Malaysia

MYS Malaysia - Economic, Social, Environmental, Health MYS-HDX-Malaysia_-_Economic,_Social,_Enviro Indicators

83,035 rows · 1 table(s) · 2024 HDX 0 1 2 3 ▶

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MYS Malaysia - Economic, Social, Environmental, Health — 83,035 rows, 3,033 columns

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Field details — 3033 fields (type, nulls, unique, samples)

Extension Fields (3033) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Fertilizer_consumption_pct_of_fertilizer_production`	Fertilizer_consumption_pct_of_fertilizer_production	float	0%	1	256.389410187668
`Fertilizer_consumption_kilograms_per_hectare_of_arable_land`	Fertilizer_consumption_kilograms_per_hectare_of_arable_land	float	0%	1	2926.42866250159
`Agricultural_land_sq._km`	Agricultural_land_sq._km	float	0%	1	85710.0
`Agricultural_land_pct_of_land_area`	Agricultural_land_pct_of_land_area	float	0%	1	26.0873535230558
`Arable_land_hectares`	Arable_land_hectares	float	0%	1	784300.0
`Arable_land_hectares_per_person`	Arable_land_hectares_per_person	float	0%	1	0.0223280005197246
`Arable_land_pct_of_land_area`	Arable_land_pct_of_land_area	float	0%	1	2.38715568406635
`Land_under_cereal_production_hectares`	Land_under_cereal_production_hectares	float	0%	1	615072.0
`Permanent_cropland_pct_of_land_area`	Permanent_cropland_pct_of_land_area	float	0%	1	22.7058286409983
`Rural_land_area_where_elevation_is_below_5_meters_sq._km`	Rural_land_area_where_elevation_is_below_5_meters_sq._km	float	0%	1	20980.5969639
`Rural_land_area_where_elevation_is_below_5_meters_pct_of_total_land_area`	Rural_land_area_where_elevation_is_below_5_meters_pct_of_total_land_area	float	0%	1	6.36517881131312
`Forest_area_sq._km`	Forest_area_sq._km	float	0%	1	189635.9
`Forest_area_pct_of_land_area`	Forest_area_pct_of_land_area	float	0%	1	57.7190381981434
`Agricultural_irrigated_land_pct_of_total_agricultural_land`	Agricultural_irrigated_land_pct_of_total_agricultural_land	float	0%	1	5.15692451289231
`Average_precipitation_in_depth_mm_per_year`	Average_precipitation_in_depth_mm_per_year	float	0%	1	2875.0
`Land_area_sq._km`	Land_area_sq._km	float	0%	1	328550.0
`Rural_land_area_sq._km`	Rural_land_area_sq._km	float	0%	1	318197.5660893
`Cereal_production_metric_tons`	Cereal_production_metric_tons	float	0%	1	2167306.44
`Crop_production_index_2014-2016_=_100`	Crop_production_index_2014-2016_=_100	float	0%	1	97.93
`Food_production_index_2014-2016_=_100`	Food_production_index_2014-2016_=_100	float	0%	1	102.8
`Livestock_production_index_2014-2016_=_100`	Livestock_production_index_2014-2016_=_100	float	0%	1	99.69
`Surface_area_sq._km`	Surface_area_sq._km	float	0%	1	330411.0
`Cereal_yield_kg_per_hectare`	Cereal_yield_kg_per_hectare	float	0%	1	3523.7
`Access_to_electricity_rural_pct_of_rural_population`	Access_to_electricity_rural_pct_of_rural_population	float	0%	1	100.0
`Rural_population_living_in_areas_where_elevation_is_below_5_meters_pct_of_total_`	Rural_population_living_in_areas_where_elevation_is_below_5_meters_pct_of_total_	float	0%	1	3.60734089468673
`Annual_freshwater_withdrawals_agriculture_pct_of_total_freshwater_withdrawal`	Annual_freshwater_withdrawals_agriculture_pct_of_total_freshwater_withdrawal	float	0%	1	45.64504373
`Agriculture_forestry_and_fishing_value_added_current_USusd`	Agriculture_forestry_and_fishing_value_added_current_USusd	float	0%	1	34320586273.8363
`Agriculture_forestry_and_fishing_value_added_pct_of_GDP`	Agriculture_forestry_and_fishing_value_added_pct_of_GDP	float	0%	1	8.12846782241948
`Employment_in_agriculture_female_pct_of_female_employment_modeled_ILO_estimate`	Employment_in_agriculture_female_pct_of_female_employment_modeled_ILO_estimate	float	0%	1	4.69720831895064
+3003 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Health Indicators MYS-HDX-Malaysia_-_Health_Indicators Indicators

21,755 rows · 1 table(s) · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Health Indicators — 21,755 rows, 17 columns

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Field details — 17 fields (type, nulls, unique, samples)

Extension Fields (17) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`GHO (CODE)`	GHO (CODE)	string	0%	63	NCD_BMI_MINUS2C, MORT_200, NUTRITION_WA_2,...
`GHO (DISPLAY)`	GHO (DISPLAY)	string	0%	63	Prevalence of thinness among children and adolescents,...
`GHO (URL)`	GHO (URL)	string	0%	62	https://www.who.int/data/gho/data/indicators/indicator-de...
`YEAR (DISPLAY)`	YEAR (DISPLAY)	string	0%	32	2009, 2011, 2016, 2012, 2009
`STARTYEAR`	STARTYEAR	string	0%	32	2009, 2011, 2016, 2012, 2009
`ENDYEAR`	ENDYEAR	string	0%	32	2009, 2011, 2016, 2012, 2009
`REGION (CODE)`	REGION (CODE)	string	0%	1	WPR, WPR, WPR, WPR, WPR
`REGION (DISPLAY)`	REGION (DISPLAY)	string	0%	1	Western Pacific, Western Pacific, Western Pacific,...
`COUNTRY (CODE)`	COUNTRY (CODE)	string	0%	1	MYS, MYS, MYS, MYS, MYS
`COUNTRY (DISPLAY)`	COUNTRY (DISPLAY)	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`DIMENSION (TYPE)`	DIMENSION (TYPE)	string	12%	10	SEX, AGEGROUP, SEX, RESIDENCEAREATYPE, SEX
`DIMENSION (CODE)`	DIMENSION (CODE)	string	12%	27	SEX_FMLE, AGEGROUP_DAYS0-27, SEX_FMLE,...
`DIMENSION (NAME)`	DIMENSION (NAME)	string	12%	27	Female, 0-27 days, Female, Total, Both sexes
`Numeric`	Numeric	string	15%	80	6.3741907, 0.0, 15.0, 97.214996553, 86.45316
`Value`	Value	string	1%	81	6.4 [5.4-7.4], 0, 15.0 [9.9-21.9], 97, 86.5 [83.6-88.9]
`Low`	Low	float	40%	54	5.4197509, 9.9, 83.57765, 4.605184802, 22.0
`High`	High	float	40%	60	7.3907971, 21.9, 88.89712, 8.352160523, 47.0

MYS Malaysia - Education MYS-HDX-Malaysia_-_Education Indicators

16,666 rows · 1 table(s) · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Education — 16,666 rows, 851 columns

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Field details — 851 fields (type, nulls, unique, samples)

Extension Fields (851) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Barro-Lee:_Percentage_of_female_population_age_15-19_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_15-19_with_no_education	float	0%	1	1.63
`Barro-Lee:_Percentage_of_population_age_15-19_with_no_education`	Barro-Lee:_Percentage_of_population_age_15-19_with_no_education	float	0%	1	1.68
`Barro-Lee:_Percentage_of_female_population_age_15+_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_15+_with_no_education	float	0%	1	9.25
`Barro-Lee:_Percentage_of_population_age_15+_with_no_education`	Barro-Lee:_Percentage_of_population_age_15+_with_no_education	float	0%	1	6.88
`Barro-Lee:_Percentage_of_female_population_age_20-24_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_20-24_with_no_education	float	0%	1	1.45
`Barro-Lee:_Percentage_of_population_age_20-24_with_no_education`	Barro-Lee:_Percentage_of_population_age_20-24_with_no_education	float	0%	1	1.48
`Barro-Lee:_Percentage_of_female_population_age_25-29_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_25-29_with_no_education	float	0%	1	1.99
`Barro-Lee:_Percentage_of_population_age_25-29_with_no_education`	Barro-Lee:_Percentage_of_population_age_25-29_with_no_education	float	0%	1	1.88
`Barro-Lee:_Percentage_of_female_population_age_25+_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_25+_with_no_education	float	0%	1	11.97
`Barro-Lee:_Percentage_of_population_age_25+_with_no_education`	Barro-Lee:_Percentage_of_population_age_25+_with_no_education	float	0%	1	8.78
`Barro-Lee:_Percentage_of_female_population_age_30-34_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_30-34_with_no_education	float	0%	1	2.27
`Barro-Lee:_Percentage_of_population_age_30-34_with_no_education`	Barro-Lee:_Percentage_of_population_age_30-34_with_no_education	float	0%	1	2.17
`Barro-Lee:_Percentage_of_female_population_age_35-39_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_35-39_with_no_education	float	0%	1	4.32
`Barro-Lee:_Percentage_of_population_age_35-39_with_no_education`	Barro-Lee:_Percentage_of_population_age_35-39_with_no_education	float	0%	1	3.71
`Barro-Lee:_Percentage_of_female_population_age_40-44_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_40-44_with_no_education	float	0%	1	4.32
`Barro-Lee:_Percentage_of_population_age_40-44_with_no_education`	Barro-Lee:_Percentage_of_population_age_40-44_with_no_education	float	0%	1	3.71
`Barro-Lee:_Percentage_of_female_population_age_45-49_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_45-49_with_no_education	float	0%	1	10.08
`Barro-Lee:_Percentage_of_population_age_45-49_with_no_education`	Barro-Lee:_Percentage_of_population_age_45-49_with_no_education	float	0%	1	7.33
`Barro-Lee:_Percentage_of_female_population_age_50-54_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_50-54_with_no_education	float	0%	1	10.08
`Barro-Lee:_Percentage_of_population_age_50-54_with_no_education`	Barro-Lee:_Percentage_of_population_age_50-54_with_no_education	float	0%	1	7.33
`Barro-Lee:_Percentage_of_female_population_age_55-59_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_55-59_with_no_education	float	0%	1	20.13
`Barro-Lee:_Percentage_of_population_age_55-59_with_no_education`	Barro-Lee:_Percentage_of_population_age_55-59_with_no_education	float	0%	1	14.1
`Barro-Lee:_Percentage_of_female_population_age_60-64_with_no_education`	Barro-Lee:_Percentage_of_female_population_age_60-64_with_no_education	float	0%	1	20.13
`Barro-Lee:_Percentage_of_population_age_60-64_with_no_education`	Barro-Lee:_Percentage_of_population_age_60-64_with_no_education	float	0%	1	14.1
`Barro-Lee:_Population_in_thousands_age_15-19_total`	Barro-Lee:_Population_in_thousands_age_15-19_total	float	0%	1	2741.0
`Barro-Lee:_Population_in_thousands_age_15-19_female`	Barro-Lee:_Population_in_thousands_age_15-19_female	float	0%	1	1336.0
`Barro-Lee:_Population_in_thousands_age_15+_total`	Barro-Lee:_Population_in_thousands_age_15+_total	float	0%	1	19391.0
`Barro-Lee:_Population_in_thousands_age_15+_female`	Barro-Lee:_Population_in_thousands_age_15+_female	float	0%	1	9611.0
`Barro-Lee:_Population_in_thousands_age_20-24_total`	Barro-Lee:_Population_in_thousands_age_20-24_total	float	0%	1	2295.0
+821 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Social Protection and Labor MYS-HDX-Malaysia_-_Social_Protection_and_La Indicators

6,525 rows · 1 table(s) · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Social Protection And Labor — 6,525 rows, 1,067 columns

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Field details — 1067 fields (type, nulls, unique, samples)

