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Login: chart-diff: ✅No charts for review.data-diff: ❌ Found differences= Dataset garden/animal_welfare/2024-06-04/bullfighting_laws
= Table bullfighting_laws
~ Column status (changed metadata)
+ + hasChartTab: false
= Dataset garden/artificial_intelligence/2024-07-16/cset
= Table cset
~ Column disclosed_investment (changed metadata)
- - Only includes private-market investment such as venture capital; excludes all investment in publicly traded companies, such as "Big Tech" firms. This data is expressed in US dollars, adjusted for inflation.
? ^^^^^^^^^^^^^^^
+ + Only includes private-market investment flows, such as venture capital; excludes all investment in publicly traded companies, such as the "Big Tech" firms. This data is expressed in US dollars, adjusted for inflation.
? ^^^^^^^^^^^^^^^^^^^^^^ ++++
- - - The data likely underestimates total global AI investment, as it only captures certain types of private equity transactions, excluding other significant channels and categories of AI-related spending.
- - - The dataset only covers private-market investment such as venture capital. It excludes non-equity financing, such as debt and grants, and publicly traded companies, including major Big Tech firms. As a result, significant investments from public companies, corporate R&D, government funding, and broader infrastructure costs (like data centers and hardware) are not captured, limiting the data's coverage of global AI investments.
? ^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^
+ + - The dataset only covers private-market investment flows, such as venture capital. It excludes non-equity financing, such as debt and grants, and omits publicly traded companies, including major Big Tech firms (e.g., Amazon, Microsoft, Meta). As a result, significant investments from public companies, corporate R&D, government funding, and broader infrastructure costs (like data centers and hardware) are not captured, limiting the dataset’s coverage of global AI investments.
? ^^^^^^^^^^^^^^^^^^^^^^ ++++++ +++ +++ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^
- - - The data's "World" aggregate reflects the total investment represented in the data, but may not represent global AI efforts comprehensively, especially in countries not included in the data.
- - - One-time events, such as large acquisitions, can distort yearly figures, while broader economic factors like interest rates and market sentiment can influence investment trends independently of AI-specific developments.
+ + - One-time events like large acquisitions can skew yearly figures, and macroeconomic conditions (e.g., interest rates, market sentiment) may impact trends independently of AI-related dynamics.
+ + - The dataset’s "World" aggregate reflects the total investment represented but does not encompass global AI efforts comprehensively, especially in countries not included in the data.
+ + - The dataset likely underestimates the total global AI investment, as it only captures certain types of private equity transactions, excluding other significant channels and categories of AI-related spending.
- - - Reporting a time series of AI investments in nominal prices would make it difficult to compare observations across time. To make these comparisons possible, one has to take into account that prices change (inflation).
- - - It is not obvious how to adjust this time series for inflation, and our team discussed the best solutions at our disposal.
+ + - Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation).
+ + - It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team.
- - - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services purchased through these investments. This would make it possible to calculate a volume measure of AI investments and tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost of some crucial AI technology has fallen rapidly in price.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^
+ + - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^
- - - In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. Ultimately, we decided to use the US Consumer Price Index (CPI).
? ^^^^^^^^^^^
+ + - In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. In the end we decided to use the US Consumer Price Index (CPI).
? ^^^^^^^^^^
- - - The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments, therefore, lets us understand the size of these investments relative to whatever else these sums of money could have purchased.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ + - The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments therefore lets us understand the size of these investments relative to whatever else these sums of money could have purchased.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
~ Column disclosed_investment_summary (changed metadata)
- - Total disclosed investment between 2013-2023. Only includes private-market investment such as venture capital; excludes all investment in publicly traded companies, such as "Big Tech" firms. This data is expressed in US dollars, adjusted for inflation.
? ^^^^^^^^^^^^^^^
+ + Total disclosed investment between 2013-2023. Only includes private-market investment flows, such as venture capital; excludes all investment in publicly traded companies, such as the "Big Tech" firms. This data is expressed in US dollars, adjusted for inflation.
