datasets
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| id ▲ | name | description | createdAt | updatedAt | namespace | isPrivate | createdByUserId | metadataEditedAt | metadataEditedByUserId | dataEditedAt | dataEditedByUserId | nonRedistributable | isArchived | sourceChecksum | shortName | version | updatePeriodDays |
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| 5071 | COVID-2019 - ECDC (2020) | 2020-03-19 11:03:55 | 2023-08-14 14:42:00 | owid | 0 | Daniel Gavrilov 29 | 2020-10-28 13:06:08 | Edouard Mathieu 46 | 2020-10-28 13:06:08 | Daniel Gavrilov 29 | 0 | 1 | |||||
| 5070 | COVID-2019 - Johns Hopkins (2020) | 2020-03-19 10:14:11 | 2023-08-14 14:41:45 | owid | 1 | Daniel Gavrilov 29 | 2020-03-19 10:14:11 | Daniel Gavrilov 29 | 2020-03-19 10:14:11 | Daniel Gavrilov 29 | 0 | 1 | |||||
| 5065 | COVID-2019 - WHO (2020) | 2020-03-12 00:11:19 | 2020-03-12 00:16:50 | owid | 1 | Breck Yunits 43 | 2020-03-12 00:16:50 | Breck Yunits 43 | 2020-03-12 00:16:50 | Daniel Gavrilov 29 | 0 | 0 | |||||
| 5058 | Trade: Crops and livestock products | The food and agricultural trade dataset is collected, processed and disseminated by FAO according to the standard International Merchandise Trade Statistics Methodology. The data is mainly provided by UNSD, Eurostat, and other national authorities as needed. This source data is checked for outliers, trade partner data is used for non-reporting countries or missing cells, and data on food aid is added to take into account total cross-border trade flows. The trade database includes the following variables: export quantity, export value, import quantity, and import value. The trade database includes all food and agricultural products imported/exported annually by all the countries in the world. | 2020-02-14 03:24:20 | 2020-02-14 03:24:20 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:24:20 | Daniel Gavrilov 29 | 2020-02-14 03:24:20 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5057 | Trade: Trade Indices | The food and agricultural trade dataset is collected, processed and disseminated by FAO according to the standard International Merchandise Trade Statistics Methodology. The data is mainly provided by UNSD, Eurostat, and other national authorities as needed. This source data is checked for outliers, trade partner data is used for non-reporting countries or missing cells, and data on food aid is added to take into account total cross-border trade flows. The trade database includes the following variables: export quantity, export value, import quantity and import value. The trade database includes all food and agricultural products imported/exported annually by all the countries in the world. | 2020-02-14 03:24:19 | 2020-02-14 03:24:19 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:24:19 | Daniel Gavrilov 29 | 2020-02-14 03:24:19 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5056 | Trade: Live animals | The food and agricultural trade dataset is collected, processed and disseminated by FAO according to the standard International Merchandise Trade Statistics Methodology. The data is mainly provided by UNSD, Eurostat, and other national authorities as needed. This source data is checked for outliers, trade partner data is used for non-reporting countries or missing cells, and data on food aid is added to take into account total cross-border trade flows. The trade database includes the following variables: export quantity, export value, import quantity and import value. The trade database includes all food and agricultural products imported/exported annually by all the countries in the world. | 2020-02-14 03:22:49 | 2020-02-14 03:22:49 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:22:49 | Daniel Gavrilov 29 | 2020-02-14 03:22:49 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5055 | Investment: Machinery Archive | 2020-02-14 03:21:58 | 2020-02-14 03:21:58 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:21:58 | Daniel Gavrilov 29 | 2020-02-14 03:21:58 | Daniel Gavrilov 29 | 0 | 1 | |||||
| 5054 | Inputs: Pesticides Trade | This domain contains data on pesticides and covers two different categories: pesticides traded in form or packagingfor retail sale or as preparations or articles, and pesticides traded as separate chemically defined compounds (if relevant for the Rotterdam Convention on the Prior Informed Consent Procedure for Certain Hazardous Chemicals and Pesticides in International Trade). The pesticides traded for retail sale or as preparations or articles are those classified under code 38.08 in the Harmonized System Nomenclature (HS) and include: hazardous pesticides, insecticides, fungicides, herbicides, disinfectants and other. For these pesticides, this domain contains trade data (imports and exports) in values only (current 1000 US dollars), and the time series extends from 1961 onwards. The pesticides traded as separate chemically defined compounds are those listed in Annex III of the Rotterdam Convention (excluding industrial chemicals) and therefore subject to the Prior Informed Consent (PIC) procedure. The correspondence with the HS Nomenclature is shown in the table at the Related Documents section. For these pesticides, this domain contains trade data (imports and exports) in both value (current 1000 US dollars) and quantity (net weight in tonnes), and the time series extends from 2007 onwards. | 2020-02-14 03:21:43 | 2020-02-14 03:21:43 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:21:43 | Daniel Gavrilov 29 | 2020-02-14 03:21:43 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5053 | Inputs: Pesticides Use | The Pesticides Use database includes data on the use of major pesticide groups (Insecticides, Herbicides, Fungicides, Plant growth regulators and Rodenticides) and of relevant chemical families. Data report the quantities (in tonnes of active ingredients) of pesticides used in or sold to the agricultural sector for crops and seeds. Information on quantities applied to single crops is not available. | 2020-02-14 03:21:37 | 2020-02-14 03:21:37 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:21:37 | Daniel Gavrilov 29 | 2020-02-14 03:21:37 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5052 | Investment: Machinery | Important notice: FAOSTAT database on Agriculture Machinery is no longer active. The latest online version of the database has as reference year 2009 (with data collected in year 2011). FAOSTAT database on Agriculture Machinery provides statistical series on Agricultural Machinery and Equipment statistical series referring to the following items: tractors, harvesters and threshers, irrigation pumps, milking machines, hand tools, and soil machines. The database includes estimates of agriculture machinery in use and value of import and export of agriculture machinery. | 2020-02-14 03:21:15 | 2020-02-14 03:21:15 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:21:15 | Daniel Gavrilov 29 | 2020-02-14 03:21:15 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5051 | Inputs: Land Use | The FAOSTAT Land Use domain contains data on forty-seven categories of land use, irrigation and agricultural practices, relevant to monitor agriculture, forestry and fisheries activities at national, regional and global level. Data are available by country and year, with global coverage and annual updates. | 2020-02-14 03:20:46 | 2020-02-14 03:20:46 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:20:46 | Daniel Gavrilov 29 | 2020-02-14 03:20:46 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5050 | Inputs: Fertilizers by Nutrient | The Fertilizers by Nutrient dataset contains information on the totals in nutrients for Production, Trade, Agriculture Use and Other Uses of chemical and mineral fertilizers, over the time series 2002-present. The fertilizer statistics data are validated in terms of summary totals of Production, Import, Export, non-Fertilizer Use and Agricultural Use, separately for the three main plant nutrients: nitrogen (N), phosphate (P2O5), potash (K2O). Both straight and compound fertilizers are included. Detailed information on Fertilizers by Nutrient Methodology at: http://fenixservices.fao.org/faostat/static/documents/RFN/RFN_EN_README.pdf | 2020-02-14 03:20:40 | 2020-02-14 03:20:40 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:20:40 | Daniel Gavrilov 29 | 2020-02-14 03:20:40 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5049 | Inputs: Fertilizers by Product | The Fertilizers by Product dataset contains information on product amounts for the Production, Trade, Agriculture Use and Other Uses of chemical and mineral fertilizers products, over the time series 2002-present. The fertilizer statistics data are validated separately for a set of over thirty individual products. Both straight and compound fertilizers are included. Detailed information on Fertilizers by Product Methodology at: http://fenixservices.fao.org/faostat/static/documents/RFB/RFB_EN_README.pdf | 2020-02-14 03:20:11 | 2020-02-14 03:20:11 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:20:11 | Daniel Gavrilov 29 | 2020-02-14 03:20:11 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5048 | Inputs: Fertilizers archive | The Fertilizer archive dataset containsinformation on the Production, Trade and Consumption of chemical and mineral fertilizers products, both in total nutrients and in amount of product, over the time series 1961 to 2002.The datasetalso contains data on Prices paid by farmers expressed in local currencies (as a consequence