📊 global flourishing study: add private dataset #3522
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Login: chart-diff: ❌
data-diff: ❌ Found differences= Dataset garden/animal_welfare/2024-06-04/bullfighting_laws
= Table bullfighting_laws
~ Column status (changed metadata)
+ + hasChartTab: false
= Dataset garden/antibiotics/2024-10-09/gram
= Table gram
~ Dim country
- - Removed values: 114 / 3876 (2.94%)
year country
2007 Africa
2010 Africa
2005 Asia
2014 Asia
2014 Oceania
~ Dim year
- - Removed values: 114 / 3876 (2.94%)
country year
Africa 2007
Africa 2010
Asia 2005
Asia 2014
Oceania 2014
~ Column antibiotic_consumption__ddd_1_000_day (changed metadata, changed data)
- - description: Population by country and year.
- - description_short: Estimated [Defined Daily Doses](#dod:defined-daily-doses) per 1,000 people per day.
? --------------------------- ^ ^
+ + description_short: Estimated defined daily doses (DDD) per 1,000 people per day.
? ^ ^ +++++
- - - producer: Browne AJ et al. (2021)
? -------
+ + - producer: Browne AJ et al.
+ + description: |-
+ + The Global Research on Antimicrobial Resistance (GRAM) Project is a partnership between the University of Oxford and the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, to provide rigorous quantitative estimates of antimicrobial resistance (AMR) burden; to increase global-, regional-, and country-level awareness of AMR; to boost surveillance efforts, particularly in low and middle income countries (LMICs); and, to promote the rational use of antimicrobials worldwide.
+ + title_snapshot: Antibiotic usage and consumption
+ + description_snapshot: |-
+ + For modeled estimates of total antibiotic consumption: IQVIA MIDASTM database, [European Center for Disease Control](https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/esac-net), World Health Organization, and published literature.
- - licenses:
? -
+ + license:
? ++++
- - - name: Creative Commons BY 4.0
- - url: https://docs.google.com/document/d/1-RmthhS2EPMK_HIpnPctcXpB0n7ADSWnXa5Hb3PxNq4/edit?usp=sharing
+ + name: © 2024 Global Research on Antimicrobial Resistance
+ + url: https://www.ox.ac.uk/legal
- - short_unit: ''
- - processing_level: major
- - attribution: HYDE (2023); Gapminder (2022); UN WPP (2024)
- - Removed values: 114 / 3876 (2.94%)
country year antibiotic_consumption__ddd_1_000_day
Africa 2007 8.395011
Africa 2010 9.500336
Asia 2005 9.062757
Asia 2014 12.298692
Oceania 2014 20.982134
~ Column lower_uncertainty_interval (changed metadata, changed data)
- - description: Population by country and year.
- - description_short: Population by country, available from 10,000 BCE to 2100, based on data and estimates from different sources.
- - - producer: Browne AJ et al. (2021)
? -------
+ + - producer: Browne AJ et al.
+ + description: |-
+ + The Global Research on Antimicrobial Resistance (GRAM) Project is a partnership between the University of Oxford and the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, to provide rigorous quantitative estimates of antimicrobial resistance (AMR) burden; to increase global-, regional-, and country-level awareness of AMR; to boost surveillance efforts, particularly in low and middle income countries (LMICs); and, to promote the rational use of antimicrobials worldwide.
+ + title_snapshot: Antibiotic usage and consumption
+ + description_snapshot: |-
+ + For modeled estimates of total antibiotic consumption: IQVIA MIDASTM database, [European Center for Disease Control](https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/esac-net), World Health Organization, and published literature.
- - licenses:
? -
+ + license:
? ++++
- - - name: Creative Commons BY 4.0
- - url: https://docs.google.com/document/d/1-RmthhS2EPMK_HIpnPctcXpB0n7ADSWnXa5Hb3PxNq4/edit?usp=sharing
+ + name: © 2024 Global Research on Antimicrobial Resistance
+ + url: https://www.ox.ac.uk/legal
- - short_unit: ''
- - display:
- - numDecimalPlaces: 0
- - processing_level: major
- - attribution: HYDE (2023); Gapminder (2022); UN WPP (2024)
- - Removed values: 114 / 3876 (2.94%)
country year lower_uncertainty_interval
Africa 2007 6.762672
Africa 2010 7.718959
Asia 2005 8.511994
Asia 2014 11.61167
Oceania 2014 20.256237
~ Column upper_uncertainty_interval (changed metadata, changed data)
- - description: Population by country and year.