Extension Fields (1067) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Adequacy_of_benefits_in_extreme_poor_<usd2.15_a_day_pct_-All_Social_Protection_a`	Adequacy_of_benefits_in_extreme_poor_<usd2.15_a_day_pct_-All_Social_Protection_a	float	0%	1	21.2632124809488
`Adequacy_of_benefits_pct_-All_Social_Protection_and_Labor_-rural`	Adequacy_of_benefits_pct_-All_Social_Protection_and_Labor_-rural	float	0%	1	8.7520618680703
`Adequacy_of_benefits_pct_-All_Social_Protection_and_Labor_-urban`	Adequacy_of_benefits_pct_-All_Social_Protection_and_Labor_-urban	float	0%	1	7.44653043515593
`Adequacy_of_benefits_in_1st_quintile_poorest_pct_-All_Social_Protection_and_Labo`	Adequacy_of_benefits_in_1st_quintile_poorest_pct_-All_Social_Protection_and_Labo	float	0%	1	8.331593503447
`Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_preT`	Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_preT	float	0%	1	9.20537742098638
`Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_-rural`	Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_-rural	float	0%	1	9.54086986406926
`Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor`	Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor	float	0%	1	7.53928868755877
`Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_-urban`	Adequacy_of_benefits_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_-urban	float	0%	1	7.30162834797261
`Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_preT`	Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_preT	float	0%	1	8.05441021753944
`Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_-rural`	Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_-rural	float	0%	1	8.44562228693007
`Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor`	Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor	float	0%	1	7.30784703602366
`Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_-urban`	Adequacy_of_benefits_in_3rd_quintile_pct_-All_Social_Protection_and_Labor_-urban	float	0%	1	6.93708839887744
`Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_preT`	Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_preT	float	0%	1	7.05883862165199
`Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_-rural`	Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_-rural	float	0%	1	7.75819239412685
`Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor`	Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor	float	0%	1	7.20331798669103
`Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_-urban`	Adequacy_of_benefits_in_4th_quintile_pct_-All_Social_Protection_and_Labor_-urban	float	0%	1	7.11528511484203
`Adequacy_of_benefits_in_5th_quintile_richest_pct_-All_Social_Protection_and_Labo`	Adequacy_of_benefits_in_5th_quintile_richest_pct_-All_Social_Protection_and_Labo	float	0%	1	7.80732554780427
`Average_per_capita_transfer_held_by_extreme_poor_<usd2.15_a_day_-All_Social_Prot`	Average_per_capita_transfer_held_by_extreme_poor_<usd2.15_a_day_-All_Social_Prot	float	0%	1	0.39936765783767
`Average_per_capita_transfer_-All_Social_Protection_and_Labor_-rural`	Average_per_capita_transfer_-All_Social_Protection_and_Labor_-rural	float	0%	1	1.44434795974237
`Average_per_capita_transfer_-All_Social_Protection_and_Labor`	Average_per_capita_transfer_-All_Social_Protection_and_Labor	float	0%	1	1.78734730408898
`Average_per_capita_transfer_-All_Social_Protection_and_Labor_-urban`	Average_per_capita_transfer_-All_Social_Protection_and_Labor_-urban	float	0%	1	1.91276179565424
`Average_per_capita_transfer_held_by_1st_quintile_poorest_-All_Social_Protection_`	Average_per_capita_transfer_held_by_1st_quintile_poorest_-All_Social_Protection_	float	0%	1	0.82716636949981
`Average_per_capita_transfer_held_by_2nd_quintile_-All_Social_Protection_and_Labo`	Average_per_capita_transfer_held_by_2nd_quintile_-All_Social_Protection_and_Labo	float	0%	1	1.23408758853938
`Average_per_capita_transfer_held_by_3rd_quintile_-All_Social_Protection_and_Labo`	Average_per_capita_transfer_held_by_3rd_quintile_-All_Social_Protection_and_Labo	float	0%	1	1.68766441481281
`Average_per_capita_transfer_held_by_4th_quintile_-All_Social_Protection_and_Labo`	Average_per_capita_transfer_held_by_4th_quintile_-All_Social_Protection_and_Labo	float	0%	1	2.52000824420938
`Average_per_capita_transfer_held_by_5th_quintile_richest_-All_Social_Protection_`	Average_per_capita_transfer_held_by_5th_quintile_richest_-All_Social_Protection_	float	0%	1	5.23483011029156
`Benefits_incidence_in_extreme_poor_<usd2.15_a_day_pct_-All_Social_Protection_and`	Benefits_incidence_in_extreme_poor_<usd2.15_a_day_pct_-All_Social_Protection_and	float	0%	1	0.0044936182922566
`Benefits_incidence_in_1st_quintile_poorest_pct_-All_Social_Protection_and_Labor_`	Benefits_incidence_in_1st_quintile_poorest_pct_-All_Social_Protection_and_Labor_	float	0%	1	11.4343737716546
`Benefits_incidence_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_preT`	Benefits_incidence_in_2nd_quintile_pct_-All_Social_Protection_and_Labor_preT	float	0%	1	18.5781818852899
+1037 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Gender MYS-HDX-Malaysia_-_Gender Indicators

4,793 rows · 1 table(s) · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Gender — 4,793 rows, 170 columns

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Field details — 170 fields (type, nulls, unique, samples)

Extension Fields (170) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Firms_with_female_top_manager_pct_of_firms`	Firms_with_female_top_manager_pct_of_firms	float	0%	1	25.54145432
`Firms_with_female_participation_in_ownership_pct_of_firms`	Firms_with_female_participation_in_ownership_pct_of_firms	float	0%	1	38.55695343
`Literacy_rate_youth_female_pct_of_females_ages_15-24`	Literacy_rate_youth_female_pct_of_females_ages_15-24	float	0%	1	98.2799987792969
`Literacy_rate_youth_ages_15-24_gender_parity_index_GPI`	Literacy_rate_youth_ages_15-24_gender_parity_index_GPI	float	0%	1	0.990000009536743
`Literacy_rate_youth_male_pct_of_males_ages_15-24`	Literacy_rate_youth_male_pct_of_males_ages_15-24	float	0%	1	99.2600021362305
`Literacy_rate_adult_female_pct_of_females_ages_15_and_above`	Literacy_rate_adult_female_pct_of_females_ages_15_and_above	float	0%	1	94.6699981689453
`Literacy_rate_adult_male_pct_of_males_ages_15_and_above`	Literacy_rate_adult_male_pct_of_males_ages_15_and_above	float	0%	1	96.8199996948242
`School_enrollment_primary_gross_gender_parity_index_GPI`	School_enrollment_primary_gross_gender_parity_index_GPI	float	0%	1	1.0134334564209
`School_enrollment_primary_and_secondary_gross_gender_parity_index_GPI`	School_enrollment_primary_and_secondary_gross_gender_parity_index_GPI	float	0%	1	1.03951001167297
`School_enrollment_secondary_gross_gender_parity_index_GPI`	School_enrollment_secondary_gross_gender_parity_index_GPI	float	0%	1	1.0560075044632
`School_enrollment_tertiary_gross_gender_parity_index_GPI`	School_enrollment_tertiary_gross_gender_parity_index_GPI	float	0%	1	1.30503585927416
`School_enrollment_preprimary_female_pct_gross`	School_enrollment_preprimary_female_pct_gross	float	0%	1	88.5871887207031
`School_enrollment_preprimary_male_pct_gross`	School_enrollment_preprimary_male_pct_gross	float	0%	1	86.4733657836914
`Primary_completion_rate_female_pct_of_relevant_age_group`	Primary_completion_rate_female_pct_of_relevant_age_group	float	0%	1	89.8850736172053
`Primary_completion_rate_male_pct_of_relevant_age_group`	Primary_completion_rate_male_pct_of_relevant_age_group	float	0%	1	85.7763433002456
`Educational_attainment_at_least_completed_primary_population_25+_years_female_pc`	Educational_attainment_at_least_completed_primary_population_25+_years_female_pc	float	0%	1	93.5199966430664
`Educational_attainment_at_least_completed_primary_population_25+_years_male_pct_`	Educational_attainment_at_least_completed_primary_population_25+_years_male_pct_	float	0%	1	96.0100021362305
`Educational_attainment_at_least_completed_primary_population_25+_years_total_pct`	Educational_attainment_at_least_completed_primary_population_25+_years_total_pct	float	0%	1	94.7900009155273
`Primary_education_pupils_pct_female`	Primary_education_pupils_pct_female	float	0%	1	48.90068
`School_enrollment_primary_female_pct_gross`	School_enrollment_primary_female_pct_gross	float	0%	1	90.3936077644114
`School_enrollment_primary_male_pct_gross`	School_enrollment_primary_male_pct_gross	float	0%	1	90.3276653777399
`Gross_intake_ratio_in_first_grade_of_primary_education_female_pct_of_relevant_ag`	Gross_intake_ratio_in_first_grade_of_primary_education_female_pct_of_relevant_ag	float	0%	1	106.20437
`Gross_intake_ratio_in_first_grade_of_primary_education_male_pct_of_relevant_age_`	Gross_intake_ratio_in_first_grade_of_primary_education_male_pct_of_relevant_age_	float	0%	1	104.35813
`School_enrollment_primary_female_pct_net`	School_enrollment_primary_female_pct_net	float	0%	1	99.8099
`School_enrollment_primary_male_pct_net`	School_enrollment_primary_male_pct_net	float	0%	1	99.97004
`Net_intake_rate_in_grade_1_female_pct_of_official_school-age_population`	Net_intake_rate_in_grade_1_female_pct_of_official_school-age_population	float	0%	1	99.502
`Net_intake_rate_in_grade_1_male_pct_of_official_school-age_population`	Net_intake_rate_in_grade_1_male_pct_of_official_school-age_population	float	0%	1	100.0
`Persistence_to_grade_5_female_pct_of_cohort`	Persistence_to_grade_5_female_pct_of_cohort	float	0%	1	88.2010726928711
`Persistence_to_grade_5_male_pct_of_cohort`	Persistence_to_grade_5_male_pct_of_cohort	float	0%	1	88.7375183105469
+140 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Human Development Indicators MYS-HDX-Malaysia_-_Human_Development_Indica Indicators

926 rows · 1 table(s) · 2024 HDX 0 1 2 3 ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

MYS Malaysia - Human Development Indicators — 926 rows, 36 columns

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Field details — 36 fields (type, nulls, unique, samples)

Extension Fields (36) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`abr`	abr	float	0%	1	5.962
`co2_prod`	co2_prod	float	0%	1	8.418
`coef_ineq`	coef_ineq	float	0%	1	13.292
`diff_hdi_phdi`	diff_hdi_phdi	float	0%	1	17.338
`eys`	eys	float	0%	1	12.676
`eys_f`	eys_f	float	0%	1	13.056
`eys_m`	eys_m	float	0%	1	12.18
`gdi_group`	gdi_group	float	0%	1	2.0
`gii_rank`	gii_rank	float	0%	1	47.0
`gni_pc_f`	gni_pc_f	float	0%	1	22511.871
`gni_pc_m`	gni_pc_m	float	0%	1	41670.457
`gnipc`	gnipc	float	0%	1	32553.091
`hdi_f`	hdi_f	float	0%	1	0.805
`hdi_m`	hdi_m	float	0%	1	0.828
`hdi_rank`	hdi_rank	float	0%	1	67.0
`ineq_edu`	ineq_edu	float	0%	1	9.02
`ineq_inc`	ineq_inc	float	0%	1	24.759
`ineq_le`	ineq_le	float	0%	1	6.096
`le`	le	float	0%	1	76.657
`le_f`	le_f	float	0%	1	79.37
`le_m`	le_m	float	0%	1	74.272
`lfpr_f`	lfpr_f	float	0%	1	55.79
`lfpr_m`	lfpr_m	float	0%	1	81.88
`loss`	loss	float	0%	1	13.675
`mf`	mf	float	0%	1	21.394
`mmr`	mmr	float	0%	1	21.132
`mys`	mys	float	0%	1	11.09
`mys_f`	mys_f	float	0%	1	10.98
`mys_m`	mys_m	float	0%	1	11.21
+6 more extension fields — download the CSV/Parquet to see them all.

MYS Malaysia - Urban Development MYS-HDX-Malaysia_-_Urban_Development Indicators

621 rows · 1 table(s) · 2024 HDX 0 1 2 3 ▶

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0 Admin 0 — 1 table(s)

MYS Malaysia - Urban Development — 621 rows, 18 columns

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Field details — 18 fields (type, nulls, unique, samples)

Extension Fields (18) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_code`	country_code	string	0%	1	MYS
`Urban_land_area_where_elevation_is_below_5_meters_sq._km`	Urban_land_area_where_elevation_is_below_5_meters_sq._km	float	0%	1	1730.753545104
`Urban_land_area_where_elevation_is_below_5_meters_pct_of_total_land_area`	Urban_land_area_where_elevation_is_below_5_meters_pct_of_total_land_area	float	0%	1	0.52508304753466
`Urban_land_area_sq._km`	Urban_land_area_sq._km	float	0%	1	11417.635397424
`Access_to_electricity_urban_pct_of_urban_population`	Access_to_electricity_urban_pct_of_urban_population	float	0%	1	100.0
`PM2.5_air_pollution_mean_annual_exposure_micrograms_per_cubic_meter`	PM2.5_air_pollution_mean_annual_exposure_micrograms_per_cubic_meter	float	0%	1	16.1938532237922
`PM2.5_air_pollution_population_exposed_to_levels_exceeding_WHO_guideline_value_p`	PM2.5_air_pollution_population_exposed_to_levels_exceeding_WHO_guideline_value_p	float	0%	1	90.537343806725
`Population_density_people_per_sq._km_of_land_area`	Population_density_people_per_sq._km_of_land_area	float	0%	1	106.913096941105
`Urban_population_living_in_areas_where_elevation_is_below_5_meters_pct_of_total_`	Urban_population_living_in_areas_where_elevation_is_below_5_meters_pct_of_total_	float	0%	1	6.51856811135229
`Population_living_in_slums_pct_of_urban_population`	Population_living_in_slums_pct_of_urban_population	float	0%	1	0.2
`Population_in_largest_city`	Population_in_largest_city	float	0%	1	9000280.0
`Population_in_the_largest_city_pct_of_urban_population`	Population_in_the_largest_city_pct_of_urban_population	float	0%	1	32.3227810517542
`Population_in_urban_agglomerations_of_more_than_1_million`	Population_in_urban_agglomerations_of_more_than_1_million	float	0%	1	9000280.0
`Population_in_urban_agglomerations_of_more_than_1_million_pct_of_total_populatio`	Population_in_urban_agglomerations_of_more_than_1_million_pct_of_total_populatio	float	0%	1	25.0161780149213
`Mortality_caused_by_road_traffic_injury_per_100000_population`	Mortality_caused_by_road_traffic_injury_per_100000_population	float	0%	1	22.5
`Urban_population_growth_annual_pct`	Urban_population_growth_annual_pct	float	0%	1	1.83816120540281
`Urban_population`	Urban_population	float	0%	1	27349906.0
`Urban_population_pct_of_total_population`	Urban_population_pct_of_total_population	float	0%	1	76.9170296648992

MYS Global Data Lab MYS-GDL Development Indices

544 rows · 1 table(s) · 2023 Global Data Lab 0 1 2 3 9 SEL 2 ext ▶

ⓘ Global Data Lab publishes its own subnational regions, which match national admin units in some countries and group several together in others. The level shown is the nearest administrative tier, not a claim that these are that tier.