? ^^^^^^^^^^^^^^^^^^^^^^ ++++
- - - The data likely underestimates total global AI investment, as it only captures certain types of private equity transactions, excluding other significant channels and categories of AI-related spending.
- - - The dataset only covers private-market investment such as venture capital. It excludes non-equity financing, such as debt and grants, and publicly traded companies, including major Big Tech firms. As a result, significant investments from public companies, corporate R&D, government funding, and broader infrastructure costs (like data centers and hardware) are not captured, limiting the data's coverage of global AI investments.
? ^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^
+ + - The dataset only covers private-market investment flows, such as venture capital. It excludes non-equity financing, such as debt and grants, and omits publicly traded companies, including major Big Tech firms (e.g., Amazon, Microsoft, Meta). As a result, significant investments from public companies, corporate R&D, government funding, and broader infrastructure costs (like data centers and hardware) are not captured, limiting the dataset’s coverage of global AI investments.
? ^^^^^^^^^^^^^^^^^^^^^^ ++++++ +++ +++ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^
- - - The data's "World" aggregate reflects the total investment represented in the data, but may not represent global AI efforts comprehensively, especially in countries not included in the data.
- - - One-time events, such as large acquisitions, can distort yearly figures, while broader economic factors like interest rates and market sentiment can influence investment trends independently of AI-specific developments.
+ + - One-time events like large acquisitions can skew yearly figures, and macroeconomic conditions (e.g., interest rates, market sentiment) may impact trends independently of AI-related dynamics.
+ + - The dataset’s "World" aggregate reflects the total investment represented but does not encompass global AI efforts comprehensively, especially in countries not included in the data.
+ + - The dataset likely underestimates the total global AI investment, as it only captures certain types of private equity transactions, excluding other significant channels and categories of AI-related spending.
- - - Reporting a time series of AI investments in nominal prices would make it difficult to compare observations across time. To make these comparisons possible, one has to take into account that prices change (inflation).
- - - It is not obvious how to adjust this time series for inflation, and our team discussed the best solutions at our disposal.
+ + - Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation).
+ + - It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team.
- - - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services purchased through these investments. This would make it possible to calculate a volume measure of AI investments and tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost of some crucial AI technology has fallen rapidly in price.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^
+ + - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^
- - - In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. Ultimately, we decided to use the US Consumer Price Index (CPI).
? ^^^^^^^^^^^
+ + - In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. In the end we decided to use the US Consumer Price Index (CPI).
? ^^^^^^^^^^
- - - The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments, therefore, lets us understand the size of these investments relative to whatever else these sums of money could have purchased.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ + - The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments therefore lets us understand the size of these investments relative to whatever else these sums of money could have purchased.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
~ Column estimated_investment_summary (changed metadata)
- - Total estimated investment between 2013-2023. Only includes private-market investment such as venture capital; excludes all investment in publicly traded companies, such as "Big Tech" firms. This data is expressed in US dollars, adjusted for inflation.
? ^^^^^^^^^^^^^^^
+ + Total estimated investment between 2013-2023. Only includes private-market investment flows, such as venture capital; excludes all investment in publicly traded companies, such as the "Big Tech" firms. This data is expressed in US dollars, adjusted for inflation.
? ^^^^^^^^^^^^^^^^^^^^^^ ++++
- - - The data likely underestimates total global AI investment, as it only captures certain types of private equity transactions, excluding other significant channels and categories of AI-related spending.
- - - The dataset only covers private-market investment such as venture capital. It excludes non-equity financing, such as debt and grants, and publicly traded companies, including major Big Tech firms. As a result, significant investments from public companies, corporate R&D, government funding, and broader infrastructure costs (like data centers and hardware) are not captured, limiting the data's coverage of global AI investments.
? ^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^
+ + - The dataset only covers private-market investment flows, such as venture capital. It excludes non-equity financing, such as debt and grants, and omits publicly traded companies, including major Big Tech firms (e.g., Amazon, Microsoft, Meta). As a result, significant investments from public companies, corporate R&D, government funding, and broader infrastructure costs (like data centers and hardware) are not captured, limiting the dataset’s coverage of global AI investments.
