wefa_bedrock_geology_metadata_faq.pdf). Please read the wefa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Connecticut Geological and Natural History Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (wefa_bedrock_geology_metadata.txt or wefa_bedrock_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:125,000 and United States National Map Accuracy Standards features are within (horizontally) 63.5 meters or 208.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Canadian Archaeological Radiocarbon Database (CARD) is a compilation of radiocarbon measurements that indicate the ages of samples primarily from archaeological sites in North America. CARD also includes samples from paleontological and geological contexts. We are slowly expanding our coverage into Central and South America. These data represent a significant investment and resource for researchers interested in human history and its context. CARD was created by Dr. Richard "Dick" Morlan of the Canadian Museum of History (formerly the Canadian Museum of Civilization), and its existence is a product of his genius and labour. In July, 2014 the Canadian Museum of History (CMH) and the Laboratory of Archaeology (LOA) at the University of British Columbia formed a partnership to revise and update the CARD platform. This current version of CARD (2.0) adds useful new features, including unlimited batch uploading/downloading of data and spatial/map visualization. However, the core of CARD remains the c14 dates painstakingly submitted by researchers across the world and compiled by Dick. We hope that this revision maintains the relationship that Dick established in one of the first crowd-sourced, big data endeavors: CARD provides utility and comprehensiveness and in exchange, researchers provide us with dates. See the HELP tab for more information and instructions on using CARD. Our efforts to update and upgrade CARD are just beginning, and we are moving in two directions: to increase the quantity and quality of CARD data and to improve the functionality of the CARD platform. We are looking for partners to assist us in both. CARD data contains some errors and is in some cases incomplete, and we are engaged in a long-term process of scrubbing the data. We are also developing new functional tools to make CARD more valuable to researchers including the generation of heat-maps of date concentrations over time, and the selection of data via a map interface. We also have longer-term plans to add calibration sockets with existing calibration services. If you have 14C data, please upload it to CARD. If you are interested in getting involved in the expansion and development of CARD, please email us at admin@card.anth.ubc.ca. Radiocarbon assessment has an effective range of about 250 to +50,000 years, and as a result most of the samples in CARD are associated with Indigenous archaeological sites. These represent a significant resource into aboriginal history. CARD fuzzes location data for public visitors to the database at 1:2,000,000 scale. Accessing CARD's full capabilities requires a security-account available only to researchers at accredited institutions. As Dick wrote in CARD 1.0, "The long term future of this database will depend upon whether or not the archaeological community finds it truly useful." We hope you do.

The Digital Geologic-GIS Map of Saint Croix National Riverway and Vicinity, Minnesota and Wisconsin is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (sacn_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (sacn_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sacn_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (sacn_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sacn_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (sacn_geology_metadata_faq.pdf). Please read the sacn_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Minnesota Geological Survey, Wisconsin Geological and Natural History Survey and National Park Service. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sacn_geology_metadata.txt or sacn_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:250,000 and United States National Map Accuracy Standards features are within (horizontally) 127 meters or 416.7 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

-This dataset is replaced by a new version, see below.-

Land use plays an important role in the climate system (Feddema et al., 2005). Many ecosystem processes are directly or indirectly climate driven, and together with human driven land use changes, they determine how the land surface will evolve through time. To assess the effects of land cover changes on the climate system, models are required which are capable of simulating interactions between the involved components of the Earth system (land, atmosphere, ocean, and carbon cycle). Since driving forces for global environmental change differ among regions, a geographically (spatially) explicit modeling approach is called for, so that it can be incorporated in global and regional (climate and/or biophysical) change models in order to enhance our understanding of the underlying processes and thus improving future projections.

Integrated records of the co-evolving human-environment system over millennia are needed to provide a basis for a deeper understanding of the present and for forecasting the future. This requires the major task of assembling and integrating regional and global historical, archaeological, and paleo-environmental records. Humans cannot predict the future. But, if we can adequately understand the past, we can use that understanding to influence our decisions and to create a better, more sustainable and desirable future.

Some researchers suggest that mankind has shifted from living in the Holocene (~emergence of agriculture) into the Anthropocene (~humans capable of changing the Earth’ atmosphere) since the start of the Industrial Revolution. But in the light of the sheer size and magnitude of some historical land use changes (e.g. collapse of the Roman Empire in the 4th century, the depopulation of Europe due to the Black Plague in the 14th century and the aftermath of the colonization of the Americas in the 16th century), some believe that this point might have occurred earlier in time (Ruddiman, 2003; Kaplan et al., 2010). Many uncertainties still remain today and gaps in our knowledge of the Antiquity and its aftermath can only be improved by interdisciplinary research.