- - description_short: Population by country, available from 10,000 BCE to 2100, based on data and estimates from different sources.
- - - producer: Browne AJ et al. (2021)
? -------
+ + - producer: Browne AJ et al.
+ + description: |-
+ + The Global Research on Antimicrobial Resistance (GRAM) Project is a partnership between the University of Oxford and the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, to provide rigorous quantitative estimates of antimicrobial resistance (AMR) burden; to increase global-, regional-, and country-level awareness of AMR; to boost surveillance efforts, particularly in low and middle income countries (LMICs); and, to promote the rational use of antimicrobials worldwide.
+ + title_snapshot: Antibiotic usage and consumption
+ + description_snapshot: |-
+ + For modeled estimates of total antibiotic consumption: IQVIA MIDASTM database, [European Center for Disease Control](https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/esac-net), World Health Organization, and published literature.
- - licenses:
? -
+ + license:
? ++++
- - - name: Creative Commons BY 4.0
- - url: https://docs.google.com/document/d/1-RmthhS2EPMK_HIpnPctcXpB0n7ADSWnXa5Hb3PxNq4/edit?usp=sharing
+ + name: © 2024 Global Research on Antimicrobial Resistance
+ + url: https://www.ox.ac.uk/legal
- - short_unit: ''
- - display:
- - numDecimalPlaces: 0
- - processing_level: major
- - attribution: HYDE (2023); Gapminder (2022); UN WPP (2024)
- - Removed values: 114 / 3876 (2.94%)
country year upper_uncertainty_interval
Africa 2007 10.357027
Africa 2010 11.702678
Asia 2005 9.724019
Asia 2014 13.079142
Oceania 2014 21.882711
= Dataset garden/antibiotics/2024-10-09/gram_level
= Table gram_level
~ Dim country
- - Removed values: 912 / 31160 (2.93%)
year atc_level_3_class country
2018 J01A-Tetracyclines Africa
2018 J01B-Amphenicols Africa
2012 J01E-Sulfonamides and trimethoprim Asia
2010 J01G-Aminoglycosides Oceania
2017 J01D-Other beta-lactams Oceania
~ Dim year
- - Removed values: 912 / 31160 (2.93%)
country atc_level_3_class year
Africa J01A-Tetracyclines 2018
Africa J01B-Amphenicols 2018
Asia J01E-Sulfonamides and trimethoprim 2012
Oceania J01G-Aminoglycosides 2010
Oceania J01D-Other beta-lactams 2017
~ Dim atc_level_3_class
- - Removed values: 912 / 31160 (2.93%)
country year atc_level_3_class
Africa 2018 J01A-Tetracyclines
Africa 2018 J01B-Amphenicols
Asia 2012 J01E-Sulfonamides and trimethoprim
Oceania 2010 J01G-Aminoglycosides
Oceania 2017 J01D-Other beta-lactams
~ Column antibiotic_consumption__ddd_1_000_day (changed metadata, changed data)
- - description: Population by country and year.
- - description_short: Estimated [Defined Daily Doses](#dod:defined-daily-doses) of << atc_level_3_class >> per 1,000 people.
? --------------------------- ^ ^
+ + description_short: Estimated defined daily doses (DDD) of << atc_level_3_class >> per 1,000 people.
? ^ ^ +++++
- - - producer: Browne AJ et al. (2021)
? -------
+ + - producer: Browne AJ et al.
+ + description: |-
+ + The Global Research on Antimicrobial Resistance (GRAM) Project is a partnership between the University of Oxford and the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, to provide rigorous quantitative estimates of antimicrobial resistance (AMR) burden; to increase global-, regional-, and country-level awareness of AMR; to boost surveillance efforts, particularly in low and middle income countries (LMICs); and, to promote the rational use of antimicrobials worldwide.
