📊 hfd: fix indicator title #3621
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
Quick links (staging server):
Login: chart-diff: ✅No charts for review.data-diff: ❌ Found differences= Dataset garden/artificial_intelligence/2024-07-16/cset
= Table cset
~ Column disclosed_investment (changed metadata)
- - 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.
+ + 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.
+ + - 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 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 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 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 (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 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.
+ + - 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.
+ + - 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.
- - - 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).
? ^^^^^^^^^^
+ + - 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).
? ^^^^^^^^^^^
- - - 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 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.
+ + 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.
+ + - 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 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 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 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 (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 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.
+ + - 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.
+ + - 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.
- - - 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).
? ^^^^^^^^^^
+ + - 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).
? ^^^^^^^^^^^
- - - 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 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.
+ + 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.
+ + - 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 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 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 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 (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 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.
+ + - 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.
+ + - 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.
- - - 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).
? ^^^^^^^^^^
+ + - 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).
? ^^^^^^^^^^^
- - - 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 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.
+ + 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.
+ + - 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 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 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 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 (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 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.
+ + - 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.
+ + - 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.
- - - 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).
? ^^^^^^^^^^
+ + - 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).
? ^^^^^^^^^^^
- - - 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/hmd/2024-11-19/hfd
= Table period_ages
~ Column asfr_period (changed metadata)
+ + <% elif birth_order == '5p' %>
+ + << title >> - Mother age: << age_str >> - Birth order: ≥5
= Table period
~ Column adjtfr (changed metadata)
+ + <%- 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)
+ + <%- elif birth_order == '5p' %>
+ + << title >> - Birth order: ≥5
~ Column tfr40 (changed metadata)
+ + <%- elif birth_order == '5p' %>
+ + << title >> - Birth order: ≥5
~ Column tmab (changed metadata)
+ + <%- elif birth_order == '5p' %>
+ + << title >> - 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 ratios - << birth_order | int >> to << (birth_order | int) + 1 >> birth
+ + title: Cohort parity progression ratios - << (birth_order | int) - 1 >> to << (birth_order | int) >> 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 cohort_ages
~ Column asfr_cohort (changed metadata)
+ + <% elif birth_order == '5p' %>
+ + << title >> - Mother age: << age_str >> - Birth order: ≥5
+ Dataset garden/imf/2024-11-25/world_economic_outlook
+ + Table world_economic_outlook
+ + Column gross_domestic_product__constant_prices__percent_change_forecast
+ + Column gross_domestic_product__constant_prices__percent_change_observation
+ + Column unemployment_rate__percent_of_total_labor_force_forecast
+ + Column unemployment_rate__percent_of_total_labor_force_observation
= Dataset garden/who/latest/avian_influenza_ah5n1
= Table avian_influenza_ah5n1_year
~ Column avian_cases_year (changed metadata, changed data)
- - date_accessed: '2024-09-30'
? ^^ ^^
+ + date_accessed: '2024-11-26'
? ^^ ^^
- - date_published: '2024-08-07'
? - ^^
+ + date_published: '2024-10-26'
? + ^^
~ Changed values: 6 / 868 (0.69%)
country year avian_cases_year - avian_cases_year +
Asia 2024 10 12
Cambodia 2024 7 10
China 2024 2 1
North America 2024 13 27
World 2024 24 40
= Table avian_influenza_ah5n1_month
~ Dim country
+ + New values: 93 / 10354 (0.90%)
date country
2024-10-01 Africa
2024-09-01 Bangladesh
2024-09-01 Cambodia
2024-08-01 Ecuador
2024-09-01 Nepal
~ Dim date
+ + New values: 93 / 10354 (0.90%)
country date
Africa 2024-10-01
Bangladesh 2024-09-01
Cambodia 2024-09-01
Ecuador 2024-08-01
Nepal 2024-09-01
~ Column avian_cases_month (changed metadata, new data, changed data)
- - date_accessed: '2024-09-30'
? ^^ ^^
+ + date_accessed: '2024-11-26'
? ^^ ^^
- - date_published: '2024-08-07'
? - ^^
+ + date_published: '2024-10-26'
? + ^^
+ + New values: 93 / 10354 (0.90%)
country date avian_cases_month
Africa 2024-10-01 0
Bangladesh 2024-09-01 0
Cambodia 2024-09-01 0
Ecuador 2024-08-01 0
Nepal 2024-09-01 0
~ Changed values: 6 / 10354 (0.06%)
country date avian_cases_month - avian_cases_month +
Asia 2024-05-01 0 1
Asia 2024-07-01 2 3
Cambodia 2024-07-01 2 3
China 2024-05-01 0 1
World 2024-07-01 12 13
Legend: +New ~Modified -Removed =Identical Details
Hint: Run this locally with etl diff REMOTE data/ --include yourdataset --verbose --snippetAutomatically updated datasets matching weekly_wildfires|excess_mortality|covid|fluid|flunet|country_profile|garden/ihme_gbd/2019/gbd_risk are not included Edited: 2024-11-26 10:11:48 UTC |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Minor edit to metadata titles.
Cohort parity progression ratiosindicators. Instead ofn to n+1 birthit should ben-1 to n birth.5pin titles, let's use≥5.