? ^^^^^^^^^^^^^^^^^^^^^^ ++++++ +++ +++ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^
- - - The data's "World" aggregate reflects the total investment represented in the data, but may not represent global AI efforts comprehensively, especially in countries not included in the data.
- - - One-time events, such as large acquisitions, can distort yearly figures, while broader economic factors like interest rates and market sentiment can influence investment trends independently of AI-specific developments.
+ + - One-time events like large acquisitions can skew yearly figures, and macroeconomic conditions (e.g., interest rates, market sentiment) may impact trends independently of AI-related dynamics.
+ + - The dataset’s "World" aggregate reflects the total investment represented but does not encompass global AI efforts comprehensively, especially in countries not included in the data.
+ + - The dataset likely underestimates the total global AI investment, as it only captures certain types of private equity transactions, excluding other significant channels and categories of AI-related spending.
- - - Reporting a time series of AI investments in nominal prices would make it difficult to compare observations across time. To make these comparisons possible, one has to take into account that prices change (inflation).
- - - It is not obvious how to adjust this time series for inflation, and our team discussed the best solutions at our disposal.
+ + - Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation).
+ + - It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team.
- - - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services purchased through these investments. This would make it possible to calculate a volume measure of AI investments and tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost of some crucial AI technology has fallen rapidly in price.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^
+ + - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^
- - - In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. Ultimately, we decided to use the US Consumer Price Index (CPI).
? ^^^^^^^^^^^
+ + - In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. In the end we decided to use the US Consumer Price Index (CPI).
? ^^^^^^^^^^
- - - The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments, therefore, lets us understand the size of these investments relative to whatever else these sums of money could have purchased.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ + - The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments therefore lets us understand the size of these investments relative to whatever else these sums of money could have purchased.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
~ Column investment_estimated (changed metadata)
- - Only includes private-market investment such as venture capital; excludes all investment in publicly traded companies, such as "Big Tech" firms. This data is expressed in US dollars, adjusted for inflation.
? ^^^^^^^^^^^^^^^
+ + Only includes private-market investment flows, such as venture capital; excludes all investment in publicly traded companies, such as the "Big Tech" firms. This data is expressed in US dollars, adjusted for inflation.
? ^^^^^^^^^^^^^^^^^^^^^^ ++++
- - - The data likely underestimates total global AI investment, as it only captures certain types of private equity transactions, excluding other significant channels and categories of AI-related spending.
- - - The dataset only covers private-market investment such as venture capital. It excludes non-equity financing, such as debt and grants, and publicly traded companies, including major Big Tech firms. As a result, significant investments from public companies, corporate R&D, government funding, and broader infrastructure costs (like data centers and hardware) are not captured, limiting the data's coverage of global AI investments.
? ^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^
+ + - The dataset only covers private-market investment flows, such as venture capital. It excludes non-equity financing, such as debt and grants, and omits publicly traded companies, including major Big Tech firms (e.g., Amazon, Microsoft, Meta). As a result, significant investments from public companies, corporate R&D, government funding, and broader infrastructure costs (like data centers and hardware) are not captured, limiting the dataset’s coverage of global AI investments.
? ^^^^^^^^^^^^^^^^^^^^^^ ++++++ +++ +++ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^
- - - The data's "World" aggregate reflects the total investment represented in the data, but may not represent global AI efforts comprehensively, especially in countries not included in the data.
- - - One-time events, such as large acquisitions, can distort yearly figures, while broader economic factors like interest rates and market sentiment can influence investment trends independently of AI-specific developments.
+ + - One-time events like large acquisitions can skew yearly figures, and macroeconomic conditions (e.g., interest rates, market sentiment) may impact trends independently of AI-related dynamics.
+ + - The dataset’s "World" aggregate reflects the total investment represented but does not encompass global AI efforts comprehensively, especially in countries not included in the data.
+ + - The dataset likely underestimates the total global AI investment, as it only captures certain types of private equity transactions, excluding other significant channels and categories of AI-related spending.