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

1 Admin 1 — 1 table(s)

MYS GDL - Subnational Indices — 544 rows, 11 columns

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Field details — 11 fields (type, nulls, unique, samples)

Standards-Aligned Fields (9)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`region_code`	Admin code	string	SEL	100%	-	-
`region_name`	Admin name	string	SEL	0%	16	Total, Johor, Kedah, Kelantan, Kuala Lumpur Federal Territory
`human_development_index`	Human development index	float	SEL	0%	72	0.689, 0.702, 0.686, 0.659, 0.738
`health_index`	Health index	float	SEL	0%	7	0.784, 0.784, 0.784, 0.784, 0.784
`education_index`	Education index	float	SEL	0%	73	0.582, 0.589, 0.572, 0.555, 0.676
`income_index`	Income index	float	SEL	0%	73	0.717, 0.748, 0.72, 0.658, 0.758
`life_expectancy`	Life expectancy	float	SEL	0%	7	70.95, 70.95, 70.95, 70.95, 70.95
`mean_years_schooling`	Mean years schooling	float	SEL	0%	87	8.731, 8.798, 8.385, 7.673, 10.57

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	7	1990, 1990, 1990, 1990, 1990
`level`	level	string	0%	2	national, subnational, subnational, subnational, subnational

MYS Malaysia - Demographic, Health, Education and Tran MYS-HDX-Malaysia_-_Demographic,_Health,_Edu Indicators

319 rows · 1 table(s) · 2024 HDX 0 1 2 3 ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

MYS Malaysia - Demographic, Health, Education And Tran — 319 rows, 11 columns

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Field details — 11 fields (type, nulls, unique, samples)

Extension Fields (11) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`category`	category	string	0%	6	Streets, Crime, Crime, Transport, Population
`indicator`	indicator	string	0%	20	road_density_national_roads, recorded_theft_rate,...
`indicator_friendly`	indicator_friendly	string	0%	20	Road density (km/100km²), Recorded theft rate per 100,...
`type_data`	type_data	string	0%	4	n, 000 population, 000 population, n, n
`latitude`	latitude	string	0%	5	2.3, n, n, 2.3, 2.3
`longitude`	longitude	float	0%	5	112.3, 2.3, 2.3, 112.3, 112.3
`region_id`	region_id	string	0%	2	789, 112.3, 112.3, 789, 789
`country_id`	country_id	string	0%	2	MY, 789, 789, MY, MY
`name`	name	string	0%	5	Malaysia, MY, MY, Malaysia, Malaysia
`year`	year	string	0%	28	2009, Malaysia, Malaysia, 2000, 2020
`value`	value	string	0%	90	30, 2000, 1999, 1931, 6

MYS Malaysia - Risk Assessment Indicators MYS-HDX-Malaysia_-_Risk_Assessment_Indicato Indicators

144 rows · 1 table(s) · 2024 HDX 0 1 2 3 ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

MYS Malaysia - Risk Assessment Indicators — 144 rows, 50 columns

Loading data…

Field details — 50 fields (type, nulls, unique, samples)

Extension Fields (50) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`ADM2_PCODE`	ADM2_PCODE	string	0%	100	MY0101, MY0102, MY0103, MY0104, MY0105
`ADM_PCODE`	ADM_PCODE	string	0%	100	MY0101, MY0102, MY0103, MY0104, MY0105
`RP10_female_pop_30cm`	RP10_female_pop_30cm	string	0%	97	25839, 2506, 606, 7439, 131
`RP10_children_u5_30cm`	RP10_children_u5_30cm	string	0%	94	4101, 398, 96, 1181, 21
`RP10_female_u5_30cm`	RP10_female_u5_30cm	string	0%	93	1977, 192, 46, 569, 10
`RP10_elderly_30cm`	RP10_elderly_30cm	string	0%	94	3726, 361, 87, 1073, 19
`RP10_pop_u15_30cm`	RP10_pop_u15_30cm	string	0%	96	11763, 1141, 276, 3386, 60
`RP10_female_u15_30cm`	RP10_female_u15_30cm	string	0%	94	5729, 556, 134, 1649, 29
`RP10_education_30cm_pct`	RP10_education_30cm_pct	string	0%	28	8, 0, 0, 5, 0
`RP10_education_30cm_count`	RP10_education_30cm_count	string	0%	18	13, 1, 0, 3, 0
`RP10_hospitals_30cm_pct`	RP10_hospitals_30cm_pct	string	0%	4	14, 0, 0, 0, 0
`RP10_hospitals_30cm_count`	RP10_hospitals_30cm_count	string	0%	3	1, 0, 0, 0, 0
`RP10_primary_healthcare_30cm_pct`	RP10_primary_healthcare_30cm_pct	string	0%	15	4, 0, 0, 0, 0
`RP10_primary_healthcare_30cm_count`	RP10_primary_healthcare_30cm_count	string	0%	9	2, 0, 0, 0, 0
`RP50_female_pop_30cm`	RP50_female_pop_30cm	string	0%	97	39581, 2976, 686, 10883, 131
`RP50_children_u5_30cm`	RP50_children_u5_30cm	string	0%	94	6282, 472, 109, 1727, 21
`RP50_female_u5_30cm`	RP50_female_u5_30cm	string	0%	93	3028, 228, 53, 833, 10
`RP50_elderly_30cm`	RP50_elderly_30cm	string	0%	96	5707, 429, 99, 1569, 19
`RP50_pop_u15_30cm`	RP50_pop_u15_30cm	string	0%	97	18018, 1355, 312, 4954, 60
`RP50_female_u15_30cm`	RP50_female_u15_30cm	string	0%	94	8775, 660, 152, 2413, 29
`RP50_education_30cm_pct`	RP50_education_30cm_pct	string	0%	30	12, 0, 0, 8, 0
`RP50_education_30cm_count`	RP50_education_30cm_count	string	0%	25	19, 1, 0, 5, 0
`RP50_hospitals_30cm_pct`	RP50_hospitals_30cm_pct	string	0%	9	43, 0, 0, 0, 0
`RP50_hospitals_30cm_count`	RP50_hospitals_30cm_count	string	0%	4	3, 0, 0, 0, 0
`RP50_primary_healthcare_30cm_pct`	RP50_primary_healthcare_30cm_pct	string	0%	23	11, 0, 0, 7, 0
`RP50_primary_healthcare_30cm_count`	RP50_primary_healthcare_30cm_count	string	0%	11	5, 0, 0, 1, 0
`RP100_female_pop_30cm`	RP100_female_pop_30cm	string	0%	97	46534, 3275, 715, 12731, 131
`RP100_children_u5_30cm`	RP100_children_u5_30cm	string	0%	94	7386, 520, 113, 2021, 21
`RP100_female_u5_30cm`	RP100_female_u5_30cm	string	0%	95	3560, 251, 55, 974, 10
`RP100_elderly_30cm`	RP100_elderly_30cm	string	0%	94	6710, 472, 103, 1836, 19
+20 more extension fields — download the CSV/Parquet to see them all.

MYS World Bank Open Data MYS-WORLD_BANK Development Indicators

132 rows · 2 table(s) · 2024 World Bank Open Data 0 1 2 3 19 SEL 4 ext ▶

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0 Admin 0 — 2 table(s)

MYS World Bank - Development Indicators — 66 rows, 9 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MY, MY, MY, MY, MY
`population_count`	Population count	float	SEL	2%	65	7956197.0, 8164443.0, 8380172.0, 8602160.0, 8828406.0
`life_expectancy`	Life expectancy	float	SEL	3%	64	57.168, 58.015, 58.855, 59.667, 60.448
`gdp_per_capita_usd`	Gdp per capita usd	float	SEL	2%	65	240.847414554537, 232.943768818315, 238.836339180717,...
`literacy_rate_pct`	Literacy rate percent	float	SEL	86%	9	69.5199966430664, 82.9199981689453, 88.6900024414062,...
`under5_mortality_per_1000`	Under5 mortality per 1000	float	SEL	3%	49	94.6, 88.3, 82.5, 77.4, 72.8
`poverty_headcount_pct`	Poverty headcount percent	float	SEL	92%	5	7.6, 5.6, 8.4, 6.2, 5.8

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`year`	year	integer	0%	66	1960, 1961, 1962, 1963, 1964

MYS World Bank - Health Indicators — 66 rows, 14 columns

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Field details — 14 fields (type, nulls, unique, samples)

Standards-Aligned Fields (12)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MY, MY, MY, MY, MY
`life_expectancy`	Life expectancy	float	SEL	3%	64	57.168, 58.015, 58.855, 59.667, 60.448
`infant_mortality_per_1000`	Infant mortality per 1000	float	SEL	3%	46	21.0, 20.1, 19.4, 18.7, 18.1
`under5_mortality_per_1000`	Under5 mortality per 1000	float	SEL	3%	49	94.6, 88.3, 82.5, 77.4, 72.8
`maternal_mortality_per_100k`	Maternal mortality per 100k	float	SEL	41%	24	63.0, 59.0, 56.0, 55.0, 52.0
`fertility_rate`	Fertility rate	float	SEL	3%	63	6.412, 6.367, 6.296, 6.191, 6.03
`birth_rate_per_1000`	Birth rate per 1000	float	SEL	3%	64	42.885, 42.187, 41.34, 40.389, 39.133
`death_rate_per_1000`	Death rate per 1000	float	SEL	3%	64	11.326, 10.676, 10.059, 9.488, 8.957
`physicians_per_1000`	Physicians per 1000	float	SEL	32%	43	0.143, 0.161, 0.232, 0.234, 0.255
`hospital_beds_per_1000`	Hospital beds per 1000	float	SEL	42%	27	3.72813272476196, 3.46970009803772, 3.34669995307922,...
`vaccination_rate_pct`	Vaccination rate percent	float	SEL	32%	20	67.0, 66.0, 73.0, 58.0, 54.0
`health_expenditure_pct_gdp`	Health expenditure percent gdp	float	SEL	64%	24	2.51463366, 2.6753974, 2.66876554, 2.92417073, 2.86016941

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`year`	year	integer	0%	66	1960, 1961, 1962, 1963, 1964

MYS Glottolog Language Catalog MYS-GLOTTOLOG Language

131 rows · 1 table(s) · 2024 Glottolog Language Catalog 0 1 2 3 1 SEL 12 CCL 3 ext ▶

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0 Admin 0 — 1 table(s)

MYS Glottolog - Languages — 131 rows, 16 columns

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Field details — 16 fields (type, nulls, unique, samples)

Standards-Aligned Fields (13)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`glottocode`	Glottocode	string	CCL	0%	100	abai1240, baba1267, bala1306, bala1311, banj1239
`name`	Name	string	CCL	0%	100	Abai Sungai, Baba Malay, Balau, Balangingi, Banjar
`iso639_3`	Iso639 3	string	CCL	1%	99	abf, mbf, blg, sse, bjn
`level`	Level	string	CCL	0%	1	language, language, language, language, language
`family_name`	Family name	string	CCL	100%	-	-
`family_glottocode`	Family glottocode	string	CCL	1%	5	aust1307, aust1307, book1242, aust1307, aust1307
`parent_name`	Parent name	string	CCL	100%	-	-
`parent_glottocode`	Parent glottocode	string	CCL	1%	68	pait1248, vehi1234, book1242, inne1244, banj1241
`endangerment_status`	Endangerment status	string	CCL	100%	-	-
`country_codes`	Country codes	string	SEL+	0%	10	['MY'], ['MY', 'SG'], ['MY'], ['MY', 'PH'], ['ID', 'MY']
`child_family_count`	Child family count	integer	CCL	0%	1	0, 0, 0, 0, 0
`child_language_count`	Child language count	integer	CCL	0%	1	0, 0, 0, 0, 0
`child_dialect_count`	Child dialect count	integer	CCL	0%	16	0, 0, 0, 6, 2

Extension Fields (3) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`latitude`	latitude	float	0%	100	5.55394, 1.75414, 1.33476, 6.011614, 0.747105
`longitude`	longitude	float	0%	99	118.306, 103.076, 110.913, 121.686646, 115.79
`classification`	classification	string	0%	1	[], [], [], [], []

MYS UNHCR Refugee Data Finder MYS-UNHCR Migration

84 rows · 2 table(s) · 2025 UNHCR Refugee Data Finder 0 1 2 3 12 CCL 36 ext ▶

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0 Admin 0 — 2 table(s)

MYS UNHCR - Hosted Populations — 49 rows, 24 columns

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Field details — 24 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_of_origin_iso`	Country of origin iso	string	CCL	0%	1	-, -, -, -, -
`refugees`	Refugees	integer	CCL	0%	75	2116011, 1952928, 1847304, 1749628, 1717966
`idps`	Idps	integer	CCL	0%	34	0, 0, 0, 0, 0
`stateless`	Stateless	integer	CCL	0%	23	0, 0, 0, 0, 0
`others_of_concern`	Others of concern	integer	CCL	0%	30	0, 0, 0, 0, 0
`total_population`	Total population	string	CCL	100%	-	-

Extension Fields (18) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	75	1951, 1952, 1953, 1954, 1955
`country_of_origin`	country_of_origin	string	100%	-	-
`country_of_asylum`	country_of_asylum	string	100%	-	-
`country_of_asylum_iso`	country_of_asylum_iso	string	0%	1	-, -, -, -, -
`population_type`	population_type	string	100%	-	-
`asylum_seekers`	asylum_seekers	integer	0%	34	0, 0, 0, 0, 0
`female_total`	female_total	string	100%	-	-
`male_total`	male_total	string	100%	-	-
`female_0_4`	female_0_4	string	100%	-	-
`female_5_11`	female_5_11	string	100%	-	-
`female_12_17`	female_12_17	string	100%	-	-
`female_18_59`	female_18_59	string	100%	-	-
`female_60_plus`	female_60_plus	string	100%	-	-
`male_0_4`	male_0_4	string	100%	-	-
`male_5_11`	male_5_11	string	100%	-	-
`male_12_17`	male_12_17	string	100%	-	-
`male_18_59`	male_18_59	string	100%	-	-
`male_60_plus`	male_60_plus	string	100%	-	-

MYS UNHCR - Origin Populations — 35 rows, 24 columns

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Field details — 24 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_of_origin_iso`	Country of origin iso	string	CCL	0%	1	-, -, -, -, -
`refugees`	Refugees	integer	CCL	0%	75	2116011, 1952928, 1847304, 1749628, 1717966
`idps`	Idps	integer	CCL	0%	34	0, 0, 0, 0, 0
`stateless`	Stateless	integer	CCL	0%	23	0, 0, 0, 0, 0
`others_of_concern`	Others of concern	integer	CCL	0%	30	0, 0, 0, 0, 0
`total_population`	Total population	string	CCL	100%	-	-

Extension Fields (18) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	75	1951, 1952, 1953, 1954, 1955
`country_of_origin`	country_of_origin	string	100%	-	-
`country_of_asylum`	country_of_asylum	string	100%	-	-
`country_of_asylum_iso`	country_of_asylum_iso	string	0%	1	-, -, -, -, -
`population_type`	population_type	string	100%	-	-
`asylum_seekers`	asylum_seekers	integer	0%	34	0, 0, 0, 0, 0
`female_total`	female_total	string	100%	-	-
`male_total`	male_total	string	100%	-	-
`female_0_4`	female_0_4	string	100%	-	-
`female_5_11`	female_5_11	string	100%	-	-
`female_12_17`	female_12_17	string	100%	-	-
`female_18_59`	female_18_59	string	100%	-	-
`female_60_plus`	female_60_plus	string	100%	-	-
`male_0_4`	male_0_4	string	100%	-	-
`male_5_11`	male_5_11	string	100%	-	-
`male_12_17`	male_12_17	string	100%	-	-
`male_18_59`	male_18_59	string	100%	-	-
`male_60_plus`	male_60_plus	string	100%	-	-

