diff --git a/global-api/importer/datasource_seeder/README.md b/global-api/importer/datasource_seeder/README.md
index c1e69af1e..1631f30cd 100644
--- a/global-api/importer/datasource_seeder/README.md
+++ b/global-api/importer/datasource_seeder/README.md
@@ -12,11 +12,12 @@ psql -U ccglobal -d ccglobal -f import_datasource_seeder.sql
 
 - `datasource_id`: unique UUID for the datasource
 - `publisher_id`: abbreviation of the original name of the datasource (eg: EPA)
-- `name`: complete name of the datasource (eg: Environmental Protection Agency)
-- `description`: brief description of the dataset from the datasource
+- `datasource_name`: complete name of the datasource (eg: Environmental Protection Agency)
+- `dataset_name`: dataset name assign after transformation phase (eg. Manufacturing Industries and Construction Direct Emitters reported in the Greenhouse Gas Reporting Program)
+- `dataset_description`: brief description of the dataset from the datasource
 - `source_type`: should always be set to `third_party` for the global API. This is set to `user` for user-supplied data.
 - `access_type`: the type of access the data (eg: free, paid, etc)
-- `URL`: the link where the data was accessed
+- `dataset_url`: the link where the data was accessed
 - `geographical_location`: it contains the geographical coverage of the data (eg: AR for Argentina data, EARTH for international datasources with global coverage)
 - `start_year`: data start date
 - `end_year`: data end date
@@ -26,9 +27,12 @@ psql -U ccglobal -d ccglobal -f import_datasource_seeder.sql
 - `language`: original language of the dataset
 - `accessibility`: if this data source can be publicly accessed or requires special access privileges (e.g. `public`, `private`, `paid`)
 - `data_quality`: quality of the data based on the GPC classification (low, medium, high)
-- `notes`: extra information about the dataset (eg: brief description of the methodology applied to obtain emission values when the raw data is activity data)
+- `notes`: extra information about the dataset
 - `units`: units of the emissions
-- `methodology_url`: link to the methodology on how the data was obtained by the source
+- `methodology_description`: description of the methodology on how the data was obtained by the source
+- `methodology_url`: link to the methodology on how the data was obtained by the source (when it is available)
+- `transformation_description`: the type of transformation that has been done to the raw data, the assumptions that were made, emission factors that were applied, etc 
 - `retrieval_method`: method used by CityCatalyst to access the data from the database
 - `api_endpoint`: Global API endpoint to access the data
-- `gpc_reference_number`: Sub-sector reference number from the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC)
+- `gpc_reference_number`: sub-sector reference number from the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC)
+- `scope`: the scope for which it includes data within the subsector 
diff --git a/global-api/importer/datasource_seeder/datasource_seeder.csv b/global-api/importer/datasource_seeder/datasource_seeder.csv
index 43a8755b1..6a62ae10a 100644
--- a/global-api/importer/datasource_seeder/datasource_seeder.csv
+++ b/global-api/importer/datasource_seeder/datasource_seeder.csv
@@ -1,36 +1,46 @@
-datasource_id;publisher_id;name;description;source_type;access_type;url;geographical_location;start_year;end_year;latest_accounting_year;frequency_of_update;spatial_resolution;language;accessibility;data_quality;notes;units;methodology_url;retrieval_method;api_endpoint;gpc_reference_number
-143F3378-17E7-4732-BF17-4253160A7CFE;EPA;Environmental Protection Agency - Fuel combustion by Manufactoring industries and construction;EPA data on emissions from fuel combusion within the city boundary;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;I.3.1
-06BB5C1D-E554-40D7-B619-DA768A5FD607;EPA;Environmental Protection Agency - Energy used in power plant auxiliary operations;EPA data for emissions from energy used in power plant auxiliary operations within the city boundary;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;I.4.1
-C309DE81-6D75-4782-98B0-A9229D43F042;EPA;Environmental Protection Agency - Solid waste in landfills;EPA data for solid waste in landfills inside the city boundary;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;III.1.1
-8F3E7542-3C50-4E33-B2F7-57F326CBF9A6;EPA;Environmental Protection Agency - Energy generation supplied to the grid;EPA data for energy generation supplied to the grid;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;I.4.4
-C57F697D-2659-450E-BAF6-6F142D18A9AF;EPA;Environmental Protection Agency - Industrial processes;EPA data for industrial processes occuring within the city boundary;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;IV.1
-930F05CF-1796-4E25-B391-686BEA746A88;EPA;Environmental Protection Agency - Solid waste treatment;EPA data for solid waste treatment;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;III.3.1
-0D7F6F31-D483-4170-B36C-C9CFC0434A09;EPA;Environmental Protection Agency - Fugitive emissions of natural gas;EPA data for fugitive emissions of natural gas;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;I.8.1
-4E235B19-E4FD-4E64-A674-9ED9D9CBA9AA;EPA;Environmental Protection Agency - Fugitive emissions of coal;EPA data for fugitive emissions of coal;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;I.7.1
-B7BCFC69-3E7F-4B5B-A7BE-B8C945BE073F;EPA;Environmental Protection Agency - Wastewater treatment;EPA data for wastewater treatment;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;III.4.1
-D213BD2F-0164-4411-84BC-1339A9D7EB94;EPA;Environmental Protection Agency - Non-specifed fuel combustion;EPA data for non-specified fuel combustion;third_party;free;https://www.epa.gov/;EARTH;2019;2022;2023;annual;point source;en;;high;Initial import;tonnes;https://www.epa.gov/;global_api;https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber;I.6.1
-124A1F4B-13FD-439B-9175-A8C40CC36E79;IEA;International Energy Agency - Energy generation supplied to the grid;IEA national emissions data for energy generation supplied to the grid;third_party;public;https://www.iea.org/statistics/co2emissions/;EARTH;2020;2022;2023;annual;country;en;;high;Initial import;tonnes;https://www.iea.org/statistics/co2emissions/;global_api_downscaled_by_population;https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber;I.4.4
-1087D232-BC25-498B-BE38-92DC330F0B15;IEA;International Energy Agency - Off-road grid energy consumption;IEA national emissions data for grid energy used for off-road transportation;third_party;public;https://www.iea.org/statistics/co2emissions/;EARTH;2020;2022;2023;annual;country;en;;high;Initial import;tonnes;https://www.iea.org/statistics/co2emissions/;global_api_downscaled_by_population;https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber;II.5.2
-D00A84CE-858E-462C-90D3-61D7BC728E30;IEA;International Energy Agency - On-road grid energy consumption;IEA national emissions data for grid data for on-road transportation;third_party;public;https://www.iea.org/statistics/co2emissions/;EARTH;2020;2022;2023;annual;country;en;;high;Initial import;tonnes;https://www.iea.org/statistics/co2emissions/;global_api_downscaled_by_population;https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber;II.1.2
-AD25F669-FC74-46D3-9E6E-EF92CD0079A5;IEA;International Energy Agency - Power plant auxiliary operations grid energy consumption;IEA national emissions data for power plant auxiliary emissions;third_party;public;https://www.iea.org/statistics/co2emissions/;EARTH;2020;2022;2023;annual;country;en;;high;Initial import;tonnes;https://www.iea.org/statistics/co2emissions/;global_api_downscaled_by_population;https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber;I.4.2
-A284E16D-F04E-46C9-A977-8BDDD396F1FB;IEA;International Energy Agency - Residential grid energy consumption;IEA national emissions data for residential use of grid-supplied emissions within the city boundary;third_party;public;https://www.iea.org/statistics/co2emissions/;EARTH;2020;2022;2023;annual;country;en;;high;Initial import;tonnes;https://www.iea.org/statistics/co2emissions/;global_api_downscaled_by_population;https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber;I.1.2
