nl2sqlproj/spider2-nl2sql huggingface dataset:
-- DBMS: snowflake
create or replace TABLE VARIANT_GENE (
	"chr_id" VARCHAR(16777216),
	"position" NUMBER(38,0),
	"ref_allele" VARCHAR(16777216),
	"alt_allele" VARCHAR(16777216),
	"gene_id" VARCHAR(16777216),
	"feature" VARCHAR(16777216),
	"type_id" VARCHAR(16777216),
	"source_id" VARCHAR(16777216),
	"fpred_labels" VARIANT,
	"fpred_scores" VARIANT,
	"fpred_max_label" VARCHAR(16777216),
	"fpred_max_score" FLOAT,
	"qtl_beta" FLOAT,
	"qtl_se" FLOAT,
	"qtl_pval" FLOAT,
	"qtl_score" FLOAT,
	"interval_score" FLOAT,
	"qtl_score_q" FLOAT,
	"interval_score_q" FLOAT,
	"d" NUMBER(38,0),
	"distance_score" FLOAT,
	"distance_score_q" FLOAT
);

INSERT INTO "OPEN_TARGETS_GENETICS_2"."OPEN_TARGETS_GENETICS"."VARIANT_GENE" (gene_id, alt_allele, ref_allele, feature, source_id, position, qtl_score, chr_id, distance_score, qtl_pval, qtl_score_q, qtl_se, fpred_max_label, fpred_labels, fpred_max_score, interval_score_q, d, type_id, interval_score, fpred_scores, qtl_beta, distance_score_q) VALUES ('ENSG00000275954', 'T', 'C', 'unspecified', 'canonical_tss', 36816582, NULL, '17', 2.6524073248880684e-06, NULL, NULL, NULL, NULL, '{
  "list": []
}', NULL, NULL, 377016, 'distance', NULL, '{
  "list": []
}', NULL, 0.2), ('ENSG00000181038', 'C', 'T', 'unspecified', 'canonical_tss', 77103846, NULL, '17', 2.6524073248880684e-06, NULL, NULL, NULL, NULL, '{
  "list": []
}', NULL, NULL, 377016, 'distance', NULL, '{
  "list": []
}', NULL, 0.2), ('ENSG00000164056', 'A', 'G', 'unspecified', 'canonical_tss', 123019779, NULL, '4', 2.6524073248880684e-06, NULL, NULL, NULL, NULL, '{
  "list": []
}', NULL, NULL, 377016, 'distance', NULL, '{
  "list": []
}', NULL, 0.2), ('ENSG00000152592', 'A', 'G', 'unspecified', 'canonical_tss', 87273264, NULL, '4', 2.6524073248880684e-06, NULL, NULL, NULL, NULL, '{
  "list": []
}', NULL, NULL, 377016, 'distance', NULL, '{
  "list": []
}', NULL, 0.2), ('ENSG00000183684', 'T', 'C', 'unspecified', 'canonical_tss', 81514570, NULL, '17', 2.6524073248880684e-06, NULL, NULL, NULL, NULL, '{
  "list": []
}', NULL, NULL, 377016, 'distance', NULL, '{
  "list": []
}', NULL, 0.2);
-- DBMS: snowflake
create or replace TABLE STUDIES_OVERLAP (
	"A_study_id" VARCHAR(16777216),
	"A_chrom" VARCHAR(16777216),
	"A_pos" NUMBER(38,0),
	"A_ref" VARCHAR(16777216),
	"A_alt" VARCHAR(16777216),
	"B_study_id" VARCHAR(16777216),
	"B_chrom" VARCHAR(16777216),
	"B_pos" NUMBER(38,0),
	"B_ref" VARCHAR(16777216),
	"B_alt" VARCHAR(16777216),
	"A_distinct" NUMBER(38,0),
	"AB_overlap" NUMBER(38,0),
	"B_distinct" NUMBER(38,0)
);

INSERT INTO "OPEN_TARGETS_GENETICS_2"."OPEN_TARGETS_GENETICS"."STUDIES_OVERLAP" (B_ref, A_study_id, B_study_id, B_alt, B_distinct, A_chrom, AB_overlap, A_distinct, A_alt, B_chrom, B_pos, A_ref, A_pos) VALUES ('G', 'NEALE2_50_raw', 'GCST90002381', 'GTAGAGGATGGCTTGAACCTAGGAAC', 4, '12', 19, 7, 'A', '12', 92111827, 'G', 93813756), ('G', 'NEALE2_50_raw', 'GCST90002382', 'GTAGAGGATGGCTTGAACCTAGGAAC', 4, '12', 19, 7, 'A', '12', 92111827, 'G', 93813756), ('G', 'NEALE2_50_raw', 'GCST90025949', 'A', 0, '12', 1, 25, 'A', '12', 93813756, 'G', 93813756), ('T', 'NEALE2_50_raw', 'GCST90025987', 'C', 0, '12', 1, 25, 'A', '12', 92112072, 'G', 93813756), ('CA', 'NEALE2_50_raw', 'NEALE2_30150', 'C', 7, '12', 21, 5, 'A', '12', 92146955, 'G', 93813756);
-- DBMS: snowflake
create or replace TABLE SA_GWAS (
	"type_id" VARCHAR(16777216),
	"study_id" VARCHAR(16777216),
	"chrom" VARCHAR(16777216),
	"pos" NUMBER(38,0),
	"ref" VARCHAR(16777216),
	"alt" VARCHAR(16777216),
	"beta" FLOAT,
	"se" FLOAT,
	"pval" FLOAT,
	"n_total" NUMBER(38,0),
	"n_cases" NUMBER(38,0),
	"eaf" FLOAT,
	"mac" FLOAT,
	"mac_cases" FLOAT,
	"info" FLOAT,
	"is_cc" BOOLEAN
);

INSERT INTO "OPEN_TARGETS_GENETICS_2"."OPEN_TARGETS_GENETICS"."SA_GWAS" (alt, chrom, mac_cases, study_id, info, mac, pos, type_id, n_total, ref, is_cc, beta, eaf, pval, se, n_cases) VALUES ('T', '6', 4652.143919872852, 'NEALE2_6152_100', 0.999724, 14458.199999999999, 32849516, 'gwas', 360527, 'C', True, 0.07621831832999174, 0.02005148019427116, 2.1656700000000002e-05, 0.017946159706930698, 116005.0), ('T', '6', 29.872799999999998, 'FINNGEN_R6_Z21_ROUTI_GENERAL_HEALTH_CHECK_DEFIN_SUBPO', nan, 8918.229599999999, 32849516, 'gwas', 137627, 'C', True, 0.57301, 0.0324, 0.00408, 0.19954, 461.0), ('T', '6', nan, 'GCST90002412', nan, 17000.91481000003, 32849516, 'gwas', 431167, 'C', False, 0.0277622, 0.019715000000000038, 5.3e-05, 0.00686612, nan), ('T', '6', 8577.74298, 'GCST005038', nan, 17183.10556, 32849516, 'gwas', 360838, 'C', True, -0.1245, 0.02381, 7.448e-08, 0.0231, 180129.0), ('T', '6', nan, 'GCST90002292', nan, 18903.15988, 32849516, 'gwas', 474001, 'C', False, -0.034161, 0.01994, 3.04e-06, 0.007313, nan);
-- DBMS: snowflake
create or replace TABLE STUDIES (
	"study_id" VARCHAR(16777216),
	"ancestry_initial" VARIANT,
	"ancestry_replication" VARIANT,
	"n_cases" NUMBER(38,0),
	"n_initial" NUMBER(38,0),
	"n_replication" NUMBER(38,0),
	"pmid" VARCHAR(16777216),
	"pub_author" VARCHAR(16777216),
	"pub_date" VARCHAR(16777216),
	"pub_journal" VARCHAR(16777216),
	"pub_title" VARCHAR(16777216),
	"has_sumstats" BOOLEAN,
	"num_assoc_loci" NUMBER(38,0),
	"source" VARCHAR(16777216),
	"trait_reported" VARCHAR(16777216),
	"trait_efos" VARIANT,
	"trait_category" VARCHAR(16777216)
);

INSERT INTO "OPEN_TARGETS_GENETICS_2"."OPEN_TARGETS_GENETICS"."STUDIES" (pmid, study_id, trait_category, num_assoc_loci, n_initial, pub_title, pub_journal, pub_date, source, trait_reported, n_cases, ancestry_replication, n_replication, has_sumstats, ancestry_initial, pub_author, trait_efos) VALUES (NULL, 'FINNGEN_R6_M13_RECUDISLOCATIO', 'phenotype', 0, 174889, NULL, NULL, '2022-01-24', 'FINNGEN', 'Recurrent dislocation and subluxation of joint', 1475, '{
  "list": []
}', 0, True, '{
  "list": [
    {
      "element": "European=174889"
    }
  ]
}', 'FINNGEN_R6', '{
  "list": [
    {
      "element": "HP_0032153"
    },
    {
      "element": "HP_0001373"
    }
  ]
