Nigeria NG: Imports: % of Total Goods Imports: The Arab World data was reported at 3.044 % in 2016. This records an increase from the previous number of 2.432 % for 2015. Nigeria NG: Imports: % of Total Goods Imports: The Arab World data is updated yearly, averaging 0.909 % from Dec 1962 (Median) to 2016, with 54 observations. The data reached an all-time high of 3.228 % in 1996 and a record low of 0.189 % in 1985. Nigeria NG: Imports: % of Total Goods Imports: The Arab World data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Imports. Merchandise imports from economies in the Arab World are the sum of merchandise imports by the reporting economy from economies in the Arab World. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

This is version 2.0.1.2016f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.0.2015p to span 1931-2016 and includes an increase in the number of stations and an updated methodology and is the final version of the 2016 data.

The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information.

The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20151231_v2-0-1-2016p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.

To keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.

For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/

References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) :

Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.

Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012

Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1

For a homogeneity assessment of HadISD please see this following reference

Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

Access 114M+ high-precision building footprints across 220 countries, enabling advanced mapping, location analysis, and strategic decision-making. With 30+ years of data expertise, we provide clean, validated, and enriched datasets to power businesses worldwide.

• Expand market reach with global-scale, high-precision data.
• Enhance mapping, navigation, and spatial analysis.
• Optimize site selection, urban planning, and infrastructure development.
• Improve logistics, delivery routes, and network optimization.
• Assess property values, competitor landscapes, and demographic trends.
• Strengthen disaster management and risk assessment with reliable insights.
• Leverage AI-driven enrichment for deeper, data-driven decision-making.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas:

1. Gain a Competitive Edge with Smarter Mapping: Use building footprint data to analyse competitors, identify high-traffic areas, and optimize locations for maximum market impact.
2. Enhance Navigation & Last-Mile Efficiency: Improve customer experiences with precise building entrances, parking areas, and optimized routes for seamless navigation and delivery.
3. Find the Perfect Site for Growth: Leverage building footprint data to select prime locations, maximize foot traffic, and drive higher sales.
4. Optimize Energy & Infrastructure Planning: Assess rooftop solar potential, utility networks, and energy distribution for smarter, more efficient urban development.
5. Improve Risk Assessment & Security: Use precise building data for insurance underwriting, security planning, and crime prevention strategies.

This data set contains mean monthly temperatures and total monthly precipitation for stations in Alaska from the mid-1800s to 1990. The values are a subset of the Global Historical Climatology Network (GHCN), archived at Oak Ridge National Laboratory.

Abstract

The survey was conducted in Kazakhstan between January and October of 2019. The survey was part of a joint project of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) and the World Bank Group (WBG). The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector. As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms’ experiences and enterprises’ perception of the environment in which they operate.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

For the Kazakhstan ES. size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for 2019 Kazakhstan ES was selected using stratified random sampling, following the methodology explained in the Sampling Note.

Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Kazakhstan 2019 Enterprise Surveys Data Set" report, Appendix C.

Industry stratification was designed in the way that follows: the universe was stratified into six manufacturing industries and two services industries: Food and Beverages (ISIC Rev. 4 codes 10 and 11), Garments (ISIC code 14), Non-Metallic Mineral Products (ISIC code 23), Fabricated Metal Products (ISIC code 25), Machinery and Equipment (ISIC code 28), Other Manufacturing (ISIC codes 12, 13, 15-22, 24, 26, 27, 29, 30-33), Retail (ISIC code 47), and Other Services (ISIC codes 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For the Kazakhstan ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees). Regional stratification for the Kazakhstan ES was done across eleven regions: Akmola Region; Aktobe Region; Almaty; Almaty Region; Nur-Sultan; Atyrau Region; Mangystau and West Kazakhstan; East Kazakhstan; Karaganda Region; Kostanay, North Kazakhstan, Pavlodar and Kyzylorda Region, South Kazakhstan, Jambyl.

Note: See Sections II and III of “The Kazakhstan 2019 Enterprise Surveys Data Set” report for additional details on the sampling procedure.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires - Manufacturing and Services were used to collect the survey data.

The Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

The number of interviews per contacted establishments was 12.5%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units.

The share of rejections per contact was 36.1%.