HYDE presents (gridded) time series of population and land use for the last 12,000 years. It is an update (v 3.2) of the History Database of the Global Environment (HYDE) from Klein Goldewijk et al. (2011, 2013) with new quantitative estimates of the underlying demographic and agricultural developments for the Holocene.

See "01_ASDN_readme.txt" (under "Download Data" tab) for data author and contact information. Field data on shorebird ecology and environmental conditions were collected from 1993-2014 at 16 field sites in Alaska, Canada, and Russia. Data were not collected in every year at all sites. Studies of the population ecology of these birds included nest-monitoring to determine timing of reproduction and reproductive success; live capture of birds to collect blood samples, feathers, and fecal samples for investigations of population structure and pathogens; banding of birds to determine annual survival rates; resighting of color-banded birds to determine space use and site fidelity; and use of light-sensitive geolocators to investigate migratory movements. Data on climatic conditions, prey abundance, and predators were also collected. Environmental data included weather stations that recorded daily climatic conditions, surveys of seasonal snowmelt, weekly sampling of terrestrial and aquatic invertebrates that are prey of shorebirds, live trapping of small mammals (alternate prey for shorebird predators), and daily counts of potential predators (jaegers, falcons, foxes). Detailed field methods for each year are available in the ASDN_protocol_201X.pdf files. All research was conducted under permits from relevant federal, state and university authorities. Potential users of these data should first contact the relevant data author(s), listed below. This will enable coordination in terms of updates/corrections to the data and ongoing analyses. Key analyses of the data are in progress and will be included in the theses and dissertations of graduate students who collected these field data. Please acknowledge this dataset and the authors in any analysis, publication, presentation, or other output that uses these data. If you use the full dataset, we suggest you cite it as: Lanctot, RB, SC Brown, and BK Sandercock. 2016. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. If you use data from only one or a few sites, we suggest you cite data for each site as per this example, using the corresponding site PIs as the authors: Lanctot, RB and ST Saalfeld. 2016. Barrow, 2014. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. Note that each updated version of the full dataset has its own unique DOI. Disclaimers: The dataset is distributed “as is” and with absolutely no warranty. The data providers have invested considerable effort to ensure that the data are of highest quality, but it is possible that undetected errors remain. Data have been processed with several steps for quality assurance, but the data providers accept no liability or guarantee that the data are up-to-date, correct, or complete. Access to data is provided on the understanding that the data providers are not responsible for any damages from inaccuracies in the data. Note: An up-to-date version of data from Barrow/Utqiagvik, including corrected and more recent data, is now housed here: https://arcticdata.io/catalog/view/doi:10.18739/A2VT1GP7Q . Please contact the relevant site PIs to seek recent data (after 2014) from any other site.

This dataset is associated with the VolcanEESM project led by the project team at the University of Leeds. The project was funded by NCAR/UCAR Atmospheric Chemistry and Modeling Visiting Scientist Program, NCAS, University of Leeds.

The global volcanic sulphur dioxide (SO2) emissions database is a combination of available information from the wider literature with as many observations of the amount and location of SO2 emitted by each volcanic eruption as possible. The database includes no information about the size, mass, distribution or optical depth of resulting aerosol. As such the database is model agnostic and it is up to each modeling group to make decisions about how to implement the emission file in their prognostic stratospheric aerosol scheme.

The dataset is divided into two parts based on the availability of satellite data. For the pre-satellite era, the necessary information about the emissions was gathered from the latest ice core records of sulphate deposition in combination historical accounts available in the wider literature (see references included in the database for specific citation for each record). In the satellite era, volcanic emissions were primarily derived from remotely sensed observations.

For the period 1850 CE to 1979 the dataset combined the most recent volcanic sulfate deposition datasets from ice cores with volcanological and, where applicable, petrological estimates of the SO2 mass emitted as well as historical records of large-magnitude volcanic eruptions. In detail, for the majority of eruptions between 1850 CE to 1979 , there are few direct measurement of SO2 emissions or quantitative observations of the plume height and very few measurements of the aerosol optical depth (AOD).

