| id | classification_Name | dimension | description | reference | meaning_attribute1 | meaning_attribute2 | meaning_attribute3 | meaning_attribute4 | meaning_attribute5 |
| 1 | chemical_elements | 2 | Chemical elements 1-118 | | element name (lowercase) | atomic number | symbol | element name (uppercase) | atomic weight (rough) |
| 2 | regions_iso_iedc | 4 | ISO 3166 classification of states and regions, with custom extensions. Numbers > 10000 are self-defined. | | Name | ISO2 | ISO3 | ISO code | continent |
| 3 | time | 1 | Years 1800-2200 | | Year | | | | |
| 4 | generic_materials_waste | 5 | List of common materials, products, and waste groups | | Material | | | | |
| 5 | NACEv2_(ProdCom) | 6 | List of ProdCom commodities | | Commodity code (NACEv2) | Commodity group name | | | |
| 6 | broad_industry_groups | 7 | List of broad industry groupts (mining, material production, ?) | | Broad industry group | | | | |
| 7 | general_product_categories | 6 | List of general product categories, residential and non-residential | | Product category | | | | |
| 8 | basic_scenario_alternatives | 9 | High-medium-low scenarios | | Scenario alternatives: High/medium/low, etc. | | | | |
| 9 | ca_time | 1 | Years with ca. prefix | | Ca. year | | | | |
| 10 | general_energy_carriers | 8 | List of general energy carriers | | Energy carrier | | | | |
| 11 | cities | 4 | List of cities | | City name | alternative city name 1 | alternative city name 2 | alternative city name 3 | Country or region ID |
| 12 | service_categories | 11 | List of services | | Service type | | | | |
| 13 | building_types | 6 | List of commonly used building types | | Building type | | | | |
| 14 | time_ranges | 1 | List of time intervals for age cohorts and years | | Range of years or age-cohorts | | | | |
| 15 | custom | 13 | Custom classification, dimension specified via aspect, placeholder only! | | None | | | | |
| 16 | steel_cycle_200R_processes | 7 | List of processes for steel cycle MFA DOI 10.1016/j.resconrec.2012.11.008 | | process_name | process_id | | | process_code |
| 17 | US_LCI_materials | 5 | List of materials (flows) used in US LCI database | | materials and substances list of US LCI database, incomplete (!) | | | | |
| 18 | LCI_material_categories | 5 | List of material categories used in LCI data | | broad material category for LCA (product, elementary, waste) | | | | |
| 19 | US_LCI_material_groups | 5 | List of material groups used in US LCI database | | grouping of flows in US LCI database into categories like 'reference product' or 'air, high population density' | | | | |
| 20 | LCI_data_layers | 12 | List of data layers used in LCI data | | data layers for LCI data (mass, energy, monetary, ?) | | | | |
| 21 | US_LCI_processes | 7 | List of processes used in US LCI database | | Process (activity) names from US LCI database | | | | |
| 22 | UK_IO_17Commodities | 6 | List of 17 commodities used for 2010 aggregated UK IO table in pxp format | | Aggregated industrial sector names of UK IO table | | | | |
| 23 | steel_cycle_200R_materials_products | 6 | materials and products for stocks and flows in dataset for global steel cycle | | material name | | | | |
| 24 | global_steel_cycle_Regions | 4 | regions used in global steel cycle project | | region name | | | | |
| 25 | EXIOBASEv3_163industries | 7 | List of industries for EXIOBASE3, official order and labelling | DOI: 10.1111/jiec.12715 | Code_number | Code_text | Name | | |
| 26 | EXIOBASEv3_163Products | 6 | List of products for EXIOBASE3, official order and labelling, 163 products (1:1 correspondence to industry resolution) | DOI: 10.1111/jiec.12715 | Code_number | Code_text | Name | | |
| 27 | EXIOBASEv3_200Products | 6 | List of products for EXIOBASE3, official order and labelling, 200 products (full resolution) | DOI: 10.1111/jiec.12715 | Code_number | Code_text | Name | | |
| 28 | Economic_indicators | 10 | List of common economic indicators | | Indicator name | | | | |
| 29 | SSP_Models | 9 | List of models used in SSP process | DOI: 10.1016/j.gloenvcha.2016.05.009 | model_name | | | | |
| 30 | SSP_32Regions | 4 | List of 32 SSP regions | DOI: 10.1016/j.gloenvcha.2016.05.009 | region_code | | | | |
| 31 | Population_classes | 9 | List of different population classes: urban, total, … | | class_name | | | | |
| 32 | EXIOBASEv2_48Regions | 4 | List of regions for EXIOBASE2, official order and labelling | DOI: 10.1111/jiec.12715 | country_name | code_2letter | code_3letter | | |
| 33 | NUTS_Regions_2010_Version | 4 | List of regions at NUTS levels 1, 2, and 3, 2010 version of NUTS | https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_CLS_DLD&StrNom=NUTS_33&StrLanguageCode=EN | NUTS_region_code | | | | NUTS_region_name |