MYS LandScan Global (ORNL) MYS-LANDSCAN Modeled Population

60 rows · 3 table(s) · 2024 LandScan Global (ORNL) 0 1 2 3 Locality 21 SEL 2 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 1 table(s)

MYS LandScan - Population Admin0 (admin_0) — 1 rows, 7 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`admin_level`	Admin level	string	SEL	0%	1	admin_0
`admin_name`	Admin name	string	SEL	0%	1	Malaysia
`admin_code`	Admin code	string	SEL	0%	1	87038898B63284295545115
`area_sqkm`	Area sqkm	float	SEL	0%	1	329656.5154
`pop_2024`	Population count	integer	SEL	0%	1	34257393
`pop_density_2024`	Population density	float	SEL	0%	1	103.92

1 Admin 1 — 1 table(s)

MYS LandScan - Population Admin1 (admin_1) — 16 rows, 7 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`admin_level`	Admin level	string	SEL	0%	1	admin_1, admin_1, admin_1, admin_1, admin_1
`admin_name`	Admin name	string	SEL	0%	16	Selangor, Johor, Sabah, Perak, Sarawak
`admin_code`	Admin code	string	SEL	0%	16	15666254B89722251658211, 15666254B42356713390762,...
`area_sqkm`	Area sqkm	float	SEL	0%	16	7936.9045, 19172.0193, 74043.8426, 20817.4217, 123770.4102
`pop_2024`	Population count	integer	SEL	0%	16	7424553, 4237897, 3542432, 2632292, 2595638
`pop_density_2024`	Population density	float	SEL	0%	16	935.45, 221.05, 47.84, 126.45, 20.97

Locality Locality — 1 table(s)

MYS LandScan - Cities (locality) — 43 rows, 9 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`admin_level`	Admin level	string	SEL	0%	1	locality, locality, locality, locality, locality
`admin_name`	Admin name	string	SEL	0%	43	Kuala Lumpur, Johor Bahru, George Town, Kota Kinabalu,...
`admin_code`	Admin code	integer	SEL	0%	43	1648, 2979, 176, 3380, 2239
`area_sqkm`	Area sqkm	float	SEL	0%	43	1331.0814, 415.2033, 133.1234, 169.8752, 202.6392
`pop_2024`	Population count	integer	SEL	0%	43	7659699, 1598115, 747181, 653346, 548073
`pop_density_2024`	Population density	float	SEL	0%	43	5754.49, 3848.99, 5612.69, 3846.04, 2704.67

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`ghs_pop_2025`	ghs_pop_2025	integer	0%	43	8413206, 1703324, 756641, 810843, 544035
`landscan_vs_ghs_ratio`	landscan_vs_ghs_ratio	float	0%	42	0.91, 0.938, 0.987, 0.806, 1.007

MYS GHS Urban Centre Database (EU JRC) MYS-GHSL_UCDB Modeled Population

43 rows · 1 table(s) · 2025 GHS Urban Centre Database (EU JRC) 0 1 2 3 Locality 3 SEL 7 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

Locality Locality — 1 table(s)

MYS GHS-UCDB - Cities (locality) — 43 rows, 10 columns

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Field details — 10 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`city_name`	Admin name	string	SEL	0%	43	Kuala Lumpur, Johor Bahru, Ipoh, Kota Kinabalu, George Town
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`population`	Population count	integer	SEL	0%	43	8413206, 1703324, 861933, 810843, 756641

Extension Fields (7) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`city_id`	city_id	integer	0%	43	1648, 2979, 1351, 3380, 176
`name_alternates`	name_alternates	string	100%	-	-
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`population_year`	population_year	integer	0%	1	2025, 2025, 2025, 2025, 2025
`latitude`	latitude	string	100%	-	-
`longitude`	longitude	string	100%	-	-
`region`	region	string	100%	-	-

MYS Department of Statistics Malaysia (DOSM) MYS-DOSM_MALAYSIA Census

17 rows · 2 table(s) · 2023 Department of Statistics Malaysia (DOSM) 0 1 2 3 10 SEL 4 ext ▶

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0 Admin 0 — 1 table(s)

MYS Dosm Malaysia - Mys Population (admin_0) — 1 rows, 7 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`admin_code`	Admin code	string	SEL	0%	1	MYS
`admin_name`	Admin name	string	SEL	0%	1	Malaysia
`population_count`	Population count	integer	SEL	0%	1	33379500
`population_male`	Population male	integer	SEL	0%	1	17460000
`population_female`	Population female	integer	SEL	0%	1	15919500

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`admin_level`	admin_level	string	0%	1	admin_0
`year`	year	integer	0%	1	2023

1 Admin 1 — 1 table(s)

MYS Dosm Malaysia - Mys Population (admin_1) — 16 rows, 7 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`admin_code`	Admin code	string	SEL	0%	16	johor, kedah, kelantan, melaka, negeri_sembilan
`admin_name`	Admin name	string	SEL	0%	16	Johor, Kedah, Kelantan, Melaka, Negeri Sembilan
`population_count`	Population count	integer	SEL	0%	16	4100900, 2187500, 1857600, 1027500, 1224300
`population_male`	Population male	integer	SEL	0%	16	2188400, 1115200, 932800, 541500, 635200
`population_female`	Population female	integer	SEL	0%	15	1912500, 1072300, 924800, 486000, 589100

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`admin_level`	admin_level	string	0%	1	admin_1, admin_1, admin_1, admin_1, admin_1
`year`	year	integer	0%	1	2023, 2023, 2023, 2023, 2023

MYS HDX COD — Population Statistics (OCHA/UNFPA) MYS-COD_PS Census Derived Population

16 rows · 1 table(s) · 2020 HDX COD — Population Statistics (OCHA/UNFPA) 0 1 2 3 3 SEL ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

1 Admin 1 — 1 table(s)

MYS Cod Ps - Mys Codps — 16 rows, 61 columns

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Field details — 61 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`pcode`	Admin code	string	SEL	0%	16	MY01, MY02, MY03, MY06, MY07
`region_name`	Region name	string	SEL	0%	16	Johor, Kedeh, Kelantan, Melaka, Negeri
`T_TL`	Total population	integer	SEL	0%	16	1978300, 1105600, 963900, 467200, 580000

Pending (58) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`admin_level`	Admin level	string	0%	1	admin_1, admin_1, admin_1, admin_1, admin_1
`year`	Reference year	integer	0%	1	2020, 2020, 2020, 2020, 2020
`ADM0_EN`	ADM0_EN	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`T_00_04`	Total population age 0-4	integer	0%	16	301600, 179400, 192000, 71500, 88400
`T_05_09`	Total population age 5-9	integer	0%	16	293900, 182200, 195700, 70200, 84800
`T_10_14`	Total population age 10-14	integer	0%	16	286400, 172500, 176100, 69300, 81900
`T_15_19`	Total population age 15-19	integer	0%	16	322200, 192800, 184700, 75600, 101900
`T_20_24`	Total population age 20-24	integer	0%	16	362500, 226700, 192800, 88500, 111600
`T_25_29`	Total population age 25-29	integer	0%	16	335400, 203300, 200300, 102400, 110400
`T_30_34`	Total population age 30-34	integer	0%	16	314900, 159900, 127300, 79500, 97500
`T_35_39`	Total population age 35-39	integer	0%	16	297500, 137200, 106500, 65300, 75100
`T_40_44`	Total population age 40-44	integer	0%	16	243300, 122300, 88000, 49900, 62500
`T_45_49`	Total population age 45-49	integer	0%	16	219300, 117000, 84500, 46500, 58900
`T_50_54`	Total population age 50-54	integer	0%	15	198600, 113000, 84200, 49100, 56100
`T_55_59`	Total population age 55-59	integer	0%	16	176700, 106300, 80300, 46300, 54900
`T_60_64`	Total population age 60-64	integer	0%	16	146800, 91700, 67400, 38300, 49400
`T_65_69`	Total population age 65-69	integer	0%	16	109000, 71100, 49800, 29400, 39000
`T_70_74`	Total population age 70-74	integer	0%	16	77600, 49700, 36800, 23100, 25600
`T_75_79`	Total population age 75-79	integer	0%	16	45300, 28200, 19700, 13300, 15200
`T_80_84`	Total population age 80-84	integer	0%	15	28500, 19200, 11500, 8100, 9400
`T_85Plus`	T_85Plus	integer	0%	15	21600, 12600, 9100, 6300, 6300
`M_00_04`	Male population age 0-4	integer	0%	16	155800, 93000, 99300, 36700, 45300
`M_05_09`	Male population age 5-9	integer	0%	16	152300, 93600, 100000, 35700, 43600
`M_10_14`	Male population age 10-14	integer	0%	16	147700, 88500, 92900, 34900, 41700
`M_15_19`	Male population age 15-19	integer	0%	16	166300, 100700, 95500, 39800, 54900
`M_20_24`	Male population age 20-24	integer	0%	16	196900, 121100, 99700, 45900, 59700
`M_25_29`	Male population age 25-29	integer	0%	16	184200, 108000, 106000, 52400, 60300
`M_30_34`	Male population age 30-34	integer	0%	16	170400, 81400, 68300, 40700, 54100
`M_35_39`	Male population age 35-39	integer	0%	16	157100, 69000, 56600, 32100, 38900
`M_40_44`	Male population age 40-44	integer	0%	16	129500, 61400, 43400, 25100, 31700
+28 more pending fields — download the CSV/Parquet to see them all.

MYS Google Community Mobility (2020-2022) MYS-GOOGLE-MOBILITY Mobility

16 rows · 1 table(s) · 2022 Google 0 1 2 3 3 SEL 8 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

1 Admin 1 — 1 table(s)

MYS Mobility by Region — 16 rows, 11 columns

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Field details — 11 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MY, MY, MY, MY, MY
`admin_level`	Admin level	integer	SEL	0%	1	1, 1, 1, 1, 1
`iso3`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS

Extension Fields (8) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`retail`	retail	float	0%	16	-35.89, -31.78, -19.52, 1.32, -19.0
`grocery`	grocery	float	0%	16	-8.4, 1.82, 10.77, 6.41, -4.59
`parks`	parks	float	0%	16	-30.95, -19.22, 0.55, 18.43, 4.72
`transit`	transit	float	0%	16	-54.65, -29.25, -1.75, -24.71, -41.34
`workplaces`	workplaces	float	0%	16	-30.08, -23.6, -16.24, -11.97, -18.75
`residential`	residential	float	0%	16	15.99, 15.68, 14.29, 14.95, 14.04
`region`	region	string	0%	16	Federal Territory of Kuala Lumpur, Johor, Kedah,...
`observation_count`	observation_count	integer	0%	3	974, 974, 974, 974, 971

MYS OpenFactBook MYS-OPENFACTBOOK Country Profile

16 rows · 16 table(s) · 2024 OpenFactBook 0 1 2 3 99 SEL 4 CCL 623 ext ▶

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0 Admin 0 — 16 table(s)

MYS OpenFactBook - Communications — 1 rows, 21 columns

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Field details — 22 fields (type, nulls, unique, samples)

Standards-Aligned Fields (8)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`telephones_fixed_lines_subscriptions_per_100_inhabitants_numeric`	Fixed line subscriptions per 100	float	SEL	0%	1	24.0
`telephones_mobile_cellular_subscriptions_per_100_inhabitants_numeric`	Mobile subscriptions per 100	float	SEL	0%	1	140.0
`internet_country_code_text`	Internet country code text	string	SEL+	0%	1	.my
`internet_users_percent_of_population_numeric`	Internet penetration percent	float	SEL	0%	1	98.0
`broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_numeric`	Broadband subscriptions per 100	float	SEL	0%	1	13.0
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (14) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`telephones_fixed_lines_total_subscriptions_text`	telephones_fixed_lines_total_subscriptions_text	string	0%	1	8.402 million (2023 est.)
`telephones_fixed_lines_total_subscriptions_numeric`	telephones_fixed_lines_total_subscriptions_numeric	float	0%	1	8.402
`telephones_fixed_lines_subscriptions_per_100_inhabitants_text`	telephones_fixed_lines_subscriptions_per_100_inhabitants_text	string	0%	1	24 (2023 est.)
`telephones_mobile_cellular_total_subscriptions_text`	telephones_mobile_cellular_total_subscriptions_text	string	0%	1	49.7 million (2024 est.)
`telephones_mobile_cellular_total_subscriptions_numeric`	telephones_mobile_cellular_total_subscriptions_numeric	float	0%	1	49.7
`telephones_mobile_cellular_subscriptions_per_100_inhabitants_text`	telephones_mobile_cellular_subscriptions_per_100_inhabitants_text	string	0%	1	140 (2024 est.)
`broadcast_media_text`	broadcast_media_text	string	0%	1	state-owned TV broadcaster operates 2 TV networks with...
`broadcast_media_numeric`	broadcast_media_numeric	float	0%	1	2.0
`internet_users_percent_of_population_text`	internet_users_percent_of_population_text	string	0%	1	98% (2023 est.)
`broadband_fixed_subscriptions_total_text`	broadband_fixed_subscriptions_total_text	string	0%	1	4.58 million (2023 est.)
`broadband_fixed_subscriptions_total_numeric`	broadband_fixed_subscriptions_total_numeric	float	0%	1	4.58
`broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text`	broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text	string	0%	1	13 (2023 est.)
`source_section`	source_section	string	0%	1	Communications
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Economy — 1 rows, 150 columns