+ + title_snapshot: Antibiotic usage and consumption
+ + description_snapshot: |-
+ + For modeled estimates of total antibiotic consumption: IQVIA MIDASTM database, [European Center for Disease Control](https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/esac-net), World Health Organization, and published literature.
- - licenses:
? -
+ + license:
? ++++
- - - name: Creative Commons BY 4.0
- - url: https://docs.google.com/document/d/1-RmthhS2EPMK_HIpnPctcXpB0n7ADSWnXa5Hb3PxNq4/edit?usp=sharing
+ + name: © 2024 Global Research on Antimicrobial Resistance
+ + url: https://www.ox.ac.uk/legal
- - short_unit: ''
- - processing_level: major
- - attribution: HYDE (2023); Gapminder (2022); UN WPP (2024)
- - Removed values: 912 / 31160 (2.93%)
country year atc_level_3_class antibiotic_consumption__ddd_1_000_day
Africa 2018 J01A-Tetracyclines 1.393283
Africa 2018 J01B-Amphenicols 0.039202
Asia 2012 J01E-Sulfonamides and trimethoprim 0.705565
Oceania 2010 J01G-Aminoglycosides 0.117554
Oceania 2017 J01D-Other beta-lactams 3.285561
~ Dataset garden/antibiotics/2024-10-18/who_glass
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS)
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS)
? +
~ Table who_glass (changed metadata)
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS)
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS)
? +
~ Column astresult (changed metadata)
+ + title: Share of bacterial confirmed <<syndrome.lower()>> infections with antibiotic susceptibility test results
- - title: |-
- - Share of samples tested and confirmed to be <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections with antibiotic susceptibility test results
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS)
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS)
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
+ + title_public: Share of bacterial confirmed <<syndrome.lower()>> infections with antibiotic susceptibility test results
- - title_public: |-
- - Share of samples tested and confirmed to be <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections with antibiotic susceptibility test results
~ Column bcispermillion (changed metadata)
+ + title: Bacteriologically confirmed <<syndrome.lower()>> infections per million population
- - title: |-
- - Samples tested and confirmed to be <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections per million population
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS)
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS)
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
- - name: << syndrome.capitalize() >>
? --
+ + name: << syndrome.capitalize >>
+ + title_public: Bacteriologically confirmed <<syndrome.lower()>> infections per million population
- - title_public: |-
- - Samples tested and confirmed to be <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections per million population
~ Column isolspermillion (changed metadata)
+ + title: Isolates of <<syndrome.lower()>> infections per million population
- - title: |-
- - Samples tested and confirmed to be <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections with antibiotic susceptibility test results per million population
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS)
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS)
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
- - unit: confirmed infections per million
+ + unit: isolates per million
- - name: << syndrome.capitalize() >>
? --
+ + name: << syndrome.capitalize >>
+ + title_public: Isolates of <<syndrome.lower()>> infections per million population
- - title_public: |-
- - Samples tested and confirmed to be <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections with antibiotic susceptibility test results per million population
~ Column totalspecimenisolates (changed metadata)
+ + title: Total specimen isolates of <<syndrome.lower()>> infections
- - title: |-
- - Total specimens collected of <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS)
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS)
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
- - unit: specimens
+ + unit: isolates
- - name: << syndrome.capitalize() >>
? --
+ + name: << syndrome.capitalize >>
+ + title_public: Total specimen isolates of <<syndrome.lower()>> infections
- - title_public: |-
- - Total specimen collected of <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections
~ Column totalspecimenisolateswithast (changed metadata)
+ + title: Bacterially confirmed <<syndrome.lower()>> infections per million population with antibiotic susceptibility test results
- - title: |-
- - Samples tested and confirmed to be <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections with antibiotic susceptibility test results
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS)
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS)
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
- - unit: confirmed infections
+ + unit: confirmed infections per million
? ++++++++++++
- - name: << syndrome.capitalize() >>
? --
+ + name: << syndrome.capitalize >>
- - title_public: |-
- - Samples tested and confirmed to be <%- if syndrome == "BLOOD" %> bloodstream <%- elif syndrome == "STOOL" %> gastrointestinal <%- elif syndrome == "URINE" %> urinary tract <%- elif syndrome == "UROGENITAL" %> gonorrohea <% endif %> infections with antibiotic susceptibility test results
+ + title_public: Bacterially confirmed <<syndrome.lower()>> infections per million population with antibiotic susceptibility
+ + test results
~ Dataset garden/antibiotics/2024-10-18/who_glass_by_antibiotic
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS) - by antibiotic