The Gridded Population of the World, Version 3 (GPWv3): Centroids consists of estimates of human population counts and densities for the years 1990, 1995, 2000, 2005, 2010, and 2015 by administrative Unit centroid location. The centroids are based on the 399,781 input administrative Units used in GPWv3. In addition to population counts and variables, the centroids have associated administrative Unit names and the land area of contained within the administrative Unit. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

Collected COVID-19 datasets from various sources as part of DAAN-888 course, Penn State, Spring 2022. Collaborators: Mohamed Abdelgayed, Heather Beckwith, Mayank Sharma, Suradech Kongkiatpaiboon, and Alex Stroud

**1 - COVID-19 Data in the United States ** Source: The data is collected from multiple public health official sources by NY Times journalists and compiled in one single file. Description: Daily count of new COVID-19 cases and deaths for each state. Data is updated daily and runs from 1/21/2020 to 2/4/2022. URL: https://github.com/nytimes/covid-19-data/blob/master/us-states.csv Data size: 38,814 row and 5 columns.

**2 - Mask-Wearing Survey Data ** Source: The New York Times is releasing estimates of mask usage by county in the United States. Description: This data comes from a large number of interviews conducted online by the global data and survey firm Dynata, at the request of The New York Times. The firm asked a question about mask usage to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. URL: https://github.com/nytimes/covid-19-data/blob/master/mask-use/mask-use-by-county.csv Data size: 3,142 rows and 6 columns

**3a - Vaccine Data – Global ** Source: This data comes from the US Centers for Disease Control and Prevention (CDC), Our World in Data (OWiD) and the World Health Organization (WHO). Description: Time series data of vaccine doses administered and the number of fully and partially vaccinated people by country. This data was last updated on February 3, 2022 URL: https://github.com/govex/COVID-19/blob/master/data_tables/vaccine_data/global_data/time_series_covid19_vaccine_global.csv
Data Size: 162,521 rows and 8 columns

**3b -Vaccine Data – United States ** Source: The data is comprised of individual State's public dashboards and data from the US Centers for Disease Control and Prevention (CDC). Description: Time series data of the total vaccine doses shipped and administered by manufacturer, the dose number (first or second) by state. This data was last updated on February 3, 2022. URL: https://github.com/govex/COVID-19/blob/master/data_tables/vaccine_data/us_data/time_series/vaccine_data_us_timeline.csv
Data Size: 141,503 rows and 13 columns

**4 - Testing Data ** Source: The data is comprised of individual State's public dashboards and data from the U.S. Department of Health & Human Services. Description: Time series data of total tests administered by county and state. This data was last updated on January 25, 2022. URL: https://github.com/govex/COVID-19/blob/master/data_tables/testing_data/county_time_series_covid19_US.csv
Data size: 322,154 rows and 8 columns

**5 – US State and Territorial Public Mask Mandates ** Source: Data from state and territory executive orders, administrative orders, resolutions, and proclamations is gathered from government websites and cataloged and coded by one coder using Microsoft Excel, with quality checking provided by one or more other coders. Description: US State and Territorial Public Mask Mandates from April 10, 2020 through August 15, 2021 by County by Day URL: https://data.cdc.gov/Policy-Surveillance/U-S-State-and-Territorial-Public-Mask-Mandates-Fro/62d6-pm5i Data Size: 1,593,869 rows and 10 columns

**6 – Case Counts & Transmission Level ** Source: This open-source dataset contains seven data items that describe community transmission levels across all counties. This dataset provides the same numbers used to show transmission maps on the COVID Data Tracker and contains reported daily transmission levels at the county level. The dataset is updated every day to include the most current day's data. The calculating procedures below are used to adjust the transmission level to low, moderate, considerable, or high.
Description: US State and County case counts and transmission level from 16-Aug-2021 to 03-Feb-2022 URL: https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-County-Level-of-Community-T/8396-v7yb Data Size: 550,702 rows and 7 columns

**7 - World Cases & Vaccination Counts ** Source: This is an open-source dataset collected and maintained by Our World in Data. OWID provides research and data to help against the world’s largest problems.
Description: This dataset includes vaccinations, tests & positivity, hospital & ICU, confirmed cases, confirmed deaths, reproduction rate, policy responses and other variables of interest. URL: https://github.com/owid/covid-19-data/tree/master/public/data Data Size: 67 columns and 157,000 rows

**8 - COVID-19 Data in the European Union ** Source: This is an open-source dataset collected and maintained by ECDC. It is an EU agency aimed at strengthening Europe's defenses against infectious diseases.
Description: This dataset co...