-CAD04116-1B60-4290-A158-85F4B2BCED28;IEA;International Energy Agency - Industrial grid energy consumption;IEA national emissions data for industrial use of grid-supplied energy;third_party;public;https://www.iea.org/statistics/co2emissions/;EARTH;2020;2022;2023;annual;country;en;;high;Initial import;tonnes;https://www.iea.org/statistics/co2emissions/;global_api_downscaled_by_population;https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber;I.3.2
-F70577EF-3E87-4750-BB48-DF48899B040E;IEA;International Energy Agency - Commercial grid energy consumption;IEA national emissions data for commercial use of grid-supplied energy;third_party;public;https://www.iea.org/statistics/co2emissions/;EARTH;2020;2022;2023;annual;country;en;;high;Initial import;tonnes;https://www.iea.org/statistics/co2emissions/;global_api_downscaled_by_population;https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber;I.2.2
-497e10f2-c3f3-3b1a-ba35-707edff58858;ClimateTRACE;ClimateTRACE Road Transportation Emissions;Hybrid emissions estimate model with two parts. 1. ML models trained to predict road transport activity 2. emission factors pipeline to convert activity to emissions.;third_party;globalapi;https://climatetrace.org/;EARTH;2021;2021;2021;annual;city;en;;medium;;kg;https://github.com/climatetracecoalition/methodology-documents/blob/main/Transportation/Transportation%20Sector-%20Road%20transportation%20(asset)%20Methodology.pdf;global_api;https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber;II.1.1
-c7c660e4-56ca-3c42-96d4-6525d2a8f6cc;ClimateTRACE;ClimateTRACE Oil and Gas Refining Emissions;ClimateTRACE uses a hybrid model to estimate emissions globally;third_party;globalapi;https://climatetrace.org/;EARTH;2015;2021;2021;annual;point source;en;;medium;;kg;https://github.com/climatetracecoalition/methodology-documents/blob/main/Fossil%20fuel%20operations/Fossil%20Fuel%20Operations%20sector-%20Oil%20and%20Gas%20Production%20and%20Transport%20Oil%2C%20and%20Gas%20Refining%20Methodology.pdf;global_api;https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber;I.4.1
-7feeb3b0-a896-3481-8c05-8d31464dcede;ClimateTRACE;ClimateTRACE Oil and Gas Production and Transport Emissions;ClimateTRACE uses a hybrid model to estimate emissions globally;third_party;globalapi;https://climatetrace.org/;EARTH;2015;2021;2021;annual;point source;en;;medium;;kg;https://github.com/climatetracecoalition/methodology-documents/blob/main/Fossil%20fuel%20operations/Fossil%20Fuel%20Operations%20sector-%20Oil%20and%20Gas%20Production%20and%20Transport%20Oil%2C%20and%20Gas%20Refining%20Methodology.pdf;global_api;https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber;I.8.1
-d58b08f3-efdb-3f2d-9320-cea8c763d05a;ClimateTRACE;ClimateTRACE Coal Mining Emissions;Estimate emissions from mining and quarrying extraction on a statistical basis by taking production numbers at national and facility level and applying specific emissions factors.;third_party;globalapi;https://climatetrace.org/;EARTH;2021;2021;2021;annual;point source;en;;medium;;kg;https://github.com/climatetracecoalition/methodology-documents/blob/main/Fossil%20fuel%20operations/Fossil%20Fuel%20Operations%20sector-%20Coal%20mining%20Methodology.pdf;global_api;https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber;I.7.1
-4fa3124f-cb69-300d-964f-57d63b04d46e;ClimateTRACE;ClimateTRACE International Aviation Emissions;Emissions estimated based on fuel consumption.;third_party;globalapi;https://climatetrace.org/;EARTH;2015;2021;2021;annual;point source;en;;medium;;kg;https://github.com/climatetracecoalition/methodology-documents/blob/main/Transportation/Transportation%20sector-%20Domestic%20and%20International%20Aviation%20Methodology.pdf;global_api;https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber;II.4.3
-d8bf703a-0b3f-305d-b2a0-6d1c9419044e;ClimateTRACE;ClimateTRACE Domestic Aviation Emissions;Emissions estimated based on fuel consumption.;third_party;globalapi;https://climatetrace.org/;EARTH;2015;2021;2021;annual;point source;en;;medium;;kg;https://github.com/climatetracecoalition/methodology-documents/blob/main/Transportation/Transportation%20sector-%20Domestic%20and%20International%20Aviation%20Methodology.pdf;global_api;https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber;II.4.1
-3b4cf72b-3bf0-32e3-af14-9dc0a05874d5;ClimateTRACE;ClimateTRACE Solid Waste Disposal Emissions;ClimateTRACE sought to combine the best available sources into a model that could be deployed globally to estimate emissions.;third_party;globalapi;https://climatetrace.org/;EARTH;2021;2021;2021;annual;point source;en;;medium;;kg;https://github.com/climatetracecoalition/methodology-documents/blob/main/Waste/Waste%20Sector-%20Solid%20Waste%20Disposal%20(asset)%20Methodology.pdf;global_api;https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber;III.1.1
-e6d533a4-0020-30bc-bbac-90627e499663;ClimateTRACE;ClimateTRACE Manure Management Emissions;Approach relies on the hypothesis that beef or daily facility area size can be used as a predictor to estimate the total cattle population, which can then be used to estimate manure management emissions.;third_party;globalapi;https://climatetrace.org/;EARTH;2020;2021;2021;annual;point source;en;;medium;;kg;https://github.com/climatetracecoalition/methodology-documents/blob/main/Agriculture/Agriculture%20sector-%20Enteric%20fermentation%20and%20Manure%20management%20(asset)%20Methodology.pdf;global_api;https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber;V.1
-3b18e434-cd0e-3686-9537-6ae38ccb5c0c;ClimateTRACE;ClimateTRACE Enteric Fermentation Emissions;Approach relies on the hypothesis that beef or daily facility area size can be used as a predictor to estimate the total cattle population, which can then be used to estimate enteric fermentation emissions.;third_party;globalapi;https://climatetrace.org/;EARTH;2020;2021;2021;annual;point source;en;;medium;;kg;https://github.com/climatetracecoalition/methodology-documents/blob/main/Agriculture/Agriculture%20sector-%20Enteric%20fermentation%20and%20Manure%20management%20(asset)%20Methodology.pdf;global_api;https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber;V.1
-3bfaac71-953d-354b-8e0c-dc3bb8ec34c3;Joint Research Centre;EDGARv7.0 Manufacturing Combustion Emissions;;third_party;globalapi;https://joint-research-centre.ec.europa.eu/index_en;EARTH;2021;2021;2021;annual;0.1 degree;en;;medium;;kg;https://edgar.jrc.ec.europa.eu/dataset_ghg70#intro;global_api;https://ccglobal.openearth.dev/api/v0/edgar/city/:locode/:year/:gpcReferenceNumber;I.3.1
-9e7138c0-510a-3f17-9464-c245842d9862;Joint Research Centre;EDGARv7.0 Road Transportation Emissions (No Resuspension);;third_party;globalapi;https://joint-research-centre.ec.europa.eu/index_en;EARTH;2021;2021;2021;annual;0.1 degree;en;;medium;;kg;https://edgar.jrc.ec.europa.eu/dataset_ghg70#intro;global_api;https://ccglobal.openearth.dev/api/v0/edgar/city/:locode/:year/:gpcReferenceNumber;II.1.1
-66403f84-41cf-4c24-8dd8-ae980e8ad687;deie_mendoza;Direccion de Estadistica e Investigaciones Economicas;Electric energy users by tariff category, according to year. Municipal Statistical System based on data provided by Epre (Provincial Electrical Regulatory Entity);third_party;public;https://deie.mendoza.gov.ar/#!/;AR;2013;2022;2022;annual;city;en;;high;Emissions data calculated using the energy consumed methodology according to the sector proposed by GPC. AR5 emission factors for Argentina.;kg;https://deie.mendoza.gov.ar/#!/;global_api;https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber;I.1.2
-ef052fea-4b6c-4421-8911-c31e01f0cc89;deie_mendoza;Direccion de Estadistica e Investigaciones Economicas;Electric energy users by tariff category, according to year. Municipal Statistical System based on data provided by Epre (Provincial Electrical Regulatory Entity);third_party;public;https://deie.mendoza.gov.ar/#!/;AR;2013;2022;2022;annual;city;en;;high;Emissions data calculated using the energy consumed methodology according to the sector proposed by GPC. AR5 emission factors for Argentina.;kg;https://deie.mendoza.gov.ar/#!/;global_api;https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber;I.2.2
-56eab4cc-b26e-44a6-b9ac-65815736b6f0;deie_mendoza;Direccion de Estadistica e Investigaciones Economicas;Electric energy users by tariff category, according to year. Municipal Statistical System based on data provided by Epre (Provincial Electrical Regulatory Entity);third_party;public;https://deie.mendoza.gov.ar/#!/;AR;2013;2022;2022;annual;city;en;;high;Emissions data calculated using the energy consumed methodology according to the sector proposed by GPC. AR5 emission factors for Argentina.;kg;https://deie.mendoza.gov.ar/#!/;global_api;https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber;I.5.2