Parameters in the database include: Day_of_Emission: The 24 hour period in which the emission is thought to have occurred. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)

Eruption: Field that contains the Volcano_Number (Which uniquely identifies each volcano in the Global Volcanism Program Database), Volcano_Name (official name from the Global Volcanism Program Database), Notes_and_References (list of notes about the observed parameters and references used to derive each entry). ( Ordered by the variable Eruption_Number starting with the first eruption in the database.)

Latitude: Latitude of each emission from -90 to +90 (Ordered by the variable Eruption_Number starting with the first eruption in the database.)

Longitude: Longitude of each emission degrees East (Ordered by the variable Eruption_Number starting with the first eruption in the database.)

VEI: Volcanic Explosively Index of each emission based on Global Volcanism Program Database (Ordered by the variable Eruption_Number starting with the first eruption in the database.)

Total_Emission_of_SO2_Tg: Total emission of SO2 in teragram for the specific database entry (Ordered by the variable Eruption_Number starting with the first eruption in the database.)

Maximum_Injection_Height_km: Maximum height of each emission in kilometers above sea level. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)

Minimum_Injection_Height_km: Minimum height of each emission in kilometers above sea level. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)

Month_of_Emission: The month in which the emission is thought to have occurred. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)

Year_of_Emission: The Year in which the emission is thought to have occurred. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)

The NCEP operational Global Forecast System analysis and forecast grids are on a 0.25 by 0.25 global latitude longitude grid. Grids include analysis and forecast time steps at a 3 hourly interval from 0 to 240, and a 12 hourly interval from 240 to 384. Model forecast runs occur at 00, 06, 12, and 18 UTC daily. For real-time data access please use the NCEP data server [http://www.nco.ncep.noaa.gov/pmb/products/gfs/].

NOTE: This dataset now has a direct, continuously updating copy located on AWS (https://noaa-gfs-bdp-pds.s3.amazonaws.com/index.html [https://noaa-gfs-bdp-pds.s3.amazonaws.com/index.html]). Therefore, the RDA will stop updating this dataset in early 2025

The first version of this data base originally was set up for testing and validation of the so-called Integrated Model of the Greenhouse Effect (the IMAGE model; see Alcamo, 1994), developed at RIVM. The main aim of the model is to use state-of-the-art models to assist policy makers in the development and evaluation of future scenarios to mitigate the negative effects of global change. The modelling framework consists of several subsystems that cover the different aspects of the earth system.

    Many calculations in IMAGE and other models are performed on a 0.5o by 0.5o
    longitude/latitude grid. This is because nearly all potential impacts of
    climate change (impacts on ecosystems, agriculture and coastal flooding) have a
    strong spatial variability. Moreover, land use related greenhouse gas emissions
    depend on local environmental conditions and human activity. There are also
    other reasons for using grid-scale information. First, policy makers are
    interested in regional/national policies to address climate change. Secondly,
    grid-scale information makes model caluclations more testable against
    observations as compared to more aggregated models.

    Nevertheless, it is infeasable to perform grid-based calculations for economic
    models, because of the difficulty in specifying economic/demographic factors on
    a country scale for the entire world over the long horizon of the model.
    Therefore, the world has been divided into 19 world regions, according to
    economic and geographic similarity. This classification also takes into account
    the regional aggregations used by the IPCC, OECD, FAO, UN and IEA. It should be
    noted, however, that IMAGE has the additional requirement that countries within
    a region be adjacent or nearby because of the model's approach to global land
    cover simulation.

    An important initiative for the update the previous version of HYDE (Klein
    Goldewijk, 2001) was the publication of a new population density data base, the
    Gridded World Population v.3 (Balk et al, 2005), which is now used as a
    starting point for historical gridded population calculations. Because
    population data are important in many calculations, it resulted in modified
    land cover estimates, as well as estimates for GDP, value added, private
    consumption. Furthermore, numerous new data have been incorporated in many
    tables.

    Besides the testing of IMAGE, HYDE has already been used for integrated
    environmental assessents such as the Global Environmental Outlook (GEO) of the
    United Nations Enviromental Programme (UNEP, 1997), technical background
    reports for GEO (RIVM/UNEP, 1997), the TARGETS project (Rotmans and De Vries,
    1997), the Dutch National Environmental Outlook (RIVM, 1997) and the Mappae
    Mundi project (Goudsblom and De Vries, 2002). Also, HYDE has contributed to
    other research e.g. in the field of historical atmospheric trace gas
    inventories (e.g. Kroeze et al, 1999; den Elzen et al, 1999; van Aardenne et
    al, 2001; Pitman et al, 2000; Pielke et al, 2003 ), biological diversity (e.g.
    Gaston et al, 2003), and climate reconstructions (e.g. Matthews et al, 2003;
    Brovkin et al, 2004).