}'), (NULL, 'FINNGEN_R6_M13_TOEDEFORMOTH', 'phenotype', 0, 174013, NULL, NULL, '2022-01-24', 'FINNGEN', 'Other deformities of toe(s)', 599, '{
  "list": []
}', 0, True, '{
  "list": [
    {
      "element": "European=174013"
    }
  ]
}', 'FINNGEN_R6', '{
  "list": [
    {
      "element": "HP_0001780"
    }
  ]
}'), (NULL, 'FINNGEN_R6_M13_TRIGGERFINGER', 'phenotype', 1, 200156, NULL, NULL, '2022-01-24', 'FINNGEN', 'Trigger finger', 2432, '{
  "list": []
}', 0, True, '{
  "list": [
    {
      "element": "European=200156"
    }
  ]
}', 'FINNGEN_R6', '{
  "list": [
    {
      "element": "HP_0012276"
    }
  ]
}'), (NULL, 'FINNGEN_R6_M13_VARUS', 'phenotype', 0, 173615, NULL, NULL, '2022-01-24', 'FINNGEN', '"Varus deformity, not elsewhere classified"', 201, '{
  "list": []
}', 0, True, '{
  "list": [
    {
      "element": "European=173615"
    }
  ]
}', 'FINNGEN_R6', '{
  "list": [
    {
      "element": "HP_0000118"
    }
  ]
}'), (NULL, 'FINNGEN_R6_N14_ABNORMALBLEED', 'phenotype', 2, 88919, NULL, NULL, '2022-01-24', 'FINNGEN', 'Other abnormal uterine and caginal bleeding', 7326, '{
  "list": []
}', 0, True, '{
  "list": [
    {
      "element": "European=88919"
    }
  ]
}', 'FINNGEN_R6', '{
  "list": [
    {
      "element": "HP_0001892"
    }
  ]
}');
-- DBMS: snowflake
create or replace TABLE LOCUS2GENE (
	"study_id" VARCHAR(16777216),
	"chrom" VARCHAR(16777216),
	"pos" NUMBER(38,0),
	"ref" VARCHAR(16777216),
	"alt" VARCHAR(16777216),
	"gene_id" VARCHAR(16777216),
	"training_clf" VARCHAR(16777216),
	"training_gs" VARCHAR(16777216),
	"training_fold" VARCHAR(16777216),
	"y_proba_dist_foot" FLOAT,
	"y_proba_dist_tss" FLOAT,
	"y_proba_full_model" FLOAT,
	"y_proba_logi_distance" FLOAT,
	"y_proba_logi_interaction" FLOAT,
	"y_proba_logi_molecularQTL" FLOAT,
	"y_proba_logi_pathogenicity" FLOAT,
	"y_proba_logo_distance" FLOAT,
	"y_proba_logo_interaction" FLOAT,
	"y_proba_logo_molecularQTL" FLOAT,
	"y_proba_logo_pathogenicity" FLOAT
);

INSERT INTO "OPEN_TARGETS_GENETICS_2"."OPEN_TARGETS_GENETICS"."LOCUS2GENE" (y_proba_logi_pathogenicity, alt, chrom, y_proba_logi_molecularQTL, pos, y_proba_dist_foot, y_proba_logo_molecularQTL, training_gs, training_clf, ref, y_proba_logo_interaction, gene_id, y_proba_logi_interaction, y_proba_logo_pathogenicity, training_fold, study_id, y_proba_logi_distance, y_proba_logo_distance, y_proba_full_model, y_proba_dist_tss) VALUES (0.06419508904218674, 'GT', '2', 0.02663401886820793, 169155682, 0.02039780281484127, 0.010547985322773457, 'high_medium', 'xgboost', 'G', 0.011236511170864105, 'ENSG00000239474', 0.027016155421733856, 0.012834045104682446, 'fold3=12|2|7|17', 'GCST011521', 0.009156674146652222, 0.021092426031827927, 0.012625353410840034, 0.028209906071424484), (0.06463130563497543, 'T', '2', 0.04719509184360504, 169139071, 0.02039780281484127, 0.009728365577757359, 'high_medium', 'xgboost', 'C', 0.009814477525651455, 'ENSG00000239474', 0.08312072604894638, 0.011792142875492573, 'fold3=12|2|7|17', 'GCST008059', 0.008775304071605206, 0.023806001991033554, 0.011795265600085258, 0.338593453168869), (0.06411707401275635, 'G', '2', 0.044152189046144485, 169013391, 0.02039780281484127, 0.008792259730398655, 'high_medium', 'xgboost', 'A', 0.009676314890384674, 'ENSG00000239474', 0.06812436133623123, 0.011257052421569824, 'fold3=12|2|7|17', 'GCST90025966', 0.007752847857773304, 0.022378509864211082, 0.011175764724612236, 0.02089153602719307), (0.06158239394426346, 'A', '17', 0.041536081582307816, 7851675, 0.02039780281484127, 0.008686564862728119, 'high_medium', 'xgboost', 'G', 0.007694853935390711, 'ENSG00000239697', 0.05952328443527222, 0.010919488966464996, 'fold3=12|2|7|17', 'GCST90025983', 0.007468224503099918, 0.013296891003847122, 0.011318651959300041, 0.020761258900165558), (0.06302739679813385, 'T', '17', 0.24548295140266418, 7176997, 0.02039780281484127, 0.010006439872086048, 'high_medium', 'xgboost', 'C', 0.022041436284780502, 'ENSG00000239697', 0.05635738745331764, 0.06870497763156891, 'fold3=12|2|7|17', 'GCST90025989', 0.008193666115403175, 0.23689737915992737, 0.0864158496260643, 0.02153369039297104);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.US_DEPARTMENT_OF_AGRICULTURE_COMMODITIES_TIMESERIES (
    VARIABLE_NAME TEXT,
    DATE DATE,
    UNIT TEXT,
    GEO_ID TEXT,
    VARIABLE TEXT,
    VALUE FLOAT,
    MARKETING_YEAR TEXT,
    CALENDAR_YEAR DATE
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."US_DEPARTMENT_OF_AGRICULTURE_COMMODITIES_TIMESERIES" (VARIABLE_NAME, DATE, UNIT, GEO_ID, VARIABLE, VALUE, MARKETING_YEAR, CALENDAR_YEAR) VALUES ('Corn: Accumulated Exports', '1999-04-01', 'Metric Tons', 'country/DEU', 'ESR_corn_accumulated_exports', 3505.0, '1999', '1970-01-01'), ('Rice-LG Brown: Current Market Year Total Commitment', '2009-09-10', 'Metric Tons', 'country/BMU', 'ESR_rice_lg_brown_current_market_year_total_commitment', 18.0, '2010', '1970-01-01'), ('Peanut Oil: Exports', '2015-03-01', 'Metric Tons', 'country/SEN', 'PSD-4234000-088', 35000.0, '2015', '2020-12-31'), ('Soybean cake & meal: Next Market Year Outstanding Sales', '2021-03-25', 'Metric Tons', 'country/JAM', 'ESR_soybean_cake__meal_next_market_year_outstanding_sales', 0.0, '2021', '1970-01-01'), ('All Upland Cotton: Weekly Exports', '2007-02-08', 'Running Bales', 'country/BEL', 'ESR_all_upland_cotton_weekly_exports', 0.0, '2007', '1970-01-01');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.WORLD_TRADE_ORGANIZATION_ATTRIBUTES (
    VARIABLE_NAME TEXT,
    VARIABLE TEXT,
    FREQUENCY TEXT,
    PRODUCT_SECTOR TEXT,
    MEASURE TEXT,
    WTO_INDICATOR TEXT,
    UNIT TEXT,