- - - Reporting a time series of AI investments in nominal prices would make it difficult to compare observations across time. To make these comparisons possible, one has to take into account that prices change (inflation).
- - - It is not obvious how to adjust this time series for inflation, and our team discussed the best solutions at our disposal.
+ + - Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation).
+ + - It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team.
- - - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services purchased through these investments. This would make it possible to calculate a volume measure of AI investments and tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost of some crucial AI technology has fallen rapidly in price.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^
+ + - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^
- - - In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. Ultimately, we decided to use the US Consumer Price Index (CPI).
? ^^^^^^^^^^^
+ + - In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. In the end we decided to use the US Consumer Price Index (CPI).
? ^^^^^^^^^^
- - - The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments, therefore, lets us understand the size of these investments relative to whatever else these sums of money could have purchased.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ + - The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments therefore lets us understand the size of these investments relative to whatever else these sums of money could have purchased.
? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
= Dataset garden/harvard/2023-09-18/colonial_dates_dataset
= Table colonial_dates_dataset
~ Column last_colonizer_grouped (changed metadata)
+ + hasChartTab: false
= Dataset garden/hmd/2024-11-19/hfd
= Table cohort_ages
~ Column asfr_cohort (changed metadata)
- - <% set title = "Cohort fertility rate" %>
+ + <% set title = "Cohort fertility rates" %>
? +
- - <% elif birth_order == '5p' %>
- - << title >> - Mother age: << age_str >> - Birth order: ≥5
= Table cohort
~ Column ccf (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column ccf40 (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column cmab (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column cmab40 (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column ppr (changed metadata)
- - title: Cohort parity progression ratio - << (birth_order | int) - 1 >> to << (birth_order | int) >> birth
+ + title: Cohort parity progression ratios - << birth_order | int >> to << (birth_order | int) + 1 >> birth
~ Column sdcmab (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column sdcmab40 (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
= Table period_ages
~ Column asfr_period (changed metadata)
- - <% set title = "Period fertility rate" %>
+ + <% set title = "Period fertility rates" %>
? +
- - <% elif birth_order == '5p' %>
- - << title >> - Mother age: << age_str >> - Birth order: ≥5
= Table period
~ Column adjtfr (changed metadata)
- - <% set title = "Tempo-adjusted total fertility rate" %>
+ + <% set title = "Tempo-adjusted total fertility rates" %>
? +
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column b (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column cbr (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column mab (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column mab40 (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column patfr (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column sdmab (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column sdmab40 (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column tfr (changed metadata)
- - <% set title = "Period total fertility rate" %>
+ + <% set title = "Period total fertility rates" %>
? +
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column tfr40 (changed metadata)
- - <% set title = "Period total fertility rate by age 40" %>
+ + <% set title = "Period total fertility rates by age 40" %>
? +
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
~ Column tmab (changed metadata)
- - <%- elif birth_order == '5p' %>
- - << title >> - Birth order: ≥5
= Dataset garden/wb/2024-10-07/world_bank_pip
= Table consumption_2011
= Table income_2017_headcount_60_median
= Table consumption_2017_decile7_thr
= Table consumption_2017_total_shortfall_2000
= Table income_2017_gini
= Table consumption_2017_headcount_ratio_50_median
= Table consumption_2017_avg_shortfall_100
= Table consumption_2017_polarization
= Table income_2017_decile5_thr
= Table income_consumption_2017_headcount_4000
= Table income_2017_poverty_gap_index_215