no country aggregates are available) for single fertilizer products. This dataset is an archive and it is disseminated as it was in the previous FAOSTAT System. No dataset updates made or to be made in the future. | 2020-02-14 03:17:19 | 2020-02-14 03:17:19 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:17:19 | Daniel Gavrilov 29 | 2020-02-14 03:17:19 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5047 | Production: Value of Agricultural Production | The data set includes data on gross and net production values, in constant international US$, and gross production values, in constant and current US$ and Local Currency Units, for various food and agriculture commodities and aggregates thereof, expressed in both total value and value per capita. | 2020-02-14 03:07:08 | 2020-02-14 03:07:08 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:07:08 | Daniel Gavrilov 29 | 2020-02-14 03:07:08 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5046 | Production: Livestock Processed | The dataset covers the following commodities: Butter and ghee, sheep milk; Butter of goat milk; Butter, buffalo milk; Butter, cow milk; Cheese of goat milk; Cheese, buffalo milk; Cheese, sheep milk; Cheese, skimmed cow milk; Cheese, whole cow milk; Cream fresh; Ghee, butteroil of cow milk; Ghee, of buffalo milk; Lard; Milk, dry buttermilk; Milk, skimmed condensed; Milk, skimmed cow; Milk, skimmed dried; Milk, skimmed evaporated; Milk, whole condensed; Milk, whole dried; Milk, whole evaporated; Silk raw; Tallow; Whey, condensed; Whey, dry; Yoghurt. | 2020-02-14 03:06:54 | 2020-02-14 03:06:54 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:06:54 | Daniel Gavrilov 29 | 2020-02-14 03:06:54 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5045 | Production: Livestock Primary | The dataset contains the following commodities and commodity aggregates thereof : Beeswax; Eggs (various types); Hair, horse; Hides buffalo, fresh; Hides, cattle, fresh; Honey, natural; Meat indigenous (ass, bird nes, buffalo, camel, cattle, chicken, duck, geese, goat, horse, mule, other camelids, pig, rabbit, rodents, sheep, turkey); Meat (ass, bird nes, buffalo, camel, cattle, chicken, duck, game, goat, goose and guinea fowl, horse, mule, Meat nes, meat other camelids, Meat other rodents, pig, rabbit, sheep, turkey); Milk (buffalo, camel, cow, goat, sheep); Offals, nes; Silk-worm cocoons, reelable; Skins, furs; Skins (goat, sheep); Snails, not sea; Wool, greasy.Meat: Data relate to animals slaughtered within national boundaries, irrespective of their origin. All data shown relate to total meat production, that is, from both commercial and farm slaughter. Data are given in terms of dressed carcass weight, excluding offal and slaughter fats. Production of beef and buffalo meat includes veal; mutton and goat meat includes meat from lambs and kids; pig meat includes bacon and ham in fresh equivalent. Poultry meat includes meat from all domestic birds and refers, wherever possible, to ready-to-cook weight. Data on poultry-meat production reported by national statistical offices could be expressed in terms of either live weight, eviscerated weight, ready-to-cook weight or dressed weight. Data for countries reporting in other than ready-to-cook weight have been converted into the ready-to-cook equivalent.Milk: Data on milk production relate to total production of whole fresh milk, excluding the milk sucked by young animals but including amounts fed to livestock. Eggs: Some countries have no statistics on egg production, and estimates had to be derived from such related data as chicken or total poultry numbers and reported or assumed rates of egg laying. Most of the countries that have statistics on egg production report either the total weight of eggs or the numbers of eggs produced. Data generally refer to total prod… | 2020-02-14 03:03:45 | 2020-02-14 03:03:45 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:03:45 | Daniel Gavrilov 29 | 2020-02-14 03:03:45 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5044 | Production: Production Indices | The dataset includes data on gross and net production indices for various food and agriculture aggregates expressed in both totals and per capita. | 2020-02-14 03:02:06 | 2020-02-14 03:02:06 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:02:06 | Daniel Gavrilov 29 | 2020-02-14 03:02:06 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5043 | Production: Crops processed | The dataset covers the following commodities: Beer of barley; Cotton lint; Cottonseed; Margarine, short; Molasses; Oil, coconut (copra); Oil, cottonseed; Oil, groundnut; Oil, linseed; Oil, maize; Oil, olive, virgin; Oil, palm; Oil, palm kernel; Oil, rapeseed; Oil, safflower; Oil, sesame; Oil, soybean; Oil, sunflower; Palm kernels; Sugar Raw Centrifugal; Wine. | 2020-02-14 03:01:50 | 2020-02-14 03:01:50 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 03:01:50 | Daniel Gavrilov 29 | 2020-02-14 03:01:50 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5042 | Production: Crops | Crop statistics are recorded for 173 products, covering the following categories: Crops Primary, Fibre Crops Primary, Cereals, Coarse Grain, Citrus Fruit, Fruit, Jute Jute-like Fibres, Oilcakes Equivalent, Oil crops Primary, Pulses, Roots and Tubers, Treenuts and Vegetables and Melons. Data are expressed in terms of area harvested, production quantity and yield. The objective is to comprehensively cover production of all primary crops for all countries and regions in the world.Cereals: Area and production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed or silage or used for grazing are therefore excluded. Area data relate to harvested area. Some countries report sown or cultivated area only; however, in these countries the sown or cultivated area does not differ significantly in normal years from the area actually harvested, either because practically the whole area sown is harvested or because the area surveys are conducted around the harvest period.Vegetables, total (including melons): Data relate to vegetable crops grown mainly for human consumption. Crops such as cabbages, pumpkins and carrots, when explicitly cultivated for animal feed, are therefore excluded. Statistics on vegetables are not available in many countries, and the coverage of the reported data differs from country to country. In general, it appears that the data refer to crops grown in field and market gardens mainly for sale, thus excluding crops cultivated in kitchen gardens or small family gardens mainly for household consumption.Fruit, total (excluding melons): Data refer to total production of fresh fruit, whether finally used for direct consumption for food or feed, or processed into different products: dry fruit, juice, jam, alcohol, etc. Generally, production data relate to plantation crops or orchard crops grown mainly for sale. Data on production from scattered trees used mainly for home consumption are not usually collected. Production from wild plants, … | 2020-02-14 02:55:51 | 2020-02-14 02:55:51 | faostat_2020 | 1 | Daniel Gavrilov 29 | 2020-02-14 02:55:51 | Daniel Gavrilov 29 | 2020-02-14 02:55:51 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5041 | Production: Live Animals | The dataset contains the following commodities and commodity aggregates thereof : Animals live n.e.s.; Asses; Beehives; Buffaloes; Camelids, other; Camels; Cattle; Chickens; Ducks; Geese and guinea fowls; Goats; Horses; Mules; Pigeons, other birds; Pigs; Rabbits and hares; Rodents, other; Sheep; Turkeys. | 2020-02-14 02:55:22 | 2020-02-14 02:55:22 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:55:22 | Daniel Gavrilov 29 | 2020-02-14 02:55:22 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5040 | Prices: Producer Prices - Annual | This sub-domain contains data on Agriculture Producer Prices. These are prices received by farmers for primary crops, live animals and livestock primary products as collected at the point of initial sale (prices paid at the farm-gate). Annual data are provided from 1991 for over 160 countries and about 200 commodities. | 2020-02-14 02:54:08 | 2020-02-14 02:54:08 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:54:08 | Daniel Gavrilov 29 | 2020-02-14 02:54:08 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5038 | Prices: Exchange rates - Annual | Annual exchange rates, national currency units per U.S. dollar. | 2020-02-14 02:54:06 | 2020-02-14 02:54:06 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:54:06 | Daniel Gavrilov 29 | 2020-02-14 02:54:06 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5037 | Prices: Deflators | The FAOSTAT Deflators database provides the following selection of implicit price deflator series by country: Gross Domestic Product (GDP) deflator, Gross Fixed Capital Formation (GFCF) deflator, Agriculture, Forestry, Fishery Value-Added (VA_AFF) deflator, andManufacturing Valued-Added (VA_MAN) deflator. A deflator is a figure expressing the change in prices over a period of time for a product or a basket of products by comparing a reference period to a base period. It is obtained by dividing a current price value of a given aggregate by its real counterpart. When calculated from the major national accounting aggregates such as GDP or agriculture VA, implicit price deflators pertains to wider ranges of goods and services in the economy than that represented by any of the individual price indexes (such as CPIs, PPIs). Movements in an implicit price deflator reflect