🐝 metadata: Improve UNODC metadata and fix issue with missing spaces in Jinja

[spoonerf]

Hey @lucasrodes,

This is very much not urgent. I'm trying to figure out why I'm getting missing spaces after the {definitions.metric} field in the titles of this dataset.

I think it's a Jinja issue, I saw you made these recent changes to Jinja formatting, I think to solve a similar issue?

Any idea what could be going on in this dataset? I tried to copy what you did but the space is still missing.

[owidbot]

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chart-diff: ✅

No charts for review.

data-diff: ❌ Found differences

```diff = Dataset garden/climate/2023-12-20/surface_temperature = Table surface_temperature ~ Dim country - - Removed values: 195 / 198315 (0.10%) time country 2024-10-15 Azerbaijan 2024-10-15 Cayman Islands 2024-10-15 Iceland 2024-10-15 Mauritania 2024-10-15 Syria ~ Dim time - - Removed values: 195 / 198315 (0.10%) country time Azerbaijan 2024-10-15 Cayman Islands 2024-10-15 Iceland 2024-10-15 Mauritania 2024-10-15 Syria 2024-10-15 ~ Column anomaly_above_0 (changed data) - - Removed values: 195 / 198315 (0.10%) country time anomaly_above_0 Azerbaijan 2024-10-15 NaN Cayman Islands 2024-10-15 0.899616 Iceland 2024-10-15 NaN Mauritania 2024-10-15 0.882162 Syria 2024-10-15 NaN ~ Changed values: 177 / 198315 (0.09%) country time anomaly_above_0 - anomaly_above_0 + Fiji 2024-08-15 0.119928 0.148529 Finland 2024-08-15 1.967826 1.964767 Greenland 2024-08-15 0.707868 0.711730 Panama 2024-08-15 0.675579 0.653290 Sri Lanka 2024-08-15 0.155777 0.169762 ~ Column anomaly_below_0 (changed data) - - Removed values: 195 / 198315 (0.10%) country time anomaly_below_0 Azerbaijan 2024-10-15 -0.453849 Cayman Islands 2024-10-15 NaN Iceland 2024-10-15 -1.903111 Mauritania 2024-10-15 NaN Syria 2024-10-15 -0.535667 ~ Changed values: 18 / 198315 (0.01%) country time anomaly_below_0 - anomaly_below_0 + Argentina 2024-08-15 -0.427680 -0.434506 Eritrea 2024-08-15 -1.489092 -1.431860 Falkland Islands 2024-08-15 -0.697932 -0.690255 Iceland 2024-08-15 -1.728457 -1.729895 South Georgia and the South Sandwich Islands 2024-08-15 -2.938580 -2.948951 ~ Column temperature_2m (changed data) - - Removed values: 195 / 198315 (0.10%) country time temperature_2m Azerbaijan 2024-10-15 13.069030 Cayman Islands 2024-10-15 28.410919 Iceland 2024-10-15 -0.399426 Mauritania 2024-10-15 30.314837 Syria 2024-10-15 20.695997 ~ Changed values: 191 / 198315 (0.10%) country time temperature_2m - temperature_2m + Guinea 2024-08-15 24.732361 24.756531 Kiribati 2024-08-15 27.161652 27.176666 Malawi 2024-08-15 20.763344 20.773182 New Caledonia 2024-08-15 20.187374 20.162069 Sri Lanka 2024-08-15 27.630537 27.644522 ~ Column temperature_anomaly (changed data) - - Removed values: 195 / 198315 (0.10%) country time temperature_anomaly Azerbaijan 2024-10-15 -0.453849 Cayman Islands 2024-10-15 0.899616 Iceland 2024-10-15 -1.903111 Mauritania 2024-10-15 0.882162 Syria 2024-10-15 -0.535667 ~ Changed values: 195 / 198315 (0.10%) country time temperature_anomaly - temperature_anomaly + Azerbaijan 2024-08-15 0.461515 0.429554 Cayman Islands 2024-08-15 0.848143 0.786985 Iceland 2024-08-15 -1.728457 -1.729895 Mauritania 2024-08-15 0.396675 0.398422 Syria 2024-08-15 1.155523 1.153339 = Dataset garden/gapminder/2023-09-22/total_fertility_rate = Table fertility_rate ~ Column children_dying_before_five_per_woman (changed metadata) - - description_key: - - - |- - - This long-run indicator is a combination of two data sources, Gapminder and the UN Inter-agency Group for Child Mortality Estimation (UN IGME). - - - |- - - The historical data is compiled by Gapminder, the full range of sources used can be found in the [Gapminder documentation](https://www.gapminder.org/data/documentation/gd005/). - - grapher_config: - - title: Child mortality rate - - subtitle: The estimated share of newborns who die before reaching the age of five. - - variantName: Long-run data; Gapminder & UN IGME - - sourceDesc: UN IGME (2023); Gapminder (2015) - - originUrl: https://ourworldindata.org/child-mortality - - hasMapTab: true - - yAxis: - - max: 0 - - min: 0 - - minTime: 1800 - - map: - - time: latest - - colorScale: - - baseColorScheme: YlOrRd - - binningStrategy: manual - - customNumericValues: - - - 0.3 - - - 0.5 - - - 1 - - - 3 - - - 5 - - - 10 - - - 30 - - - 50 - - customNumericMinValue: 0 - - timeTolerance: 0 - - selectedEntityNames: - - - United States - - - United Kingdom - - - Sweden - - - France - - - Brazil - - - India - - title_public: Under-five mortality rate - - title_variant: Long-run data - - attribution_short: UN IGME; Gapminder ~ Column children_surviving_past_five_per_woman (changed metadata) - - description_key: - - - |- - - This long-run indicator is a combination of two data sources, Gapminder and the UN Inter-agency Group for Child Mortality Estimation (UN IGME). - - - |- - - The historical data is compiled by Gapminder, the full range of sources used can be found in the [Gapminder documentation](https://www.gapminder.org/data/documentation/gd005/). - - grapher_config: - - title: Child mortality rate - - subtitle: The estimated share of newborns who die before reaching the age of five. - - variantName: Long-run data; Gapminder & UN IGME - - sourceDesc: UN IGME (2023); Gapminder (2015) - - originUrl: https://ourworldindata.org/child-mortality - - hasMapTab: true - - yAxis: - - max: 0 - - min: 0 - - minTime: 1800 - - map: - - time: latest - - colorScale: - - baseColorScheme: YlOrRd - - binningStrategy: manual - - customNumericValues: - - - 0.3 - - - 0.5 - - - 1 - - - 3 - - - 5 - - - 10 - - - 30 - - - 50 - - customNumericMinValue: 0 - - timeTolerance: 0 - - selectedEntityNames: - - - United States - - - United Kingdom - - - Sweden - - - France - - - Brazil - - - India - - title_public: Under-five mortality rate - - title_variant: Long-run data - - attribution_short: UN IGME; Gapminder ~ Dataset garden/health/2023-05-04/global_wellbeing - - publication_year: 2020 + + publication_year: 2020" ? + = Table global_wellbeing = Table global_wellbeing_index = Dataset garden/health/latest/global_health_mpox = Table global_health_mpox ~ Dim country + + New values: 1 / 93 (1.08%) date country 2024-10-21 Cameroon - - Removed values: 33 / 93 (35.48%) date country 2024-03-24 Central African Republic 2024-09-23 Central African Republic 2024-09-30 Gabon 2024-09-08 Guinea 2024-10-20 Kenya ~ Dim date + + New values: 1 / 93 (1.08%) country date Cameroon 2024-10-21 - - Removed values: 33 / 93 (35.48%) country date Central African Republic 2024-03-24 Central African Republic 2024-09-23 Gabon 2024-09-30 Guinea 2024-09-08 Kenya 2024-10-20 ~ Column reported_cases (new data, changed data) + + New values: 1 / 93 (1.08%) country date reported_cases Cameroon 2024-10-21 5.0 - - Removed values: 33 / 93 (35.48%) country date reported_cases Central African Republic 2024-03-24 2.0 Central African Republic 2024-09-23 12.0 Gabon 2024-09-30 3.0 Guinea 2024-09-08 7.0 Kenya 2024-10-20 5.0 ~ Changed values: 4 / 93 (4.30%) country date reported_cases - reported_cases + Cote d'Ivoire 2024-10-12 32.0 14.0 Gabon 2024-10-20 1.0 10.0 Liberia 2024-10-21 47.0 33.0 Rwanda 2024-10-13 531.0 298.0 ~ Column suspected_cases_cumulative (new data, changed data) + + New values: 1 / 93 (1.08%) country date suspected_cases_cumulative Cameroon 2024-10-21 210.0 - - Removed values: 33 / 93 (35.48%) country date suspected_cases_cumulative Central African Republic 2024-03-24 47.0 Central African Republic 2024-09-23 255.0 Gabon 2024-09-30 7.0 Guinea 2024-09-08 7.0 Kenya 2024-10-20 230.0 ~ Changed values: 6 / 93 (6.45%) country date suspected_cases_cumulative - suspected_cases_cumulative + Central African Republic 2024-10-27 323.0 5.0 Cote d'Ivoire 2024-10-12 269.0 251.0 Cote d'Ivoire 2024-10-27 311.0 293.0 Liberia 2024-10-20 187.0 23.0 Rwanda 2024-10-13 3329.0 2037.0 = Dataset garden/homicide/2024-10-30/unodc = Table by_mechanisms ~ Column value (changed metadata) - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> - << category >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> - << category >> - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> of <% if sex == "Total" %> all victimes<% elif sex == "Male" %>male victims<% elif sex == "Female" %>female victims<%- endif -%> <% if age == "Total" %> in all age-groups <% elif age == "30-44" %> aged 30-44 years <% elif age == "45-59" %> aged 45-59 years <% elif age == "60 and older" %> aged over 60 years <% elif age == "0-9" %> aged 0-9 years <% elif age == "10 -14" %> aged 10-14 years <% elif age == "15 -17" %> aged 15-17 years <% elif age == "18-19" %> aged 18-19 years <% elif age == "20-24" %> aged 20-24 years <% elif age == "25-29" %> aged 25-29 years <% elif age == "Unknown" %> of unknown age <%- endif -%> where the weapon was << category.lower() >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> of <% if sex == "Total" %> + + all victims + + <%- elif sex == "Male" -%> + + male victims + + <%- elif sex == "Female" -%> + + female victims + + <% endif %> <% if age == "Total" %> + + in all age-groups + + <%- elif age == "30-44" %> + + aged 30-44 years + + <%- elif age == "45-59" %> + + aged 45-59 years + + <%- elif age == "60 and older" %> + + aged over 60 years + + <%- elif age == "0-9" %> + + aged 0-9 years + + <%- elif age == "10 -14" %> + + aged 10-14 years + + <%- elif age == "15 -17" %> + + aged 15-17 years + + <%- elif age == "18-19" %> + + aged 18-19 years + + <%- elif age == "20-24" %> + + aged 20-24 years + + <%- elif age == "25-29" %> + + aged 25-29 years + + <%- elif age == "Unknown" %> + + of unknown age + + <%- endif %> where the weapon was << category.lower() >> - - <% if unit_of_measurement == "Counts" %> homicides <% elif sex == "Rate per 100,000 population" %> homicides per 100,000 population <%- endif -%> + + <% if unit_of_measurement == "Counts" %> + + homicides + + <%- elif sex == "Rate per 100,000 population" %> + + homicides per 100,000 population + + <%- endif %> + + numDecimalPlaces: |- - - numDecimalPlaces: <% if unit_of_measurement == "Counts" %>0<% elif unit_of_measurement == "Rate per 100,000 population" ? ^^^^^^^^^^^^^^^^^ + + <%- if unit_of_measurement == "Counts" -%> 0<%- elif unit_of_measurement == "Rate per 100,000 population" -%> 1<%- endif -%> ? ^ ++ + + ++ +++++++++++++++++++ - - %>1<%- endif -%> - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> of <% if sex == "Total" %> all victimes<% elif sex == "Male" %>male victims<% elif sex == "Female" %>female victims<%- endif -%> <% if age == "Total" %> in all age-groups <% elif age == "30-44" %> aged 30-44 years <% elif age == "45-59" %> aged 45-59 years <% elif age == "60 and older" %> aged over 60 years <% elif age == "0-9" %> aged 0-9 years <% elif age == "10 -14" %> aged 10-14 years <% elif age == "15 -17" %> aged 15-17 years <% elif age == "18-19" %> aged 18-19 years <% elif age == "20-24" %> aged 20-24 years <% elif age == "25-29" %> aged 25-29 years <% elif age == "Unknown" %> of unknown age <%- endif -%> where the weapon was << category.lower() >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> of <% if sex == "Total" %> + + all victims + + <%- elif sex == "Male" -%> + + male victims + + <%- elif sex == "Female" -%> + + female victims + + <% endif %> <% if age == "Total" %> + + in all age-groups + + <%- elif age == "30-44" %> + + aged 30-44 years + + <%- elif age == "45-59" %> + + aged 45-59 years + + <%- elif age == "60 and older" %> + + aged over 60 years + + <%- elif age == "0-9" %> + + aged 0-9 years + + <%- elif age == "10 -14" %> + + aged 10-14 years + + <%- elif age == "15 -17" %> + + aged 15-17 years + + <%- elif age == "18-19" %> + + aged 18-19 years + + <%- elif age == "20-24" %> + + aged 20-24 years + + <%- elif age == "25-29" %> + + aged 25-29 years + + <%- elif age == "Unknown" %> + + of unknown age + + <%- endif %> where the weapon was << category.lower() >> = Table by_situational_context ~ Column value (changed metadata) - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> - << category >> - sex: << sex >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> - << category >> - sex: << sex >> - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> of <% if sex == "Total" %> all victimes<% elif sex == "Male" %>male victims<% elif sex == "Female" %>female victims<%- endif -%> <% if age == "Total" %> in all age-groups <% elif age == "30-44" %> aged 30-44 years <% elif age == "45-59" %> aged 45-59 years <% elif age == "60 and older" %> aged over 60 years <% elif age == "0-9" %> aged 0-9 years <% elif age == "10 -14" %> aged 10-14 years <% elif age == "15 -17" %> aged 15-17 years <% elif age == "18-19" %> aged 18-19 years <% elif age == "20-24" %> aged 20-24 years <% elif age == "25-29" %> aged 25-29 years <% elif age == "Unknown" %> of unknown age <%- endif -%> where the situation was << category.lower() >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> of <% if sex == "Total" %> + + all victims + + <%- elif sex == "Male" -%> + + male victims + + <%- elif sex == "Female" -%> + + female victims + + <% endif %> <% if age == "Total" %> + + in all age-groups + + <%- elif age == "30-44" %> + + aged 30-44 years + + <%- elif age == "45-59" %> + + aged 45-59 years + + <%- elif age == "60 and older" %> + + aged over 60 years + + <%- elif age == "0-9" %> + + aged 0-9 years + + <%- elif age == "10 -14" %> + + aged 10-14 years + + <%- elif age == "15 -17" %> + + aged 15-17 years + + <%- elif age == "18-19" %> + + aged 18-19 years + + <%- elif age == "20-24" %> + + aged 20-24 years + + <%- elif age == "25-29" %> + + aged 25-29 years + + <%- elif age == "Unknown" %> + + of unknown age + + <%- endif %> where the situation was << category.lower() >> - - <% if unit_of_measurement == "Counts" %> homicides <% elif sex == "Rate per 100,000 population" %> homicides per 100,000 population <%- endif -%> + + <% if unit_of_measurement == "Counts" %> + + homicides + + <%- elif sex == "Rate per 100,000 population" %> + + homicides per 100,000 population + + <%- endif %> + + numDecimalPlaces: |- - - numDecimalPlaces: <% if unit_of_measurement == "Counts" %>0<% elif unit_of_measurement == "Rate per 100,000 population" ? ^^^^^^^^^^^^^^^^^ + + <%- if unit_of_measurement == "Counts" -%> 0<%- elif unit_of_measurement == "Rate per 100,000 population" -%> 1<%- endif -%> ? ^ ++ + + ++ +++++++++++++++++++ - - %>1<%- endif -%> - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> of <% if sex == "Total" %> all victimes<% elif sex == "Male" %>male victims<% elif sex == "Female" %>female victims<%- endif -%> <% if age == "Total" %> in all age-groups <% elif age == "30-44" %> aged 30-44 years <% elif age == "45-59" %> aged 45-59 years <% elif age == "60 and older" %> aged over 60 years <% elif age == "0-9" %> aged 0-9 years <% elif age == "10 -14" %> aged 10-14 years <% elif age == "15 -17" %> aged 15-17 years <% elif age == "18-19" %> aged 18-19 years <% elif age == "20-24" %> aged 20-24 years <% elif age == "25-29" %> aged 25-29 years <% elif age == "Unknown" %> of unknown age <%- endif -%> where the situation was << category.lower() >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> of <% if sex == "Total" %> + + all victims + + <%- elif sex == "Male" -%> + + male victims + + <%- elif sex == "Female" -%> + + female victims + + <% endif %> <% if age == "Total" %> + + in all age-groups + + <%- elif age == "30-44" %> + + aged 30-44 years + + <%- elif age == "45-59" %> + + aged 45-59 years + + <%- elif age == "60 and older" %> + + aged over 60 years + + <%- elif age == "0-9" %> + + aged 0-9 years + + <%- elif age == "10 -14" %> + + aged 10-14 years + + <%- elif age == "15 -17" %> + + aged 15-17 years + + <%- elif age == "18-19" %> + + aged 18-19 years + + <%- elif age == "20-24" %> + + aged 20-24 years + + <%- elif age == "25-29" %> + + aged 25-29 years + + <%- elif age == "Unknown" %> + + of unknown age + + <%- endif %> where the situation was << category.lower() >> = Table by_relationship_to_perpetrator ~ Column value (changed metadata) - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> - << category >> - sex: << sex >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> - << category >> - sex: << sex >> - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> of <% if sex == "Total" %> all victimes<% elif sex == "Male" %>male victims<% elif sex == "Female" %>female victims<%- endif -%> <% if age == "Total" %> in all age-groups <% elif age == "30-44" %> aged 30-44 years <% elif age == "45-59" %> aged 45-59 years <% elif age == "60 and older" %> aged over 60 years <% elif age == "0-9" %> aged 0-9 years <% elif age == "10 -14" %> aged 10-14 years <% elif age == "15 -17" %> aged 15-17 years <% elif age == "18-19" %> aged 18-19 years <% elif age == "20-24" %> aged 20-24 years <% elif age == "25-29" %> aged 25-29 years <% elif age == "Unknown" %> of unknown age <%- endif -%> where the << category.lower() >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> of <% if sex == "Total" %> + + all victims + + <%- elif sex == "Male" -%> + + male victims + + <%- elif sex == "Female" -%> + + female victims + + <% endif %> <% if age == "Total" %> + + in all age-groups + + <%- elif age == "30-44" %> + + aged 30-44 years + + <%- elif age == "45-59" %> + + aged 45-59 years + + <%- elif age == "60 and older" %> + + aged over 60 years + + <%- elif age == "0-9" %> + + aged 0-9 years + + <%- elif age == "10 -14" %> + + aged 10-14 years + + <%- elif age == "15 -17" %> + + aged 15-17 years + + <%- elif age == "18-19" %> + + aged 18-19 years + + <%- elif age == "20-24" %> + + aged 20-24 years + + <%- elif age == "25-29" %> + + aged 25-29 years + + <%- elif age == "Unknown" %> + + of unknown age + + <%- endif %> where the << category.lower() >> - - <% if unit_of_measurement == "Counts" %> homicides <% elif sex == "Rate per 100,000 population" %> homicides per 100,000 population <%- endif -%> + + <% if unit_of_measurement == "Counts" %> + + homicides + + <%- elif sex == "Rate per 100,000 population" %> + + homicides per 100,000 population + + <%- endif %> + + numDecimalPlaces: |- - - numDecimalPlaces: <% if unit_of_measurement == "Counts" %>0<% elif unit_of_measurement == "Rate per 100,000 population" ? ^^^^^^^^^^^^^^^^^ + + <%- if unit_of_measurement == "Counts" -%> 0<%- elif unit_of_measurement == "Rate per 100,000 population" -%> 1<%- endif -%> ? ^ ++ + + ++ +++++++++++++++++++ - - %>1<%- endif -%> - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> of <% if sex == "Total" %> all victimes<% elif sex == "Male" %>male victims<% elif sex == "Female" %>female victims<%- endif -%> <% if age == "Total" %> in all age-groups <% elif age == "30-44" %> aged 30-44 years <% elif age == "45-59" %> aged 45-59 years <% elif age == "60 and older" %> aged over 60 years <% elif age == "0-9" %> aged 0-9 years <% elif age == "10 -14" %> aged 10-14 years <% elif age == "15 -17" %> aged 15-17 years <% elif age == "18-19" %> aged 18-19 years <% elif age == "20-24" %> aged 20-24 years <% elif age == "25-29" %> aged 25-29 years <% elif age == "Unknown" %> of unknown age <%- endif -%> where the << category.lower() >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> of <% if sex == "Total" %> + + all victims + + <%- elif sex == "Male" -%> + + male victims + + <%- elif sex == "Female" -%> + + female victims + + <% endif %> <% if age == "Total" %> + + in all age-groups + + <%- elif age == "30-44" %> + + aged 30-44 years + + <%- elif age == "45-59" %> + + aged 45-59 years + + <%- elif age == "60 and older" %> + + aged over 60 years + + <%- elif age == "0-9" %> + + aged 0-9 years + + <%- elif age == "10 -14" %> + + aged 10-14 years + + <%- elif age == "15 -17" %> + + aged 15-17 years + + <%- elif age == "18-19" %> + + aged 18-19 years + + <%- elif age == "20-24" %> + + aged 20-24 years + + <%- elif age == "25-29" %> + + aged 25-29 years + + <%- elif age == "Unknown" %> + + of unknown age + + <%- endif %> where the << category.lower() >> = Table total ~ Column value (changed metadata) - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> - sex: << sex >> - age: << age >> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> - sex: << sex >> - age: << age >> - - <% if unit_of_measurement == "Counts" %> Number of homicides <% elif unit_of_measurement == "Rate per 100,000 