    MEASUREMENT_TYPE TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."WORLD_TRADE_ORGANIZATION_ATTRIBUTES" (VARIABLE_NAME, VARIABLE, FREQUENCY, PRODUCT_SECTOR, MEASURE, WTO_INDICATOR, UNIT, MEASUREMENT_TYPE) VALUES ('HS Pref - Best preferential simple average ad valorem duty: Goats (010420) (Harmonized System) [Least-developed countries (LDC) duties for 6 countries]', 'a0cb8c65e9dea7c45a97105bf3e27b66', 'Annual', 'Goats', 'Preferential by detailed HS 6-digit', 'TPA_PRF_HS_HS_P_0070_HS_010420', 'Percent', NULL), ('HS Pref - Best preferential simple average ad valorem duty: Other (010690) (Harmonized System) [Free-trade agreement duty rates for Cambodia under ASEAN, Least-developed countries (LDC) duties]', 'bb8bfe9d6ef6049cf68b65d1a2cd25ca', 'Annual', 'Other', 'Preferential by detailed HS 6-digit', 'TPA_PRF_HS_HS_P_0070_HS_010690', 'Percent', NULL), ('HS Pref - Best preferential simple average ad valorem duty: Sheep (010410) (Harmonized System) [Free-trade agreement duty rate for CAFTA, Dominican Republic and United States]', 'ccca9ea12075bfede6aa76eb298a70f3', 'Annual', 'Sheep', 'Preferential by detailed HS 6-digit', 'TPA_PRF_HS_HS_P_0070_HS_010410', 'Percent', NULL), ('HS Pref - Best preferential simple average ad valorem duty: Whales, dolphins and porpoises (mammals of the order Cetacea); manatees and dugongs (mammals of the order Sirenia) (010612) (Harmonized System) [ASEAN-Japan free trade area duty rates (ASEAN-Japan Economic Partnership Agreement (AJCEPA)), Philippines-Japan free trade area duty rates (Japan-Philippines Economic Partnership Agreement (JPEPA))]', '958c7736f6ff967096963976ab54ae97', 'Annual', 'Whales, dolphins and porpoises (mammals of the order Cetacea); manatees and dugongs (mammals of the order Sirenia)', 'Preferential by detailed HS 6-digit', 'TPA_PRF_HS_HS_P_0070_HS_010612', 'Percent', NULL), ('HS Pref - Best preferential simple average ad valorem duty: Camels and other camelids (Camelidae) (010613) (Harmonized System) [EU-Montenegro, CEFTA-Croatia]', 'a5fcb51f308d40a4f0eb8e2379201c7c', 'Annual', 'Camels and other camelids (Camelidae)', 'Preferential by detailed HS 6-digit', 'TPA_PRF_HS_HS_P_0070_HS_010613', 'Percent', NULL);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.FEDERAL_RESERVE_ATTRIBUTES (
    UNIT TEXT,
    FREQUENCY TEXT,
    SECTOR TEXT,
    SEASONALLY_ADJUSTED TEXT,
    VARIABLE TEXT,
    NAICS TEXT,
    CATEGORY TEXT,
    VARIABLE_NAME TEXT,
    MEASURE TEXT,
    TERMS TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."FEDERAL_RESERVE_ATTRIBUTES" (UNIT, FREQUENCY, SECTOR, SEASONALLY_ADJUSTED, VARIABLE, NAICS, CATEGORY, VARIABLE_NAME, MEASURE, TERMS) VALUES ('USD', 'Quarterly', 'Domestic nonfinancial sectors', 'Not seasonally adjusted', 'Z1_FU383020005.Q', '', 'Checkable deposits and currency; asset', 'Transactions: Domestic nonfinancial sectors; checkable deposits and currency; asset - Computed series, Quarterly (Not seasonally adjusted)', 'US Financial Accounts', NULL), ('USD', 'Quarterly', 'Domestic nonfinancial sectors', 'Not seasonally adjusted', 'Z1_FU383064105.Q', '', 'Corporate equities; asset', 'Transactions: Domestic nonfinancial sectors; corporate equities; asset - Computed series, Quarterly (Not seasonally adjusted)', 'US Financial Accounts', NULL), ('USD', 'Quarterly', 'Domestic nonfinancial sectors', 'Not seasonally adjusted', 'Z1_FU383069105.Q', '', 'Open market paper; asset', 'Transactions: Domestic nonfinancial sectors; commercial paper; asset - Computed series, Quarterly (Not seasonally adjusted)', 'US Financial Accounts', NULL), ('USD', 'Annual', 'Domestic nonfinancial sectors', 'Not seasonally adjusted', 'Z1_FU383070005.A', '', 'Trade receivables; asset', 'Transactions: Domestic nonfinancial sectors; trade receivables; asset - Computed series, Annual (Not seasonally adjusted)', 'US Financial Accounts', NULL), ('USD', 'Quarterly', 'Domestic nonfinancial sectors', 'Not seasonally adjusted', 'Z1_FU383078005.Q', '', 'Taxes receivable; asset', 'Transactions: Domestic nonfinancial sectors; taxes receivable; asset - Computed series, Quarterly (Not seasonally adjusted)', 'US Financial Accounts', NULL);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.AIRCRAFT_CARRIER_INDEX (
    CARRIER_WORLD_AREA_CODE NUMBER,
    AIRCRAFT_CARRIER_ID TEXT,
    CARRIER_TYPE TEXT,
    CARRIER_NAME TEXT,
    COUNTRY_GEO_ID TEXT,
    OAI_CARRIER_TYPE TEXT,
    STATE_GEO_ID TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."AIRCRAFT_CARRIER_INDEX" (CARRIER_WORLD_AREA_CODE, AIRCRAFT_CARRIER_ID, CARRIER_TYPE, CARRIER_NAME, COUNTRY_GEO_ID, OAI_CARRIER_TYPE, STATE_GEO_ID) VALUES (1, '01212_11', 'Domestic Carrier', 'Paklook Air, Inc d/b/a/ Airlift Alaska (2017 - 2017)', 'country/USA', 'Large Regional Carrier - $20-100M in revenue', 'geoId/02'), (10, '01006_31', 'Domestic Carrier', 'Virgin America (2007 - 2018)', NULL, 'Major Carrier - $1B+ in revenue', NULL), (10, '06993_51', 'Domestic Carrier', 'Bellair Inc. (1) (Domestic)', NULL, 'Small Certified Carrier', NULL), (10, '16770 (1)_11', 'Domestic Carrier', 'Reliant Airlines (1998 - 2003)', NULL, 'Large Regional Carrier - $20-100M in revenue', NULL), (10, '06752_11', 'Domestic Carrier', 'Pan American Airways Corp. (1999 - 2004)', NULL, 'Large Regional Carrier - $20-100M in revenue', NULL);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.UNITED_NATIONS_INDUSTRIAL_DEVELOPMENT_ORGANIZATION_TIMESERIES (
    VALUE TEXT,
    UNIT TEXT,
    VARIABLE_NAME TEXT,
    VARIABLE TEXT,
    GEO_ID TEXT,
    DATE DATE
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."UNITED_NATIONS_INDUSTRIAL_DEVELOPMENT_ORGANIZATION_TIMESERIES" (VALUE, UNIT, VARIABLE_NAME, VARIABLE, GEO_ID, DATE) VALUES ('116439336', 'USD', 'Wages and salaries: C Total mining and quarrying (INDSTAT 2024, ISIC Revision 3) (in USD)', 'wages_and_salaries_c_total_mining_and_quarrying_indstat_2024_isic_revision_3_in_usd', 'country/BGR', '2005-12-31'), ('344', 'Count', 'Establishments: 26 Non-metallic mineral products (INDSTAT 2024, ISIC Revision 3)', 'establishments_26_non-metallic_mineral_products_indstat_2024_isic_revision_3', 'country/TJK', '2014-12-31'), ('0', 'USD', 'Gross fixed capital formation: 323 TV and radio receivers and associated goods (INDSTAT 2024, ISIC Revision 3) (in USD)', 'gross_fixed_capital_formation_323_tv_and_radio_receivers_and_associated_goods_indstat_2024_isic_revision_3_in_usd', 'country/MKD', '2003-12-31'), ('40303332', 'USD', 'Wages and salaries: 132 Non-ferrous metal ores,excl.uranium,thorium (INDSTAT 2024, ISIC Revision 3) (in USD)', 'wages_and_salaries_132_non-ferrous_metal_oresexcluraniumthorium_indstat_2024_isic_revision_3_in_usd', 'country/IRL', '2001-12-31'), ('59626344', 'USD', 'Output: 251 Rubber products (INDSTAT 2024, ISIC Revision 3) (in USD)', 'output_251_rubber_products_indstat_2024_isic_revision_3_in_usd', 'country/MLT', '2004-12-31');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.US_ECONOMIC_CENSUS_ATTRIBUTES (