both changes in price and changes in the composition of the aggregate for which the deflator is calculated. In the FAOSTAT Deflators database, all series are derived from the United Nations Statistics Division (UNSD) National Accounts Estimates of Main Aggregates database (UNSD NAE). In particular, the implicit GDP deflator, the implicit GFCF deflator and the implicit value added deflator in Agriculture, Forestry, Fishery are obtained by dividing the series in current prices by those in constant 2010 prices (base year). | 2020-02-14 02:53:54 | 2020-02-14 02:53:54 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:53:54 | Daniel Gavrilov 29 | 2020-02-14 02:53:54 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5036 | Inputs: Employment Indicators | The FAOSTAT Employment indicators module contains data on:- Agriculture value added per worker;- Employment in agriculture (absolute numbers and shares, by sex);- Share of Employees in agriculture (by sex);- Labour force participation in rural areas (by sex);- Employment-to-population ratio in rural areas (by sex).The series consist of data taken from Labour Force Surveys as available from the original sources, namely the International Labour Organization (ILO). | 2020-02-14 02:53:48 | 2020-02-14 02:53:48 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:53:48 | Daniel Gavrilov 29 | 2020-02-14 02:53:48 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5035 | Macro-Statistics: Macro Indicators | TheFAOSTAT Macro Indicatorsdatabase provides a selection of country-level macroeconomic indicators relating to total economy (TE); agriculture (Ag); agriculture, forestry and fishing (AFF); manufacturing (MAN); manufacturing of food products and beverages (FB); manufacturing of tobacco products (Tob); and manufacturing of food, beverage and tobacco products (FBT). It releases time series for a selection of National Accounts variables, including gross domestic product, gross fixed capital formation, industry-level value added and gross output. The database also proposes additional indicators such as per capita GDP, year-on-year growth rates and measures ofindustry contribution to GDP. All data relating to TE, AFF, and to MAN originates from the United Nations Statistics Division (UNSD) National Accounts Estimates of Main Aggregates database, which consists of a complete and consistent set of time series of the main National Accounts (NA) aggregates of all UN Members States and other territories in the world for which National Accounts information is available. The UNSD database's content is based on the countries' official NA data reported to UNSD through the annual National Accounts Questionnaire, supplemented with data estimates for any years and countries with incomplete or inconsistent information (Seehttp://unstats.un.org/unsd/snaama/Introduction.asp). Series relating to the sub-industry Ag are obtained from the OECD Annual National Accounts and UNSD NA Official Country Data databases while serieson the FBT industry originates - in order of priority - from OECD Annual National Accounts and UNIDO INDSTAT2 databases. In order to ensure that sub-industry series are consistent in levels with National Accounts based series, which is needed to support comparability across industries (agriculture vs. agro-industry and sub-industries), we proceed to a rescaling exercise of UNIDO originating series on UNSD National Accounts Estimates of Main Aggregates data series (See Section 20 for a more detailed description of t… | 2020-02-14 02:51:58 | 2020-02-14 02:51:58 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:51:58 | Daniel Gavrilov 29 | 2020-02-14 02:51:58 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5034 | Agri-Environmental Indicators: Land Cover | The FAOSTAT domain Land Cover under the Agri-Environmental Indicators section contains land cover information organized by the land cover classes of the international standard system for Environmental and Economic Accounting Central Framework (SEEA CF). The land cover information is compiled from publicly available Global Land Cover (GLC) maps: a) the International Geosphere-Biosphere Programme (IGBP)-MODIS land cover type (20012012) and the European Spatial Agency (ESA) Climate Change Initiative (CCI) annual land cover maps (19922015) produced by the Université catholique de Louvain (UCL)-Geomatics. | 2020-02-14 02:51:41 | 2020-02-14 02:51:41 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:51:41 | Daniel Gavrilov 29 | 2020-02-14 02:51:41 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5033 | Investment: Government Expenditure | The Statistics Division of FAO collects annually data on Government Expenditure on Agriculture through a questionnaire, which was developed in partnership with the International Monetary Fund. The IMF is the responsible institution for the Government Finance Statistics (GFS) methodology and annually collects GFS data, including Expenditure by Functions of Government (COFOG). The Classification of the Functions of Government (COFOG) is an international classification developed by Organisation for Economic Co-operation and Development (OECD) and published by the United Nations Statistical Division (UNSD), with the aim of categorise governments' functions according to their purposes. The FAO questionnaire aligns with Table 7 of the IMF GFS questionnaire, replicates the relevant aggregates and drills down to request additional detail related to Agriculture. The FAO dataset consists of a time series, from 2001 onwards, of Total Government Expenditure and expenditure in: Economic affairs; Agriculture, Forestry, Fishing and Hunting, along with its three disaggregated subsectors of Agriculture, Forestry and Fishing; and Environmental Protection. In addition, expenditure in each detailed function are further disaggregated into Recurrent and Capital expenditure. Additional indicators include the Agriculture Share of Government Expenditure, and the Agriculture Orientation Index (ratio between the Agriculture Share of Government Expenditure and the Agriculture Value Added as Share of GDP). Though the goal is to have complete and consistent coverage for all countries, different stages of implementation of the GFS methodology and COFOG classification, and differences in the data collection and reporting at country level creates some challenges in providing a complete and consistent global dataset. | 2020-02-14 02:51:31 | 2020-02-14 02:51:31 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:51:31 | Daniel Gavrilov 29 | 2020-02-14 02:51:31 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5032 | Investment: Credit to Agriculture | The Credit to Agriculture dataset provides national data for over 100 countries on the amount of loans provided by the private/commercial banking sector to producers in agriculture, forestry and fisheries, including household producers, cooperatives, and agro-businesses. For some countries, the three subsectors of agriculture, forestry, and fishing are completely specified. In other cases, complete disaggregations are not available. The dataset also provides statistics on the total credit to all industries, indicators on the share of credit to agricultural producers, and an agriculture orientation index (the agriculture share of credit, over the agriculture share of GDP). | 2020-02-14 02:51:29 | 2020-02-14 02:51:29 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:51:29 | Daniel Gavrilov 29 | 2020-02-14 02:51:29 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5031 | Emissions - Agriculture: Synthetic Fertilizers | Greenhouse gas (GHG) emissions from synthetic fertilizers consist of nitrous oxide gas from synthetic nitrogen additions to managed soils. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the addition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq. Implied emission factor for N2O and activity data (consumption) are also provided. | 2020-02-14 02:51:06 | 2020-02-14 02:51:06 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:51:06 | Daniel Gavrilov 29 | 2020-02-14 02:51:06 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5030 | Emissions - Agriculture: Cultivation of Organic Soils | The domain Cultivation of Organic soils contains nitrous oxide (N2O) gas emissions from organic soils under cropland (item: Cropland organic soils) and grassland (item: Grassland organic soils). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html) applied to geospatial data. GHG emissions are provided by country, region and special groups, with global coverage, relative to the period 1990-2016 (with annual updates) and with projections for 2030 and 2050, expressed both as Gg N2O and Gg CO2eq, by cropland, grassland and by their aggregation. Implied emission factor for N2O as well activity data (areas) are also provided. | 2020-02-14 02:51:00 | 2020-02-14 02:51:00 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:51:00 | Daniel Gavrilov 29 | 2020-02-14 02:51:00 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5029 | Emissions - Agriculture: Manure applied to Soils | GHG emissions from manure applied to soils consist of direct and indirect nitrous oxide (N2O) emissions from manure nitrogen (N) added to agricultural soils by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the application site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), chickens (broilers and layers), ducks, goats, horses, llamas, mules, sheep, swine (breeding and market) and turkeys) and by species aggregates (all animals, camels and llamas, cattle, chickens, mules and asses, poultry birds, sheep and goats, swine). Implied emission factor for N2O and activity data (N content in manure) are also provided. | 2020-02-14 02:44:27 | 2020-02-14 02:44:27 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:44:27 | Daniel Gavrilov 29 | 2020-02-14 02:44:27 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5028 | Emissions - Agriculture: Agriculture Total | Agriculture Total contains all the emissions produced in the different agricultural emissions sub-domains (enteric fermentation, manure management, rice cultivation, synthetic fertilizers, manure applied to soils, manure left on pastures, crop residues, cultivation of organic soils, burning of crop residues, burning of savanna, energy use), providing a picture of the contribution to the total amount of GHG emissions from agriculture. GHG emissions from agriculture consist of non-CO2 gases, namely methane (CH4) and nitrous oxide (N2O), produced by crop and livestock production and management activities. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg CO2 and CO2eq (from CH4 and N2O), by underlying agricultural emission sub-domain and by aggregate (agriculture total, agriculture total plus energy, agricultural soils). | 2020-02-14 02:43:06 | 2020-02-14 02:43:06 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:43:06 | Daniel Gavrilov 29 | 2020-02-14 02:43:06 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5027 | Emissions - Agriculture: Rice Cultivation | Greenhouse gas (GHG) emissions from rice cultivation consist of methane gas from the anaerobic decomposition of organic matter in paddy fields. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html) and the IPCC 2000 Good Practice Guidance and Uncertainty Management in National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/gp/english/). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4 and Gg CO2eq. Implied emission factor for CH4 and activity data are also provided. | 2020-02-14 02:43:01 | 2020-02-14 02:43:01 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:43:01 | Daniel Gavrilov 29 | 2020-02-14 02:43:01 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5026 | Emissions - Agriculture: Manure left on Pasture | GHG emissions from manure left on pastures consist of direct and indirect nitrous oxide (N2O) emissions from manure nitrogen (N) left on pastures by grazing livestock. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the deposition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as direct, indirect and total Gg N2O and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), chickens (broilers and layers), ducks, goats, horses, llamas, mules, sheep, swine (breeding, market), turkeys) and by species aggregates (all animals, camels and llamas, cattle, chickens, mules and asses, poultry birds, sheep and goats, swine). Implied emission factor for N2O and N content in manure are also provided. | 2020-02-14 02:35:16 | 2020-02-14 02:35:16 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:35:16 | Daniel Gavrilov 29 | 2020-02-14 02:35:16 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5025 | Emissions - Agriculture: Energy Use | Greenhouse gas (GHG) emissions from direct energy use consist of carbon dioxide, methane and nitrous oxide gases associated with fuel burning and generation of electricity used in agriculture (including fisheries). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories, vol. 2, ch. 2 and 3 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol2.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1970-present (with annual updates), expressed both as Gg CH4, Gg N2O, Gg CO2, Gg CO2eq and Gg CO2eq from both CH4 and N2O, by motor gasoline (gas-diesel oils, gasoline, natural gas, liquefied petroleum gas, residual fuel oil, hard coal, electricity, gas-diesel oils in fisheries, residual fuel oil in fisheries and energy for power irrigation) and by aggregates (total energy, transport fuel consumed in agriculture excluding fishery, energy consumed in fishery). Implied emission factors for N2O, CH4 and CO2 as well activity data (consumption of gasoline's in agriculture) are also provided. | 2020-02-14 02:34:08 | 2020-02-14 02:34:08 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:34:08 | Daniel Gavrilov 29 | 2020-02-14 02:34:08 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5024 | Emissions - Agriculture: Manure Management | Greenhouse gas (GHG) emissions from manure management consist of methane and nitrous oxide gases from aerobic and anaerobic manure decomposition processes. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4, Gg N2O and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), chickens (broilers and layers), ducks, goats, horses, llamas, mules, sheep, swine (breeding, market), turkeys) and by species aggregates (all animals, camels and llamas, cattle, chickens, mules and asses, poultry birds, sheep and goats, swine). Implied emission factors, direct and indirect emissions (for both N2O and CO2eq) as well as N content in manure are also provided. | 2020-02-14 02:26:44 | 2020-02-14 02:26:44 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:26:44 | Daniel Gavrilov 29 | 2020-02-14 02:26:44 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5023 | Emissions - Land Use: Land Use Total | Land Use Total contains all GHG emissions and removals produced in the different Land Use sub-domains, representing the three IPCC Land Use categories: cropland, forest land, and grassland, collectively called emissions/removals from the Forestry and Other Land Use (FOLU) sector. FOLU emissions consist of CO2 (carbon dioxide), CH4 (methane) and N2O (nitrous oxide) associated with land management activities. CO2 emissions/removals are derived from estimated net carbon stock changes in above and below-ground biomass pools of forest land, including forest land converted to other land uses. CH4 and N2O, and additional CO2 emissions are estimated for fires and drainage of organic soils. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html). GHG emissions are provided as by country, regions and special groups, with global coverage, relative to the period 1990-most recent available year (with annual updates), expressed as Gg CO2eq from CH4 and N2O, net emissions/removals as GG CO2 and Gg CO2eq, by underlying land use emission sub-domain and by aggregate (land use total). | 2020-02-14 02:26:30 | 2020-02-14 02:26:30 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:26:30 | Daniel Gavrilov 29 | 2020-02-14 02:26:30 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5022 | Emissions - Land Use: Burning - Biomass | Greenhouse Gas (GHG) emissions from burning of biomass consist of methane and nitrous oxide gases from biomass combustion of forest land cover classes Humid and Tropical Forest and Other Forests, and of methane, nitrous oxide, and carbon dioxide gases from combustion of organic soils. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, with global coverage, relative to the period 1990-present (with annual updates), expressed as Gg CH4, Gg N2O, Gg CO2, Gg CO2eq and Gg CO2eq from both CH4 and N2O, by land cover class (humid tropical forest, other forest, organic soils) and by aggregate (burning - all categories). Implied emission factors for N2O, CH4 and CO2 as well activity data (burned area and biomass burned) are also provided. | 2020-02-14 02:25:55 | 2020-02-14 02:25:55 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:25:55 | Daniel Gavrilov 29 | 2020-02-14 02:25:55 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5021 | Emissions - Agriculture: Burning - Savanna | Greenhouse Gas (GHG) emissions from burning of savanna consist of methane (CH4) and nitrous oxide (N2O) gases produced from the burning of vegetation biomass in the following five land cover types: Savanna, Woody Savanna, Open Shrublands, Closed Shrublands, and Grasslands. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1990-present (with annual updates), expressed as Gg CH4, Gg N2O, Gg CO2eq and Gg CO2eq from both CH4 and N2O, by land cover class (savanna, woody savanna, closed shrubland, open shrubland, grassland) and by aggregates (all categories, savanna and woody savanna, closed and open shrubland). Implied emission factors for N2O and CH4 as well activity data (burned area and biomass burned) are also provided. | 2020-02-14 02:24:44 | 2020-02-14 02:24:44 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:24:44 | Daniel Gavrilov 29 | 2020-02-14 02:24:44 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5020 | Emissions - Land Use: Grassland | Greenhouse gas (GHG) emissions in the domain "Grassland" are currently limited to the CO2 emissions from grassland organic soils. They are those associated with carbon losses from drained histosols under grassland. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol6.html)applied to geospatial data.GHG emissions are provided by country, region and special groups, with global coverage, relative to the period 1990-present (with annual updates), expressed as net emissions/removal Gg CO2 and Gg CO2eq. Implied emission factor for C, net stock change Gg C and activity data (area) are also provided. | 2020-02-14 02:24:42 | 2020-02-14 02:24:42 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:24:42 | Daniel Gavrilov 29 | 2020-02-14 02:24:42 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5019 | Emissions - Land Use: Forest Land | Annual net CO2 emission/removal from Forest Land consist of net carbon stock gain/loss in the living biomass pool (aboveground and belowground biomass) associated with Forest and Net Forest Conversion. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html) and using area and carbon stocks data compiled by countries in the FAO Global Forest Resource Assessments (http://www.fao.org/forestry/fra/en/). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1990-present (with annual updates), expressed as net stock change Gg C, net emissions/removals Gg CO2 and CO2eq, by forest or net forest conversion and by aggregate (forest land). Implied emission factor for CO2 as well as activity data (area, net area difference, total forest area and carbon stock in living biomass) are also given. | 2020-02-14 02:24:31 | 2020-02-14 02:24:31 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:24:31 | Daniel Gavrilov 29 | 2020-02-14 02:24:31 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5018 | Emissions - Agriculture: Enteric Fermentation | Greenhouse gas (GHG) emissions from enteric fermentation consist of methane gas produced in digestive systems of ruminants and to a lesser extent of non-ruminants. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories vol. 4, ch. 10 and 11 (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4 and Gg CO2eq, by livestock species (asses, buffaloes, camels, cattle (dairy and non-dairy), goats, horses, llamas, mules, sheep, swine (breeding and market)) and by species aggregates (all animals, camels and llamas, cattle, mules and asses, sheep and goats, swine). Implied emission factor for CH4 and activity data are also provided. | 2020-02-14 02:22:53 | 2020-02-14 02:22:53 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:22:53 | Daniel Gavrilov 29 | 2020-02-14 02:22:53 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5017 | Emissions - Land Use: Cropland | Greenhouse gas (GHG) emissions in the domain "Cropland" are currently limited to CO2 emissions from cropland organic soils. They are those associated with carbon losses from drained histosols under cropland. The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol5.html) applied to geospatial data. GHG emissions are provided by country, region and special groups, with global coverage, relative to the period 1990-2016 (with annual updates), expressed as net emissions/removal Gg CO2 and Gg CO2eq. Implied emission factor for C, net stock change Gg C and activity data (area) are also provided. | 2020-02-14 02:22:50 | 2020-02-14 02:22:50 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:22:50 | Daniel Gavrilov 29 | 2020-02-14 02:22:50 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5016 | Emissions - Agriculture: Burning - Crop Residues | Greenhouse Gas (GHG) emissions from burning crop residues consist of methane (CH4) and nitrous oxide (N2O) gases produced by the combustion of a percentage of crop residues burnt on-site. The mass of fuel available for burning should be estimated taking into account the fractions removed before burning due to animal consumption, decay in the field, and use in other sectors (e.g., biofuel, domestic livestock feed, building materials, etc.). FAOSTAT emission estimates are computed at Tier 1 following the IPCC 2006 Guidelines for National GHG Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided by country, reguions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed both as Gg CH4, Gg N2O, Gg CO2eq and CO2eq from CH4 and N2O, by crop (maize, rice, sugarcane and wheat) and by aggregates. Implied emission factors for N2O and CH4 as well activity data (biomass burned) are also provided. | 2020-02-14 02:21:58 | 2020-02-14 02:21:58 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:21:58 | Daniel Gavrilov 29 | 2020-02-14 02:21:58 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5015 | Emissions - Agriculture: Crop Residues | Greenhouse gas (GHG) emissions from crop residues consist of direct and indirect nitrous oxide (N2O) emissions from nitrogen (N) in crop residues and forage/pasture renewal left on agricultural fields by farmers. Specifically, N2O is produced by microbial processes of nitrification and de-nitrification taking place on the deposition site (direct emissions), and after volatilization/re-deposition and leaching processes (indirect emissions). The FAOSTAT emissions database is computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories, Vol. 4, Ch. 2 and 11(http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html). GHG emissions are provided as direct, indirect and total by country, regions and special groups, with global coverage, relative to the period 1961-present (with annual updates) and with projections for 2030 and 2050, expressed as Gg N2O and Gg CO2eq, by crop and N content in residues. | 2020-02-14 02:20:08 | 2020-02-14 02:20:08 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:20:08 | Daniel Gavrilov 29 | 2020-02-14 02:20:08 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5014 | Food Security: Suite of Food Security Indicators | For detailed description of the indicators below see attached document: Average Dietary Supply Adequacy; Average Value of Food Production; Share of Dietary Energy Supply Derived from Cereals, Roots and Tubers; Average Protein Supply; Average Supply of Protein of Animal Origin; Rail lines Density (per 100 square km of land area); Percentage of Population Using At Least Basic Drinking Water Sources; Percentage of Population Using Safely Managed Drinking Water Sources; Percentage of Population Using At Least Basic Sanitation Services; Percentage of Population Using Safely Managed Sanitation Services; Cereal Import Dependency Ratio; Percent of Arable Land Equipped for Irrigation; Value of Food Imports in Total Merchandise Exports; Political Stability and Absence of Violence; Domestic Food Price Volatility Index; Per capita food production variability; Per Capita Food Supply Variability; Prevalence of Undernourishment; Prevalence of Severe Food Insecurity; Prevalence of Moderate or Severe Food Insecurity; Children aged <5 years wasted (%); Children aged <5 years stunted (%); Children aged <5 years overweight (%); Percentage of Adult Obesity; Prevalence of Anaemia among Women of Reproductive Age; Prevalence of Exclusive Breastfeeding among Infants 0-5 Months of Age; Prevalence of Low Birthweight; Number of Undernourished People; Number of Severely Food Insecure People; Prevalence of Moderately or Severely Food Insecure People; Minimum Dietary Energy Requirement (MDER); Average Dietary Energy Requirement (ADER); Coefficient of Variation of Habitual Caloric Consumption Distribution (CV); Skewness of Habitual Caloric Consumption Distribution (SK); Incidence of Caloric Losses at Retail Distribution Level; Dietary Energy Supply (DES); Average Fat Supply | 2020-02-14 02:19:55 | 2020-02-14 02:19:55 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:19:55 | Daniel Gavrilov 29 | 2020-02-14 02:19:55 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5013 | Forestry: Forestry Production and Trade | The database contains data on the production and trade in roundwood and primary wood and paper products for all countries and territories in the world.The main types of primary forest products included in are: roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. These products are detailed further. The definitions are available. The database contains details of the following topics:- Roundwood removals (production) by type of wood and assortment- Production and trade in roundwood, woodfuel and other basic products- Industrial roundwood by assortment and species- Sawnwood, panels and other primary products- Pulp and paper & paperboard.More detailed information on wood products, including definitions, can be found at http://www.fao.org/forestry/statistics/80572/en/ | 2020-02-14 02:14:54 | 2020-02-14 02:14:54 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:14:54 | Daniel Gavrilov 29 | 2020-02-14 02:14:54 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5012 | Investment: Foreign Direct Investment (FDI) | All data originates from the United Nations Conference on Trade and Development (UNCTAD), the International Trade Centre (INTRACEN) and the Organisation for Economic Co-operation and Development (OECD). Foreign Direct Investment (FDI) data is collected following the International Monetary Fund's Balance of Payments Manual, Fifth Edition, BPM5, IMF 1993, the OECD's Detailed Benchmark Definition of Foreign Direct Investment,Third Edition, BMD3, OECD 1996 and the updated OECD's benchmark definition (BMD4, OECD, 2008). .All datasets contain FDI data for the whole economy, for agriculture, forestry and fishery (AFF) and for food, beverages and tobacco (FBT) sectors. The FDI sectoral disaggregation follows the International Standard Industrial Classification of All Economic Activities, Rev.4 (ISIC, Rev. 4).UNCTAD database presents time-series data from 1980 onwards to 2017 of FDI for most UN Members States and other territories. Data availability extends to approximately 80 countries for AFF and FBT.See link to UNCTAD data:- For the whole economy: http://unctadstat.unctad.org/wds/ReportFolders/reportFolders.aspx - For AFF and FBT: provided by UNCTAD (not available to the public).INTRACEN dataset extends from 2007 to 2016 for approximately 80 countries. See link to INTRACEN data: http://www.investmentmap.org/SelectionMenu.aspx (a username and a password need to be created to login).OECD dataset extends from 2005 to 2016 for approximately 34 