-ba4e85e8-8292-4a5d-93a7-2cec57e7dee7;deie_mendoza;Direccion de Estadistica e Investigaciones Economicas;Electric energy users by tariff category, according to year. Municipal Statistical System based on data provided by Epre (Provincial Electrical Regulatory Entity);third_party;public;https://deie.mendoza.gov.ar/#!/;AR;2013;2022;2022;annual;city;en;;high;Emissions data calculated using the energy consumed methodology according to the sector proposed by GPC. AR5 emission factors for Argentina.;kg;https://deie.mendoza.gov.ar/#!/;global_api;https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber;I.3.2
-ff4c21b4-574b-4f4d-bd19-7d3f9dda9093;deie_mendoza;Direccion de Estadistica e Investigaciones Economicas;Gas distributed by type of user, according to year. Municipal Statistical System based on data provided by Epre (Provincial Electrical Regulatory Entity);third_party;public;https://deie.mendoza.gov.ar/#!/;AR;2018;2022;2022;annual;city;en;;high;Emissions data calculated using the fuel sales methodology according to the sector proposed by GPC. AR5 emission factors for Argentina.;kg;https://deie.mendoza.gov.ar/#!/;global_api;https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber;I.1.1
-ae849774-309e-4091-8461-1be91db5a958;deie_mendoza;Direccion de Estadistica e Investigaciones Economicas;Gas distributed by type of user, according to year. Municipal Statistical System based on data provided by Epre (Provincial Electrical Regulatory Entity);third_party;public;https://deie.mendoza.gov.ar/#!/;AR;2018;2022;2022;annual;city;en;;high;Emissions data calculated using the fuel sales methodology according to the sector proposed by GPC. AR5 emission factors for Argentina.;kg;https://deie.mendoza.gov.ar/#!/;global_api;https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber;I.2.1
-70f80ab9-622a-4665-8176-e4e21bf9a634;deie_mendoza;Direccion de Estadistica e Investigaciones Economicas;Gas distributed by type of user, according to year. Municipal Statistical System based on data provided by Epre (Provincial Electrical Regulatory Entity);third_party;public;https://deie.mendoza.gov.ar/#!/;AR;2018;2022;2022;annual;city;en;;high;Emissions data calculated using the fuel sales methodology according to the sector proposed by GPC. AR5 emission factors for Argentina.;kg;https://deie.mendoza.gov.ar/#!/;global_api;https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber;I.3.1
\ No newline at end of file
+datasource_id,publisher_id,datasource_name,dataset_name,dataset_descrption,source_type,access_type,dataset_url,geographical_location,start_year,end_year,latest_accounting_year,frequency_of_update,spatial_resolution,language,accessibility,data_quality,notes,units,methodology_description,methodology_url,transformation_description,retrieval_method,api_endpoint,gpc_reference_number,scope
+143F3378-17E7-4732-BF17-4253160A7CFE,EPA,Environmental Protection Agency,Manufacturing Industries and Construction Direct Emitters reported in the Greenhouse Gas Reporting Program,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,"Direct-emitting facilities report emissions from combustion or process sources, such as fuel combustion and chemical transformations, using methods like continuous emission monitoring systems (CEMS) or default emission factors. ",https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,I.3.1,1
+06BB5C1D-E554-40D7-B619-DA768A5FD607,EPA,Environmental Protection Agency,Power Plant Auxiliary Operations as Direct Emitters reported in the Greenhouse Gas Reporting Program ,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,"Direct-emitting facilities report emissions from combustion or process sources, such as fuel combustion and chemical transformations, using methods like continuous emission monitoring systems (CEMS) or default emission factors. ",https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,I.4.1,1
+C309DE81-6D75-4782-98B0-A9229D43F042,EPA,Environmental Protection Agency,Solid Waste emissions by landfills as Direct Emitters reported in the Greenhouse Gas Reporting Program,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,"Direct-emitting facilities report emissions from combustion or process sources, such as fuel combustion and chemical transformations, using methods like continuous emission monitoring systems (CEMS) or default emission factors. ",https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,III.1.1,1
+8F3E7542-3C50-4E33-B2F7-57F326CBF9A6,EPA,Environmental Protection Agency,Power Plants as Direct Emitters reported in the Greenhouse Gas Reporting Program ,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,"Direct-emitting facilities report emissions from combustion or process sources, such as fuel combustion and chemical transformations, using methods like continuous emission monitoring systems (CEMS) or default emission factors. ",https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,I.4.4,1
+C57F697D-2659-450E-BAF6-6F142D18A9AF,EPA,Environmental Protection Agency,Industries as Direct Emitters reported in the Greenhouse Gas Reporting Program ,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,Process emissions are determined through mass balance approaches or emission factors specific to each source category.,https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,IV.1,1
+930F05CF-1796-4E25-B391-686BEA746A88,EPA,Environmental Protection Agency,Solid Waste Treatment Plants as Direct Emitters reported in the Greenhouse Gas Reporting Program ,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,"Direct-emitting facilities report emissions from combustion or process sources, such as fuel combustion and chemical transformations, using methods like continuous emission monitoring systems (CEMS) or default emission factors. ",https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,III.3.1,1
+0D7F6F31-D483-4170-B36C-C9CFC0434A09,EPA,Environmental Protection Agency,Fugitive emissions of Natural Gas as Direct Emitters reported in the Greenhouse Gas Reporting Program,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,source category. Suppliers report emissions based on mass balance methods or direct measurement of carbon quantities. CO2 injection facilities report CO2 quantities received for injection and must develop EPA-approved monitoring plans.,https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,I.8.1,1
+4E235B19-E4FD-4E64-A674-9ED9D9CBA9AA,EPA,Environmental Protection Agency,Fugitive emissions of coal as Direct Emitters reported in the Greenhouse Gas Reporting Program ,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,source category. Suppliers report emissions based on mass balance methods or direct measurement of carbon quantities. CO2 injection facilities report CO2 quantities received for injection and must develop EPA-approved monitoring plans.,https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,I.7.1,1
+B7BCFC69-3E7F-4B5B-A7BE-B8C945BE073F,EPA,Environmental Protection Agency,Wastewater Treatment Plants as Direct Emitters reported in the Greenhouse Gas Reporting Program,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,"Direct-emitting facilities report emissions from combustion or process sources, such as fuel combustion and chemical transformations, using methods like continuous emission monitoring systems (CEMS) or default emission factors. ",https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,III.4.1,1
+D213BD2F-0164-4411-84BC-1339A9D7EB94,EPA,Environmental Protection Agency,Non-specifed sources as Direct Emitters reported in the Greenhouse Gas Reporting Program ,"
+EPA provides information about GHG emissions from large facilities in the U.S. These facilities are required to report annual data about GHG emissions to EPA as part of the Greenhouse Gas Reporting Program (GHGRP). ",third_party,public,https://www.epa.gov/,US,2019,2022,2023,annual,point source,en,,high,Initial import,kg,"Direct-emitting facilities report emissions from combustion or process sources, such as fuel combustion and chemical transformations, using methods like continuous emission monitoring systems (CEMS) or default emission factors. ",https://www.epa.gov/ghgreporting/learn-about-greenhouse-gas-reporting-program-ghgrp,"Facility emissions in the GHGRP are adjusted to fit the GPC format. The EPA categorizes facilities into nine industry groups, and they report direct emissions from 23 facility-level processes. Facilities often engage in multiple emission-generating processes. If a facility reports emissions from a single activity, it's placed in that industry group. If reporting includes stationary combustion, those emissions are added, and the facility is categorized accordingly. In cases of multiple activities with stationary combustion, the highest-emission activity determines the industry group, while others are classified separately.",global_api,https://ccglobal.openearth.dev/api/v0/ghgrp_epa/city/:locode/:year/:gpcReferenceNumber,I.6.1,1