population" %> Homicide rate per 100,000 population <%- endif -%> of <% if sex == "Total" %> all victimes<% elif sex == "Male" %>male victims<% elif sex == "Female" %>female victims<%- endif -%> <% if age == "Total" %> in all age-groups <% elif age == "30-44" %> aged 30-44 years <% elif age == "45-59" %> aged 45-59 years <% elif age == "60 and older" %> aged over 60 years <% elif age == "0-9" %> aged 0-9 years <% elif age == "10 -14" %> aged 10-14 years <% elif age == "15 -17" %> aged 15-17 years <% elif age == "18-19" %> aged 18-19 years <% elif age == "20-24" %> aged 20-24 years <% elif age == "25-29" %> aged 25-29 years <% elif age == "Unknown" %> of unknown age <%- endif -%> + + <% if unit_of_measurement == "Counts" %> + + Number of homicides + + <%- elif unit_of_measurement == "Rate per 100,000 population" %> + + Homicide rate per 100,000 population + + <%- endif %> of <% if sex == "Total" %> + + all victims + + <%- elif sex == "Male" -%> + + male victims + + <%- elif sex == "Female" -%> + + female victims + + <% endif %> <% if age == "Total" %> + + in all age-groups + + <%- elif age == "30-44" %> + + aged 30-44 years + + <%- elif age == "45-59" %> + + aged 45-59 years + + <%- elif age == "60 and older" %> + + aged over 60 years + + <%- elif age == "0-9" %> + + aged 0-9 years + + <%- elif age == "10 -14" %> + + aged 10-14 years + + <%- elif age == "15 -17" %> + + aged 15-17 years + + <%- elif age == "18-19" %> + + aged 18-19 years + + <%- elif age == "20-24" %> + + aged 20-24 years + + <%- elif age == "25-29" %> + + aged 25-29 years + + <%- elif age == "Unknown" %> + + of unknown age + + <%- endif %> - - <% if unit_of_measurement == "Counts" %> homicides <% elif sex == "Rate per 100,000 population" %> homicides per 100,000 population <%- endif -%> + + <% if unit_of_measurement == "Counts" %> + + homicides + + <%- elif sex == "Rate per 100,000 population" %> + + homicides per 100,000 population + + <%- endif %> + + numDecimalPlaces: |- - - numDecimalPlaces: <% if unit_of_measurement == "Counts" %>0<% elif unit_of_measurement == "Rate per 100,000 population" ? ^^^^^^^^^^^^^^^^^ + + <%- if unit_of_measurement == "Counts" -%> 0<%- elif unit_of_measurement == "Rate per 100,000 population" -%> 1<%- endif -%> ? ^ ++ + + ++ +++++++++++++++++++ - - %>1<%- endif -%> = Dataset garden/regions/2023-01-01/regions = Table regions ~ Dim code - - Removed values: 22 / 312 (7.05%) code UNSD_CAM UNSD_NAF UNSD_POL UNSD_SAF UNSD_WAS ~ Column defined_by (changed data) - - Removed values: 22 / 312 (7.05%) code defined_by UNSD_CAM unsd UNSD_NAF unsd UNSD_POL unsd UNSD_SAF unsd UNSD_WAS unsd ~ Column is_historical (changed data) - - Removed values: 22 / 312 (7.05%) code is_historical UNSD_CAM False UNSD_NAF False UNSD_POL False UNSD_SAF False UNSD_WAS False ~ Column members (changed data) - - Removed values: 22 / 312 (7.05%) code members UNSD_CAM ["BLZ", "CRI", "SLV", "GTM", "HND", "MEX", "NIC", "PAN"] UNSD_NAF ["DZA", "EGY", "LBY", "MAR", "SDN", "TUN", "ESH"] UNSD_POL ["ASM", "COK", "PYF", "NIU", "PCN", "WSM", "TKL", "TON", "TUV", "WLF"] UNSD_SAF ["BWA", "LSO", "NAM", "ZAF", "SWZ"] UNSD_WAS ["ARM", "AZE", "BHR", "CYP", "GEO", "IRQ", "ISR", "JOR", "KWT", "LBN", "OMN", "QAT", "SAU", "SYR", "TUR", "ARE", "YEM"] ~ Column name (changed data) - - Removed values: 22 / 312 (7.05%) code name UNSD_CAM Central America (UNSD) UNSD_NAF Northern Africa (UNSD) UNSD_POL Polynesia (UNSD) UNSD_SAF Southern Africa (UNSD) UNSD_WAS Western Asia (UNSD) ~ Column region_type (changed data) - - Removed values: 22 / 312 (7.05%) code region_type UNSD_CAM aggregate UNSD_NAF aggregate UNSD_POL aggregate UNSD_SAF aggregate UNSD_WAS aggregate ~ Column short_name (changed data) - - Removed values: 22 / 312 (7.05%) code short_name UNSD_CAM Central America (UNSD) UNSD_NAF Northern Africa (UNSD) UNSD_POL Polynesia (UNSD) UNSD_SAF Southern Africa (UNSD) UNSD_WAS Western Asia (UNSD) ~ Changed values: 2 / 312 (0.64%) code short_name - short_name + BIH Bosnia and Herz. Bosnia and Herzegovina TCA Turks and Caicos Turks and Caicos Islands = Dataset