OECD countries. See link to OECD data:http://www.oecd.org/investment/statistics.htmBased on the FDI sources, the FAO FDI dataset extends from 1991 to 2017. The availability of data was the highest between years 1997 to 2011. | 2020-02-14 02:14:49 | 2020-02-14 02:14:49 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 02:14:49 | Daniel Gavrilov 29 | 2020-02-14 02:14:49 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5011 | Food Balance: Food Balances (old methodology and population) | Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content. | 2020-02-14 01:46:25 | 2020-02-14 01:46:25 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:46:25 | Daniel Gavrilov 29 | 2020-02-14 01:46:25 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5010 | Food Balance: New Food Balances | Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content. | 2020-02-14 01:43:38 | 2020-02-14 01:43:38 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:43:38 | Daniel Gavrilov 29 | 2020-02-14 01:43:38 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5009 | Agri-Environmental Indicators: Pesticides indicators | Agri-environmental indicator on the Use of pesticides per area of cropland (which is the sum of arable land and land under permanent crops) at national level for the period 1990 to 2016. | 2020-02-14 01:43:37 | 2020-02-14 01:43:37 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:43:37 | Daniel Gavrilov 29 | 2020-02-14 01:43:37 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5008 | Agri-Environmental Indicators: Livestock Manure | The Livestock Manure domain under the FAOSTAT section of Agri-Environmental Indicators contains estimates of nitrogen (N) inputs to agricultural soils from livestock manure. These estimates are compiled using official FAOSTAT statistics of animal stocks and by applying the internationally approved Guidelines of the Intergovernmental Panel on Climate Change (IPCC). Data are available by country, with global coverage and relative to the period 19612017, with annual updates. The following elements are disseminated: 1) Stocks; 2) Amount excreted in manure (N content); 3) Manure left on pasture (N content); 4) Manure left on pasture that volatilises (N content); 5) Manure left on pasture that leaches (N content); 6) Manure treated (N content); 7) Losses from manure treated (N content); 8) Manure applied to soils (N content); 9) Manure applied to soils that volatilises (N content); 10) Manure applied to soils that leaches (N content). | 2020-02-14 01:37:34 | 2020-02-14 01:37:34 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:37:34 | Daniel Gavrilov 29 | 2020-02-14 01:37:34 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5007 | Agri-Environmental Indicators: Emissions shares | The FAOSTAT Emissions shares domain of FAOSTAT Agri-Environmental Indicators disseminates data on the greenhouse gas (GHG) emissions shares of agriculture and related land use to the total emissions from all economic sectors, by gas, country and year, for the period 19902017. Emissions data are also disseminated, for transparency. The economic sectors considered as emission sources are those defined by the Intergovernmental Panel on Climate Change (IPCC) in the 2006 guidelines (Vol.1, ch.8): Energy, Industrial Processes and Product Use, Waste, and Agriculture. Agriculture-related land use emissions are also considered and used to compute emissions shares. Emissions from agriculture and associated land use are taken from the relevant FAOSTAT GHG emissions domains of Emissions-Agriculture and Emissions-Land Use (2019). Agriculture-related land use emissions include emissions from cropland, grassland, net forest conversion, and fires from burning of organic soils and humid tropical forests. Emissions from the other sectors are taken from the third-party PRIMAP-hist dataset v2.1 (Gütschow et al., 2016; Gütschow et al., 2019). Shares are computed and disseminated with respect to total CO2eq as well as single gas emissions. Total emissions are computed by summing emissions in CO2 gas with emissions of the other trace gases, the latter converted in CO2eq via Global Warming Potentials (GWP) coefficients. Results are disseminated separately for GWPs corresponding to three different options used in various IPCC reporting processes, namely GWPs from: a) the IPCC Second Assessment Report (SAR)(IPCC, 1996); b) the IPCC Fourth Assessment Report (AR4) (IPCC, 2007); and c) the IPCC Fifth Assessment Report (AR5)(IPCC, 2014). Data are available by country, by FAOSTAT regional aggregation and special group, including the Annex I and Non-Annex I Parties to the United Nations Framework Convention on Climate Change (UNFCCC). A complete methodological note is available at: http://fenixservices.fao.org/faostat/static/documents/EM… | 2020-02-14 01:35:38 | 2020-02-14 01:35:38 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:35:38 | Daniel Gavrilov 29 | 2020-02-14 01:35:38 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5006 | Agri-Environmental Indicators: Land use indicators | The Agri-environmental IndicatorsLand Use domain provides information on the distribution of agricultural and forest land, and their sub-components, including irrigated areas and areas under organic agriculture, at national, regional and global levels. | 2020-02-14 01:35:17 | 2020-02-14 01:35:17 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:35:17 | Daniel Gavrilov 29 | 2020-02-14 01:35:17 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5005 | Agri-Environmental Indicators: Livestock Patterns | The Livestock Patterns domain of the FAOSTAT Agri-Environmental Indicators contains data on livestock numbers, shares of major livestock species and livestock densities in the agricultural land area. Values are calculated using Livestock Units (LSU), which facilitate aggregating information for different livestock types. Data are available by country, with global coverage, for the period 19612017. This methodology applies the LSU coefficients reported in the "Guidelines for the preparation of livestock sector reviews" (FAO, 2011). From this publication, LSU coefficients are computed by livestock type and by country. The reference unit used for the calculation of livestock units (=1 LSU) is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, fed without additional concentrated foodstuffs. FAOSTAT agri-environmental indicators on livestock patterns closely follow the structure of the indicators in EUROSTAT. | 2020-02-14 01:34:13 | 2020-02-14 01:34:13 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:34:13 | Daniel Gavrilov 29 | 2020-02-14 01:34:13 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5004 | Agri-Environmental Indicators: Emissions intensities | Intensities of greenhouse gas (GHG) emissions by unit of product for a selection of agricultural commodities. | 2020-02-14 01:33:18 | 2020-02-14 01:33:18 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:33:18 | Daniel Gavrilov 29 | 2020-02-14 01:33:18 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5003 | Agri-Environmental Indicators: Fertilizers indicators | The data describe the use of chemical and mineral fertilizers per area of cropland (which corresponds to the sum of arable land and permanent crops) at national, regional, and global level in a time series from 2002 to 2017 | 2020-02-14 01:33:17 | 2020-02-14 01:33:17 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:33:17 | Daniel Gavrilov 29 | 2020-02-14 01:33:17 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5002 | Macro-Statistics: Capital Stock | As part of the FAO Agriculture Capital Stock (ACS) database, ESS-FAO publishes country-by-country data on physical investment in agriculture, forestry and fishing as measured by the System of National Accounts (SNA) concept of Gross Fixed Capital Formation (GFCF). Additional variables included in the ACS are Net and Gross Capital Stock, Consumption of Fixed Capital, the Agriculture Investment ratio, and the Gross Fixed Capital Formation Agriculture Orientation Index. The FAO Agriculture Capital Stock Database is an analytical database: whenever available, the database integrates official National Accounts data harvested from the UNSD National Accounts Main Aggregates Database (UNSD AMA) and the OECD Annual National Accounts Database (OECD ANA). If the full set of official data is not available for any specific country, imputation methods are applied to obtain estimates over the complete time series. Many data points in ACS are estimated and are flagged as such; they do not represent official Member countries' submissions. With a view of producing internationally comparable net capital stock estimates, we employ the Perpetual Inventory Method (PIM) with a time invariant geometric depreciation rate to impute missing data. The Perpetual Inventory Method is a well-established economic model to calculate Net Capital Stocks (NCS) and Consumption of Fixed Capital (CFC) from time series of Gross Fixed Capital Formation (GFCF). Specifically, annual measures of the NCS are obtained from cumulating historical series on physical investment flows and deducting the part of assets that are depreciated (the Consumption of Fixed Capital that occurs in every period). In order to implement the PIM, long time series on aggregate GFCF in agriculture, forestry and fishing is required. As much as possible, we rely on National Accounts data published by the OECD and UNSD. When country data are partially or fully missing, we employ econometric techniques