| 34 | 34_EXIOBASEv3_49Regions | 4 | List of regions for EXIOBASE3, official order and labelling | DOI: 10.1111/jiec.12715 | country_name | code_2letter | code_3letter | | |
| 35 | NUTS_Regions_2006_Version | 4 | List of regions at NUTS levels 1, 2, and 3, 2006 version of NUTS | http://database.espon.eu/db2/jsf/DicoSpatialUnits/DicoSpatialUnits_html/ch01s01.html | NUTS_region_code | | | | NUTS_region_name1 |
| 36 | Literature | 13 | List of literature sources and their description | None | literature_id | literature_key | | | authors |
| 37 | YSTAFDB_criticality_regional_scope | 4 | regional scopes of YSTAFDB (Yale stocks and flow database) criticality indicators | None | regional scope | | | | |
| 38 | YSTAFDB_materials | 5 | materials list of the YSTAFDB (Yale stocks and flow database) | None | material_shortname | material_name | material_id | | atomic_weight |
| 39 | YSTAFDB_criticality_timeframe | 1 | time frames of YSTAFDB (Yale stocks and flow database) criticality indicators | None | time frame | | | | |
| 40 | YSTAFDB_criticality_indicators | 12 | YSTAFDB (Yale stocks and flow database) criticality indicators | None | criticality_indicator_name | criticality_indicator_symbol | | | |
| 41 | YSTAFDB_material_groups | 5 | YSTAFDB (Yale stocks and flow database) material groups, for sector split and recycling shares | None | material_group_application | | | | |
| 42 | YSTAFDB_use_phase_layer | 12 | YSTAFDB (Yale stocks and flow database) sector splits and recyclability_layers, described as 'recycling_use_type' in the original YSTAFDB | None | use_phase_and_recyclability_layer | | | | |
| 43 | YSTAFDB_UsePhase_Applications | 7 | YSTAFDB (Yale stocks and flow database) use phase applications | None | use_phase_applications | | | | |
| 44 | YSTAFDB_Time | 1 | YSTAFDB (Yale stocks and flow database) time intervals and years | None | time | | | | |
| 45 | YSTAFDB_Region | 4 | YSTAFDB (Yale stocks and flow database) regions (countries, regions, cities) | None | region | | | | |
| 46 | YSTAFDB_Commodities | 6 | YSTAFDB (Yale stocks and flow database) commodities/product groups | None | commodity | | | | |
| 47 | YSTAFDB_Processes | 7 | YSTAFDB (Yale stocks and flow database) processes | None | process, consisting of [aggregate_subsystem_module];[subsystem_name];[process_name] | | | | |
| 48 | StockChangeLayers | 12 | stock change layers like 'net addition, 'withdrawal', 'deposition' | None | stock change layers like 'net addition, 'withdrawal', 'deposition' | | | | |
| 49 | Wood_MFA_Indonesia_Aryapratama_2019_processes | 7 | processes used in MFA study on wood in Indonesia, by Rio Aryapratama | None | process name | | | | |
| 10000 | origin_process__1_F_steel_SankeyFlows_2008_Global | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_1' aspect of dataset | | | | |
| 10001 | destination_process__1_F_steel_SankeyFlows_2008_Global | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_2' aspect of dataset | | | | |
| 10002 | commodity__1_F_steel_SankeyFlows_2008_Global | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10003 | commodity__3_LT_AluCycle_LIU_2012 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_1' aspect of dataset | | | | |
| 10004 | scenario__3_LT_AluCycle_LIU_2012 | 9 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_4' aspect of dataset | | | | |
| 10005 | commodity__3_LT_AluCycle_LIU_2013 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_1' aspect of dataset | | | | |
| 10006 | region__3_LT_AluCycle_LIU_2013 | 4 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10007 | commodity__3_LT_IAI_GARC_2011 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_1' aspect of dataset | | | | |
| 10008 | commodity__3_LT_MetalDemand_DEETMAN_2018 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_1' aspect of dataset | | | | |
| 10009 | age-cohort__3_MC_Buildings_Sinha_2016 | 1 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_4' aspect of dataset | | | | |
| 10010 | commodity__3_MC_MetalDemand_DEETMAN_2018 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_1' aspect of dataset | | | | |
| 10011 | commodity__3_SHA_EndUseShares_56Metals_Ciacci_2015 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_2' aspect of dataset | | | | |
| 10012 | process__3_SHA_FabricationYield_CollectionRate_Alu_Liu_2012 | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10013 | process__4_PE_EnergyIntensity_AluminiumCycle_Liu_2012 | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_2' aspect of dataset | | | | |