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Field details — 156 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`real_gdp_per_capita_real_gdp_per_capita_2024_numeric`	Real gdp per capita 2024 (numeric)	float	SEL	0%	1	34100.0
`gdp_official_exchange_rate_numeric`	Gdp total usd	float	SEL	0%	1	421.972
`population_below_poverty_line_numeric`	Poverty headcount percent	float	SEL	0%	1	6.2
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (150) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`economic_overview_text`	economic_overview_text	string	0%	1	upper middle-income Southeast Asian economy;...
`economic_overview_numeric`	economic_overview_numeric	float	0%	1	40.0
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2024_text`	Real gdp purchasing power parity 2024 (text)	string	0%	1	$1.212 trillion (2024 est.)
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2024_numeric`	Real gdp purchasing power parity 2024 (numeric)	float	0%	1	1.212
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_text`	Real gdp purchasing power parity 2023 (text)	string	0%	1	$1.153 trillion (2023 est.)
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_numeric`	Real gdp purchasing power parity 2023 (numeric)	float	0%	1	1.153
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_text`	Real gdp purchasing power parity 2022 (text)	string	0%	1	$1.113 trillion (2022 est.)
`real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_numeric`	Real gdp purchasing power parity 2022 (numeric)	float	0%	1	1.113
`real_gdp_purchasing_power_parity_note`	real_gdp_purchasing_power_parity_note	string	0%	1	note: data in 2021 dollars
`real_gdp_growth_rate_real_gdp_growth_rate_2024_text`	Real gdp growth rate 2024 (text)	string	0%	1	5.1% (2024 est.)
`real_gdp_growth_rate_real_gdp_growth_rate_2024_numeric`	Real gdp growth rate 2024 (numeric)	float	0%	1	5.1
`real_gdp_growth_rate_real_gdp_growth_rate_2023_text`	Real gdp growth rate 2023 (text)	string	0%	1	3.6% (2023 est.)
`real_gdp_growth_rate_real_gdp_growth_rate_2023_numeric`	Real gdp growth rate 2023 (numeric)	float	0%	1	3.6
`real_gdp_growth_rate_real_gdp_growth_rate_2022_text`	Real gdp growth rate 2022 (text)	string	0%	1	8.9% (2022 est.)
`real_gdp_growth_rate_real_gdp_growth_rate_2022_numeric`	Real gdp growth rate 2022 (numeric)	float	0%	1	8.9
`real_gdp_growth_rate_note`	real_gdp_growth_rate_note	string	0%	1	note: annual GDP % growth based on constant local currency
`real_gdp_per_capita_real_gdp_per_capita_2024_text`	Real gdp per capita 2024 (text)	string	0%	1	$34,100 (2024 est.)
`real_gdp_per_capita_real_gdp_per_capita_2023_text`	Real gdp per capita 2023 (text)	string	0%	1	$32,800 (2023 est.)
`real_gdp_per_capita_real_gdp_per_capita_2023_numeric`	Real gdp per capita 2023 (numeric)	float	0%	1	32800.0
`real_gdp_per_capita_real_gdp_per_capita_2022_text`	Real gdp per capita 2022 (text)	string	0%	1	$32,100 (2022 est.)
`real_gdp_per_capita_real_gdp_per_capita_2022_numeric`	Real gdp per capita 2022 (numeric)	float	0%	1	32100.0
`real_gdp_per_capita_note`	real_gdp_per_capita_note	string	0%	1	note: data in 2021 dollars
`gdp_official_exchange_rate_text`	gdp_official_exchange_rate_text	string	0%	1	$421.972 billion (2024 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2024_text`	Inflation rate consumer prices 2024 (text)	string	0%	1	1.8% (2024 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2024_numeric`	Inflation rate consumer prices 2024 (numeric)	float	0%	1	1.8
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_text`	Inflation rate consumer prices 2023 (text)	string	0%	1	2.5% (2023 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_numeric`	Inflation rate consumer prices 2023 (numeric)	float	0%	1	2.5
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_text`	Inflation rate consumer prices 2022 (text)	string	0%	1	3.4% (2022 est.)
`inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_numeric`	Inflation rate consumer prices 2022 (numeric)	float	0%	1	3.4
`inflation_rate_consumer_prices_note`	inflation_rate_consumer_prices_note	string	0%	1	note: annual % change based on consumer prices
+120 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Energy — 1 rows, 53 columns

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Field details — 53 fields (type, nulls, unique, samples)

Standards-Aligned Fields (4)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`electricity_access_electrification_total_population_numeric`	Electricity access percent	float	SEL	0%	1	100.0
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (49) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`electricity_access_electrification_total_population_text`	electricity_access_electrification_total_population_text	string	0%	1	100% (2022 est.)
`electricity_installed_generating_capacity_text`	electricity_installed_generating_capacity_text	string	0%	1	37.22 million kW (2023 est.)
`electricity_installed_generating_capacity_numeric`	electricity_installed_generating_capacity_numeric	float	0%	1	37.22
`electricity_consumption_text`	electricity_consumption_text	string	0%	1	178.653 billion kWh (2023 est.)
`electricity_consumption_numeric`	electricity_consumption_numeric	float	0%	1	178.653
`electricity_exports_text`	electricity_exports_text	string	0%	1	1.2 billion kWh (2023 est.)
`electricity_exports_numeric`	electricity_exports_numeric	float	0%	1	1.2
`electricity_imports_text`	electricity_imports_text	string	0%	1	61.678 million kWh (2023 est.)
`electricity_imports_numeric`	electricity_imports_numeric	float	0%	1	61.678
`electricity_transmission_distribution_losses_text`	electricity_transmission_distribution_losses_text	string	0%	1	13.188 billion kWh (2023 est.)
`electricity_transmission_distribution_losses_numeric`	electricity_transmission_distribution_losses_numeric	float	0%	1	13.188
`electricity_generation_sources_fossil_fuels_text`	electricity_generation_sources_fossil_fuels_text	string	0%	1	81.9% of total installed capacity (2023 est.)
`electricity_generation_sources_fossil_fuels_numeric`	electricity_generation_sources_fossil_fuels_numeric	float	0%	1	81.9
`electricity_generation_sources_solar_text`	electricity_generation_sources_solar_text	string	0%	1	1.1% of total installed capacity (2023 est.)
`electricity_generation_sources_solar_numeric`	electricity_generation_sources_solar_numeric	float	0%	1	1.1
`electricity_generation_sources_hydroelectricity_text`	electricity_generation_sources_hydroelectricity_text	string	0%	1	16.3% of total installed capacity (2023 est.)
`electricity_generation_sources_hydroelectricity_numeric`	electricity_generation_sources_hydroelectricity_numeric	float	0%	1	16.3
`electricity_generation_sources_biomass_and_waste_text`	electricity_generation_sources_biomass_and_waste_text	string	0%	1	0.6% of total installed capacity (2023 est.)
`electricity_generation_sources_biomass_and_waste_numeric`	electricity_generation_sources_biomass_and_waste_numeric	float	0%	1	0.6
`coal_production_text`	coal_production_text	string	0%	1	4.476 million metric tons (2023 est.)
`coal_production_numeric`	coal_production_numeric	float	0%	1	4.476
`coal_consumption_text`	coal_consumption_text	string	0%	1	35.741 million metric tons (2023 est.)
`coal_consumption_numeric`	coal_consumption_numeric	float	0%	1	35.741
`coal_exports_text`	coal_exports_text	string	0%	1	462,000 metric tons (2023 est.)
`coal_exports_numeric`	coal_exports_numeric	float	0%	1	462000.0
`coal_imports_text`	coal_imports_text	string	0%	1	31.706 million metric tons (2023 est.)
`coal_imports_numeric`	coal_imports_numeric	float	0%	1	31.706
`coal_proven_reserves_text`	coal_proven_reserves_text	string	0%	1	226 million metric tons (2023 est.)
`coal_proven_reserves_numeric`	coal_proven_reserves_numeric	float	0%	1	226.0
`petroleum_total_petroleum_production_text`	petroleum_total_petroleum_production_text	string	0%	1	582,000 bbl/day (2023 est.)
+19 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Environment — 1 rows, 54 columns

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Field details — 60 fields (type, nulls, unique, samples)

Standards-Aligned Fields (8)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`land_use_agricultural_land_numeric`	Agricultural land percent	float	SEL	0%	1	26.1
`land_use_forest_numeric`	Forest area percent	float	SEL	0%	1	57.8
`urbanization_urban_population_numeric`	Urban population percent	float	SEL	0%	1	78.7
`urbanization_rate_of_urbanization_numeric`	Urban growth rate percent	float	SEL	0%	1	1.87
`waste_and_recycling_municipal_solid_waste_generated_annually_numeric`	Municipal waste kg per capita	float	SEL	0%	1	12.983
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (52) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`environmental_issues_text`	environmental_issues_text	string	0%	1	air pollution from industrial and vehicular emissions;...
`international_environmental_agreements_party_to_text`	international_environmental_agreements_party_to_text	string	0%	1	Antarctic-Environmental Protection, Antarctic Treaty,...
`international_environmental_agreements_party_to_numeric`	international_environmental_agreements_party_to_numeric	float	0%	1	2006.0
`international_environmental_agreements_signed_but_not_ratified_text`	international_environmental_agreements_signed_but_not_ratified_text	string	0%	1	none of the selected agreements
`climate_text`	climate_text	string	0%	1	tropical; annual southwest (April to October) and...
`land_use_agricultural_land_text`	land_use_agricultural_land_text	string	0%	1	26.1% (2023 est.)
`land_use_agricultural_land_arable_land_text`	land_use_agricultural_land_arable_land_text	string	0%	1	arable land: 2.4% (2023 est.)
`land_use_agricultural_land_arable_land_numeric`	land_use_agricultural_land_arable_land_numeric	float	0%	1	2.4
`land_use_agricultural_land_permanent_crops_text`	land_use_agricultural_land_permanent_crops_text	string	0%	1	permanent crops: 22.7% (2023 est.)
`land_use_agricultural_land_permanent_crops_numeric`	land_use_agricultural_land_permanent_crops_numeric	float	0%	1	22.7
`land_use_agricultural_land_permanent_pasture_text`	land_use_agricultural_land_permanent_pasture_text	string	0%	1	permanent pasture: 0.9% (2023 est.)
`land_use_agricultural_land_permanent_pasture_numeric`	land_use_agricultural_land_permanent_pasture_numeric	float	0%	1	0.9
`land_use_forest_text`	land_use_forest_text	string	0%	1	57.8% (2023 est.)
`land_use_other_text`	land_use_other_text	string	0%	1	16% (2023 est.)
`land_use_other_numeric`	land_use_other_numeric	float	0%	1	16.0
`urbanization_urban_population_text`	urbanization_urban_population_text	string	0%	1	78.7% of total population (2023)
`urbanization_rate_of_urbanization_text`	urbanization_rate_of_urbanization_text	string	0%	1	1.87% annual rate of change (2020-25 est.)
`carbon_dioxide_emissions_total_emissions_text`	carbon_dioxide_emissions_total_emissions_text	string	0%	1	260.005 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_total_emissions_numeric`	carbon_dioxide_emissions_total_emissions_numeric	float	0%	1	260.005
`carbon_dioxide_emissions_from_coal_and_metallurgical_coke_text`	carbon_dioxide_emissions_from_coal_and_metallurgical_coke_text	string	0%	1	76.78 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_from_coal_and_metallurgical_coke_numeric`	carbon_dioxide_emissions_from_coal_and_metallurgical_coke_numeric	float	0%	1	76.78
`carbon_dioxide_emissions_from_petroleum_and_other_liquids_text`	carbon_dioxide_emissions_from_petroleum_and_other_liquids_text	string	0%	1	90.273 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric`	carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric	float	0%	1	90.273
`carbon_dioxide_emissions_from_consumed_natural_gas_text`	carbon_dioxide_emissions_from_consumed_natural_gas_text	string	0%	1	92.951 million metric tonnes of CO2 (2023 est.)
`carbon_dioxide_emissions_from_consumed_natural_gas_numeric`	carbon_dioxide_emissions_from_consumed_natural_gas_numeric	float	0%	1	92.951
`particulate_matter_emissions_text`	particulate_matter_emissions_text	string	0%	1	23.7 micrograms per cubic meter (2019 est.)
`particulate_matter_emissions_numeric`	particulate_matter_emissions_numeric	float	0%	1	23.7
`methane_emissions_energy_text`	methane_emissions_energy_text	string	0%	1	818.9 kt (2022-2024 est.)
`methane_emissions_energy_numeric`	methane_emissions_energy_numeric	float	0%	1	818.9
`methane_emissions_agriculture_text`	methane_emissions_agriculture_text	string	0%	1	182.2 kt (2019-2021 est.)
+22 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Ethnic Groups — 1 rows, 8 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (6) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`nationality_noun_text`	nationality_noun_text	string	0%	1	Malaysian(s)
`nationality_adjective_text`	nationality_adjective_text	string	0%	1	Malaysian
`ethnic_groups_text`	ethnic_groups_text	string	0%	1	Bumiputera 63.8% (Malay 52.8% and indigenous peoples,...
`ethnic_groups_numeric`	ethnic_groups_numeric	float	0%	1	63.8
`source_section`	source_section	string	0%	1	People and Society:ethnic_groups
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Geography — 1 rows, 50 columns

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Field details — 55 fields (type, nulls, unique, samples)