+124A1F4B-13FD-439B-9175-A8C40CC36E79,IEA,International Energy Agency,Energy generation supplied to the grid reported by the International Energy Agency ,"Electricity and heat production contains the sum of emissions from electricity production, combined heat and power plants and heat plants. It is the sum of main activity producers and autoproducers. Emissions from own on-site use of fuel are included. ",third_party,public,https://www.iea.org/statistics/co2emissions/,EARTH,2020,2022,2023,annual,country,en,,high,Initial import,tonnes,"IEA estimates CO2 emissions from fuel combustion using a Tier 1 method with globally collected energy data. Average net calorific values are applied, varying for oil and coal types. The IEA uses default carbon content values but recognizes country experts may have better information. Autoproducer emissions are unallocated, forming a category called ""Unallocated autoproducers."" The estimates encompass all CO2 emissions from fuel combustion, even though countries may categorize some differently. ",https://iea.blob.core.windows.net/assets/e6e332ed-24ab-4977-9ef9-cf3865934d63/Databasedocumentation2023Worldedition.pdf,"A reassignment of the categories proposed by IEA was carried out to consider some of the subsectors of the GPC. For this case, the reassignment was ""Electricity and heat production"" as 'I.4.4'",global_api_downscaled_by_population,https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber,I.4.4,1
+1087D232-BC25-498B-BE38-92DC330F0B15,IEA,International Energy Agency,Off-road grid energy consumption reported by the International Energy Agency ,Includes all emissions from transport not elsewhere specified. International marine bunkers and international aviation bunkers are not included in transport at a country or regional level (except for World transport emissions).  And this flow is included for CO2 emissions from fuel combustion and excludes non-CO2 greenhouse gases. ,third_party,public,https://www.iea.org/statistics/co2emissions/,EARTH,2020,2022,2023,annual,country,en,,high,Initial import,tonnes,"IEA estimates CO2 emissions from fuel combustion using a Tier 1 method with globally collected energy data. Average net calorific values are applied, varying for oil and coal types. The IEA uses default carbon content values but recognizes country experts may have better information. Autoproducer emissions are unallocated, forming a category called ""Unallocated autoproducers."" The estimates encompass all CO2 emissions from fuel combustion, even though countries may categorize some differently. ",https://iea.blob.core.windows.net/assets/e6e332ed-24ab-4977-9ef9-cf3865934d63/Databasedocumentation2023Worldedition.pdf,"A reassignment of the categories proposed by IEA was carried out to consider some of the subsectors of the GPC. For this case, the reassignment was ""Other Transport"" as 'I.5.1'",global_api_downscaled_by_population,https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber,II.5.1,1
+D00A84CE-858E-462C-90D3-61D7BC728E30,IEA,International Energy Agency,On-road grid energy consumption reported by the International Energy Agency ,"Road contains the emissions arising from fuel use in road vehicles, including the use of agricultural vehicles on highways. This corresponds to the IPCC Source/Sink Category 1 A 3 b. Excludes emissions from military consumption as well as motor gasoline used in stationary engines and diesel oil for use in tractors that are not for highway use. ",third_party,public,https://www.iea.org/statistics/co2emissions/,EARTH,2020,2022,2023,annual,country,en,,high,Initial import,tonnes,"IEA estimates CO2 emissions from fuel combustion using a Tier 1 method with globally collected energy data. Average net calorific values are applied, varying for oil and coal types. The IEA uses default carbon content values but recognizes country experts may have better information. Autoproducer emissions are unallocated, forming a category called ""Unallocated autoproducers."" The estimates encompass all CO2 emissions from fuel combustion, even though countries may categorize some differently. ",https://iea.blob.core.windows.net/assets/e6e332ed-24ab-4977-9ef9-cf3865934d63/Databasedocumentation2023Worldedition.pdf,"A reassignment of the categories proposed by IEA was carried out to consider some of the subsectors of the GPC. For this case, the reassignment was ""Road Transport"" as 'II.1.1'",global_api_downscaled_by_population,https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber,II.1.1,1
+AD25F669-FC74-46D3-9E6E-EF92CD0079A5,IEA,International Energy Agency,Power plant auxiliary operations grid energy consumption reported by the International Energy Agency ,"Electricity and heat production contains the sum of emissions from electricity production, combined heat and power plants and heat plants. It is the sum of main activity producers and autoproducers. Emissions from own on-site use of fuel are included. ",third_party,public,https://www.iea.org/statistics/co2emissions/,EARTH,2020,2022,2023,annual,country,en,,high,Initial import,tonnes,"IEA estimates CO2 emissions from fuel combustion using a Tier 1 method with globally collected energy data. Average net calorific values are applied, varying for oil and coal types. The IEA uses default carbon content values but recognizes country experts may have better information. Autoproducer emissions are unallocated, forming a category called ""Unallocated autoproducers."" The estimates encompass all CO2 emissions from fuel combustion, even though countries may categorize some differently. ",https://iea.blob.core.windows.net/assets/e6e332ed-24ab-4977-9ef9-cf3865934d63/Databasedocumentation2023Worldedition.pdf,"A reassignment of the categories proposed by IEA was carried out to consider some of the subsectors of the GPC. For this case, the reassignment was ""Other Energy Industry Own Use"" as 'I.4.1'",global_api_downscaled_by_population,https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber,I.4.1,1
+A284E16D-F04E-46C9-A977-8BDDD396F1FB,IEA,International Energy Agency,Residential grid energy consumption reported by the International Energy Agency ,Residential contains all emissions from fuel combustion in households. This corresponds to IPCC Source/Sink Category 1 A 4 b. ,third_party,public,https://www.iea.org/statistics/co2emissions/,EARTH,2020,2022,2023,annual,country,en,,high,Initial import,tonnes,"IEA estimates CO2 emissions from fuel combustion using a Tier 1 method with globally collected energy data. Average net calorific values are applied, varying for oil and coal types. The IEA uses default carbon content values but recognizes country experts may have better information. Autoproducer emissions are unallocated, forming a category called ""Unallocated autoproducers."" The estimates encompass all CO2 emissions from fuel combustion, even though countries may categorize some differently. ",https://iea.blob.core.windows.net/assets/e6e332ed-24ab-4977-9ef9-cf3865934d63/Databasedocumentation2023Worldedition.pdf,"A reassignment of the categories proposed by IEA was carried out to consider some of the subsectors of the GPC. For this case, the reassignment was ""Residential"" as 'I.1.1'",global_api_downscaled_by_population,https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber,I.1.1,1