= Table consumption_2017_decile7_share
= Table income_consumption_2017_total_shortfall_2000
= Table consumption_2017_top1_avg
= Table income_2011
= Table income_consumption_2017_top90_99_share
= Table consumption_2017_headcount_365
= Table income_consumption_2017_income_gap_ratio_60_median
= Table income_consumption_2017_headcount_ratio_60_median
= Table consumption_2017_spr
= Table income_2017_decile9_share
= Table income_2017_poverty_gap_index_3000
= Table income_consumption_2017_p90_p50_ratio
= Table consumption_2017_poverty_gap_index_215
= Table consumption_2017_headcount_ratio_3000
= Table income_consumption_2017_top1_thr
= Table consumption_2017_avg_shortfall_3000
= Table income_consumption_2017_headcount_ratio_50_median
= Table consumption_2017_decile6_thr
= Table income_2017_headcount_100
= Table income_consumption_2017_headcount_50_median
= Table income_consumption_2017_headcount_ratio_40_median
= Table income_2017_decile2_share
= Table income_2017_decile1_share
= Table income_2017_income_gap_ratio_4000
= Table consumption_2017_headcount_100
= Table income_2017_total_shortfall_50_median
= Table consumption_2017_decile2_thr
= Table consumption_2017_pg
= Table consumption_2017_mean
= Table income_consumption_2017_bottom50_share
= Table income_2017_poverty_gap_index_60_median
= Table consumption_2017_decile5_avg
= Table consumption_2017_poverty_gap_index_685
= Table income_2017_poverty_gap_index_50_median
= Table income_consumption_2017_total_shortfall_1000
= Table income_consumption_2017_total_shortfall_685
= Table consumption_2017_total_shortfall_685
= Table income_consumption_2017_avg_shortfall_1000
= Table income_consumption_2017_headcount_3000
= Table consumption_2017_headcount_2000
= Table income_consumption_2017_decile1_share
= Table income_2017_decile8_thr
= Table income_consumption_2017_unsmoothed
= Table income_2017_top1_thr
= Table income_2017_decile7_avg
= Table consumption_2017_headcount_ratio_2000
= Table income_consumption_2017_headcount_215
= Table consumption_2017_income_gap_ratio_3000
= Table consumption_2017_poverty_gap_index_40_median
= Table consumption_2017_poverty_gap_index_365
= Table consumption_2017_headcount_4000
= Table income_2017_poverty_gap_index_40_median
= Table consumption_2017_p90_p10_ratio
= Table income_2017_headcount_ratio_50_median
= Table income_consumption_2017_decile3_thr
= Table consumption_2017_decile4_thr
= Table income_consumption_2017_decile7_avg
= Table income_consumption_2017_avg_shortfall_685
= Table income_2017_decile5_share
= Table consumption_2017_income_gap_ratio_215
= Table income_consumption_2017_decile4_thr
= Table consumption_2017_decile1_avg
= Table consumption_2017_income_gap_ratio_4000
= Table consumption_2017_headcount_ratio_60_median
= Table income_consumption_2017_total_shortfall_365
= Table income_2017_headcount_ratio_1000
= Table income_2017_decile4_avg
= Table consumption_2017_headcount_ratio_1000
= Table income_consumption_2017_top1_avg
= Table income_consumption_2017_decile6_share
= Table consumption_2017_decile8_avg
= Table income_consumption_2017_decile8_share
= Table income_consumption_2017_decile8_thr
= Table income_2017_income_gap_ratio_215
= Table income_consumption_2017_poverty_gap_index_50_median
= Table income_2017_p90_p50_ratio
= Table income_consumption_2017_decile2_avg
= Table income_2017_decile2_thr
= Table consumption_2011_2017
= Table income_consumption_2017_poverty_gap_index_100
= Table income_consumption_2017_income_gap_ratio_1000
= Table income_consumption_2017_total_shortfall_100
= Table income_2017_decile9_thr
= Table income_consumption_2017_poverty_gap_index_365
= Table income_2017_avg_shortfall_100
= Table income_2017_headcount_4000
= Table income_consumption_2017_middle40_share
= Table income_2017_total_shortfall_365
= Table income_consumption_2017_avg_shortfall_50_median
= Table consumption_2017_decile7_avg
= Table income_2017_poverty_gap_index_100
= Table income_consumption_2011_unsmoothed
= Table income_consumption_2017_income_gap_ratio_4000
= Table income_consumption_2017_decile9_avg
= Table income_consumption_2017_income_gap_ratio_50_median
= Table income_2017_income_gap_ratio_50_median
= Table income_2017_p90_p10_ratio
= Table income_consumption_2017_avg_shortfall_40_median
= Table income_consumption_2017_decile10_share
= Table income_consumption_2017_income_gap_ratio_40_median
= Table income_consumption_2017_decile8_avg
= Table income_2017_headcount_2000
= Table consumption_2017_decile9_avg
= Table consumption_2017_decile6_share
= Table income_consumption_2017_avg_shortfall_100
= Table consumption_2017_gini
= Table consumption_2017_decile1_share
= Table consumption_2017_total_shortfall_50_median
= Table income_consumption_2017_income_gap_ratio_2000
= Table income_consumption_2017_headcount_685