garden/un/2024-04-09/undp_hdr = Table undp_hdr ~ Column abr (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column co2_prod (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column coef_ineq (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column diff_hdi_phdi (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column eys (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column eys_f (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column eys_m (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column gdi (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column gdi_group (changed metadata, changed data) + + description_processing: |- + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ~ Changed values: 11 / 7161 (0.15%) country year gdi_group - gdi_group + Europe 2022 1.268419 High-income countries 2022 1.392950 Lower-middle-income countries 2022 4.389009 South America 2022 1.150919 Upper-middle-income countries 2022 2.057359 ~ Column gii (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column gii_rank (changed metadata, changed data) + + description_processing: |- + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ~ Changed values: 9 / 7161 (0.13%) country year gii_rank - gii_rank + Asia 2022 3579 Europe 2022 1089 High-income countries 2022 1832 South America 2022 1092 Upper-middle-income countries 2022 3799 ~ Column gni_pc_f (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column gni_pc_m (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column gnipc (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column hdi (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column hdi_f (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column hdi_m (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column hdi_rank (changed metadata, changed data) + + description_processing: |- + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ~ Changed values: 11 / 7161 (0.15%) country year hdi_rank - hdi_rank + Europe 2022 1537 High-income countries 2022 2161 Lower-middle-income countries 2022 7099 South America 2022 1054 Upper-middle-income countries 2022 4964 ~ Column ihdi (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column ineq_edu (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column ineq_inc (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column ineq_le (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column le (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column le_f (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column le_m (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column lfpr_f (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column lfpr_m (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column loss (changed metadata, changed data) + + description_processing: |- + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ~ Changed values: 82 / 7161 (1.15%) country year loss - loss + Africa 2015 NaN 1714.844482 Africa 2021 NaN 1684.291626 Europe 2020 NaN 351.625641 High-income countries 2019 NaN 535.104187 Lower-middle-income countries 2018 NaN 1293.413940 ~ Column mf (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column mmr (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column mys (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column mys_f (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column mys_m (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column phdi (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column pop_total (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column pr_f (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column pr_m (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column rankdiff_hdi_phdi (changed metadata, changed data) + + description_processing: |- + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ~ Changed values: 6 / 7161 (0.08%) country year rankdiff_hdi_phdi - rankdiff_hdi_phdi + Africa 2022 98 Asia 2022 -340 Europe 2022 100 European Union (27) 2022 79 South America 2022 130 ~ Column se_f (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ ~ Column se_m (changed metadata) - - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. + + - We calculated averages over continents and income groups by taking the population-weighted average of the countries in each group. If less than 80% of countries in an area report data for a given year, we do not calculate the average for that area. ? ++ = Dataset garden/un/2024-07-12/un_wpp = Table median_age = Table fertility_rate = Table growth_rate = Table life_expectancy = Table population = Table deaths = Table migration ~ Column net_migration_rate (changed metadata) - - title: Annual net migration rate ? ^^^^^^^^ + + title: Net migration rate ? ^ = Table mortality_rate = Table dependency_ratio = Table births = Table natural_change_rate = Table sex_ratio = Dataset garden/un/2024-09-16/long_run_child_mortality = Table long_run_child_mortality = Table long_run_child_mortality_selected ~ Column under_five_mortality (changed metadata) - - title: Under-five mortality rate + + title: Under-five mortality rate (selected) ? +++++++++++ + + description_short: Population by country, available from 10,000 BCE to 2100, based on data and estimates from different sources. - - description_short: The long-run estimated share of newborns who die before reaching the age of five. - - description_key: - - - |- - - This long-run indicator is a combination of two data sources, Gapminder and the UN Inter-agency Group for Child Mortality Estimation (UN IGME). - - - |- - - The historical data is compiled by Gapminder, the full range of sources used can be found in the [Gapminder documentation](https://www.gapminder.org/data/documentation/gd005/). - - grapher_config: - - title: Child mortality rate - - subtitle: The estimated share of newborns who die before reaching the age of five. - - variantName: Long-run data; Gapminder & UN IGME - - sourceDesc: UN IGME (2023); Gapminder (2015) - - originUrl: https://ourworldindata.org/child-mortality - - hasMapTab: true - - yAxis: - - max: 0 - - min: 0 - - minTime: 1800 - - map: - - time: latest - - colorScale: - - baseColorScheme: YlOrRd - - binningStrategy: manual - - customNumericValues: - - - 0.3 - - - 0.5 - - - 1 - - - 3 - - - 5 - - - 10 - - - 30 - - - 50 - - customNumericMinValue: 0 - - timeTolerance: 0 - - selectedEntityNames: - - - United States - - - United Kingdom - - - Sweden - - - France - - - Brazil - - - India - - title_public: Under-five mortality rate - - title_variant: Long-run data - - attribution_short: UN IGME; Gapminder - - description_processing: |- - - This indicator is a combination of data from two sources: - - - The UN Inter-agency Group for Child Mortality Estimation (UN IGME) provides estimates of child mortality rates, which is available for some countries from 1932. - - - Gapminder provides estimates of child mortality rates for the years 1800 to 2015. - - - - We combine the two datasets, for years where both are available, we have a preference for the UN IGME data. = Dataset garden/who/2024-09-09/flu_test = Table flu_test ~ Dim country - - Removed values: 112 / 71972 (0.16%) date country 2024-10-28 Canada 2024-10-28 Guyana 2024-09-23 Namibia 2024-11-04 Seychelles 2024-07-22 United States ~ Dim date - - Removed values: 112 / 71972 (0.16%) country date Canada 2024-10-28 Guyana 2024-10-28 Namibia 2024-09-23 Seychelles 2024-11-04 United States 2024-07-22 ~ Column denomcombined (changed data) - - Removed values: 112 / 71972 (0.16%) country date denomcombined Canada 2024-10-28 24888 Guyana 2024-10-28 45 Namibia 2024-09-23 3 Seychelles 2024-11-04 1 United States 2024-07-22 45337 ~ Changed values: 139 / 71972 (0.19%) country date denomcombined - denomcombined + Canada 2024-10-14 25439 25441 Indonesia 2023-08-14 32 31 South Africa 2024-04-08 176 175 ...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: 2024-11-20 10:36:18 UTC Execution time: 3.89 seconds

[lucasrodes]

@spoonerf I've corrected the spacings! Sorry for the mess

[spoonerf]

Thanks @lucasrodes! Jinja spacing confuses me but it looks like what you did worked!