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS) - by antibiotic
? +
~ Table bci_table (changed metadata)
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS) - by antibiotic
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS) - by antibiotic
? +
~ Column bcis_per_million (changed metadata)
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS) - by antibiotic
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS) - by antibiotic
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
~ Column total_bcis (changed metadata)
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS) - by antibiotic
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS) - by antibiotic
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
~ Table antibiotic_table (changed metadata)
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS) - by antibiotic
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS) - by antibiotic
? +
~ Column bcis_with_ast_per_million (changed metadata)
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS) - by antibiotic
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS) - by antibiotic
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
~ Column share_bcis_with_ast (changed metadata)
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS) - by antibiotic
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS) - by antibiotic
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
~ Column total_bcis_with_ast (changed metadata)
- - title: Global Antimicrobial Resistance and Use Surveillance System (GLASS) - by antibiotic
? -
+ + title: Global Antimicrobial Resitsance and Use Surveillance System (GLASS) - by antibiotic
? +
- - citation_full: Global AMR data - Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization
? -
+ + citation_full: Global AMR data - Global Antimicrobial Resitsance and Use Surveillance System (GLASS), World Health Organization
? +
= Dataset garden/antibiotics/2024-10-25/esvac_sales_corrected
= Table esvac_sales_corrected
⚠ Error: Index must be unique.
= Dataset garden/artificial_intelligence/2023-06-14/ai_deepfakes
= Table ai_deepfakes
⚠ Error: Index must be unique.
⚠ Error: Index must be unique.
= Dataset garden/artificial_intelligence/2024-02-15/epoch_llms
= Table epoch_llms
~ Column dataset_size__tokens (changed metadata)
- - Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epoch.ai. Retrieved from: 'https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^ ^^^
+ + Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^^^^ ^^^^^^
- - url_main: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url_main: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
- - url: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
~ Column mmlu_avg (changed metadata)
- - Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epoch.ai. Retrieved from: 'https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^ ^^^
+ + Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^^^^ ^^^^^^
- - url_main: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url_main: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
- - url: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
~ Column model_size__parameters (changed metadata)
- - Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epoch.ai. Retrieved from: 'https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^ ^^^
+ + Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^^^^ ^^^^^^
- - url_main: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url_main: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
- - url: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
~ Column organisation (changed metadata)
- - Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epoch.ai. Retrieved from: 'https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^ ^^^
+ + Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^^^^ ^^^^^^
- - url_main: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url_main: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
- - url: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
~ Column training_computation_petaflop (changed metadata)
- - Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epoch.ai. Retrieved from: 'https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^ ^^^
+ + Owen, David. (2023). Large Language Model performance and compute, Epoch (2023) [Data set]. In Extrapolating performance in language modeling benchmarks. Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks' .
? ^^^^^^^ ^^^^^^
- - url_main: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url_main: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
- - url: https://epoch.ai/blog/extrapolating-performance-in-language-modelling-benchmarks
? -
+ + url: https://epochai.org/blog/extrapolating-performance-in-language-modelling-benchmarks
? ++++
= Dataset garden/artificial_intelligence/2024-06-06/epoch_compute_cost
= Table epoch_compute_cost
~ Column cost__inflation_adjusted (changed metadata)
- - url_main: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url_main: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
~ Column domain (changed metadata)
- - url_main: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url_main: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
~ Column publication_date (changed metadata)
- - url_main: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url_main: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
= 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, l
...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: 2024-12-02 15:11:20 UTC |
lucasrodes
changed the title
added 9 commits
November 14, 2024 16:22
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(resolve owid/owid-issues#1649)