= Table consumption_2017_avg_shortfall_215
= Table income_consumption_2017_poverty_gap_index_2000
= Table income_2017_headcount_1000
= Table income_consumption_2017_decile9_thr
= Table income_consumption_2017_headcount_ratio_365
= Table consumption_2017_avg_shortfall_50_median
= Table income_2017_middle40_share
= Table consumption_2017_headcount_1000
= Table income_consumption_2017_top1_share
= Table consumption_2017_poverty_gap_index_3000
= Table income_2017_income_gap_ratio_40_median
= Table income_consumption_2017_poverty_gap_index_3000
= Table income_consumption_2017_decile6_avg
= Table income_consumption_2017_s80_s20_ratio
= Table income_2011_2017
= Table income_2017_avg_shortfall_40_median
= Table income_consumption_2017_headcount_ratio_1000
= Table income_consumption_2017_total_shortfall_60_median
= Table income_consumption_2017_total_shortfall_215
= Table income_2017_avg_shortfall_685
= Table income_2017_income_gap_ratio_2000
= Table income_2017_poverty_gap_index_1000
= Table income_consumption_2017_decile6_thr
= Table income_2017_total_shortfall_2000
= Table income_2017_headcount_40_median
= Table income_2017_decile7_thr
= Table income_consumption_2017_decile4_share
= Table income_consumption_2017_headcount_100
= Table income_2017_decile10_avg
= Table income_consumption_2017_decile2_thr
= Table consumption_2017_total_shortfall_100
= Table income_consumption_2011
= Table income_consumption_2017_headcount_ratio_2000
= Table income_2017_avg_shortfall_50_median
= Table income_consumption_2017_mean
= Table consumption_2017_headcount_215
= Table income_2017_decile5_avg
= Table income_2017_decile8_avg
= Table income_2017_poverty_gap_index_365
= Table consumption_2017_income_gap_ratio_1000
= Table income_2017_total_shortfall_4000
= Table consumption_2017_decile6_avg
= Table income_consumption_2017_decile7_thr
= Table consumption_2017_poverty_gap_index_4000
= Table income_2017_top1_share
= Table consumption_2017_poverty_gap_index_60_median
= Table consumption_2017_total_shortfall_40_median
= Table income_2017_total_shortfall_685
= Table income_consumption_2017_median
= Table income_2017_decile6_thr
= Table income_2017_income_gap_ratio_1000
= Table income_2017_total_shortfall_1000
= Table income_consumption_2017_decile3_avg
= Table income_2017_headcount_ratio_3000
= Table consumption_2017_top1_thr
= Table consumption_2017_avg_shortfall_2000
= Table income_consumption_2017_decile2_share
= Table income_consumption_2017_headcount_40_median
= Table income_2017_headcount_ratio_4000
= Table income_2017_avg_shortfall_60_median
= Table income_consumption_2017_poverty_gap_index_4000
= Table income_2017_avg_shortfall_1000
= Table income_consumption_2017_headcount_ratio_4000
= Table consumption_2017_decile8_share
= Table consumption_2017_decile5_share
= Table consumption_2017_s80_s20_ratio
= Table income_consumption_2017_poverty_gap_index_215
= Table income_2017_decile4_thr
= Table income_2017_headcount_3000
= Table percentiles_income_consumption_2017
= Table income_2017_headcount_ratio_60_median
= Table income_2017_headcount_ratio_365
= Table income_consumption_2017_income_gap_ratio_3000
= Table income_consumption_2017_headcount_2000
= Table income_2017_avg_shortfall_365
= Table income_2017_decile10_share
= Table income_2017_decile6_avg
= Table income_2017_decile6_share
= Table consumption_2017_total_shortfall_60_median
= Table income_2017_avg_shortfall_2000
= Table income_2017_decile3_share
= Table income_2017_headcount_ratio_100
= Table income_consumption_2017_polarization
= Table income_2017_spr
= Table income_consumption_2017_headcount_ratio_3000
= Table income_consumption_2017_headcount_60_median
= Table income_2017_total_shortfall_60_median
= Table income_consumption_2017_spl
= Table percentiles_income_consumption_2011
= Table consumption_2017_p50_p10_ratio
= Table income_2017_mld
= Table income_2017_decile1_thr
= Table consumption_2017_headcount_685
= Table income_consumption_2017_palma_ratio
= Table income_consumption_2017_spr
= Table income_2017_headcount_ratio_215
= Table income_consumption_2017_decile5_share
= Table income_2017_polarization
= Table consumption_2017_decile10_avg
= Table income_consumption_2017_income_gap_ratio_365
= Table consumption_2017_headcount_ratio_685
= Table consumption_2017_decile3_avg
= Table income_2017
= Table income_consumption_2017_pg
= Table consumption_2017_headcount_ratio_215
= Table income_consumption_2017_total_shortfall_50_median
= Table consumption_2017_bottom50_share
= Table income_2017_poverty_gap_index_2000
= Table income_consumption_2017_decile3_share
= Table income_consumption_2017_avg_shortfall_2000
= Table income_2017_top1_avg
= Table income_consumption_2017_poverty_gap_index_1000