+CAD04116-1B60-4290-A158-85F4B2BCED28,IEA,International Energy Agency,Manufacturing industries and construction grid energy consumption reported by the International Energy Agency ,"Manufacturing and construction industries contribute to emissions through fuel combustion, classified under IPCC Source/Sink Category 1 A 2. The 2006 GLs include emissions from industry autoproducers generating electricity and/or heat in this category. IEA data lacks the specific end-use breakdown, resulting in unallocated autoproducers. ",third_party,public,https://www.iea.org/statistics/co2emissions/,EARTH,2020,2022,2023,annual,country,en,,high,Initial import,tonnes,"IEA estimates CO2 emissions from fuel combustion using a Tier 1 method with globally collected energy data. Average net calorific values are applied, varying for oil and coal types. The IEA uses default carbon content values but recognizes country experts may have better information. Autoproducer emissions are unallocated, forming a category called ""Unallocated autoproducers."" The estimates encompass all CO2 emissions from fuel combustion, even though countries may categorize some differently. ",https://iea.blob.core.windows.net/assets/e6e332ed-24ab-4977-9ef9-cf3865934d63/Databasedocumentation2023Worldedition.pdf,"A reassignment of the categories proposed by IEA was carried out to consider some of the subsectors of the GPC. For this case, the reassignment was ""Manufactoring Industries and Construction"" as 'I.3.1'",global_api_downscaled_by_population,https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber,I.3.1,1
+F70577EF-3E87-4750-BB48-DF48899B040E,IEA,International Energy Agency,Commercial grid energy consumption reported by the International Energy Agency ,"Commercial and public services includes emissions from all activities of ISIC Rev. 4 Divisions 33, 36-39, 45-47, 52, 53, 5556, 58-66, 68-75, 77-82, 84 (excluding Class 8422), 85-88, 9096 and 99. ",third_party,public,https://www.iea.org/statistics/co2emissions/,EARTH,2020,2022,2023,annual,country,en,,high,Initial import,tonnes,"IEA estimates CO2 emissions from fuel combustion using a Tier 1 method with globally collected energy data. Average net calorific values are applied, varying for oil and coal types. The IEA uses default carbon content values but recognizes country experts may have better information. Autoproducer emissions are unallocated, forming a category called ""Unallocated autoproducers."" The estimates encompass all CO2 emissions from fuel combustion, even though countries may categorize some differently. ",https://iea.blob.core.windows.net/assets/e6e332ed-24ab-4977-9ef9-cf3865934d63/Databasedocumentation2023Worldedition.pdf,"A reassignment of the categories proposed by IEA was carried out to consider some of the subsectors of the GPC. For this case, the reassignment was ""Commercial and Public Services"" as 'I.2.1'",global_api_downscaled_by_population,https://ccglobal.openearth.dev/api/v0/source/IEA_energy/country/:country/:year/:gpcReferenceNumber,I.2.1,1
+497e10f2-c3f3-3b1a-ba35-707edff58858,ClimateTRACE,ClimateTRACE,Road Transportation Estimated Emissions ,"GHG emission estimatations from on-road transportation, integrating data on road segments, vehicle distribution, and fuel types for environmental impact assessments.",third_party,globalapi,https://climatetrace.org/,EARTH,2021,2021,2021,annual,city,en,,medium,,kg,"The on-road transportation emissions methodology involves classifying road segments, considering factors like road type from OpenStreetMap data. Vehicle distribution by type and fuel efficiency is determined using registration data, kilometers traveled, and US FHWA estimates. Fuel types and efficiencies are analyzed based on gasoline, diesel, and alternative fuels, using U.S. EPA emissions factors. Machine learning models assist in estimating traffic volume per road segment, contributing to emissions calculations. Uncertainty estimates are provided, with ongoing efforts to improve accuracy.",https://github.com/climatetracecoalition/methodology-documents/blob/main/Transportation/Transportation%20Sector-%20Road%20transportation%20(asset)%20Methodology.pdf,"Latitude and longitude information are utilized to apply a reverse geocode methodology, assigning the corresponding city locode to each emission point. Once identified, all data points within the city boundary are aggregated to calculate the total emissions for the sector.",global_api,https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber,II.1.1,1
+c7c660e4-56ca-3c42-96d4-6525d2a8f6cc,ClimateTRACE,ClimateTRACE,Oil and Gas Refining Estimated Emissions ,"Oil and Gas Refining Estimated Emissions- Point source GHG estimates using the OCI+ tool, incorporating models like OPGEE for upstream activities and PRELIM for refining.",third_party,globalapi,https://climatetrace.org/,EARTH,2015,2021,2021,annual,point source,en,,medium,,kg,"Climate TRACE utilizes the OCI+ tool, incorporating models like OPGEE and PRELIM, to estimate emissions. PRELIM assesses emissions from midstream oil refining, considering various sources such as heat, steam, and hydrogen, along with non-GHG gases. Key inputs like crude assays and refinery configurations are used to estimate emissions intensities, with throughput and capacity factored in to derive emissions estimates. For US refineries, increased data availability allows for more detailed categorization. Confidence categories and uncertainty analysis are applied to provide a measure of data quality and variation in estimates.",https://github.com/climatetracecoalition/methodology-documents/blob/main/Fossil%20fuel%20operations/Fossil%20Fuel%20Operations%20sector-%20Oil%20and%20Gas%20Production%20and%20Transport%20Oil%2C%20and%20Gas%20Refining%20Methodology.pdf,"Latitude and longitude information are utilized to apply a reverse geocode methodology, assigning the corresponding city locode to each emission point. Once identified, all data points within the city boundary are aggregated to calculate the total emissions for the sector.",global_api,https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber,I.4.1,1
+7feeb3b0-a896-3481-8c05-8d31464dcede,ClimateTRACE,ClimateTRACE,Oil and Gas Production and Transport Estimated Emissions,"Oil and Gas Production and Transport Estimated Emissions - Point source GHG estimates using OCI+ tool, covering upstream to downstream operations, incorporating over 100 emission sources and integrating ground truthing and VIIRS remote sensing data for precision.",third_party,globalapi,https://climatetrace.org/,EARTH,2015,2021,2021,annual,point source,en,,medium,,kg,"The methodology for calculating emissions from oil and gas production and transport involves using the OCI+ tool, which consists of three models: OPGEE for upstream operations, PRELIM for midstream refining, and OPEM for downstream consumption. OPGEE focuses on all stages of producing and transporting crude hydrocarbons and gas to end-use points. It accounts for over 100 emission sources, including flaring, venting, fugitive losses, and more. Key inputs such as field characteristics, production volumes, and transport methods are considered, integrating ground truthing and remote sensing data like VIIRS for accurate estimations. ",https://github.com/climatetracecoalition/methodology-documents/blob/main/Fossil%20fuel%20operations/Fossil%20Fuel%20Operations%20sector-%20Oil%20and%20Gas%20Production%20and%20Transport%20Oil%2C%20and%20Gas%20Refining%20Methodology.pdf,"Latitude and longitude information are utilized to apply a reverse geocode methodology, assigning the corresponding city locode to each emission point. Once identified, all data points within the city boundary are aggregated to calculate the total emissions for the sector.",global_api,https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber,I.8.1,1
+d58b08f3-efdb-3f2d-9320-cea8c763d05a,ClimateTRACE,ClimateTRACE,Coal Mining Estimated Emissions,Estimate emissions from mining and quarrying extraction on a statistical basis by taking production numbers at national and facility level and applying specific emissions factors,third_party,globalapi,https://climatetrace.org/,EARTH,2021,2021,2021,annual,point source,en,,medium,,kg,"The methodology for calculating coal mine emissions involves utilizing data from the Global Coal Mine Tracker, which includes production and capacity data for coal mines globally. Methane emissions are estimated based on methane gas content and capacity factors obtained from literature. The methane gas content is converted to emissions using a conversion factor provided by the EPA. The emissions factor is calculated based on the methane gas content and an average emission factor coefficient. The emissions are then estimated for each mine using the emissions factor and activity data. Finally, emissions data is reported on the Climate TRACE website in terms of methane (CH4) and CO2 equivalent (CO2e) values.",https://github.com/climatetracecoalition/methodology-documents/blob/main/Fossil%20fuel%20operations/Fossil%20Fuel%20Operations%20sector-%20Coal%20mining%20Methodology.pdf,"Latitude and longitude information are utilized to apply a reverse geocode methodology, assigning the corresponding city locode to each emission point. Once identified, all data points within the city boundary are aggregated to calculate the total emissions for the sector.",global_api,https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber,I.7.1,1