    Furthermore, this effort very much fits within the Land-Use and Land-Cover
    Change LUCC project, (activity 3; database development), part of the the
    International Human Dimensions Project (IHDP), and the PAGES (Human
    Interactions in Past Environmental Changes) - focus 3: Human Impacts on
    Terrestrial Ecosystems (HITE) initiative. PAGES is the International
    Geosphere-Biosphere Programme (IGBP) core project charged with providing a
    quantitative understanding of the Earth's past climate and environment.

    Please note that this data base is far from complete. Work is continuous in
    progress to update and extent the data series where possible.

    [Summary provided by MNP]

The Smithsonian Institution's Global Volcanism Program (GVP) is housed in the Department of Mineral Sciences, National Museum of Natural History, in Washington D.C. We are devoted to a better understanding of Earth's active volcanoes and their eruptions during the last 10,000 years.

The mission of GVP is to document, understand, and disseminate information about global volcanic activity. We do this through four core functions: reporting, archiving, research, and outreach. The data systems that lie at our core have been in development since 1968 when GVP began documenting the eruptive histories of volcanoes.

Reporting. GVP is unique in its documentation of current and past activity for all volcanoes on the planet active during the last 10,000 years. During the early stages of an eruption anywhere in the world we act as a clearinghouse of reports, data, and imagery. Reports are released in two formats. The Smithsonian / USGS Weekly Volcanic Activity Report provides timely information vetted by GVP staff about current eruptions. The Bulletin of the Global Volcanism Network provides comprehensive reporting on recent eruptions on a longer time horizon to allow incorporation of peer-reviewed literature and observatory reports.

Archiving. Complementing our effort toward reporting of current eruptive activity is our database of volcanoes and eruptions that documents the last 10,000 years of Earth's volcanism. These databases and interpretations based on them were published in three editions of the book "Volcanoes of the World".

Research. GVP researchers are curators in the Department of Mineral Sciences and maintain active research programs on volcanic products, processes, and the deep Earth that is the ultimate source of volcanism.

Outreach. This website presents more than 7,000 reports on volcanic activity, provides access to the baseline data and eruptive histories of Holocene volcanoes, and makes available other resources to our international partners, scientists, civil-authorities, and the public.

The Global Volcanism Program relies on an international network of collaborating individuals, programs and organizations, many of which are listed below:

United States Geological Survey Volcano Hazards Program (USA). The Volcano Hazards Program monitors active and potentially active volcanoes, assesses their hazards, responds to volcanic crises, and conducts research on volcanoes. The Volcano Disaster Assistance Program (VDAP) (with the U.S. Office of Foreign Disaster Assistance) works to reduce fatalities and economic losses in countries experiencing a volcano emergency.

Global Volcano Model (Bristol University and the British Geological Survey, UK). GVM is a growing international network that aims to create a sustainable, accessible information platform on volcanic hazard and risk.

WOVOdat (Earth Observatory of Singapore). A collective record of volcano monitoring, worldwide - brought to you by the WOVO (World Organization of Volcano Observatories).

Integrated Earth Data Applications (Lamont-Doherty Earth Observatory of Columbia University, USA). A community-based data facility to support, sustain, and advance the geosciences by providing data services for observational solid earth data from the Ocean, Earth, and Polar Sciences.

VHub (The State University of New York at Buffalo, USA). An online resource for collaboration in volcanology research and risk mitigation.

International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI). IAVCEI represents the primary international focus for: (1) research in volcanology, (2) efforts to mitigate volcanic disasters, and (3) research into closely related disciplines, such as igneous geochemistry and petrology, geochronology, volcanogenic mineral deposits, and the physics of the generation and ascent of magmas in the upper mantle and crust. IAVCEI has charged GVP with providing the official names and unique identifier numbers for the world's volcanoes.

National Oceanographic and Atmospheric Administration (NOAA). Volcanic Ash Advisory Centers (VAACs) The International Civil Aviation Organization (ICAO) has established nine Volcanic Ash Advisory Centers tasked with monitoring Volcanic Ash plumes within their assigned airspace.