= Table income_2017_total_shortfall_215
= Table income_consumption_2017_decile4_avg
= Table income_2017_top90_99_share
= Table income_consumption_2017_mld
= Table consumption_2017_avg_shortfall_40_median
= Table income_2017_income_gap_ratio_685
= Table income_consumption_2017_total_shortfall_4000
= Table consumption_2017_avg_shortfall_60_median
= Table income_2017_headcount_365
= Table consumption_2017_decile4_share
= Table income_2017_decile3_avg
= Table income_2017_headcount_685
= Table consumption_2017_income_gap_ratio_685
= Table income_2017_poverty_gap_index_4000
= Table consumption_2017_income_gap_ratio_2000
= Table income_2017_headcount_ratio_685
= Table income_consumption_2017_headcount_ratio_215
= Table consumption_2017_headcount_3000
= Table income_consumption_2017_income_gap_ratio_685
= Table income_2017_avg_shortfall_215
= Table income_consumption_2017_income_gap_ratio_215
= Table consumption_2017_headcount_ratio_4000
= Table consumption_2017_decile1_thr
= Table income_consumption_2011_2017
= Table income_2017_total_shortfall_100
= Table consumption_2017_avg_shortfall_365
= Table consumption_2017_total_shortfall_3000
= Table income_2017_decile1_avg
= Table consumption_2017_poverty_gap_index_2000
= Table income_2017_decile4_share
= Table consumption_2017_poverty_gap_index_50_median
= Table consumption_2017_poverty_gap_index_100
= Table income_consumption_2017_headcount_1000
= Table income_2017_median
= Table income_consumption_2017_decile10_avg
= Table income_2017_income_gap_ratio_100
= Table income_consumption_2017_decile9_share
= Table income_2017_income_gap_ratio_60_median
= Table consumption_2017_decile4_avg
= Table consumption_2017_decile8_thr
= Table income_consumption_2017_decile1_thr
= Table consumption_2017_top1_share
= Table consumption_2017_income_gap_ratio_365
= Table consumption_2017
= Table income_consumption_2017_poverty_gap_index_685
= Table consumption_2017_middle40_share
= Table income_2017_income_gap_ratio_365
= Table consumption_2017_avg_shortfall_1000
= Table consumption_2017_avg_shortfall_685
= Table consumption_2017_decile9_share
= Table income_consumption_2017_avg_shortfall_3000
= Table consumption_2017_decile5_thr
= Table income_consumption_2017_poverty_gap_index_40_median
= Table consumption_2017_income_gap_ratio_40_median
= Table income_consumption_2017_income_gap_ratio_100
= Table consumption_2017_top90_99_share
= Table consumption_2017_headcount_40_median
= Table income_consumption_2017_decile5_avg
= Table income_consumption_2017_avg_shortfall_60_median
= Table income_consumption_2017_poverty_gap_index_60_median
= Table income_consumption_2017_avg_shortfall_365
= Table consumption_2017_decile2_avg
= Table income_consumption_2017_gini
= Table income_2017_decile8_share
= Table income_consumption_2017_decile7_share
= Table income_consumption_2017_total_shortfall_40_median
= Table income_2017_decile7_share
= Table income_2017_s80_s20_ratio
= Table consumption_2017_headcount_ratio_365
= Table consumption_2017_median
= Table income_2017_headcount_ratio_2000
= Table consumption_2017_headcount_ratio_40_median
= Table consumption_2017_avg_shortfall_4000
= Table consumption_2017_total_shortfall_4000
= Table consumption_2017_decile9_thr
= Table income_2017_pg
= Table consumption_2017_decile10_share
= Table income_2017_total_shortfall_3000
= Table consumption_2017_headcount_60_median
= Table income_consumption_2017_avg_shortfall_215
= Table consumption_2017_p90_p50_ratio
= Table income_2017_bottom50_share
= Table income_consumption_2017_decile5_thr
= Table consumption_2017_decile3_thr
= Table income_2017_decile9_avg
= Table consumption_2017_palma_ratio
= Table income_2017_headcount_50_median
= Table income_2017_poverty_gap_index_685
= Table consumption_2017_total_shortfall_365
= Table consumption_2017_total_shortfall_215
= Table income_consumption_2017
~ Column avg_shortfall_100 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_1000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_2000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_215 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_3000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_365 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_4000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_40_median (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_50_median (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_60_median (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column avg_shortfall_685 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column bottom50_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile10_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile10_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile1_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile1_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile1_thr (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile2_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile2_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile2_thr (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile3_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile3_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile3_thr (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile4_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile4_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile4_thr (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile5_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile5_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile5_thr (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile6_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile6_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile6_thr (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile7_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile7_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile7_thr (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile8_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile8_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile8_thr (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile9_avg (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile9_share (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column decile9_thr (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column gini (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_100 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_1000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_2000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_215 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.005.json
~ Column headcount_215_regions (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
- - chartTypes:
- - - StackedArea
? ^^^
+ + type: StackedArea
? ^^^^^
~ Column headcount_3000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_365 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_4000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_40_median (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_50_median (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_60_median (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_685 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_above_100 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_above_1000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_above_2000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_above_215 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_above_3000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_above_365 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_above_4000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_above_685 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_between_1000_2000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_between_1000_3000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_between_100_215 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_between_2000_3000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_between_215_1000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_between_215_365 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_between_3000_4000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_between_365_685 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_between_685_1000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_ratio_100 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_ratio_1000 (changed metadata)
+ + $schema: https://files.ourworldindata.org/schemas/grapher-schema.003.json
~ Column headcount_ratio_2000 (changed metad
...diff too long, truncated...Automatically updated datasets matching weekly_wildfires|excess_mortality|covid|fluid|flunet|country_profile|garden/ihme_gbd/2019/gbd_risk are not included Edited: 2024-11-27 08:19:43 UTC |
pabloarosado
approved these changes
Nov 28, 2024
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Wow, this is a lot of work. I've quickly skimmed through the changes, and they look good. If you want me to review something in particular, or have a look at the outcome of certain steps, let me know. Otherwise, feel free to merge, and thanks a lot for maintaining these steps!
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Fixes #3616 (comment)
I went through all fast-track snapshots mentioning Google Sheets and reran fast-track on them, which fixed it. This was only happening for old fast-track datasets, newer ones don't have this problem.