to impute missing observations. Depending on the pattern of data missingness fo… | 2020-02-14 01:33:08 | 2020-02-14 01:33:08 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:33:08 | Daniel Gavrilov 29 | 2020-02-14 01:33:08 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5001 | Food Balance: Food Supply - Livestock and Fish Primary Equivalent | Food supply data is some of the most important data in FAOSTAT. In fact, this data is for the basis for estimation of global and national undernourishment assessment, when it is combined with parameters and other data sets. This data has been the foundation of food balance sheets ever since they were first constructed. The data is accessed by both business and governments for economic analysis and policy setting, as well as being used by the academic community. | 2020-02-14 01:28:04 | 2020-02-14 01:28:04 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:28:04 | Daniel Gavrilov 29 | 2020-02-14 01:28:04 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 5000 | Investment: Country Investment Statistics Profile | The Country Investment Statistics Profile domain provides an overall view of the information about investment in agriculture at country level. Data are collected from other FAOSTAT domains, in particular from Investment and Macro Indicators. The purpose is to give to the users a comprehensive dataset that allows making comparison among the different flows to agriculture within each country. The dataset consists of a time series of more than 200 countries, from 2001 onwards. The information included regards the levels of central government expenditure on agriculture, credit to agriculture, official development flows (commitment) and foreign direct investment on agriculture. Besides the levels of investment flows, the dataset also includes the information on agriculture value added and agriculture gross fixed capital formation. Additional reported indicators are -the share of total flow allocated to agriculture (for Government Expenditure on Agriculture, Credit to Agriculture, Development Flows, Foreign direct Investment to Agriculture), -the agriculture share of total GDP, -the agriculture share of total gross fixed capital formation, -the agriculture orientation index (ratio of the agriculture share of total flow, over the agriculture value added share of total GDP) for Government Expenditure on Agriculture, Credit to Agriculture, Development Flows to Agriculture, -the investment agriculture orientation index (which is the ratio between the agriculture share of gross fixed capital formation over the agriculture share of GDP), -the annual growth,-the investment ratio (ratio between gross fixed capital formation over GDP),-the agriculture investment ratio (ratio between agriculture gross fixed capital formation and agriculture value added). Though the goal is to have complete and consistent coverage for all countries, relatively low response rates for the different databases belonging to the investment domain and country level differences in data collection and reporting creates some challenges in providing a compl… | 2020-02-14 01:27:55 | 2020-02-14 01:27:55 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:27:55 | Daniel Gavrilov 29 | 2020-02-14 01:27:55 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4999 | Food Balance: Food Supply - Crops Primary Equivalent | Food supply data is some of the most important data in FAOSTAT. In fact, this data is for the basis for estimation of global and national undernourishment assessment, when it is combined with parameters and other data sets. This data has been the foundation of food balance sheets ever since they were first constructed. The data is accessed by both business and governments for economic analysis and policy setting, as well as being used by the academic community. | 2020-02-14 01:15:14 | 2020-02-14 01:15:14 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:15:14 | Daniel Gavrilov 29 | 2020-02-14 01:15:14 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4998 | Food Balance: Commodity Balances - Livestock and Fish Primary Equivalent | Food supply data is some of the most important data in FAOSTAT. In fact, this data is for the basis for estimation of global and national undernourishment assessment, when it is combined with parameters and other data sets. This data has been the foundation of food balance sheets ever since they were first constructed. The data is accessed by both business and governments for economic analysis and policy setting, as well as being used by the academic community. | 2020-02-14 01:09:03 | 2020-02-14 01:09:03 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 01:09:03 | Daniel Gavrilov 29 | 2020-02-14 01:09:03 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4997 | Food Balance: Commodity Balances - Crops Primary Equivalent | Commodity balances show balances of food and agricultural commodities in a standardized form. The scope of standardization is to present these data in a less detailed form for a selected number of commodities without causing any significant loss of the basic variables monitoring the agricultural sector. The selected commodities include the equivalents of their derived products falling in the same commodity group, but exclude the equivalents of by-products and derived commodities, which through processing, change their nature and become part of different commodity groups. A number of commodity/item aggregates have been included to offer synthetic information. Some of these are included with the aim of simplifying the extraction of all component commodities. Data shown in the item aggregates represent the sum of the component commodities as presented in this domain (standardized form). Commodity coverage: The commodity list in this domain has been generally confined to primary commodities - except for sugar, oils and fats and beverages. Whenever possible trade in processed commodities is expressed in the originating primary commodity equivalent. Rice is expressed in milled equivalent. | 2020-02-14 00:52:11 | 2020-02-14 00:52:11 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 00:52:11 | Daniel Gavrilov 29 | 2020-02-14 00:52:11 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4996 | ASTI R&D Indicators: ASTI-Researchers | ASTI collects primary time-series data on agricultural research capacity and spending levels through national survey rounds in over 80 low-and middle-income countries. Data collection is carried out by country focal points, who distribute survey forms to all agencies known to conduct agricultural research in a given country, including government, nonprofit, and higher education agencies. Private-for profit sector coverage is limited, and hence excluded from this dataset. More detailed country- and regional-level data on agricultural research capacity, investment, and outputs are available on www.asti.cgiar.org/data. | 2020-02-14 00:52:10 | 2020-02-14 00:52:10 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 00:52:10 | Daniel Gavrilov 29 | 2020-02-14 00:52:10 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4995 | ASTI R&D Indicators: ASTI-Expenditures | ASTI collects primary time-series data on agricultural research capacity and spending levels through national survey rounds in over 80 low-and middle-income countries. Data collection is carried out by country focal points, who distribute survey forms to all agencies known to conduct agricultural research in a given country, including government, nonprofit, and higher education agencies. Private-for profit sector coverage is limited, and hence excluded from this dataset. More detailed country- and regional-level data on agricultural research capacity, investment, and outputs are available on www.asti.cgiar.org/data. | 2020-02-14 00:52:09 | 2020-02-14 00:52:09 | faostat_2020 | 0 | Daniel Gavrilov 29 | 2020-02-14 00:52:09 | Daniel Gavrilov 29 | 2020-02-14 00:52:09 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4874 | BP Statistical Review of Global Energy | This is a dataset imported by the automated fetcher | 2019-11-20 13:19:59 | 2019-11-20 13:19:59 | bpstatreview_2019 | 0 | Daniel Gavrilov 29 | 2019-11-20 13:19:59 | Daniel Gavrilov 29 | 2019-11-20 13:19:59 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4862 | Proportion of population covered by a mobile network, by technology (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:40 | 2019-11-15 20:26:40 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:40 | Daniel Gavrilov 29 | 2019-11-15 20:26:40 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4861 | Proportion of medium and high-tech industry value added in total value added (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:39 | 2019-11-15 20:26:39 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:39 | Daniel Gavrilov 29 | 2019-11-15 20:26:39 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4860 | Total official flows for infrastructure, by recipient countries (millions of constant 2017 United States dollars) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:39 | 2019-11-15 20:26:39 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:39 | Daniel Gavrilov 29 | 2019-11-15 20:26:39 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4859 | Researchers (in full-time equivalent) per million inhabitants (per 1,000,000 population) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:38 | 2019-11-15 20:26:38 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:38 | Daniel