    VARIABLE_NAME TEXT,
    CREDIT_CARD_SERVICES_INCOME TEXT,
    MEASURE TEXT,
    VARIABLE TEXT,
    NAICS_CODE TEXT,
    FREQUENCY TEXT,
    TAX_STATUS TEXT,
    PRODUCT_SERVICES_LINE TEXT,
    NAICS_DESCRIPTION TEXT,
    UNIT TEXT,
    OPERATION_TYPE TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."US_ECONOMIC_CENSUS_ATTRIBUTES" (VARIABLE_NAME, CREDIT_CARD_SERVICES_INCOME, MEASURE, VARIABLE, NAICS_CODE, FREQUENCY, TAX_STATUS, PRODUCT_SERVICES_LINE, NAICS_DESCRIPTION, UNIT, OPERATION_TYPE) VALUES ('Value of construction work on privately owned projects | Paint or body repair (NAICS 2012 code: 8111211)', NULL, 'Construction work', 'PRIDL_8111211_2012', '8111211', 'Annual', NULL, NULL, 'Paint or body repair', 'USD', NULL), ('Relative standard error of estimate for total capital expenditures for buildings, structures, machinery, and equipment (new and used) | Other consumer goods rental (NAICS 2012 code: 53229)', NULL, 'Capital expenditures', 'CEXTOT_S_53229_2012', '53229', 'Annual', NULL, NULL, 'Other consumer goods rental', 'Percent', NULL), ('Cost of resales | All other professional, scientific, and technical services (NAICS 2012 code: 54199)', NULL, 'Resales', 'CSTRSL_54199_2012', '54199', 'Annual', NULL, NULL, 'All other professional, scientific, and technical services', 'USD', NULL), ('Relative standard error of estimate of value of construction work for establishments not reporting inventories | Other nonferrous metal foundries (except die-casting) (NAICS 2012 code: 331529)', NULL, 'Construction work', 'RCPCNR_S_331529_2012', '331529', 'Annual', NULL, NULL, 'Other nonferrous metal foundries (except die-casting)', 'Percent', NULL), ('Relative standard error of rental payments for machinery and equipment | Pet care (except veterinary) services (NAICS 2012 code: 81291)', NULL, 'Rental payments', 'RPMCH_S_81291_2012', '81291', 'Annual', NULL, NULL, 'Pet care (except veterinary) services', 'Percent', NULL);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.US_DEPARTMENT_OF_LABOR_UNEMPLOYMENT_INSURANCE_CLAIMS_ATTRIBUTES (
    MEASURE TEXT,
    VARIABLE_NAME TEXT,
    UNIT TEXT,
    VARIABLE TEXT,
    FREQUENCY TEXT,
    MEASUREMENT_TYPE TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."US_DEPARTMENT_OF_LABOR_UNEMPLOYMENT_INSURANCE_CLAIMS_ATTRIBUTES" (MEASURE, VARIABLE_NAME, UNIT, VARIABLE, FREQUENCY, MEASUREMENT_TYPE) VALUES ('Employment', 'Covered Employment', 'Count', 'CE', 'Weekly', 'Reports the 12-month average monthly covered employment for the first 4 of the last 6 completed calendar quarters prior to the end of the last week of the current 13-week period to which the insured unemployment data relate'), ('Insurance Claims', 'State Unemployment Insurance Initial Claims without Intrastate Transitional Claims', 'Count', 'IC', 'Weekly', NULL), ('Insurance Claims', 'Short Time Compensation (Workshare) Equivalent Initial Claims', 'Count', 'WSEIC', 'Weekly', NULL), ('Insurance Claims', 'Short Time Compensation (Workshare) Total Initial Claims', 'Count', 'WSIC', 'Weekly', NULL), ('Insurance Claims', 'Total Continued Weeks Claimed under a State Additional Benefit Program', 'Count', 'ABT', 'Weekly', 'Includes Unemployment Compensation for Federal Employees adn Ex-Service Members');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.INTERNATIONAL_LABOUR_ORGANIZATION_ATTRIBUTES (
    SEX TEXT,
    ECONOMIC_CLASS TEXT,
    MEASUREMENT_TYPE TEXT,
    SERIES_ID TEXT,
    UNIT TEXT,
    VARIABLE_NAME TEXT,
    FREQUENCY TEXT,
    AGE_GROUP TEXT,
    GEOGRAPHY_TYPE TEXT,
    ECONOMIC_ACTIVITY TEXT,
    OCCUPATION TEXT,
    VARIABLE TEXT,
    MEASURE TEXT,
    EMPLOYMENT_STATUS TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."INTERNATIONAL_LABOUR_ORGANIZATION_ATTRIBUTES" (SEX, ECONOMIC_CLASS, MEASUREMENT_TYPE, SERIES_ID, UNIT, VARIABLE_NAME, FREQUENCY, AGE_GROUP, GEOGRAPHY_TYPE, ECONOMIC_ACTIVITY, OCCUPATION, VARIABLE, MEASURE, EMPLOYMENT_STATUS) VALUES ('Total', NULL, 'Estimate', 'EAP_TEAP_SEX_AGE_GEO_NB,A', 'Count', 'Labour force by sex, age and rural / urban areas | Total | Age (10-year bands): 15-24 | Not elsewhere classified, Annual (Count), Estimate', 'Annual', 'Age (10-year bands): 15-24', 'Not elsewhere classified', NULL, NULL, 'EAP_TEAP_SEX_AGE_GEO_NB,A_SEX_T_AGE_10YRBANDS_Y15-24_GEO_COV_X_E', 'Labour force by sex, age and rural / urban areas', NULL), ('Male', NULL, 'Estimate', 'EAP_TEAP_SEX_AGE_GEO_NB,A', 'Count', 'Labour force by sex, age and rural / urban areas | Male | Age (10-year bands): 25-34 | National, Annual (Count), Estimate', 'Annual', 'Age (10-year bands): 25-34', 'National', NULL, NULL, 'EAP_TEAP_SEX_AGE_GEO_NB,A_SEX_M_AGE_10YRBANDS_Y25-34_GEO_COV_NAT_E', 'Labour force by sex, age and rural / urban areas', NULL), ('Female', NULL, 'Estimate', 'EAP_TEAP_SEX_AGE_GEO_NB,Q', 'Count', 'Labour force by sex, age and rural / urban areas | Female | Age (10-year bands): 25-34 | National, Quarterly (Count), Estimate', 'Quarterly', 'Age (10-year bands): 25-34', 'National', NULL, NULL, 'EAP_TEAP_SEX_AGE_GEO_NB,Q_SEX_F_AGE_10YRBANDS_Y25-34_GEO_COV_NAT_E', 'Labour force by sex, age and rural / urban areas', NULL), ('Total', NULL, 'Estimate', 'EAP_TEAP_SEX_AGE_GEO_NB,Q', 'Count', 'Labour force by sex, age and rural / urban areas | Total | Age (Youth, adults): 15-64 | Rural, Quarterly (Count), Estimate', 'Quarterly', 'Age (Youth, adults): 15-64', 'Rural', NULL, NULL, 'EAP_TEAP_SEX_AGE_GEO_NB,Q_SEX_T_AGE_YTHADULT_Y15-64_GEO_COV_RUR_E', 'Labour force by sex, age and rural / urban areas', NULL), ('Male', NULL, 'Estimate', 'EAP_TEAP_SEX_AGE_GEO_NB,Q', 'Count', 'Labour force by sex, age and rural / urban areas | Male | Age (Youth, adults): 15-64 | Rural, Quarterly (Count), Estimate', 'Quarterly', 'Age (Youth, adults): 15-64', 'Rural', NULL, NULL, 'EAP_TEAP_SEX_AGE_GEO_NB,Q_SEX_M_AGE_YTHADULT_Y15-64_GEO_COV_RUR_E', 'Labour force by sex, age and rural / urban areas', NULL);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.AIRPORT_INDEX (