+4fa3124f-cb69-300d-964f-57d63b04d46e,ClimateTRACE,ClimateTRACE,International Aviation Estimated Emissions,"Point source estimates of GHG emissions from international aviation, employing the ICAO Tier 3a methodology, Version 11 of the ICAO Carbon Emissions Calculator, and OAG Historical Flight Status Data to calculate emissions based on fuel consumption, including CO2, CH4, and N2O, and attributing them to countries and airports.",third_party,globalapi,https://climatetrace.org/,EARTH,2015,2021,2021,annual,point source,en,,medium,,kg,"To calculate international aviation emissions, Climate TRACE utilizes the ICAO methodology, employing a Tier 3a approach defined by the IPCC. They use Version 11 of the ICAO Carbon Emissions Calculator Methodology along with OAG Historical Flight Status Data. The methodology estimates emissions based on fuel consumption, including CO2, CH4, and N2O. Flight data, aircraft types, and fuel consumption factors are used to estimate fuel burned for each trip. Emissions are attributed either fully to a country for domestic flights or divided equally between countries for international flights. Finally, emissions data is aggregated by country and airport for reporting.",https://github.com/climatetracecoalition/methodology-documents/blob/main/Transportation/Transportation%20sector-%20Domestic%20and%20International%20Aviation%20Methodology.pdf,"Latitude and longitude information are utilized to apply a reverse geocode methodology, assigning the corresponding city locode to each emission point. Once identified, all data points within the city boundary are aggregated to calculate the total emissions for the sector.",global_api,https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber,II.4.3,3
+d8bf703a-0b3f-305d-b2a0-6d1c9419044e,ClimateTRACE,ClimateTRACE,Domestic Aviation Estimated Emissions,"Point source estimates of GHG emissions from domestic aviation, utilizing ICAO's Tier 3a approach, Carbon Emissions Calculator Methodology, and OAG Historical Flight Status Data from January 2015 to June 2023, attributing emissions fully to the country of origin based on detailed aircraft movement data and fuel consumption factors for accurate estimations.",third_party,globalapi,https://climatetrace.org/,EARTH,2015,2021,2021,annual,point source,en,,medium,,kg,"To calculate domestic aviation emissions, Climate TRACE uses ICAO's Tier 3a approach, considering detailed aircraft movement data. They employ ICAO's Carbon Emissions Calculator Methodology and OAG Historical Flight Status Data from January 2015 to June 2023. This methodology estimates emissions based on fuel consumption, including CO2, CH4, and N2O. They calculate emissions for each flight between origin and destination pairs, excluding specific aircraft types, adjusting for factors like stacking and weather. Fuel consumption is estimated using ICAO's data, and emissions are attributed fully to the country of origin.",https://github.com/climatetracecoalition/methodology-documents/blob/main/Transportation/Transportation%20sector-%20Domestic%20and%20International%20Aviation%20Methodology.pdf,"Latitude and longitude information are utilized to apply a reverse geocode methodology, assigning the corresponding city locode to each emission point. Once identified, all data points within the city boundary are aggregated to calculate the total emissions for the sector.",global_api,https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber,II.4.1,1
+3b4cf72b-3bf0-32e3-af14-9dc0a05874d5,ClimateTRACE,ClimateTRACE,Solid Waste Disposal Estimated Emissions,"Point source estimates of GHG emissions from solid waste disposal, employing a Bayesian statistical approach, considering waste site capacities, income groups, and regions for robust methane emission predictions.",third_party,globalapi,https://climatetrace.org/,EARTH,2021,2021,2021,annual,point source,en,,medium,,kg,"This methodology involves a Bayesian statistical approach, implemented using PyMC3 in Python. The process begins with defining hierarchical regression structures to predict methane emissions from waste sites based on their capacities. Parameters are initialized with priors selected through prior predictive simulations and model cross-validation. The core of the model entails regressing the mean emissions of waste sites as a function of observed capacities, utilizing coefficients specific to income groups or regions. Posterior predictions are then generated by sampling from the posterior distributions on the parameters, with predicted emissions simulated for each site. To prevent unrealistic predictions, a ""saturation effect"" is integrated into the model, capping predicted emissions at reasonable values. This methodology operates in a two-stage process, where the emissions prediction model serves as the second stage of a composite model, with the first stage predicting waste capacities from areas. This approach allows for robust modeling of methane emissions from solid waste sites while considering variability within and between income groups or regions",https://github.com/climatetracecoalition/methodology-documents/blob/main/Waste/Waste%20Sector-%20Solid%20Waste%20Disposal%20(asset)%20Methodology.pdf,"Latitude and longitude information are utilized to apply a reverse geocode methodology, assigning the corresponding city locode to each emission point. Once identified, all data points within the city boundary are aggregated to calculate the total emissions for the sector.",global_api,https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber,III.1.1,1
+e6d533a4-0020-30bc-bbac-90627e499663,ClimateTRACE,ClimateTRACE,Manure Management Estimated Emissions,"Point source estimates of GHG emissions from beef and dairy feedlots, employing IPCC equations, default regional emission factors, and Climate Trace's approach incorporating temperature data, facility-level population counts, and regional manure management variations.",third_party,globalapi,https://climatetrace.org/,EARTH,2020,2021,2021,annual,point source,en,,medium,,kg,"The manure management calculation process involves the estimation of methane and nitrous oxide emissions from beef and dairy feedlots using IPCC equations and default regional emission factors. Climate Trace's approach incorporates temperature data, ground-truthed facility-level population counts, and regional variations in manure management practices to develop emission estimates. These estimates are presented for different regions, highlighting the significant contributions of beef and dairy feedlots to greenhouse gas emissions",https://github.com/climatetracecoalition/methodology-documents/blob/main/Agriculture/Agriculture%20sector-%20Enteric%20fermentation%20and%20Manure%20management%20(asset)%20Methodology.pdf,"Latitude and longitude information are utilized to apply a reverse geocode methodology, assigning the corresponding city locode to each emission point. Once identified, all data points within the city boundary are aggregated to calculate the total emissions for the sector.",global_api,https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber,V.1,1
+3b18e434-cd0e-3686-9537-6ae38ccb5c0c,ClimateTRACE,ClimateTRACE,Enteric Fermentation Estimated Emissions,"Point source estimates of GHG emissions from beef and dairy feedlots, utilizing advanced spatial data processing techniques and machine learning algorithms to predict methane and nitrous oxide emissions, considering factors such as cattle populations, feedlot area size, and regional variations in manure management practices, with accuracy validated through statistical measures.",third_party,globalapi,https://climatetrace.org/,EARTH,2020,2021,2021,annual,point source,en,,medium,,kg,"The enteric fermentation calculation process  involves the utilization of advanced spatial data processing techniques and machine learning algorithms to estimate methane and nitrous oxide emissions from beef and dairy feedlots. This method, developed by Climate Trace, involves spatially joining data, adding ancillary information, and performing data cleaning to create training datasets for model development. The models developed utilize linear regression to predict cattle populations at individual facilities, with separate models for beef and dairy feedlots, while also considering factors such as feedlot area size and regional variations in manure management practices. The accuracy of the models is evaluated through various statistical measures, and emission estimates are provided for different regions",https://github.com/climatetracecoalition/methodology-documents/blob/main/Agriculture/Agriculture%20sector-%20Enteric%20fermentation%20and%20Manure%20management%20(asset)%20Methodology.pdf,"Latitude and longitude information are utilized to apply a reverse geocode methodology, assigning the corresponding city locode to each emission point. Once identified, all data points within the city boundary are aggregated to calculate the total emissions for the sector.",global_api,https://ccglobal.openearth.dev/api/v0/climatetrace/city/:locode/:year/:gpcReferenceNumber,V.1,1