Standards-Aligned Fields (13)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`area_total_numeric`	Area sqkm	float	SEL	0%	1	329847.0
`area_land_numeric`	Area land sqkm	float	SEL	0%	1	328657.0
`area_water_numeric`	Area water sqkm	float	SEL	0%	1	1190.0
`land_boundaries_total_numeric`	Land boundary km	float	SEL	0%	1	2742.0
`coastline_numeric`	Coastline km	float	SEL	0%	1	4675.0
`elevation_highest_point_numeric`	Elevation max m	float	SEL	0%	1	4095.0
`elevation_lowest_point_numeric`	Elevation min m	float	SEL	0%	1	0.0
`land_use_agricultural_land_numeric`	Agricultural land percent	float	SEL	0%	1	26.1
`land_use_forest_numeric`	Forest area percent	float	SEL	0%	1	57.8
`irrigated_land_numeric`	Irrigated land sqkm	float	SEL	0%	1	4420.0
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (42) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`location_text`	location_text	string	0%	1	Southeastern Asia, peninsula bordering Thailand and...
`geographic_coordinates_text`	geographic_coordinates_text	string	0%	1	2 30 N, 112 30 E
`geographic_coordinates_numeric`	geographic_coordinates_numeric	float	0%	1	2.0
`map_references_text`	map_references_text	string	0%	1	Southeast Asia
`area_total_text`	area_total_text	string	0%	1	329,847 sq km
`area_land_text`	area_land_text	string	0%	1	328,657 sq km
`area_water_text`	area_water_text	string	0%	1	1,190 sq km
`area_comparative_text`	area_comparative_text	string	0%	1	slightly larger than New Mexico
`land_boundaries_total_text`	land_boundaries_total_text	string	0%	1	2,742 km
`land_boundaries_border_countries_text`	land_boundaries_border_countries_text	string	0%	1	Brunei 266 km; Indonesia 1,881 km; Thailand 595 km
`land_boundaries_border_countries_numeric`	land_boundaries_border_countries_numeric	float	0%	1	266.0
`coastline_text`	coastline_text	string	0%	1	4,675 km (Peninsular Malaysia 2,068 km; East Malaysia 2,607 km)
`maritime_claims_territorial_sea_text`	maritime_claims_territorial_sea_text	string	0%	1	12 nm
`maritime_claims_territorial_sea_numeric`	maritime_claims_territorial_sea_numeric	float	0%	1	12.0
`maritime_claims_exclusive_economic_zone_text`	maritime_claims_exclusive_economic_zone_text	string	0%	1	200 nm
`maritime_claims_exclusive_economic_zone_numeric`	maritime_claims_exclusive_economic_zone_numeric	float	0%	1	200.0
`maritime_claims_continental_shelf_text`	maritime_claims_continental_shelf_text	string	0%	1	200-m depth or to the depth of exploitation; specified...
`maritime_claims_continental_shelf_numeric`	maritime_claims_continental_shelf_numeric	float	0%	1	200.0
`climate_text`	climate_text	string	0%	1	tropical; annual southwest (April to October) and...
`terrain_text`	terrain_text	string	0%	1	coastal plains rising to hills and mountains
`elevation_highest_point_text`	elevation_highest_point_text	string	0%	1	Gunung Kinabalu 4,095 m
`elevation_lowest_point_text`	elevation_lowest_point_text	string	0%	1	Indian Ocean 0 m
`elevation_mean_elevation_text`	elevation_mean_elevation_text	string	0%	1	419 m
`elevation_mean_elevation_numeric`	elevation_mean_elevation_numeric	float	0%	1	419.0
`natural_resources_text`	natural_resources_text	string	0%	1	tin, petroleum, timber, copper, iron ore, natural gas, bauxite
`land_use_agricultural_land_text`	land_use_agricultural_land_text	string	0%	1	26.1% (2023 est.)
`land_use_agricultural_land_arable_land_text`	land_use_agricultural_land_arable_land_text	string	0%	1	arable land: 2.4% (2023 est.)
`land_use_agricultural_land_arable_land_numeric`	land_use_agricultural_land_arable_land_numeric	float	0%	1	2.4
`land_use_agricultural_land_permanent_crops_text`	land_use_agricultural_land_permanent_crops_text	string	0%	1	permanent crops: 22.7% (2023 est.)
`land_use_agricultural_land_permanent_crops_numeric`	land_use_agricultural_land_permanent_crops_numeric	float	0%	1	22.7
+12 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Government — 1 rows, 82 columns

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Field details — 113 fields (type, nulls, unique, samples)

Standards-Aligned Fields (2)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (111) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name_conventional_long_form_text`	country_name_conventional_long_form_text	string	0%	1	none
`country_name_conventional_short_form_text`	country_name_conventional_short_form_text	string	0%	1	Malaysia
`country_name_local_long_form_text`	country_name_local_long_form_text	string	0%	1	none
`country_name_local_short_form_text`	country_name_local_short_form_text	string	0%	1	Malaysia
`country_name_former_text`	country_name_former_text	string	0%	1	British Malaya, Malayan Union, Federation of Malaya
`country_name_etymology_text`	country_name_etymology_text	string	0%	1	devised in the early 19th century by British...
`country_name_etymology_numeric`	country_name_etymology_numeric	float	0%	1	19.0
`government_type_text`	government_type_text	string	0%	1	federal parliamentary constitutional monarchy
`capital_name_text`	capital_name_text	string	0%	1	Kuala Lumpur
`capital_geographic_coordinates_text`	capital_geographic_coordinates_text	string	0%	1	3 10 N, 101 42 E
`capital_geographic_coordinates_numeric`	capital_geographic_coordinates_numeric	float	0%	1	3.0
`capital_time_difference_text`	capital_time_difference_text	string	0%	1	UTC+8 (13 hours ahead of Washington, DC, during Standard Time)
`capital_time_difference_numeric`	capital_time_difference_numeric	float	0%	1	8.0
`capital_etymology_text`	capital_etymology_text	string	0%	1	the name means "muddy river junction," referring to the...
`capital_note`	capital_note	string	0%	1	note: nearby Putrajaya is referred to as a federal...
`administrative_divisions_text`	administrative_divisions_text	string	0%	1	13 states (negeri-negeri, singular - negeri); Johor,...
`administrative_divisions_numeric`	administrative_divisions_numeric	float	0%	1	13.0
`legal_system_text`	legal_system_text	string	0%	1	mixed system of English common law, Islamic law...
`constitution_history_text`	constitution_history_text	string	0%	1	previous 1948; latest drafted 21 February 1957,...
`constitution_history_numeric`	constitution_history_numeric	float	0%	1	1948.0
`constitution_amendment_process_text`	constitution_amendment_process_text	string	0%	1	proposed as a bill by Parliament; passage requires at...
`international_law_organization_participation_text`	international_law_organization_participation_text	string	0%	1	has not submitted an ICJ jurisdiction declaration;...
`citizenship_citizenship_by_birth_text`	Citizenship by birth (text)	string	0%	1	no
`citizenship_citizenship_by_descent_only_text`	Citizenship by descent only (text)	string	0%	1	at least one parent must be a citizen of Malaysia
`citizenship_dual_citizenship_recognized_text`	citizenship_dual_citizenship_recognized_text	string	0%	1	no
`citizenship_residency_requirement_for_naturalization_text`	citizenship_residency_requirement_for_naturalization_text	string	0%	1	10 out 12 years preceding application
`citizenship_residency_requirement_for_naturalization_numeric`	citizenship_residency_requirement_for_naturalization_numeric	float	0%	1	10.0
`suffrage_text`	suffrage_text	string	0%	1	18 years of age; universal
`suffrage_numeric`	suffrage_numeric	float	0%	1	18.0
`executive_branch_chief_of_state_text`	executive_branch_chief_of_state_text	string	0%	1	King Sultan IBRAHIM ibni al-Marhum Sultan Iskandar...
+81 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Introduction — 1 rows, 6 columns

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Field details — 7 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (4) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`background_text`	background_text	string	0%	1	Malaysia’s location has long made it an important...
`background_numeric`	background_numeric	float	0%	1	14.0
`source_section`	source_section	string	0%	1	Introduction
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Languages — 1 rows, 8 columns

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Field details — 8 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (5) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`languages_languages_text`	Languages (text)	string	0%	1	Bahasa Malaysia (official), English, Chinese (Cantonese,...
`languages_major_language_sample_s_text`	languages_major_language_sample_s_text	string	0%	1	Buku Fakta Dunia, sumber yang diperlukan untuk maklumat...
`languages_note`	languages_note	string	0%	1	note: Malaysia has 134 languages (112 indigenous and 22...
`source_section`	source_section	string	0%	1	People and Society:languages
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Migration — 1 rows, 9 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (6) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`refugees_and_internally_displaced_persons_refugees_text`	refugees_and_internally_displaced_persons_refugees_text	string	0%	1	191,343 (2024 est.)
`refugees_and_internally_displaced_persons_refugees_numeric`	refugees_and_internally_displaced_persons_refugees_numeric	float	0%	1	191343.0
`refugees_and_internally_displaced_persons_stateless_persons_text`	refugees_and_internally_displaced_persons_stateless_persons_text	string	0%	1	120,857 (2024 est.)
`refugees_and_internally_displaced_persons_stateless_persons_numeric`	refugees_and_internally_displaced_persons_stateless_persons_numeric	float	0%	1	120857.0
`source_section`	source_section	string	0%	1	Transnational Issues:migration
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Military And Security — 1 rows, 23 columns

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Field details — 27 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (24) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`military_and_security_forces_text`	military_and_security_forces_text	string	0%	1	Malaysian Armed Forces (Angkatan Tentera Malaysia, ATM):...
`military_and_security_forces_numeric`	military_and_security_forces_numeric	float	0%	1	2025.0
`military_expenditures_military_expenditures_2024_text`	Military expenditures 2024 (text)	string	0%	1	1% of GDP (2024 est.)
`military_expenditures_military_expenditures_2024_numeric`	Military expenditures 2024 (numeric)	float	0%	1	1.0
`military_expenditures_military_expenditures_2023_text`	Military expenditures 2023 (text)	string	0%	1	0.9% of GDP (2023 est.)
`military_expenditures_military_expenditures_2023_numeric`	Military expenditures 2023 (numeric)	float	0%	1	0.9
`military_expenditures_military_expenditures_2022_text`	Military expenditures 2022 (text)	string	0%	1	1.1% of GDP (2022 est.)
`military_expenditures_military_expenditures_2022_numeric`	Military expenditures 2022 (numeric)	float	0%	1	1.1
`military_expenditures_military_expenditures_2021_text`	Military expenditures 2021 (text)	string	0%	1	1% of GDP (2021 est.)
`military_expenditures_military_expenditures_2021_numeric`	Military expenditures 2021 (numeric)	float	0%	1	1.0
`military_expenditures_military_expenditures_2020_text`	Military expenditures 2020 (text)	string	0%	1	1.1% of GDP (2020 est.)
`military_expenditures_military_expenditures_2020_numeric`	Military expenditures 2020 (numeric)	float	0%	1	1.1
`military_and_security_service_personnel_strengths_text`	military_and_security_service_personnel_strengths_text	string	0%	1	approximately 110,000 active Malaysian Armed Forces (2025)
`military_and_security_service_personnel_strengths_numeric`	military_and_security_service_personnel_strengths_numeric	float	0%	1	110000.0
`military_equipment_inventories_and_acquisitions_text`	military_equipment_inventories_and_acquisitions_text	string	0%	1	the military fields a diverse array of mostly older but...
`military_equipment_inventories_and_acquisitions_numeric`	military_equipment_inventories_and_acquisitions_numeric	float	0%	1	2025.0
`military_service_age_and_obligation_text`	military_service_age_and_obligation_text	string	0%	1	17 years 6 months of age for voluntary military service...
`military_service_age_and_obligation_numeric`	military_service_age_and_obligation_numeric	float	0%	1	17.0
`military_deployments_text`	military_deployments_text	string	0%	1	825 Lebanon (UNIFIL) (2025)
`military_deployments_numeric`	military_deployments_numeric	float	0%	1	825.0
`military_note_text`	military_note_text	string	0%	1	the Malaysian military is responsible for defense of the...
`military_note_numeric`	military_note_numeric	float	0%	1	1971.0
`source_section`	source_section	string	0%	1	Military and Security
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - People And Society — 1 rows, 129 columns

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Field details — 143 fields (type, nulls, unique, samples)