| 10014 | time__4_PE_EnergyIntensity_AluminiumCycle_Liu_2012 | 1 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10015 | commodity__4_PY_FabricationYield_Alu_Liu_2012 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10016 | commodity__4_PY_FabricationYield_Cullen2012 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10017 | commodity__4_PY_FabricationYield_Dahlstroem2004 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10018 | commodity__4_PY_FabricationYield_Hatayama2010 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10019 | input_commodity__4_PY_WorldSteel_EoL_RR_SteelScrap | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_4' aspect of dataset | | | | |
| 10020 | time__5_CAP_PowerGenCapacity_Germany_2018 | 1 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_2' aspect of dataset | | | | |
| 10021 | age-cohort__5_CAP_PowerGenCapacity_Germany_2018 | 1 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10022 | commodity__5_CAP_PowerGenCapacity_Germany_2018 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_4' aspect of dataset | | | | |
| 10023 | region__5_CAP_PowerGenCapacity_Germany_2018 | 4 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_5' aspect of dataset | | | | |
| 10024 | technology__5_CAP_PowerGenCapacity_Germany_2018 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_6' aspect of dataset | | | | |
| 10025 | product_type__6_PCS_Buildings_Indonesia_1985 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_4' aspect of dataset | | | | |
| 10026 | input_commodity__1_F_IOT_UK_2010_pxp_v | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10027 | process__1_F_IOT_UK_2010_pxp_x | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_4' aspect of dataset | | | | |
| 10028 | process__1_F_IOT_UK_2010_pxp_Y | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_4' aspect of dataset | | | | |
| 10029 | commodity__1_F_MetalDemand_DEETMAN_2018 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_6' aspect of dataset | | | | |
| 10030 | origin_process__1_F_Waste_EXIOBASE2_Global_aggregated | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_1' aspect of dataset | | | | |
| 10031 | destination_process__1_F_Waste_EXIOBASE2_Global_aggregated | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_2' aspect of dataset | | | | |
| 10032 | engineering_material__1_F_Waste_EXIOBASE2_Global_aggregated | 5 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10033 | region__1_F_Waste_EXIOBASE2_Global_aggregated | 4 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_4' aspect of dataset | | | | |
| 10034 | commodity__1_F_WIO_Japan_Nakamura_Kondo_2002 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_1' aspect of dataset | | | | |
| 10035 | origin_process__1_F_WIO_Japan_Nakamura_Kondo_2002 | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_2' aspect of dataset | | | | |
| 10036 | destination_process__1_F_WIO_Japan_Nakamura_Kondo_2002 | 7 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_6' aspect of dataset | | | | |
| 10037 | commodity__3_LT_MaTraceGlobal_Pauliuk_2017 | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_1' aspect of dataset | | | | |
| 10038 | region__3_LT_MaTraceGlobal_Pauliuk_2017 | 4 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_3' aspect of dataset | | | | |
| 10039 | input_commodity__4_PY_IAI_EoL_RR_AluScrap | 6 | Custom classification, generated by IEDC_tools v0.4.0 | | 'aspect_4' aspect of dataset | | | | |
| 10040 | age-cohort__3_MC_Buildings_Heeren_Fishman_ScientificData_2019_V1.1 | 1 | Custom classification, generated by IEDC_tools v0.4.1 | | 'aspect_3' aspect of dataset | | | | |
| 10041 | impact_indicator__6_IMI_EU_Regional_Carbon_Footprint_Ivanova_2017 | 10 | Custom classification, generated by IEDC_tools v0.4.1 | | 'aspect_1' aspect of dataset | | | | |
| 10042 | impact_indicator__6_IMI_Global_Household_EnvFootprints_Ivanova_2016 | 10 | Custom classification, generated by IEDC_tools v0.4.1 | | 'aspect_1' aspect of dataset | | | | |
| 10043 | scenario__2_IUS_Wood_Carbon_MFA_Indonesia_Aryapratama_2019 | 9 | Custom classification, generated by IEDC_tools v0.4.2 | | 'aspect_4' aspect of dataset | | | | |
| 10083 | city__6_URB_MetabolismOfCities_Jan2019_DOI_7326485.v1 | 4 | Custom classification, generated by IEDC_tools v0.4.2 | | 'aspect_1' aspect of dataset | | | | |
| 10084 | layer__6_URB_MetabolismOfCities_Jan2019_DOI_7326485.v1 | 12 | Custom classification, generated by IEDC_tools v0.4.2 | | 'aspect_2' aspect of dataset | | | | |
| 10085 | impact_indicator__6_URB_MetabolismOfCities_Jan2019_DOI_7326485.v1 | 10 | Custom classification, generated by IEDC_tools v0.4.2 | | 'aspect_3' aspect of dataset | | | | |