The Museum is committed to open access and open science, and has launched the Data Portal to make its research and collections datasets available online. It allows anyone to explore, download and reuse the data for their own research. Our natural history collection is one of the most important in the world, documenting 4.5 billion years of life, the Earth and the solar system. Almost all animal, plant, mineral and fossil groups are represented. The portal's main dataset consists of specimens from the Museum's collection database, with 5,989,739 records from the Museum’s Entomology, Zoology, Botany, Palaeontology, Mineralogy collections. We also have 828,349 species-level (index lot) records, which denote the presence of a taxon in the Museum collection. These datasets will increase exponentially. Under the Museum's ambitious digital collections programme we aim to have 20 million specimens digitised in the next five years. More about our collections The Museum has over 600 curators, researchers, scientific associates, students and volunteers. Our scientists publish circa 1,000 scientific papers annually and datasets from these publications will be available through the portal. Digitising and databasing our vast collections is a huge and evolving task. As a result some records in the Data Portal may contain erroneous data. If you find a record that needs correcting, please tell us at data@nhm.ac.uk, or use the feedback mechanism embedded on the page.

See "01_ASDN_readme.txt" (under "Download Data" tab) for data author and contact information. Field data on shorebird ecology and environmental conditions were collected from 1993-2014 at 16 field sites in Alaska, Canada, and Russia. Data were not collected in every year at all sites. Studies of the population ecology of these birds included nest-monitoring to determine timing of reproduction and reproductive success; live capture of birds to collect blood samples, feathers, and fecal samples for investigations of population structure and pathogens; banding of birds to determine annual survival rates; resighting of color-banded birds to determine space use and site fidelity; and use of light-sensitive geolocators to investigate migratory movements. Data on climatic conditions, prey abundance, and predators were also collected. Environmental data included weather stations that recorded daily climatic conditions, surveys of seasonal snowmelt, weekly sampling of terrestrial and aquatic invertebrates that are prey of shorebirds, live trapping of small mammals (alternate prey for shorebird predators), and daily counts of potential predators (jaegers, falcons, foxes). Detailed field methods for each year are available in the ASDN_protocol_201X.pdf files. All research was conducted under permits from relevant federal, state and university authorities.

Potential users of these data should first contact the relevant data author(s), listed below. This will enable coordination in terms of updates/corrections to the data and ongoing analyses. Key analyses of the data are in progress and will be included in the theses and dissertations of graduate students who collected these field data.

Please acknowledge this dataset and the authors in any analysis, publication, presentation, or other output that uses these data. If you use the full dataset, we suggest you cite it as:
Lanctot, RB, SC Brown, and BK Sandercock. 2017. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. If you use data from only one or a few sites, we suggest you cite data for each site as per this example:
Lanctot, RB and ST Saalfeld. 2017. Barrow, 2014. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. Note that each updated version of the dataset has its own unique DOI.

Disclaimers: The dataset is distributed “as is” and with absolutely no warranty. The data providers have invested considerable effort to ensure that the data are of highest quality, but it is possible that undetected errors remain. Data have been processed with several steps for quality assurance, but the data providers accept no liability or guarantee that the data are up-to-date, correct, or complete. Access to data is provided on the understanding that the data providers are not responsible for any damages from inaccuracies in the data.

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.

This data publication includes the half-hourly Hp30 and ap30 indices as well as the hourly Hp60 and ap60 indices, collectively denoted as Hpo. This dataset is based on near real-time geomagnetic observatory data provided by 13 contributing observatories. It is derived and distributed by GFZ German Research Centre for Geosciences. When using the Hpo index, please cite this data publication as well as the accompanying publication Yamazaki et al. (submitted), which serves as documentation of the Hpo. The dataset is organised in yearly files, which, for the current year, are updated on a monthly basis. Typically, during the second week of a month, the data for the previous month is appended to the current year's file. The files are in ASCII files and start with header lines marked with # (hash). The Hpo index was developed within the H2020 project SWAMI (grant agreement No 776287) and is produced by Geomagnetic Observatory Niemegk, GFZ German Research Centre for Geosciences. It derives from the same 13 geomagnetic observatories that also contribute to the Kp index (Matzka et al., 2021, https://doi.org/10.5880/Kp.0001). They are listed as contributors to this data publication. With the introduction of the DOI for the Hpo index (Matzka et al, 2021, https://doi.org/10.5880/Hpo.0001), this DOI landing page and the associated HTTPS server linked to the DOI become the primary archive of Hpo (while the other established index distribution mechanisms at GFZ will be maintained in parallel). With the DOI, the dataset can grow with time, but a change of the data, once published, is not possible. If necessity arises in the future to correct already published values, then the corrected dataset will be published with a new DOI. Older DOIs and data sets will then still be available. For each DOI, an additional versioning mechanism will be available to document changes to the files such as header or format changes, which do not affect the integrity of the data. The DOI https://doi.org/10.5880/Hpo.0002 identifies the current version. A format description and a version history are provided in the data download folder.