    AIRPORT_NAME TEXT,
    STATE_GEO_ID TEXT,
    AIRPORT_CITY_NAME TEXT,
    AIRPORT_ALPHA_CODE TEXT,
    AIRPORT_DOT_CODE NUMBER,
    AIRPORT_WORLD_AREA_CODE NUMBER,
    COUNTRY_GEO_ID TEXT,
    LOCATION TEXT,
    AIRPORT_ID TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."AIRPORT_INDEX" (AIRPORT_NAME, STATE_GEO_ID, AIRPORT_CITY_NAME, AIRPORT_ALPHA_CODE, AIRPORT_DOT_CODE, AIRPORT_WORLD_AREA_CODE, COUNTRY_GEO_ID, LOCATION, AIRPORT_ID) VALUES ('Birmingham-Shuttlesworth International', 'geoId/01', 'Birmingham', 'BHM', 1059, 51, 'country/USA', 'Birmingham, AL: Birmingham-Shuttlesworth International', '1059BHM'), ('Kansas City International', 'geoId/29', 'Kansas City', 'MCI', 42650, 64, 'country/USA', 'Kansas City, MO: Kansas City International', '42650MCI'), ('Tucson International', 'geoId/04', 'Tucson', 'TUS', 91940, 81, 'country/USA', 'Tucson, AZ: Tucson International', '91940TUS'), ('Long Island Airport', 'geoId/02', 'Long Island', 'LIJ', 1298, 1, 'country/USA', 'Long Island, AK: Long Island Airport', '1298LIJ'), ('Hattiesburg-Laurel Regional', 'geoId/28', 'Hattiesburg/Laurel', 'PIB', 49005, 53, 'country/USA', 'Hattiesburg/Laurel, MS: Hattiesburg-Laurel Regional', '49005PIB');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.US_TREASURY_ATTRIBUTES (
    VARIABLE TEXT,
    MEASURE TEXT,
    VARIABLE_NAME TEXT,
    FREQUENCY TEXT,
    TAX_CATEGORY TEXT,
    MEASUREMENT_TYPE TEXT,
    UNIT TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."US_TREASURY_ATTRIBUTES" (VARIABLE, MEASURE, VARIABLE_NAME, FREQUENCY, TAX_CATEGORY, MEASUREMENT_TYPE, UNIT) VALUES ('paper_savings_bonds_outstanding_series_i', 'Paper Savings Bonds', 'Paper Savings Bonds Outstanding: Series I', 'Monthly', NULL, 'Series I', 'Count'), ('treasurydirect_sales_by_term_amount_marketable_security_tips_5_years_from_certificates_of_indebtedness', 'TreasuryDirect Sales by Term', 'TreasuryDirect Sales by Term Amount: Marketable Security TIPS (5 years) (From Certificates of Indebtedness)', 'Monthly', NULL, 'Marketable Security TIPS (5 years) (From Certificates of Indebtedness)', 'USD'), ('paper_savings_bonds_redeemed_series_j', 'Paper Savings Bonds', 'Paper Savings Bonds Redeemed: Series J', 'Monthly', NULL, 'Series J', 'Count'), ('matured_unredeemed_debt_bonds_outstanding_series_j', 'Matured Unredeemed Debt', 'Matured Unredeemed Debt: Bonds Outstanding, Series J', 'Monthly', NULL, 'Series J', 'Count'), ('treasurydirect_sales_by_term_count_marketable_series_bill_7_weeks_from_certificates_of_indebtedness', 'TreasuryDirect Sales by Term', 'TreasuryDirect Sales by Term Count: Marketable Series Bill (7 weeks) (From Certificates of Indebtedness)', 'Monthly', NULL, 'Marketable Series Bill (7 weeks) (From Certificates of Indebtedness)', 'Count');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.EUROPEAN_CENTRAL_BANK_TIMESERIES (
    UNIT TEXT,
    DATE DATE,
    VARIABLE_NAME TEXT,
    VALUE_COMMENT TEXT,
    GEO_ID TEXT,
    VARIABLE TEXT,
    VALUE FLOAT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."EUROPEAN_CENTRAL_BANK_TIMESERIES" (UNIT, DATE, VARIABLE_NAME, VALUE_COMMENT, GEO_ID, VARIABLE, VALUE) VALUES ('Percent', '2000-06-30', 'Harmonized Index Of Consumer Prices - All-Items Excluding Energy And Food, Annual Rate Of Change, Eurostat, Neither Seasonally Nor Working Day Adjusted, Percent', '', 'country/FRA', 'harmonized_index_of_consumer_prices_allitems_excluding_energy_and_food_annual_rate_of_change_eurostat_neither_seasonally_nor_working_day_adjusted_percent', 0.002), ('Index, 2015=100', '2005-05-31', 'Harmonized Index Of Consumer Prices - Other Paramedical Services, Monthly Index, Eurostat, Neither Seasonally Nor Working Day Adjusted, Index, 2015=100', '', 'country/BEL', 'harmonized_index_of_consumer_prices_other_paramedical_services_monthly_index_eurostat_neither_seasonally_nor_working_day_adjusted_index_2015=100', 116.0), ('Percent', '2024-03-31', 'Harmonized Index Of Consumer Prices - Beef And Veal, Annual Rate Of Change, Eurostat, Neither Seasonally Nor Working Day Adjusted, Percent', '', 'country/LTU', 'harmonized_index_of_consumer_prices_beef_and_veal_annual_rate_of_change_eurostat_neither_seasonally_nor_working_day_adjusted_percent', 0.039), ('Percent', '2021-05-31', 'Harmonized Index Of Consumer Prices - Non-Durable Household Goods, Annual Rate Of Change, Eurostat, Neither Seasonally Nor Working Day Adjusted, Percent', '', 'country/CZE', 'harmonized_index_of_consumer_prices_nondurable_household_goods_annual_rate_of_change_eurostat_neither_seasonally_nor_working_day_adjusted_percent', -0.022), ('Index, 2015=100', '2015-06-30', 'Harmonized Index Of Consumer Prices - Hairdressing For Men And Children, Monthly Index, Eurostat, Neither Seasonally Nor Working Day Adjusted, Index, 2015=100', '', 'country/POL', 'harmonized_index_of_consumer_prices_hairdressing_for_men_and_children_monthly_index_eurostat_neither_seasonally_nor_working_day_adjusted_index_2015=100', 99.9);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.DATACOMMONS_ATTRIBUTES (
    UNIT TEXT,
    SOURCE_DOMAIN TEXT,
    VARIABLE_GROUP TEXT,
    CATEGORY TEXT,
    VARIABLE TEXT,