Standards-Aligned Fields (29)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`population_total_numeric`	Population count	float	SEL	0%	1	34905275.0
`population_male_numeric`	Population male	float	SEL	0%	1	17833074.0
`population_female_numeric`	Population female	float	SEL	0%	1	17072201.0
`age_structure_0_14_years_numeric`	Population 0 14 percent	float	SEL	0%	1	22.2
`age_structure_15_64_years_numeric`	Population 15 64 percent	float	SEL	0%	1	69.4
`age_structure_65_years_and_over_numeric`	Population 65 plus percent	float	SEL	0%	1	8.4
`dependency_ratios_total_dependency_ratio_numeric`	Total dependency ratio	float	SEL	0%	1	44.3
`dependency_ratios_youth_dependency_ratio_numeric`	Youth dependency ratio	float	SEL	0%	1	31.7
`dependency_ratios_elderly_dependency_ratio_numeric`	Elderly dependency ratio	float	SEL	0%	1	12.6
`median_age_total_numeric`	Median age	float	SEL	0%	1	32.2
`population_growth_rate_numeric`	Population growth rate percent	float	SEL	0%	1	0.97
`birth_rate_numeric`	Birth rate per 1000	float	SEL	0%	1	14.05
`death_rate_numeric`	Death rate per 1000	float	SEL	0%	1	5.8
`net_migration_rate_numeric`	Net migration per 1000	float	SEL	0%	1	1.43
`urbanization_urban_population_numeric`	Urban population percent	float	SEL	0%	1	78.7
`urbanization_rate_of_urbanization_numeric`	Urban growth rate percent	float	SEL	0%	1	1.87
`sex_ratio_at_birth_numeric`	Sex ratio at birth	float	SEL	0%	1	1.07
`sex_ratio_total_population_numeric`	Sex ratio overall	float	SEL	0%	1	1.05
`maternal_mortality_ratio_numeric`	Maternal mortality per 100k	float	SEL	0%	1	26.0
`infant_mortality_rate_total_numeric`	Infant mortality per 1000	float	SEL	0%	1	6.3
`life_expectancy_at_birth_total_population_numeric`	Life expectancy	float	SEL	0%	1	76.6
`total_fertility_rate_numeric`	Fertility rate	float	SEL	0%	1	1.73
`gross_reproduction_rate_numeric`	Gross reproduction rate	float	SEL	0%	1	0.83
`physician_density_numeric`	Physicians per 1000	float	SEL	0%	1	2.34
`hospital_bed_density_numeric`	Hospital beds per 1000	float	SEL	0%	1	2.0
`literacy_total_population_numeric`	Literacy rate percent	float	SEL	0%	1	95.8
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (114) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`population_total_text`	population_total_text	string	0%	1	34,905,275 (2025 est.)
`population_male_text`	population_male_text	string	0%	1	17,833,074
`population_female_text`	population_female_text	string	0%	1	17,072,201
`age_structure_0_14_years_text`	age_structure_0_14_years_text	string	0%	1	22.2% (male 3,947,914/female 3,730,319)
`age_structure_15_64_years_text`	age_structure_15_64_years_text	string	0%	1	69.4% (male 12,308,938/female 11,666,947)
`age_structure_65_years_and_over_text`	age_structure_65_years_and_over_text	string	0%	1	8.4% (2024 est.) (male 1,409,360/female 1,501,332)
`dependency_ratios_total_dependency_ratio_text`	dependency_ratios_total_dependency_ratio_text	string	0%	1	44.3 (2025 est.)
`dependency_ratios_youth_dependency_ratio_text`	dependency_ratios_youth_dependency_ratio_text	string	0%	1	31.7 (2025 est.)
`dependency_ratios_elderly_dependency_ratio_text`	dependency_ratios_elderly_dependency_ratio_text	string	0%	1	12.6 (2025 est.)
`dependency_ratios_potential_support_ratio_text`	dependency_ratios_potential_support_ratio_text	string	0%	1	7.9 (2025 est.)
`dependency_ratios_potential_support_ratio_numeric`	dependency_ratios_potential_support_ratio_numeric	float	0%	1	7.9
`median_age_total_text`	median_age_total_text	string	0%	1	32.2 years (2025 est.)
`median_age_male_text`	median_age_male_text	string	0%	1	31.7 years
`median_age_male_numeric`	median_age_male_numeric	float	0%	1	31.7
`median_age_female_text`	median_age_female_text	string	0%	1	31.9 years
`median_age_female_numeric`	median_age_female_numeric	float	0%	1	31.9
`population_growth_rate_text`	population_growth_rate_text	string	0%	1	0.97% (2025 est.)
`birth_rate_text`	birth_rate_text	string	0%	1	14.05 births/1,000 population (2025 est.)
`death_rate_text`	death_rate_text	string	0%	1	5.8 deaths/1,000 population (2025 est.)
`net_migration_rate_text`	net_migration_rate_text	string	0%	1	1.43 migrant(s)/1,000 population (2025 est.)
`population_distribution_text`	population_distribution_text	string	0%	1	a highly uneven distribution, with over 80% of the...
`population_distribution_numeric`	population_distribution_numeric	float	0%	1	80.0
`urbanization_urban_population_text`	urbanization_urban_population_text	string	0%	1	78.7% of total population (2023)
`urbanization_rate_of_urbanization_text`	urbanization_rate_of_urbanization_text	string	0%	1	1.87% annual rate of change (2020-25 est.)
`major_urban_areas_population_text`	major_urban_areas_population_text	string	0%	1	8.622 million KUALA LUMPUR (capital), 1.086 million...
`major_urban_areas_population_numeric`	major_urban_areas_population_numeric	float	0%	1	8.622
`sex_ratio_at_birth_text`	sex_ratio_at_birth_text	string	0%	1	1.07 male(s)/female
`sex_ratio_0_14_years_text`	sex_ratio_0_14_years_text	string	0%	1	1.06 male(s)/female
`sex_ratio_0_14_years_numeric`	sex_ratio_0_14_years_numeric	float	0%	1	1.06
`sex_ratio_15_64_years_text`	sex_ratio_15_64_years_text	string	0%	1	1.06 male(s)/female
+84 more extension fields — download the CSV/Parquet to see them all.

MYS OpenFactBook - Religions — 1 rows, 6 columns

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Field details — 24 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national
`composition_religion_buddhist_pct_synth`	Buddhist	numeric	CCL	0%	-	18.7
`composition_religion_christian_pct_synth`	Christian	numeric	CCL	0%	-	9.1
`composition_religion_hindu_pct_synth`	Hindu	numeric	CCL	0%	-	6.1
`composition_ethnicity_primary_label_synth`	Bumiputera	string	CCL	0%	-	Bumiputera

Extension Fields (4) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`religions_text`	religions_text	string	0%	1	Muslim (official) 63.5%, Buddhist 18.7%, Christian 9.1%,...
`religions_numeric`	religions_numeric	float	0%	1	63.5
`source_section`	source_section	string	0%	1	People and Society:religions
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

Pending (13) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`composition_religion_muslim_official_pct_synth`	Muslim (official)	numeric	0%	-	63.5
`composition_religion_other_confucianism_taoism_other_traditional_chinese_religions_pct_synth`	other (Confucianism, Taoism, other traditional Chinese religions)	numeric	0%	-	0.9
`composition_religion_none_unspecified_pct_synth`	none/unspecified	numeric	0%	-	1.8
`composition_ethnicity_bumiputera_pct_synth`	Bumiputera	numeric	0%	-	63.8
`composition_ethnicity_bumiputera__malay_and_indigenous_peoples_pct_synth`	Malay and indigenous peoples	numeric	0%	-	52.8
`composition_ethnicity_bumiputera__including_orang_asli_pct_synth`	including Orang Asli	numeric	0%	-	-
`composition_ethnicity_bumiputera__dayak_pct_synth`	Dayak	numeric	0%	-	-
`composition_ethnicity_bumiputera__anak_negeri_pct_synth`	Anak Negeri	numeric	0%	-	-
`composition_ethnicity_chinese_pct_synth`	Chinese	numeric	0%	-	20.6
`composition_ethnicity_indian_pct_synth`	Indian	numeric	0%	-	6.0
`composition_ethnicity_other_pct_synth`	other	numeric	0%	-	0.6
`composition_ethnicity_non_citizens_pct_synth`	non-citizens	numeric	0%	-	9.0
`composition_ethnicity_primary_share_pct_synth`	Bumiputera	numeric	0%	-	63.8

MYS OpenFactBook - Space — 1 rows, 9 columns

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Field details — 13 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (10) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`space_agency_agencies_text`	space_agency_agencies_text	string	0%	1	Malaysian Space Agency (MYSA; established 2019) (2025)
`space_agency_agencies_numeric`	space_agency_agencies_numeric	float	0%	1	2019.0
`space_launch_site_s_text`	space_launch_site_s_text	string	0%	1	has launched feasibility studies for potential space...
`space_launch_site_s_numeric`	space_launch_site_s_numeric	float	0%	1	2025.0
`space_program_overview_text`	space_program_overview_text	string	0%	1	has a national space policy and program focused on the...
`space_program_overview_numeric`	space_program_overview_numeric	float	0%	1	2025.0
`key_space_program_milestones_text`	key_space_program_milestones_text	string	0%	1	1996 - first of a series of domestically produced...
`key_space_program_milestones_numeric`	key_space_program_milestones_numeric	float	0%	1	1996.0
`source_section`	source_section	string	0%	1	Space
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Terrorism — 1 rows, 6 columns

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Field details — 6 fields (type, nulls, unique, samples)

Standards-Aligned Fields (3)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (3) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`terrorist_group_s_text`	terrorist_group_s_text	string	0%	1	Abu Sayyaf Group, al-Qa'ida, Islamic State of Iraq and...
`source_section`	source_section	string	0%	1	Terrorism
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS OpenFactBook - Transportation — 1 rows, 31 columns

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Field details — 34 fields (type, nulls, unique, samples)

Standards-Aligned Fields (5)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`civil_aircraft_registration_country_code_prefix_text`	Civil aircraft registration country code prefix text	string	SEL+	0%	1	9M
`airports_numeric`	Airports count	float	SEL	0%	1	100.0
`country_code`	Country code	string	SEL	0%	1	MYS
`country_name`	Country name	string	SEL	0%	1	Malaysia
`admin_level`	Admin level	string	SEL	0%	1	national

Extension Fields (29) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`civil_aircraft_registration_country_code_prefix_numeric`	civil_aircraft_registration_country_code_prefix_numeric	float	0%	1	9.0
`airports_text`	airports_text	string	0%	1	100 (2025)
`heliports_text`	heliports_text	string	0%	1	24 (2025)
`heliports_numeric`	heliports_numeric	float	0%	1	24.0
`railways_total_text`	railways_total_text	string	0%	1	1,851 km (2014)
`railways_total_numeric`	railways_total_numeric	float	0%	1	1851.0
`railways_standard_gauge_text`	railways_standard_gauge_text	string	0%	1	59 km (2014) 1.435-m gauge (59 km electrified)
`railways_standard_gauge_numeric`	railways_standard_gauge_numeric	float	0%	1	59.0
`railways_narrow_gauge_text`	railways_narrow_gauge_text	string	0%	1	1,792 km (2014) 1.000-m gauge (339 km electrified)
`railways_narrow_gauge_numeric`	railways_narrow_gauge_numeric	float	0%	1	1792.0
`merchant_marine_total_text`	merchant_marine_total_text	string	0%	1	1,750 (2023)
`merchant_marine_total_numeric`	merchant_marine_total_numeric	float	0%	1	1750.0
`merchant_marine_by_type_text`	merchant_marine_by_type_text	string	0%	1	bulk carrier 14, container ship 35, general cargo 169,...
`merchant_marine_by_type_numeric`	merchant_marine_by_type_numeric	float	0%	1	14.0
`ports_total_ports_text`	ports_total_ports_text	string	0%	1	35 (2024)
`ports_total_ports_numeric`	ports_total_ports_numeric	float	0%	1	35.0
`ports_large_text`	ports_large_text	float	0%	1	3
`ports_large_numeric`	ports_large_numeric	float	0%	1	3.0
`ports_medium_text`	ports_medium_text	float	0%	1	4
`ports_medium_numeric`	ports_medium_numeric	float	0%	1	4.0
`ports_small_text`	ports_small_text	float	0%	1	10
`ports_small_numeric`	ports_small_numeric	float	0%	1	10.0
`ports_very_small_text`	ports_very_small_text	float	0%	1	18
`ports_very_small_numeric`	ports_very_small_numeric	float	0%	1	18.0
`ports_ports_with_oil_terminals_text`	Ports with oil terminals (text)	float	0%	1	24
`ports_ports_with_oil_terminals_numeric`	Ports with oil terminals (numeric)	float	0%	1	24.0
`ports_key_ports_text`	ports_key_ports_text	string	0%	1	Johor, Kota Kinabalu, Port Dickson, Port Klang, Pulau...
`source_section`	source_section	string	0%	1	Transportation
`source_profile_path`	source_profile_path	string	0%	1	east-n-southeast-asia/my.json

MYS NGA GEOnet Names Server (GNS) MYS-NGA_GNS Language

10 rows · 2 table(s) · 2026 NGA GEOnet Names Server (GNS) 0 1 2 3 4 SEL 12 CCL 24 ext ▶

or use the per-admin-level buttons below. Free downloads return a 10-row sample. Contact us for the full pack.

0 Admin 0 — 2 table(s)

MYS Nga Gns - Language — 9 rows, 9 columns

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Field details — 9 fields (type, nulls, unique, samples)

Standards-Aligned Fields (9)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	country_code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`gns_language_code`	gns_language_code	string	CCL	0%	9	msa, mly, ind, eng, tha
`gns_language_name`	gns_language_name	string	CCL	0%	9	Malay (generic), Malay (specific), Indonesian, English, Thai
`gns_toponym_count`	gns_toponym_count	integer	CCL	0%	7	36588, 627, 66, 26, 20
`gns_toponym_share_pct`	gns_toponym_share_pct	float	CCL	0%	7	98.0044, 1.6795, 0.1768, 0.0696, 0.0536
`gns_non_roman_toponym_count`	gns_non_roman_toponym_count	integer	CCL	0%	2	0, 0, 0, 0, 6
`gns_dominant_script_code`	gns_dominant_script_code	string	CCL	0%	2	, , , , Thai
`gns_dominant_script_name`	gns_dominant_script_name	string	CCL	0%	2	, , , , Thai
`gns_script_count`	gns_script_count	integer	CCL	0%	2	0, 0, 0, 0, 1

MYS Nga Gns - Summary — 1 rows, 31 columns

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Field details — 31 fields (type, nulls, unique, samples)

Standards-Aligned Fields (7)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	country_code	string	SEL	0%	1	MYS
`admin_level`	admin_level	integer	SEL	0%	1	0
`gns_country_name`	gns_country_name	string	SEL	0%	1	Malaysia
`gns_language_count`	gns_language_count	integer	CCL	0%	1	9
`gns_script_count`	gns_script_count	integer	CCL	0%	1	1
`gns_endonym_share_pct`	gns_endonym_share_pct	float	CCL	0%	1	99.99
`gns_non_roman_name_count`	gns_non_roman_name_count	integer	CCL	0%	1	13

Extension Fields (24) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`gns_name_count`	gns_name_count	integer	0%	1	90178
`gns_feature_count`	gns_feature_count	integer	0%	1	67798
`gns_endonym_count`	gns_endonym_count	integer	0%	1	90169
`gns_exonym_count`	gns_exonym_count	integer	0%	1	9
`gns_source_build_date`	gns_source_build_date	string	0%	1	Wed, 05 Aug 2026
`gns_source_change_date`	gns_source_change_date	string	0%	1	2026-08-05
`gns_name_count_hypsographic`	gns_name_count_hypsographic	integer	0%	1	11181
`gns_feature_count_hypsographic`	gns_feature_count_hypsographic	integer	0%	1	8000
`gns_name_count_populated_places`	gns_name_count_populated_places	integer	0%	1	29860
`gns_feature_count_populated_places`	gns_feature_count_populated_places	integer	0%	1	20252
`gns_name_count_hydrographic`	gns_name_count_hydrographic	integer	0%	1	37949
`gns_feature_count_hydrographic`	gns_feature_count_hydrographic	integer	0%	1	30779
`gns_name_count_administrative_regions`	gns_name_count_administrative_regions	integer	0%	1	588
`gns_feature_count_administrative_regions`	gns_feature_count_administrative_regions	integer	0%	1	389
`gns_name_count_spot_features`	gns_name_count_spot_features	integer	0%	1	6631
`gns_feature_count_spot_features`	gns_feature_count_spot_features	integer	0%	1	4975
`gns_name_count_areas_localities`	gns_name_count_areas_localities	integer	0%	1	3337
`gns_feature_count_areas_localities`	gns_feature_count_areas_localities	integer	0%	1	2835
`gns_name_count_vegetation`	gns_name_count_vegetation	integer	0%	1	591
`gns_feature_count_vegetation`	gns_feature_count_vegetation	integer	0%	1	538
`gns_name_count_undersea`	gns_name_count_undersea	integer	0%	1	14
`gns_feature_count_undersea`	gns_feature_count_undersea	integer	0%	1	6
`gns_name_count_transportation_networks`	gns_name_count_transportation_networks	integer	0%	1	27
`gns_feature_count_transportation_networks`	gns_feature_count_transportation_networks	integer	0%	1	24

MYS D-PLACE MYS-DPLACE Ethnographic Societies

7 rows · 1 table(s) · 2024 D-PLACE 0 1 2 3 1 SEL 10 CCL 7 ext ▶

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0 Admin 0 — 1 table(s)

MYS D-PLACE - Dplace Ea Societies — 7 rows, 18 columns

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Field details — 18 fields (type, nulls, unique, samples)

Standards-Aligned Fields (11)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`society_id`	Society id	string	CCL	0%	7	Ej14, Ej16, Ej3, Ej8, Ib1
`society_name`	Society name	string	CCL	0%	7	Senoi, Negri Sembilan, Semang, Malays, Iban
`language_glottocode`	Language glottocode	string	CCL	0%	7	sema1266, nege1240, kens1248, mala1479, iban1264
`language_name`	Language name	string	CCL	0%	1	, , , ,
`kinship_system`	Kinship system	string	CCL	0%	6	EA001:1; EA002:1; EA003:2; EA004:0; EA005:6, EA001:0;...
`marriage_pattern`	Marriage pattern	string	CCL	0%	7	EA006:6; EA007:8; EA008:8; EA009:2; EA010:2, EA006:1;...
`subsistence_pattern`	Subsistence pattern	string	CCL	0%	7	EA028:3; EA029:6; EA030:4; EA031:2; EA032:3, EA028:6;...
`political_complexity`	Political complexity	string	CCL	0%	7	EA033:1; EA034:1; EA035:1, EA033:4; EA034:4; EA035:NA,...
`religion_importance`	Religion importance	string	CCL	0%	7	EA034:1; EA112:4, EA034:4; EA112:NA, EA034:3; EA112:4,...
`residence_pattern`	Residence pattern	string	CCL	0%	6	EA011:2; EA012:2; EA013:9, EA011:3; EA012:5; EA013:9,...