Gavrilov 29 | 2019-11-15 20:26:38 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4858 | Research and development expenditure as a proportion of GDP (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:38 | 2019-11-15 20:26:38 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:38 | Daniel Gavrilov 29 | 2019-11-15 20:26:38 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4857 | Carbon dioxide emissions per unit of manufacturing value added (kilogrammes of CO2 per constant 2010 United States dollars) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:37 | 2019-11-15 20:26:37 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:37 | Daniel Gavrilov 29 | 2019-11-15 20:26:37 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4856 | Carbon dioxide emissions per unit of GDP (kilogrammes of CO2 per constant 2010 United States dollars) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:37 | 2019-11-15 20:26:37 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:37 | Daniel Gavrilov 29 | 2019-11-15 20:26:37 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4855 | Carbon dioxide emissions from fuel combustion (millions of tonnes) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:36 | 2019-11-15 20:26:36 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:36 | Daniel Gavrilov 29 | 2019-11-15 20:26:36 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4854 | Proportion of small-scale industries with a loan or line of credit (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:36 | 2019-11-15 20:26:36 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:36 | Daniel Gavrilov 29 | 2019-11-15 20:26:36 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4853 | Proportion of small-scale industries in total industry value added (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:36 | 2019-11-15 20:26:36 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:36 | Daniel Gavrilov 29 | 2019-11-15 20:26:36 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4852 | Manufacturing employment as a proportion of total employment (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:35 | 2019-11-15 20:26:35 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:35 | Daniel Gavrilov 29 | 2019-11-15 20:26:35 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4851 | Manufacturing value added per capita (constant 2010 United States dollars) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:34 | 2019-11-15 20:26:34 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:34 | Daniel Gavrilov 29 | 2019-11-15 20:26:34 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4850 | Manufacturing value added as a proportion of GDP (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:34 | 2019-11-15 20:26:34 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:34 | Daniel Gavrilov 29 | 2019-11-15 20:26:34 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4849 | Passenger volume (passenger kilometres), by mode of transport | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:33 | 2019-11-15 20:26:33 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4848 | Freight volume, by mode of transport (tonne kilometres) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:33 | 2019-11-15 20:26:33 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4847 | Total official flows (disbursement) for Aid for Trade, by recipient countries (millions of constant 2017 United States dollars) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:33 | 2019-11-15 20:26:33 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4846 | Total official flows (disbursement) for Aid for Trade, by donor countries (millions of constant 2017 United States dollars) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:33 | 2019-11-15 20:26:33 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4845 | Total official flows (commitments) for Aid for Trade, by recipient countries (millions of constant 2017 United States dollars) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:33 | 2019-11-15 20:26:33 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 2019-11-15 20:26:33 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4844 | Total official flows (commitments) for Aid for Trade, by donor countries (millions of constant 2017 United States dollars) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:32 | 2019-11-15 20:26:32 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:32 | Daniel Gavrilov 29 | 2019-11-15 20:26:32 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4843 | Non-fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:32 | 2019-11-15 20:26:32 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:32 | Daniel Gavrilov 29 | 2019-11-15 20:26:32 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4842 | Fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:31 | 2019-11-15 20:26:31 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:31 | Daniel Gavrilov 29 | 2019-11-15 20:26:31 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4841 | Proportion of children engaged in economic activity and household chores, by sex and age (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:31 | 2019-11-15 20:26:31 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:31 | Daniel Gavrilov 29 | 2019-11-15 20:26:31 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4840 | Proportion of children engaged in economic activity, by sex and age (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:31 | 2019-11-15 20:26:31 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:31 | Daniel Gavrilov 29 | 2019-11-15 20:26:31 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4839 | Proportion of youth not in education, employment or training, by sex and age (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:30 | 2019-11-15 20:26:30 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:30 | Daniel Gavrilov 29 | 2019-11-15 20:26:30 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4838 | Unemployment rate, by sex and disability (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:30 | 2019-11-15 20:26:30 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:30 | Daniel Gavrilov 29 | 2019-11-15 20:26:30 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4837 | Unemployment rate, by sex and age (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:26 | 2019-11-15 20:26:26 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:26 | Daniel Gavrilov 29 | 2019-11-15 20:26:26 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4836 | Average hourly earnings of managers (ISCO-08) (local currency) | This is a dataset imported by the automated fetcher | 2019-11-15 20:26:24 | 2019-11-15 20:26:24 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:26:24 | Daniel Gavrilov 29 | 2019-11-15 20:26:24 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4829 | Proportion of informal employment in non-agriculture employment, by sex (ILO harmonized estimates) (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:25:50 | 2019-11-15 20:25:50 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:25:50 | Daniel Gavrilov 29 | 2019-11-15 20:25:50 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4828 | Annual growth rate of real GDP per employed person (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:25:49 | 2019-11-15 20:25:49 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:25:49 | Daniel Gavrilov 29 | 2019-11-15 20:25:49 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4827 | Proportion of adults (15 years and older) with an account at a financial institution or mobile-money-service provider, by sex (% of adults aged 15 years and older) | This is a dataset imported by the automated fetcher | 2019-11-15 20:25:49 | 2019-11-15 20:25:49 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:25:49 | Daniel Gavrilov 29 | 2019-11-15 20:25:49 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4826 | Number of commercial bank branches per 100,000 adults | This is a dataset imported by the automated fetcher | 2019-11-15 20:25:49 | 2019-11-15 20:25:49 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:25:49 | Daniel Gavrilov 29 | 2019-11-15 20:25:49 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4825 | Number of automated teller machines (ATMs) per 100,000 adults | This is a dataset imported by the automated fetcher | 2019-11-15 20:25:48 | 2019-11-15 20:25:48 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:25:48 | Daniel Gavrilov 29 | 2019-11-15 20:25:48 | Daniel Gavrilov 29 | 0 | 1 | ||||
| 4824 | Annual growth rate of real GDP per capita (%) | This is a dataset imported by the automated fetcher | 2019-11-15 20:25:47 | 2019-11-15 20:25:47 | un_sdg_2019 | 1 | Daniel Gavrilov 29 | 2019-11-15 20:25:47 | Daniel Gavrilov 29 | 2019-11-15 20:25:47 | Daniel Gavrilov 29 | 0 | 1 |
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FOREIGN KEY("createdByUserId") REFERENCES "users" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT,
FOREIGN KEY("dataEditedByUserId") REFERENCES "users" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT,
FOREIGN KEY("metadataEditedByUserId") REFERENCES "users" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT
);
CREATE INDEX "datasets_createdByUserId" ON "datasets" ("createdByUserId");
CREATE INDEX "datasets_dataEditedByUserId" ON "datasets" ("dataEditedByUserId");
CREATE INDEX "datasets_metadataEditedByUserId" ON "datasets" ("metadataEditedByUserId");
CREATE UNIQUE INDEX "unique_short_name_version_namespace" ON "datasets" ("shortName", "version", "namespace");