    VARIABLE_NAME TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."DATACOMMONS_ATTRIBUTES" (UNIT, SOURCE_DOMAIN, VARIABLE_GROUP, CATEGORY, VARIABLE, VARIABLE_NAME) VALUES ('Count', 'europa.eu', 'Count of Birth Event', 'BirthEvent', 'Count_BirthEvent_europa.eu', 'Count of Birth Event, europa.eu'), ('Percent', 'cdc.gov', 'Percentage of Population Who Binge Drink', 'Person_HealthBehavior-BingeDrinking', 'Percent_Person_BingeDrinking', 'Percentage of Population Who Binge Drink'), ('Celsius', 'nasa.gov', 'Temperature in a Location', 'ObservationalWeather', 'Temperature', 'Temperature in a Location'), ('Grams', 'dea.gov', 'Retail Drug Distribution of Drug Distribution: Drug/dea/9180 L', 'DrugDistribution_DrugPrescribed', 'RetailDrugDistribution_DrugDistribution_Ecgonine', 'Retail Drug Distribution of Drug Distribution: Drug/dea/9180 L'), ('Count', 'abs.gov.au', 'Female Population', 'Person_Gender-Female', 'Count_Person_Female_abs.gov.au', 'Female Population, abs.gov.au');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.US_DEPARTMENT_OF_TRANSPORTATION_ATTRIBUTES (
    MEASURE TEXT,
    FREQUENCY TEXT,
    UNIT TEXT,
    VARIABLE TEXT,
    VARIABLE_NAME TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."US_DEPARTMENT_OF_TRANSPORTATION_ATTRIBUTES" (MEASURE, FREQUENCY, UNIT, VARIABLE, VARIABLE_NAME) VALUES ('Cargo', 'Monthly', 'Pounds', 'MAIL_TRANSPORTED', 'Non-Stop Segment Mail Transported'), ('Departures', 'Monthly', 'Count', 'DEPARTURES_PERFORMED', 'Departures Performed'), ('Departures', 'Monthly', 'Count', 'DEPARTURES_SCHEDULED', 'Departures Scheduled'), ('Cargo', 'Monthly', 'Pounds', 'FREIGHT_TRANSPORTED', 'Non-Stop Segment Freight Transported'), ('Distance', 'Monthly', 'Miles', 'DISTANCE', 'Distance Between Airports');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.WORLD_HEALTH_ORGANIZATION_TIMESERIES (
    VARIABLE_NAME TEXT,
    VARIABLE TEXT,
    DATE DATE,
    GEO_ID TEXT,
    UNIT TEXT,
    WHO_SOURCE TEXT,
    VALUE_COMMENT TEXT,
    VALUE FLOAT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."WORLD_HEALTH_ORGANIZATION_TIMESERIES" (VARIABLE_NAME, VARIABLE, DATE, GEO_ID, UNIT, WHO_SOURCE, VALUE_COMMENT, VALUE) VALUES ('Household air pollution attributable deaths | Sex: Both sexes | Age Group: Total (All ages) | Cause: Total | Estimate', 'AIR_11_SEX_BTSX_AGEGROUP_AGEALL_ENVCAUSE_ENVCAUSE000', '2019-12-31', 'WHORegion/AFR', 'Count', NULL, NULL, 638583.0321), ('Age-specific death rate between ages x and x+n | Sex: Female | Age Group: 55-59 years | Estimate', 'LIFE_0000000029_SEX_FMLE_AGEGROUP_AGE55-59', '2000-12-31', 'WorldBankIncomeGroup/WB_LMI', 'Rate', NULL, NULL, 0.01276), ('Projection of deaths | Sex: Female | Age Group: 70 plus  years of age (please delete. It does not work with menu.xml) | Causes: Birth asphyxia and birth trauma | Estimate', 'MORT_600_SEX_FMLE_AGEGROUP_YEARS70+_GHECAUSES_GHE051', '2030-12-31', 'MGHERegion/WBDCP_EAP', 'Count', NULL, NULL, 8.0), ('Percent of population with primary reliance on fuels and technologies for cooking, by fuel type | Residence Area Type: Rural | Cooking fuel: Charcoal | Margin of Error: Upper Bound', 'PHE_HHAIR_PROP_POP_CATEGORY_FUELS_RESIDENCEAREATYPE_RUR_HOUSEHOLDCOOKINGFUEL_FUEL_CHARCOAL_HIGH', '2002-12-31', 'WHORegion/WPR', 'Percent', NULL, NULL, 1.68), ('Years of life lost from mortality (YLLs), per 100,000 people | Sex: Female | Age Group: 50-69 years | Causes: Back and neck pain | Estimate', 'GHE_YLLRATE_SEX_FMLE_AGEGROUP_YEARS50-69_GHECAUSES_GHE138', '2012-12-31', 'MGHERegion/WBINC_LOINC', 'Count', NULL, NULL, 0.06066);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.PUBLIC_HOLIDAY_CALENDAR (
    SUBDIVISION TEXT,
    HOLIDAY_NAME TEXT,
    ISO_ALPHA2 TEXT,
    IS_FINANCIAL BOOLEAN,
    DATE DATE,
    GEO_ID TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."PUBLIC_HOLIDAY_CALENDAR" (SUBDIVISION, HOLIDAY_NAME, ISO_ALPHA2, IS_FINANCIAL, DATE, GEO_ID) VALUES (NULL, 'Heiliger Abend', 'LI', False, '1970-12-24', 'country/LIE'), (NULL, 'Berchtoldstag', 'LI', False, '1971-01-02', 'country/LIE'), (NULL, 'Ostersonntag', 'LI', False, '1971-04-11', 'country/LIE'), (NULL, 'Ostermontag', 'LI', True, '1971-04-12', 'country/LIE'), (NULL, 'Maria Geburt', 'LI', False, '1971-09-08', 'country/LIE');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.AIRCRAFT_INDEX (
    AIRCRAFT_NAME TEXT,
    AIRCRAFT_ID TEXT,
    CABIN_CONFIGURATION TEXT,
    AIRCRAFT_GROUP TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."AIRCRAFT_INDEX" (AIRCRAFT_NAME, AIRCRAFT_ID, CABIN_CONFIGURATION, AIRCRAFT_GROUP) VALUES ('McDonnell Douglas DC-8-71', '86082', 'Cargo', 'Jet, 4-Engine/6-Engine'), ('De Havilland DHC2 Beaver', '4031', 'Passenger', 'Helicopter/Stol'), ('De Havilland DHC3 Otter', '4203', 'Combination Passenger/Cargo', 'Piston, 1-Engine/Combined Single Engine (Piston/Turbine)'), (NULL, '1982', 'Cargo', 'Jet, 4-Engine/6-Engine'), ('De Havilland DHC2 Beaver', '4061', 'Passenger', 'Jet, 2-Engine');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.FBI_CRIME_ATTRIBUTES (
    MEASURE TEXT,
    VARIABLE TEXT,
    UNIT TEXT,
    VARIABLE_NAME TEXT,
    OFFENSE_CATEGORY TEXT,
    FREQUENCY TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."FBI_CRIME_ATTRIBUTES" (MEASURE, VARIABLE, UNIT, VARIABLE_NAME, OFFENSE_CATEGORY, FREQUENCY) VALUES ('Incidents', 'annual_count_of_incidents_aggravated_assault', 'Count', 'Annual Count of Incidents, aggravated assault', 'Aggravated Assault', 'Annual'), ('Incidents', 'annual_count_of_incidents_burglary', 'Count', 'Annual Count of Incidents, burglary', 'Burglary', 'Annual'), ('Incidents', 'annual_count_of_incidents_homicide', 'Count', 'Annual Count of Incidents, homicide', 'Homicide', 'Annual'), ('Incidents', 'annual_count_of_incidents_larceny', 'Count', 'Annual Count of Incidents, larceny', 'Larceny', 'Annual'), ('Incidents', 'annual_count_of_incidents_motor_vehicle_theft', 'Count', 'Annual Count of Incidents, motor vehicle theft', 'Motor Vehicle Theft', 'Annual');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.UNITED_KINGDOM_ATTRIBUTES (
    SOURCE TEXT,
    MEASURE TEXT,
    INDUSTRY TEXT,
    SEX TEXT,
    VARIABLE_NAME TEXT,
    MEASUREMENT_TYPE TEXT,
    FREQUENCY TEXT,
    RELIGION TEXT,
    AGE TEXT,
    UNIT TEXT,
    SEASONALLY_ADJUSTED TEXT,