Extension Fields (7) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`country_name`	country_name	string	0%	1	Malaysia, Malaysia, Malaysia, Malaysia, Malaysia
`dataset`	dataset	string	0%	1	EA, EA, EA, EA, EA
`region`	region	string	0%	1	, , , ,
`latitude`	latitude	float	0%	6	4.0, 2.58, 5.0, 5.0, 2.0
`longitude`	longitude	float	0%	7	102.0, 102.25, 101.0, 103.0, 112.0
`assignment_method`	assignment_method	string	0%	1	point_in_polygon, point_in_polygon, point_in_polygon,...
`assignment_confidence`	assignment_confidence	string	0%	1	approximate, approximate, approximate, approximate, approximate

MYS Ethnic Power Relations Dataset MYS-EPR Political Ethnicity

6 rows · 1 table(s) · 2021 Ethnic Power Relations Dataset 0 1 2 3 1 SEL 5 CCL 2 ext ▶

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0 Admin 0 — 1 table(s)

MYS EPR - Groups — 6 rows, 8 columns

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Field details — 8 fields (type, nulls, unique, samples)

Standards-Aligned Fields (6)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS, MYS, MYS, MYS, MYS
`ethnic_group_name`	Ethnic group name	string	CCL	0%	6	Malays, Chinese, East Indians, Dayaks, Kadazans
`ethnic_power_status`	Ethnic power status	string	CCL	0%	3	SENIOR PARTNER, JUNIOR PARTNER, JUNIOR PARTNER,...
`ethnic_population_share`	Ethnic population share	float	CCL	0%	6	0.5, 0.226, 0.067, 0.026, 0.021
`ethnic_group_id`	Ethnic group id	float	CCL	0%	6	82005000, 82001000, 82003000, 82002000, 82004000
`regional_autonomy`	Regional autonomy	string	CCL	0%	1	false, false, false, false, false

Extension Fields (2) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`year`	year	integer	0%	1	2021, 2021, 2021, 2021, 2021
`group_relevance`	group_relevance	string	0%	1	, , , ,

MYS Global Organized Crime Index MYS-OCINDEX Crime Index

1 rows · 1 table(s) · 2025 GI-TOC / ENACT 0 1 2 3 13 CCL 22 ext ▶

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0 Admin 0 — 1 table(s)

MYS Organized Crime Scores 2025 — 1 rows, 35 columns

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Field details — 139 fields (type, nulls, unique, samples)

Standards-Aligned Fields (13)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`oc_anti_money_laundering`	oc_anti_money_laundering	numeric	CCL	0%	1	-
`oc_arms_trafficking`	oc_arms_trafficking	numeric	CCL	0%	1	-
`oc_criminal_actors`	oc_criminal_actors	numeric	CCL	0%	1	-
`oc_criminal_markets`	oc_criminal_markets	numeric	CCL	0%	1	-
`oc_criminality`	oc_criminality	numeric	CCL	0%	1	-
`oc_cyber_dependent_crimes`	oc_cyber_dependent_crimes	numeric	CCL	0%	1	-
`oc_financial_crimes`	oc_financial_crimes	numeric	CCL	0%	1	-
`oc_human_smuggling`	oc_human_smuggling	numeric	CCL	0%	1	-
`oc_human_trafficking`	oc_human_trafficking	numeric	CCL	0%	1	-
`oc_judicial_system_and_detention`	oc_judicial_system_and_detention	numeric	CCL	0%	1	-
`oc_law_enforcement`	oc_law_enforcement	numeric	CCL	0%	1	-
`oc_political_leadership_and_governance`	oc_political_leadership_and_governance	numeric	CCL	0%	1	-
`oc_resilience`	oc_resilience	numeric	CCL	0%	1	-

Extension Fields (22) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`oc_cannabis_trade`	oc_cannabis_trade	numeric	0%	1	-
`oc_cocaine_trade`	oc_cocaine_trade	numeric	0%	1	-
`oc_criminal_networks`	oc_criminal_networks	numeric	0%	1	-
`oc_economic_regulatory_capacity`	oc_economic_regulatory_capacity	numeric	0%	1	-
`oc_extortion_and_protection_racketeering`	oc_extortion_and_protection_racketeering	numeric	0%	1	-
`oc_fauna_crimes`	oc_fauna_crimes	numeric	0%	1	-
`oc_flora_crimes`	oc_flora_crimes	numeric	0%	1	-
`oc_foreign_actors`	oc_foreign_actors	numeric	0%	1	-
`oc_government_transparency_and_accountability`	oc_government_transparency_and_accountability	numeric	0%	1	-
`oc_heroin_trade`	oc_heroin_trade	numeric	0%	1	-
`oc_illicit_trade_in_excisable_goods`	oc_illicit_trade_in_excisable_goods	numeric	0%	1	-
`oc_international_cooperation`	oc_international_cooperation	numeric	0%	1	-
`oc_mafia_style_groups`	oc_mafia_style_groups	numeric	0%	1	-
`oc_national_policies_and_laws`	oc_national_policies_and_laws	numeric	0%	1	-
`oc_non_renewable_resource_crimes`	oc_non_renewable_resource_crimes	numeric	0%	1	-
`oc_non_state_actors`	oc_non_state_actors	numeric	0%	1	-
`oc_prevention`	oc_prevention	numeric	0%	1	-
`oc_private_sector_actors`	oc_private_sector_actors	numeric	0%	1	-
`oc_state_embedded_actors`	oc_state_embedded_actors	numeric	0%	1	-
`oc_synthetic_drug_trade`	oc_synthetic_drug_trade	numeric	0%	1	-
`oc_territorial_integrity`	oc_territorial_integrity	numeric	0%	1	-
`oc_trade_in_counterfeit_goods`	oc_trade_in_counterfeit_goods	numeric	0%	1	-

Pending (104) — awaiting mapping

Field Name	Label	Type	Nulls	Unique	Samples
`iso3`	iso3	string	0%	1	MYS
`source`	source	string	0%	1	Global Organized Crime Index
`source_url`	source_url	string	0%	1	https://ocindex.net/
`year`	year	integer	0%	1	2025
`license`	license	string	0%	1	Creative Commons (GI-TOC / ENACT)
`oc_anti_money_laundering_rank`	oc_anti_money_laundering_rank	integer	0%	1	31
`oc_anti_money_laundering_2023`	oc_anti_money_laundering_2023	integer	0%	1	6
`oc_anti_money_laundering_2021`	oc_anti_money_laundering_2021	float	0%	1	5.5
`oc_arms_trafficking_rank`	oc_arms_trafficking_rank	integer	0%	1	90
`oc_arms_trafficking_2023`	oc_arms_trafficking_2023	float	0%	1	5.5
`oc_arms_trafficking_2021`	oc_arms_trafficking_2021	float	0%	1	5.5
`oc_cannabis_trade_rank`	oc_cannabis_trade_rank	integer	0%	1	100
`oc_cannabis_trade_2023`	oc_cannabis_trade_2023	integer	0%	1	5
`oc_cannabis_trade_2021`	oc_cannabis_trade_2021	integer	0%	1	5
`oc_cocaine_trade_rank`	oc_cocaine_trade_rank	integer	0%	1	126
`oc_cocaine_trade_2023`	oc_cocaine_trade_2023	integer	0%	1	4
`oc_cocaine_trade_2021`	oc_cocaine_trade_2021	integer	0%	1	5
`oc_criminal_actors_rank`	oc_criminal_actors_rank	integer	0%	1	40
`oc_criminal_actors_2023`	oc_criminal_actors_2023	float	0%	1	5.8
`oc_criminal_actors_2021`	oc_criminal_actors_2021	float	0%	1	5.63
`oc_criminal_markets_rank`	oc_criminal_markets_rank	integer	0%	1	23
`oc_criminal_markets_2023`	oc_criminal_markets_2023	float	0%	1	6.67
`oc_criminal_markets_2021`	oc_criminal_markets_2021	float	0%	1	6.25
`oc_criminal_networks_rank`	oc_criminal_networks_rank	integer	0%	1	74
`oc_criminal_networks_2023`	oc_criminal_networks_2023	float	0%	1	5.5
`oc_criminal_networks_2021`	oc_criminal_networks_2021	float	0%	1	5.5
`oc_criminality_rank`	oc_criminality_rank	integer	0%	1	35
`oc_criminality_2023`	oc_criminality_2023	float	0%	1	6.23
`oc_criminality_2021`	oc_criminality_2021	float	0%	1	5.94
`oc_cyber_dependent_crimes_rank`	oc_cyber_dependent_crimes_rank	integer	0%	1	7
+74 more pending fields — download the CSV/Parquet to see them all.

MYS World Values Survey MYS-WVS Attitudes Values

1 rows · 1 table(s) · 2022 World Values Survey 0 1 2 3 1 SEL 25 ext ▶

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0 Admin 0 — 1 table(s)

MYS WVS - Country Aggregates — 1 rows, 26 columns

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Field details — 26 fields (type, nulls, unique, samples)

Standards-Aligned Fields (1)

Field Name	Label	Type	Schema	Nulls	Unique	Samples
`country_code`	Country code	string	SEL	0%	1	MYS

Extension Fields (25) — supplemental data preserved outside core SEL/CCL schema

Field Name	Label	Type	Nulls	Unique	Samples
`wave`	wave	integer	0%	1	7
`wave_years`	wave_years	string	0%	1	2017-2022
`source`	source	string	0%	1	World Values Survey
`importance_religion`	importance_religion	integer	0%	1	1
`importance_family`	importance_family	integer	0%	1	1
`importance_friends`	importance_friends	integer	0%	1	1
`trust_people`	trust_people	integer	0%	1	1
`trust_family`	trust_family	integer	0%	1	1
`life_satisfaction`	life_satisfaction	integer	0%	1	1
`happiness`	happiness	integer	0%	1	1
`freedom_choice`	freedom_choice	integer	0%	1	1
`gender_jobs_scarce`	gender_jobs_scarce	integer	0%	1	1
`gender_political_leaders`	gender_political_leaders	integer	0%	1	1
`gender_university`	gender_university	integer	0%	1	1
`justifiable_divorce`	justifiable_divorce	integer	0%	1	1
`justifiable_homosexuality`	justifiable_homosexuality	integer	0%	1	1
`immigration_policy`	immigration_policy	integer	0%	1	1
`immigrants_jobs`	immigrants_jobs	integer	0%	1	1
`immigrants_culture`	immigrants_culture	integer	0%	1	1
`confidence_government`	confidence_government	integer	0%	1	1
`confidence_parliament`	confidence_parliament	integer	0%	1	1
`confidence_police`	confidence_police	integer	0%	1	1
`confidence_courts`	confidence_courts	integer	0%	1	1
`confidence_press`	confidence_press	integer	0%	1	1
`democracy_importance`	democracy_importance	integer	0%	1	1

HERA · Human Entity Reference Atlas by Masego Inc

Build a download

Language	Place names	Share	Script
Malay (generic) (msa)	36,588	98.0%	—
Malay (specific) (mly)	627	1.7%	—
Indonesian (ind)	66	0.2%	—
English (eng)	26	0.1%	—
Thai (tha)	20	0.1%	Thai

Source	Type	Access
Department of Statistics Malaysia (DOSM)	official_api	api
HDX COD — Population Statistics (OCHA/UNFPA)	international_organization	bulk_download
HDX (Humanitarian Data Exchange)	international_organization	bulk_download
Google	commercial	bulk_download
GI-TOC / ENACT (Global Initiative Against Transnational Organized Crime · ENACT)	academic	bulk_download
Ethnic Power Relations Dataset	academic	bulk_download
D-PLACE (Database of Places, Language, Culture & Environment)	academic	bulk_download
World Bank Open Data	international_organization	api
UNHCR Refugee Data Finder (UN High Commissioner for Refugees)	international_organization	api
NGA GEOnet Names Server (GNS)	official_government	bulk_download
Global Data Lab	academic	api
World Values Survey	academic	metadata_catalog
Glottolog Language Catalog	academic	bulk_download
LandScan Global (ORNL — Oak Ridge National Laboratory)	research_institution	earth_engine
GHS Urban Centre Database (Global Human Settlement · EU Joint Research Centre)	international_organization	bulk_download
OpenFactBook	community_compilation	bulk_download