Abstract The dataset was derived by the Bioregional Assessment Programme from the Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset provides a userguide and setup information relating to accessing the Geoscience Australia, 1 second SRTM Digital Elevation …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from the Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset provides a userguide and setup information relating to accessing the Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM), for visualisation and analysis using ESRI ArcMap and ArcCatalog. The 1 second DSM, DEM, DEM-S and DEM-H are national elevation data products derived from the Shuttle Radar Topography Mission (SRTM) data. The SRTM data is not suitable for routine application due to various artifacts and noise. The data has been treated with several processes to produce more usable products: * A cleaned digital surface model (DSM) o regular grid representing ground surface topography as well as other features including vegetation and man-made structures * A bare-earth digital elevation model (DEM) o regular grid representing ground surface topography, and where possible, excluding other features such as vegetation and man-made structures. * A smoothed digital elevation model (DEM-S) o A smoothed DEM based on the bare-earth DEM that has been adaptively smoothed to reduce random noise typically associated with the SRTM data in low relief areas. * A hydrologically enforced digital elevation model (DEM-H) o A hydrologically enforced DEM is based on DEM-S that has had drainage lines imposed and been further smoothed using the ANUDEM interpolation software. The last product, a hydrologically enforced DEM, is most similar to the DEMs commonly in use around Australia, such as the GEODATA 9 Second DEM and the 25 m resolution DEMs produced by State and Territory agencies from digitised topographic maps. For any analysis where surface shape is important, one of the smoothed DEMs (DEM-S or DEM-H) should be used. DEM-S is preferred for shape and vertical accuracy and DEM-H for hydrological connectivity. The DSM is suitable if you want to see the vegetation as well as the land surface height. There are few cases where DEM is the best data source, unless access to a less processed product is necessary. The 1 second DEM (in its various incarnations) has quite different characteristics to DEMs derived by interpolation from topographic data. Those DEMs are typically quite smooth and are based on fairly accurate but sparse source data, usually contours and spot heights supplemented by drainage lines. The SRTM data is derived from radar measurements that are dense (there is essentially a measurement at almost every grid cell) but noisy. Version 1.0 of the DSM was released in early 2009 and version 1.0 of the DEM was released in late 2009. Version 1.0 of the DEM-S was released in July 2010 and version 1.0 of the hydrologically enforced DEM-H was released in October 2011. These products provide substantial improvements in the quality and consistency of the data relative to the original SRTM data, but are not free from artefacts. Improved products will be released over time. The 3 second products were derived from the 1 second data and version 1.0 was released in August 2010. Future releases of these products will occur when the 1 second products have been improved. At this stage there is no 3 second DEM-H product, which requires re-interpolation with drainage enforcement at that resolution. Dataset History See readme file: readme file for gloucester basin 1sec srtm.xyz This is ascii file created by CSIRO 3 september 2013 using Geosoft Oasis Montaj software file is 1 second shuttle radar data (28.6 x 28.6 m) which has had buildings and vegetation removed (processing by CSIRO and GA) DEM-S product file format is gda94 easting, gda94 northing, height above sea level mga zone 56 coordinates, all data in metres origin (bottom left) is 379007E, 6400022N 1260 pts in east direction 2798 pts in north direction Dataset Citation Bioregional Assessment Programme (XXXX) GLO DEM 1sec SRTM MGA56. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/ca38ed31-e15d-4bb5-a7ef-0aeba3dad3f4. Dataset Ancestors Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

Neodymium fell to 582,500 CNY/T on July 15, 2025, down 1.69% from the previous day. Over the past month, Neodymium's price has risen 5.43%, and is up 28.73% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Neodymium Rare Earth - values, historical data, forecasts and news - updated on July of 2025.