Central African Republic CF: Imports: % of Total Goods Imports: The Arab World data was reported at 6.163 % in 2023. This records an increase from the previous number of 5.903 % for 2022. Central African Republic CF: Imports: % of Total Goods Imports: The Arab World data is updated yearly, averaging 1.615 % from Dec 1960 (Median) to 2023, with 60 observations. The data reached an all-time high of 11.412 % in 2015 and a record low of 0.026 % in 1980. Central African Republic CF: Imports: % of Total Goods Imports: The Arab World data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Imports. Merchandise imports from economies in the Arab World are the sum of merchandise imports by the reporting economy from economies in the Arab World. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.;World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.;Weighted average;

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleoceanography with a geographic location of Global. The time period coverage is from 39253 to 9 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

1. Overview This dataset is a collection of 10,000+ high quality images of supermarket & store display shelves that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Use case The dataset could be used for various AI & Computer Vision models: Store Management, Stock Monitoring, Customer Experience, Sales Analysis, Cashierless Checkout,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ or contact via our email admin.bi@pixta.co.jp.

The Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to landslide hazard. Mortality loss estimates per hazard event are caculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of landslide hazard are obtained from the Global Landslide Hazard Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of landslide-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

The Global Retail Sales Data provided here is a self-generated synthetic dataset created using Random Sampling techniques provided by the Numpy Package. The dataset emulates information regarding merchandise sales through a retail website set up by a popular fictional influencer based in the US between the '23-'24 period. The influencer would sell clothing, ornaments and other products at variable rates through the retail website to all of their followers across the world. Imagine that the influencer executes high levels of promotions for the materials they sell, prompting more ratings and reviews from their followers, pushing more user engagement.

This dataset is placed to help with practicing Sentiment Analysis or/and Time Series Analysis of sales, etc. as they are very important topics for Data Analyst prospects. The column description is given as follows:

Order ID: Serves as an identifier for each order made.

Order Date: The date when the order was made.

Product ID: Serves as an identifier for the product that was ordered.

Product Category: Category of Product sold(Clothing, Ornaments, Other).

Buyer Gender: Genders of people that have ordered from the website (Male, Female).

Buyer Age: Ages of the buyers.

Order Location: The city where the order was made from.

International Shipping: Whether the product was shipped internationally or not. (Yes/No)

Sales Price: Price tag for the product.

Shipping Charges: Extra charges for international shipments.

Sales per Unit: Sales cost while including international shipping charges.

Quantity: Quantity of the product bought.

Total Sales: Total sales made through the purchase.

Rating: User rating given for the order.

Review: User review given for the order.

San Francisco Airport Runway Use

Late Night Departure Preferences

By City of San Francisco [source]

About this dataset

This dataset explores the late night departing runways used by aircraft at San Francisco International Airport (SFO). From 1:00 a.m. to 6:00 a.m., aircraft are directed to either 10L/R, 01L/R or 28L/R with an immediate right turn when safety and weather conditions permit to reduce noise in the area's surrounding residential communities by following over-water departure procedures, directing aircraft over the bay instead. Providing insight into which late night runways are most frequently used, data from this dataset is broken down by runway, month and year of departure as well as what percent of total departures for each month come from each runway - allowing for a comprehensive look at SFO's preferential late night use of airport runways!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to analyze the degree of aircraft late night departure from San Francisco Airport in order to study the impact of runway usage on air and noise pollution in residential communities. This dataset contains information about departures from each runway (01L/R, 10L/R, 19L/R and 28L/R) at San Francisco Airport for a specified year and month. By studying the percentage of total departures by runway we can understand how much aircraft are using which runways during late night hours.

To use this dataset one needs to first become familiar with the column names such as Year, Month, 01L/R(number of departures from 01L/R runway),01L/R Percent of Departures (percentage of departures from 01LR runway) etc. It is also important to become more familiar with terms such as departure and late-night which are prominently used in this dataset.