    VARIABLE TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."UNITED_KINGDOM_ATTRIBUTES" (SOURCE, MEASURE, INDUSTRY, SEX, VARIABLE_NAME, MEASUREMENT_TYPE, FREQUENCY, RELIGION, AGE, UNIT, SEASONALLY_ADJUSTED, VARIABLE) VALUES (NULL, 'Deaths', NULL, NULL, 'Death Occurrences of 70-74 year olds', NULL, 'Weekly', NULL, NULL, 'Count', NULL, 'weekly-deaths-age-sex_occurrences_all_70-74'), (NULL, 'Deaths', NULL, NULL, 'Death Occurrences of 20-24 year old Females', NULL, 'Weekly', NULL, NULL, 'Count', NULL, 'weekly-deaths-age-sex_occurrences_female_20-24'), (NULL, 'Deaths', NULL, NULL, 'Death Occurrences of 30-34 year old Females', NULL, 'Weekly', NULL, NULL, 'Count', NULL, 'weekly-deaths-age-sex_occurrences_female_30-34'), (NULL, 'Deaths', NULL, NULL, 'Death Occurrences of 65-69 year old Females', NULL, 'Weekly', NULL, NULL, 'Count', NULL, 'weekly-deaths-age-sex_occurrences_female_65-69'), (NULL, 'Deaths', NULL, NULL, 'Death Occurrences of 80-84 year old Females', NULL, 'Weekly', NULL, NULL, 'Count', NULL, 'weekly-deaths-age-sex_occurrences_female_80-84');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.WORLD_BANK_ATTRIBUTES (
    UNIT TEXT,
    FREQUENCY TEXT,
    VARIABLE_NAME TEXT,
    VARIABLE TEXT,
    MEASURE TEXT,
    ESG_PILLAR TEXT,
    RELATED_TOPICS ARRAY,
    SOURCE TEXT,
    WORLD_BANK_SOURCE TEXT,
    SOURCE_NOTE TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."WORLD_BANK_ATTRIBUTES" (UNIT, FREQUENCY, VARIABLE_NAME, VARIABLE, MEASURE, ESG_PILLAR, RELATED_TOPICS, SOURCE, WORLD_BANK_SOURCE, SOURCE_NOTE) VALUES ('USD', 'Quarterly', 'External Debt Service Payment, Publicly Guaranteed Private Sector External Debt, 4 years, All instruments, Interest, USD', 'QEDS_DT.INT.PRVS.CD.04.YR.US', 'External Debt Service Payment, Publicly Guaranteed Private Sector External Debt', NULL, '[]', NULL, 'Quarterly External Debt Statistics GDDS', NULL), ('USD', 'Quarterly', 'External Debt Service Payment, Publicly Guaranteed Private Sector External Debt, 18 to 24 months, All instruments, Interest, USD', 'QEDS_DT.INT.PRVS.CD.19.24.MO.US', 'External Debt Service Payment, Publicly Guaranteed Private Sector External Debt', NULL, '[]', NULL, 'Quarterly External Debt Statistics GDDS', NULL), ('USD', 'Quarterly', 'External Debt Service Payment, Publicly Guaranteed Private Sector External Debt, Immediately, All instruments, Interest, USD', 'QEDS_DT.INT.PRVS.CD.IQ.00.US', 'External Debt Service Payment, Publicly Guaranteed Private Sector External Debt', NULL, '[]', NULL, 'Quarterly External Debt Statistics GDDS', NULL), ('USD', 'Quarterly', 'External Debt Service Payment, Public Sector External Debt, 3 years, All instruments, Interest, USD', 'QEDS_DT.INT.PUBS.CD.03.YR.US', 'External Debt Service Payment, Public Sector External Debt', NULL, '[]', NULL, 'Quarterly External Debt Statistics GDDS', NULL), ('USD', 'Quarterly', 'External Debt Service Payment, Public Sector External Debt, 6 to 9 months, All instruments, Interest, USD', 'QEDS_DT.INT.PUBS.CD.07.09.MO.US', 'External Debt Service Payment, Public Sector External Debt', NULL, '[]', NULL, 'Quarterly External Debt Statistics GDDS', NULL);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.HOUSING_URBAN_DEVELOPMENT_TIMESERIES (
    VALUE FLOAT,
    DATE DATE,
    UNIT TEXT,
    GEO_ID TEXT,
    VARIABLE TEXT,
    VARIABLE_NAME TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."HOUSING_URBAN_DEVELOPMENT_TIMESERIES" (VALUE, DATE, UNIT, GEO_ID, VARIABLE, VARIABLE_NAME) VALUES (0.0, '2017-12-31', 'Count', 'CoC/MI-504', 'overall_homeless_veterans_transgender', 'Overall Homeless Veterans - Transgender'), (0.0, '2020-12-31', 'Count', 'CoC/CA-518', 'sheltered_safe_haven_sh_homeless_over_24', 'Sheltered Safe Haven (SH) Homeless - Over 24'), (2.0, '2018-12-31', 'Count', 'CoC/MD-600', 'overall_homeless_individuals_under_18', 'Overall Homeless Individuals - Under 18'), (0.0, '2019-12-31', 'Count', 'CoC/FL-515', 'sheltered_transitional_housing_th_homeless_people_in_families_native_hawaiian_or_other_pacific_islander', 'Sheltered Transitional Housing (TH) Homeless People in Families - Native Hawaiian or Other Pacific Islander'), (0.0, '2016-12-31', 'Count', 'CoC/PA-603', 'sheltered_emergency_shelter_es_homeless_individuals_under_18', 'Sheltered Emergency Shelter (ES) Homeless Individuals - Under 18');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.US_TREASURY_TIMESERIES (
    GEO_ID TEXT,
    UNIT TEXT,
    VARIABLE TEXT,
    DATE DATE,
    VARIABLE_NAME TEXT,
    VALUE FLOAT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."US_TREASURY_TIMESERIES" (GEO_ID, UNIT, VARIABLE, DATE, VARIABLE_NAME, VALUE) VALUES ('country/USA', 'USD', '121', '2017-03-20', 'Non-Tax Net Collections Amount: Fiscal Service Processed Electronically (Over-the-Counter)', 64657026.0), ('country/USA', 'USD', '131', '2022-08-29', 'Non-Tax Net Collections Amount: Fiscal Service Settled Electronically (Over-the-Counter)', 753.0), ('country/USA', 'USD', '213', '2017-08-10', 'IRS Non-Tax Net Collections Amount: Fully Electronic (Internet)', 99641.0), ('country/USA', 'USD', '332', '2013-10-09', 'IRS Tax Net Collections Amount: Fiscal Service Settled Electronically (Mail)', 704080197.0), ('country/USA', 'Count', 'paper_savings_bonds_unmatured_&_unredeemed_all_series', '2020-11-30', 'Paper Savings Bonds Unmatured & Unredeemed: All Series', 294438314.0);
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.GEOGRAPHY_RELATIONSHIPS (
    RELATED_GEO_NAME TEXT,
    RELATED_LEVEL TEXT,
    RELATIONSHIP_TYPE TEXT,
    RELATED_GEO_ID TEXT,
    GEO_ID TEXT,
    LEVEL TEXT,
    GEO_NAME TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."GEOGRAPHY_RELATIONSHIPS" (RELATED_GEO_NAME, RELATED_LEVEL, RELATIONSHIP_TYPE, RELATED_GEO_ID, GEO_ID, LEVEL, GEO_NAME) VALUES ('61317', 'CensusZipCodeTabulationArea', 'Contains', 'zip/61317', 'geoId/17011', 'County', 'Bureau County'), ('67487', 'CensusZipCodeTabulationArea', 'Overlaps', 'zip/67487', 'geoId/20061000800', 'CensusTract', 'Census Tract 8, Geary County, Kansas'), ('24180065', 'DisseminationArea', 'Contains', 'statcanId/S051224180065', 'country/CAN', 'Country', 'Canada'), ('Census Tract 4087.07, Los Angeles County, California', 'CensusTract', 'Contains', 'geoId/06037408707', 'geoId/06', 'State', 'California'), ('United States', 'Country', 'Overlaps', 'country/USA', 'zip/04346', 'CensusZipCodeTabulationArea', '04346');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.INTERNATIONAL_LABOUR_ORGANIZATION_TIMESERIES (