+3bfaac71-953d-354b-8e0c-dc3bb8ec34c3,EDGAR,Emissions Database for Global Atmospheric Research,Grid Manufacturing Combustion Estimated Emissions,"Grid cell estimates of GHG emissions (CO2, CH4, N2O) from manufacturing combustion, derived from EDGARv7.0 annual gridmaps (1970-2022) expressed in ton substance per 0.1-degree x 0.1-degree per year and sector specification using IPCC 1996 and 2006 codes",third_party,globalapi,https://joint-research-centre.ec.europa.eu/index_en,EARTH,2021,2021,2021,annual,0.1 degree,en,,medium,,kg,"The emission calculation method utilizes a standardized approach across all countries, employing technology-based emission factors to estimate annual emissions for each compound and sector. This involves multiplying country-specific activity data with the mix of technologies and their associated abatement measures, considering both emission factors and reductions due to installed abatement measures. Spatial allocation of emissions is achieved through a grid system, utilizing geographical databases and spatial proxy datasets to distribute emissions across a country's area based on relevant spatial factors such as population density and land use.",https://edgar.jrc.ec.europa.eu/dataset_ghg70#intro,"Utilizing the central latitude and longitude coordinates of the grid, the assignment of the corresponding city locode is performed. Following identification, the aggregation of all the grid cells within the city boundary ensues to derive the total sector emissions. In instances where the grid extends beyond the city limits, the proportional fraction is calculated, and that specific emission fraction is assigned to the respective city.",global_api,https://ccglobal.openearth.dev/api/v0/edgar/city/:locode/:year/:gpcReferenceNumber,I.3.1,1
+9e7138c0-510a-3f17-9464-c245842d9862,EDGAR,Emissions Database for Global Atmospheric Research,Grid Road Transportation Estimated Emissions (No Resuspension),"Grid cell estimates of GHG emissions from road transportation, employing a standardized method utilizing technology-based emission factors and spatial allocation through a grid system, considering country-specific activity data and relevant spatial factors.",third_party,globalapi,https://joint-research-centre.ec.europa.eu/index_en,EARTH,2021,2021,2021,annual,0.1 degree,en,,medium,,kg,"The emission calculation method utilizes a standardized approach across all countries, employing technology-based emission factors to estimate annual emissions for each compound and sector. This involves multiplying country-specific activity data with the mix of technologies and their associated abatement measures, considering both emission factors and reductions due to installed abatement measures. Spatial allocation of emissions is achieved through a grid system, utilizing geographical databases and spatial proxy datasets to distribute emissions across a country's area based on relevant spatial factors such as population density and land use.",https://edgar.jrc.ec.europa.eu/dataset_ghg70#intro,"Utilizing the central latitude and longitude coordinates of the grid, the assignment of the corresponding city locode is performed. Following identification, the aggregation of all the grid cells within the city boundary ensues to derive the total sector emissions. In instances where the grid extends beyond the city limits, the proportional fraction is calculated, and that specific emission fraction is assigned to the respective city.",global_api,https://ccglobal.openearth.dev/api/v0/edgar/city/:locode/:year/:gpcReferenceNumber,II.1.1,1
+66403f84-41cf-4c24-8dd8-ae980e8ad687,DEIE Mendoza,Direccion de Estadisticas e Investigaciones Economicas Gobierno Mendoza,Residential electricity consumption in Mendoza cities,"Electric energy users by tariff category, according to year reported in the Directorate of Statistics and Economic Research based on data provided by Epre (Provincial Electrical Regulatory Entity)",third_party,public,https://deie.mendoza.gov.ar/#!/,AR,2013,2022,2022,annual,city,en,,high,,kg,The Directorate of Statistics and Economic Research is a public technical body that coordinates the statistical activities carried out in the territory of the province of Mendoza. Compiling and systematizing economic data relevant to the territory.,https://deie.mendoza.gov.ar/#!/,Emissions data calculated using the grid energy consumed methodology for residential buildings according to the sector proposed by GPC. AR5 emission factors for Argentina were applied.,global_api,https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber,I.1.2,2
+ef052fea-4b6c-4421-8911-c31e01f0cc89,DEIE Mendoza,Direccion de Estadisticas e Investigaciones Economicas Gobierno Mendoza,Commercial electricity consumption in Mendoza cities,"Electric energy users by tariff category, according to year reported in the Directorate of Statistics and Economic Research based on data provided by Epre (Provincial Electrical Regulatory Entity)",third_party,public,https://deie.mendoza.gov.ar/#!/,AR,2013,2022,2022,annual,city,en,,high,,kg,The Directorate of Statistics and Economic Research is a public technical body that coordinates the statistical activities carried out in the territory of the province of Mendoza. Compiling and systematizing economic data relevant to the territory.,https://deie.mendoza.gov.ar/#!/,Emissions data calculated using the grid energy consumed methodology for commercial and institutional buildings according to the sector proposed by GPC. AR5 emission factors for Argentina were applied.,global_api,https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber,I.2.2,2
+56eab4cc-b26e-44a6-b9ac-65815736b6f0,DEIE Mendoza,Direccion de Estadisticas e Investigaciones Economicas Gobierno Mendoza,Agriculture activities electricity consumption in Mendoza cities,"Electric energy users by tariff category, according to year reported in the Directorate of Statistics and Economic Research based on data provided by Epre (Provincial Electrical Regulatory Entity)",third_party,public,https://deie.mendoza.gov.ar/#!/,AR,2013,2022,2022,annual,city,en,,high,,kg,The Directorate of Statistics and Economic Research is a public technical body that coordinates the statistical activities carried out in the territory of the province of Mendoza. Compiling and systematizing economic data relevant to the territory.,https://deie.mendoza.gov.ar/#!/,Emissions data calculated using the grid energy consumed methodology for agriculture industries according to the sector proposed by GPC. AR5 emission factors for Argentina were applied.,global_api,https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber,I.5.2,2
+ba4e85e8-8292-4a5d-93a7-2cec57e7dee7,DEIE Mendoza,Direccion de Estadisticas e Investigaciones Economicas Gobierno Mendoza,Manufactoring Industries and Construction electricity consumption in Mendoza cities,"Electric energy users by tariff category, according to year reported in the Directorate of Statistics and Economic Research based on data provided by Epre (Provincial Electrical Regulatory Entity)",third_party,public,https://deie.mendoza.gov.ar/#!/,AR,2013,2022,2022,annual,city,en,,high,,kg,The Directorate of Statistics and Economic Research is a public technical body that coordinates the statistical activities carried out in the territory of the province of Mendoza. Compiling and systematizing economic data relevant to the territory.,https://deie.mendoza.gov.ar/#!/,Emissions data calculated using the grid energy consumed methodology for manufacturing industries and construction according to the sector proposed by GPC. AR5 emission factors for Argentina were applied.,global_api,https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber,I.3.2,2