Once you have familiarized yourself with these details you can start exploring the data for further insights into how specific runways are being used for late night flight operations in San Francisco Airport and also note any patterns or trends that may emerge when looking at multiple months or years within this data set. Additionally, by comparing percentages between different runways we can measure which runways are preferred more often than others during times when congested traffic is more common such as holidays or summer months when residents take trips more often

Research Ideas

• To identify areas of the San Francisco Airport prone to noise pollution from aircraft and develop ways to limit it.
• To analyze the impacts of changing departure runway preferences on noise pollution levels over residential communities near the airport.
• To monitor seasonal trends in aircraft late night departures by runways, along with identifying peak hours for each runway, in order to inform flight controllers and develop improved flight control regulations and procedures at the San Francisco Airport

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: late-night-preferential-runway-use-1.csv | Column name | Description | |:--------------------------------|:--------------------------------------------------------| | Year | The year of the data. (Integer) | | Month | The month of the data. (String) | | 01L/R | The number of departures from runway 01L/R. (Integer) | | 01L/R Percent of Departures | The percentage of departures from runway 01L/R. (Float) | | 10L/R | The number of departures from runway 10L/R. (Integer) | | 10L/R Percent of Departures | The percentage of departures from runway 10L/R. (Float) | | 19L/R | The number of departures from runway 19L/R. (Integer) | | 19L/R Percent of Departures | The percentage of departures from runway 19L/R. (Float) | | 28L/R | The number of departures from runway 28L/R. (Integer) | | 28L/R Percent of Departures | The percentage of departures from runway 28L/R. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit City of San Francisco.

World Cities provides a basemap layer for the cities of the world. The cities include national capitals, provincial capitals, major population centers, and landmark cities. Population estimates are provided for those cities listed in open source data from the United Nations Statistics Division, United Nations Human Settlements Programme, and U.S. Census Bureau.

The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.

This dataset contains all their Version 5.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Data are also available as monthly climatologies.

Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.

This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)

Please note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)

Version 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0

Saint Lucia LC: Exports: % of Total Goods Exports: The Arab World data was reported at 0.183 % in 2016. This records an increase from the previous number of 0.048 % for 2015. Saint Lucia LC: Exports: % of Total Goods Exports: The Arab World data is updated yearly, averaging 0.013 % from Dec 1986 (Median) to 2016, with 26 observations. The data reached an all-time high of 0.981 % in 1995 and a record low of 0.000 % in 1992. Saint Lucia LC: Exports: % of Total Goods Exports: The Arab World data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s St. Lucia – Table LC.World Bank: Exports. Merchandise exports to economies in the Arab World are the sum of merchandise exports by the reporting economy to economies in the Arab World. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Profitability by Crops Field

Harvested Crops and Their Value by Country and Year

By Andy Kriebel [source]

About this dataset

This dataset contains information on the world's harvested crops. The data includes the value of the crop, the country of origin, the year of harvest, and more. This data can be used to understand which crops are the most valuable, and how this value has changed over time

How to use the dataset

This dataset provides information on the world's most profitable cash crops. The data includes the value of the crop, the country of origin, and the year of harvest. This dataset can be used to understand which crops are most valuable and how this value has changed over time

Research Ideas

• To find out which crops are grown in which countries
• To find out the value of harvested crops by country and year
• To find out the world's biggest cash crop

Acknowledgements

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: Harvested Crops.csv | Column name | Description | |:---------------------|:------------------------------------------------------------| | Area | The country where the crop was grown. (String) | | Element | The type of crop. (String) | | Item | The name of the crop. (String) | | Year | The year the crop was harvested. (Integer) | | Unit | The unit of measurement for the value of the crop. (String) | | Value | The value of the crop. (Float) | | Flag | A code that indicates the quality of the data. (String) | | Flag Description | A description of the flag code. (String) |

File: Harvested Crops Summary.csv

Acknowledgements

If you use this dataset in your research, please credit Andy Kriebel.

This dataset contains the predicted prices of the asset World On Base over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This study presents a dataset on global afforestation and reforestation efforts compiled from primary (meta-)information and augmented with time-series satellite imagery and other secondary data. Our dataset covers 1,289,068 planting sites from 45,628 projects spanning 33 years.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.