    VARIABLE_NAME TEXT,
    VARIABLE TEXT,
    VALUE FLOAT,
    DATE DATE,
    GEO_ID TEXT,
    VALUE_COMMENT TEXT,
    UNIT TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."INTERNATIONAL_LABOUR_ORGANIZATION_TIMESERIES" (VARIABLE_NAME, VARIABLE, VALUE, DATE, GEO_ID, VALUE_COMMENT, UNIT) VALUES ('Population by sex and age -- UN estimates and projections | Male | Age (5-year bands): 5-9, Annual (Count), Estimate', 'POP_2POP_SEX_AGE_NB,A_SEX_M_AGE_5YRBANDS_Y05-09_E', 95073.0, '2026-12-31', 'country/ARM', 'Source: Modeled Estimates', 'Count'), ('Employment by sex and age -- ILO modelled estimates | Female | Age (Youth, adults): 15+, Annual (Count), Estimate', 'EMP_2EMP_SEX_AGE_NB,A_SEX_F_AGE_YTHADULT_YGE15_E', 353757.0, '2008-12-31', 'country/LBN', 'Source: Modeled Estimates', 'Count'), ('Employment by sex, age and economic class -- ILO modelled estimates | Female | Age (Youth, adults): 25+ | Extremely poor < USD 1.90 PPP, Annual (Count), Estimate', 'EMP_2EMP_SEX_AGE_CLA_NB,A_SEX_F_AGE_YTHADULT_YGE25_CLA_ECOCLA_USDLT2_E', 0.0, '1997-12-31', 'country/BHS', 'Value Status: Adjusted | Source: Modeled Estimates', 'Count'), ('Population by sex and age -- UN estimates and projections | Male | Age (5-year bands): 40-44, Annual (Count), Estimate', 'POP_2POP_SEX_AGE_NB,A_SEX_M_AGE_5YRBANDS_Y40-44_E', 11109349.0, '2022-12-31', 'countryGroup/X70', 'Source: Modeled Estimates', 'Count'), ('Population by sex and age -- UN estimates and projections | Female | Age (10-year bands): Total, Annual (Count), Estimate', 'POP_2POP_SEX_AGE_NB,A_SEX_F_AGE_10YRBANDS_TOTAL_E', 994573.0, '1992-12-31', 'country/MKD', 'Source: Modeled Estimates', 'Count');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.US_ECONOMIC_CENSUS_TIMESERIES (
    UNIT TEXT,
    DATE TEXT,
    VARIABLE TEXT,
    VALUE FLOAT,
    VARIABLE_NAME TEXT,
    GEO_ID TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."US_ECONOMIC_CENSUS_TIMESERIES" (UNIT, DATE, VARIABLE, VALUE, VARIABLE_NAME, GEO_ID) VALUES ('Count', '2017-12-31', 'ECTGE100_5324_2017_00_00', 0.0, 'Establishments with 100 employees or more | Commercial and industrial machinery and equipment rental and leasing (NAICS 2017 code: 5324) | Type of Operation: All establishments | Tax Status: All establishments', 'geoId/C37740'), ('Percent', '2017-12-31', 'ESTAB_S_5312_2017_00_00', 0.0, 'Relative standard error of estimate of count of establishments | Offices of real estate agents and brokers (NAICS 2017 code: 5312) | Type of Operation: All establishments | Tax Status: All establishments', 'geoId/4170200'), ('USD', '2012-12-31', 'CSTCNT_722513_2012', 0.0, 'Cost of contract work | Limited-service restaurants (NAICS 2012 code: 722513)', 'geoId/1236550'), ('Count', '2017-12-31', 'EMP_81_2017_00_Y', 24.0, 'Count of employees | Other services (except public administration) (NAICS 2017 code: 81) | Type of Operation: All establishments | Tax Status: Establishments exempt from federal income tax', 'geoId/1711007'), ('USD', '2012-12-31', 'MSCRSLTT_722310_2012', 0.0, 'Value of resales | Food service contractors (NAICS 2012 code: 722310)', 'geoId/3479040');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.US_DEPARTMENT_OF_LABOR_UNEMPLOYMENT_INSURANCE_CLAIMS_TIMESERIES (
    UNIT TEXT,
    GEO_ID TEXT,
    VARIABLE TEXT,
    DATE DATE,
    VALUE FLOAT,
    VARIABLE_NAME TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."US_DEPARTMENT_OF_LABOR_UNEMPLOYMENT_INSURANCE_CLAIMS_TIMESERIES" (UNIT, GEO_ID, VARIABLE, DATE, VALUE, VARIABLE_NAME) VALUES ('Count', 'geoId/24', 'CW', '2007-04-28', 30739.0, 'Adjusted Continued Weeks Claimed for State Unemployment Insurance'), ('Count', 'geoId/24', 'WSEIC', '2016-04-09', 0.0, 'Short Time Compensation (Workshare) Equivalent Initial Claims'), ('Count', 'geoId/40', 'CE', '1992-11-21', 1182846.0, 'Covered Employment'), ('Count', 'geoId/42', 'WSECW', '2008-07-19', 0.0, 'Short Time Compensation (Workshare) Equivalent Continued Weeks Claimed'), ('Count', 'geoId/24', 'EBUI', '2022-02-26', 5.0, 'Total State Unemployment Insurance Continued Weeks Claimed under the Federal/State Extended Benefit Program');
-- DBMS: snowflake
CREATE TABLE GLOBAL_GOVERNMENT.CYBERSYN.FEDERAL_RESERVE_TIMESERIES (
    DATE DATE,
    VARIABLE_NAME TEXT,
    VALUE FLOAT,
    VARIABLE TEXT,
    GEO_ID TEXT,
    UNIT TEXT
);

INSERT INTO "GLOBAL_GOVERNMENT"."CYBERSYN"."FEDERAL_RESERVE_TIMESERIES" (DATE, VARIABLE_NAME, VALUE, VARIABLE, GEO_ID, UNIT) VALUES ('2008-12-31', 'Levels: Government-sponsored enterprises; total assets held by Farmer Mac (balance sheet) - Input series with zero seasonal factor, Quarterly (Not seasonally adjusted)', 5107000000.0, 'Z1_FL404090453.Q', 'country/USA', 'USD'), ('2018-06-30', 'Transactions: Monetary authority; Treasury bills; asset - Input series with zero seasonal factor, Quarterly (Not seasonally adjusted)', 0.0, 'Z1_FU713061113.Q', 'country/USA', 'USD'), ('1984-03-31', 'Transactions: Domestic financial sectors; gross fixed investment, nonresidential software - Computed series, Quarterly (Not seasonally adjusted)', 1443000000.0, 'Z1_FU795013035.Q', 'country/USA', 'USD'), ('1998-12-31', 'Transactions: Domestic financial sectors; other deposits including time and savings deposits; liability (IMA) - Computed series, Annual (Not seasonally adjusted)', 259475000000.0, 'Z1_FU793130205.A', 'country/USA', 'USD'), ('2019-09-30', 'Transactions: Banks in U.S.-affiliated areas; consumption of fixed capital, nonresidential intellectual property products, current cost basis - Computed series, Quarterly (Seasonally adjusted)', 83000000.0, 'Z1_FA746330075.Q', 'country/USA', 'USD');