+ff4c21b4-574b-4f4d-bd19-7d3f9dda9093,DEIE Mendoza,Direccion de Estadisticas e Investigaciones Economicas Gobierno Mendoza,Natural Gas consumption by Residential Buildings in Mendoza cities,"Gas distributed by type of user, according to year reported in the Directorate of Statistics and Economic Research based on data provided by Epre (Provincial Electrical Regulatory Entity)",third_party,public,https://deie.mendoza.gov.ar/#!/,AR,2018,2022,2022,annual,city,en,,high,,kg,The Directorate of Statistics and Economic Research is a public technical body that coordinates the statistical activities carried out in the territory of the province of Mendoza. Compiling and systematizing economic data relevant to the territory.,https://deie.mendoza.gov.ar/#!/,Emissions data calculated for natural gas consumption using the fuel sales methodology for residential buildings proposed by GPC. AR5 emission factors for Argentina were applied.,global_api,https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber,I.1.1,1
+ae849774-309e-4091-8461-1be91db5a958,DEIE Mendoza,Direccion de Estadisticas e Investigaciones Economicas Gobierno Mendoza,Natural Gas consumption by Commercial Buildings in Mendoza cities,"Gas distributed by type of user, according to year reported in the Directorate of Statistics and Economic Research based on data provided by Epre (Provincial Electrical Regulatory Entity)",third_party,public,https://deie.mendoza.gov.ar/#!/,AR,2018,2022,2022,annual,city,en,,high,,kg,The Directorate of Statistics and Economic Research is a public technical body that coordinates the statistical activities carried out in the territory of the province of Mendoza. Compiling and systematizing economic data relevant to the territory.,https://deie.mendoza.gov.ar/#!/,Emissions data calculated for natural gas consumption using the fuel sales methodology for commercial buildings proposed by GPC. AR5 emission factors for Argentina were applied.,global_api,https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber,I.2.1,1
+70f80ab9-622a-4665-8176-e4e21bf9a634,DEIE Mendoza,Direccion de Estadisticas e Investigaciones Economicas Gobierno Mendoza,Natural Gas consumption by Manufactoring Industries and Construction in Mendoza cities,"Gas distributed by type of user, according to year reported in the Directorate of Statistics and Economic Research based on data provided by Epre (Provincial Electrical Regulatory Entity)",third_party,public,https://deie.mendoza.gov.ar/#!/,AR,2018,2022,2022,annual,city,en,,high,,kg,The Directorate of Statistics and Economic Research is a public technical body that coordinates the statistical activities carried out in the territory of the province of Mendoza. Compiling and systematizing economic data relevant to the territory.,https://deie.mendoza.gov.ar/#!/,Emissions data calculated for natural gas consumption using the fuel sales methodology for manufacturing industries and construction proposed by GPC. AR5 emission factors for Argentina were applied.,global_api,https://ccglobal.openearth.dev/api/v0/deie_mendoza/city/:locode/:year/:gpcReferenceNumber,I.3.1,1
\ No newline at end of file
diff --git a/global-api/importer/datasource_seeder/import_datasource_seeder.sql b/global-api/importer/datasource_seeder/import_datasource_seeder.sql
index 048b7e5bf..beb2a692b 100644
--- a/global-api/importer/datasource_seeder/import_datasource_seeder.sql
+++ b/global-api/importer/datasource_seeder/import_datasource_seeder.sql
@@ -6,21 +6,19 @@ TRUNCATE datasource_staging;
 
 /* Load the staging table from the transformed file */
 
-\copy datasource_staging (datasource_id,publisher_id,name,description,source_type,access_type,url,geographical_location,start_year,end_year,latest_accounting_year,frequency_of_update,spatial_resolution,language,accessibility,data_quality,notes,units,methodology_url,retrieval_method,api_endpoint,gpc_reference_number) from 'datasource_seeder.csv' with CSV HEADER;
+\copy datasource_staging (datasource_id,publisher_id,source_type,dataset_url,access_type,geographical_location,start_year,end_year,latest_accounting_year,frequency_of_update,spatial_resolution,language,accessibility,data_quality,notes,units,methodology_url,retrieval_method,api_endpoint,gpc_reference_number,datasource_name,dataset_name,methodology_description,transformation_description,scope) from 'datasource_seeder.csv' with CSV HEADER;
 
 /* Update the main table with the staging table */
 
-INSERT INTO datasource (datasource_id,publisher_id,name,description,source_type,access_type,url,geographical_location,start_year,end_year,latest_accounting_year,frequency_of_update,spatial_resolution,language,accessibility,data_quality,notes,units,methodology_url,retrieval_method,api_endpoint,gpc_reference_number)
-SELECT datasource_id,publisher_id,name,description,source_type,access_type,url,geographical_location,start_year,end_year,latest_accounting_year,frequency_of_update,spatial_resolution,language,accessibility,data_quality,notes,units,methodology_url,retrieval_method,api_endpoint,gpc_reference_number
+INSERT INTO datasource (datasource_id,publisher_id,source_type,dataset_url,access_type,geographical_location,start_year,end_year,latest_accounting_year,frequency_of_update,spatial_resolution,language,accessibility,data_quality,notes,units,methodology_url,retrieval_method,api_endpoint,gpc_reference_number,datasource_name,dataset_name,methodology_description,transformation_description,scope)
+SELECT datasource_id,publisher_id,source_type,dataset_url,access_type,geographical_location,start_year,end_year,latest_accounting_year,frequency_of_update,spatial_resolution,language,accessibility,data_quality,notes,units,methodology_url,retrieval_method,api_endpoint,gpc_reference_number,datasource_name,dataset_name,methodology_description,transformation_description,scope
 FROM datasource_staging
 ON CONFLICT ON CONSTRAINT datasource_pkey
 DO UPDATE SET
   publisher_id = EXCLUDED.publisher_id,
-  name = EXCLUDED.name,
-  description = EXCLUDED.description,
   source_type = EXCLUDED.source_type,
+  dataset_url = EXCLUDED.dataset_url,
   access_type = EXCLUDED.access_type,
-  url = EXCLUDED.url,
   geographical_location = EXCLUDED.geographical_location,
   start_year = EXCLUDED.start_year,
   end_year = EXCLUDED.end_year,
@@ -36,6 +34,11 @@ DO UPDATE SET
   retrieval_method = EXCLUDED.retrieval_method,
   api_endpoint = EXCLUDED.api_endpoint,
   gpc_reference_number = EXCLUDED.gpc_reference_number,
+  datasource_name = EXCLUDED.datasource_name,
+  dataset_name = EXCLUDED.dataset_name,
+  methodology_description = EXCLUDED.methodology_description,
+  transformation_description = EXCLUDED.transformation_description,
+  scope = EXCLUDED.scope,
   modified_date = now();
 
 DROP TABLE datasource_staging;
diff --git a/global-api/migrations/versions/191489d19e2a_datacatalog_updatev01.py b/global-api/migrations/versions/191489d19e2a_datacatalog_updatev01.py
new file mode 100644
index 000000000..950125913
--- /dev/null
+++ b/global-api/migrations/versions/191489d19e2a_datacatalog_updatev01.py
@@ -0,0 +1,39 @@
+"""datacatalog_updateV01
+
+Revision ID: 191489d19e2a
+Revises: 0ca75cb48d2c
+Create Date: 2024-03-11 13:29:48.302595
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '191489d19e2a'
+down_revision: Union[str, None] = '0ca75cb48d2c'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+def upgrade():
+    op.add_column('datasource', sa.Column("datasource_name", sa.String(), nullable=True))
+    op.add_column('datasource', sa.Column("dataset_name", sa.TEXT(), nullable=True))
+    op.add_column('datasource', sa.Column("methodology_description", sa.TEXT(), nullable=True))
+    op.add_column('datasource', sa.Column("transformation_description", sa.TEXT(), nullable=True))
+    op.add_column('datasource', sa.Column("scope", sa.String(), nullable=True))
+    op.alter_column("datasource", "url", new_column_name="dataset_url")
+    op.drop_column("datasource", "name")
+    op.drop_column("datasource", "description")
+
+def downgrade():
+    op.drop_column('datasource', 'datasource_name')
+    op.drop_column('datasource', 'dataset_name')
+    op.drop_column('datasource', 'methodology_description')
+    op.drop_column('datasource', 'transformation_description')
+    op.drop_column('datasource', 'scope')
+    op.alter_column("datasource", "dataset_url", new_column_name="url")
+    op.add_column("datasource", sa.Column("name", sa.String(), nullable=True))
+    op.add_column("datasource", sa.Column("description", sa.TEXT(), nullable=True))
+