📊 population: exploration (shared externally) #3502
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data-diff: ❌ Found differences= 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/2024-10-25/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/2024-11-03/epoch
= Table epoch
~ Column domain (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column organization_categorization (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column parameters (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column publication_date (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column training_computation_petaflop (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column training_dataset_size__datapoints (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
= Dataset garden/artificial_intelligence/2024-11-03/epoch_aggregates_affiliation
= Table epoch_aggregates_affiliation
~ Column cumulative_count (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column yearly_count (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
= Dataset garden/artificial_intelligence/2024-11-03/epoch_aggregates_domain
= Table epoch_aggregates_domain
~ Column cumulative_count (changed metadata)
- - Describes the specific area, application, or field in which an AI system is designed to operate. An AI system can operate in more than one domain, thus contributing to the count for multiple domains. The 2024 data is incomplete and was last updated 03 November 2024.
? ^^
+ + Describes the specific area, application, or field in which an AI system is designed to operate. An AI system can operate in more than one domain, thus contributing to the count for multiple domains. The 2024 data is incomplete and was last updated 6 November 2024.
? ^
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column yearly_count (changed metadata)
- - Describes the specific area, application, or field in which an AI system is designed to operate. An AI system can operate in more than one domain, thus contributing to the count for multiple domains. The 2024 data is incomplete and was last updated 03 November 2024.
? ^^
+ + Describes the specific area, application, or field in which an AI system is designed to operate. An AI system can operate in more than one domain, thus contributing to the count for multiple domains. The 2024 data is incomplete and was last updated 6 November 2024.
? ^
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
= Dataset garden/artificial_intelligence/2024-11-03/epoch_compute_intensive
= Table epoch_compute_intensive
~ Column domain (changed metadata)
- - Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models'
? ^^^
+ + Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/tracking-compute-intensive-ai-models'
? ^^^^^^
- - url_main: https://epoch.ai/blog/tracking-compute-intensive-ai-models
? -
+ + url_main: https://epochai.org/blog/tracking-compute-intensive-ai-models
? ++++
- - url_download: https://epoch.ai/data/epochdb/large_scale_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/large_scale_ai_models.csv
? ++++
- - url: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
~ Column parameters (changed metadata)
- - Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models'
? ^^^
+ + Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/tracking-compute-intensive-ai-models'
? ^^^^^^
- - url_main: https://epoch.ai/blog/tracking-compute-intensive-ai-models
? -
+ + url_main: https://epochai.org/blog/tracking-compute-intensive-ai-models
? ++++
- - url_download: https://epoch.ai/data/epochdb/large_scale_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/large_scale_ai_models.csv
? ++++
- - url: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
~ Column publication_date (changed metadata)
- - Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models'
? ^^^
+ + Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/tracking-compute-intensive-ai-models'
? ^^^^^^
- - url_main: https://epoch.ai/blog/tracking-compute-intensive-ai-models
? -
+ + url_main: https://epochai.org/blog/tracking-compute-intensive-ai-models
? ++++
- - url_download: https://epoch.ai/data/epochdb/large_scale_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/large_scale_ai_models.csv
? ++++
- - url: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
~ Column training_computation_petaflop (changed metadata)
- - Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models'
? ^^^
+ + Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/tracking-compute-intensive-ai-models'
? ^^^^^^
- - url_main: https://epoch.ai/blog/tracking-compute-intensive-ai-models
? -
+ + url_main: https://epochai.org/blog/tracking-compute-intensive-ai-models
? ++++
- - url_download: https://epoch.ai/data/epochdb/large_scale_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/large_scale_ai_models.csv
? ++++
- - url: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
~ Column training_dataset_size__datapoints (changed metadata)
- - Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models'
? ^^^
+ + Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/tracking-compute-intensive-ai-models'
? ^^^^^^
- - url_main: https://epoch.ai/blog/tracking-compute-intensive-ai-models
? -
+ + url_main: https://epochai.org/blog/tracking-compute-intensive-ai-models
? ++++
- - url_download: https://epoch.ai/data/epochdb/large_scale_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/large_scale_ai_models.csv
? ++++
- - url: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
= Dataset garden/artificial_intelligence/2024-11-03/epoch_compute_intensive_countries
= Table epoch_compute_intensive_countries
~ Column cumulative_count (changed metadata)
- - Refers to the location of the primary organization with which the authors of a large-scale AI systems are affiliated. The 2024 data is incomplete and was last updated 03 November 2024.
? ^^
+ + Refers to the location of the primary organization with which the authors of a large-scale AI systems are affiliated. The 2024 data is incomplete and was last updated 6 November 2024.
? ^
- - Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models'
? ^^^
+ + Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/tracking-compute-intensive-ai-models'
? ^^^^^^
- - url_main: https://epoch.ai/blog/tracking-compute-intensive-ai-models
? -
+ + url_main: https://epochai.org/blog/tracking-compute-intensive-ai-models
? ++++
- - url_download: https://epoch.ai/data/epochdb/large_scale_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/large_scale_ai_models.csv
? ++++
- - url: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
~ Column yearly_count (changed metadata)
- - Refers to the location of the primary organization with which the authors of a large-scale AI systems are affiliated. The 2024 data is incomplete and was last updated 03 November 2024.
? ^^
+ + Refers to the location of the primary organization with which the authors of a large-scale AI systems are affiliated. The 2024 data is incomplete and was last updated 6 November 2024.
? ^
- - Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models'
? ^^^
+ + Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/tracking-compute-intensive-ai-models'
? ^^^^^^
- - url_main: https://epoch.ai/blog/tracking-compute-intensive-ai-models
? -
+ + url_main: https://epochai.org/blog/tracking-compute-intensive-ai-models
? ++++
- - url_download: https://epoch.ai/data/epochdb/large_scale_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/large_scale_ai_models.csv
? ++++
- - url: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
= Dataset garden/artificial_intelligence/2024-11-03/epoch_compute_intensive_domain
= Table epoch_compute_intensive_domain
~ Column cumulative_count (changed metadata)
- - Describes the specific area, application, or field in which a large-scale AI model is designed to operate. The 2024 data is incomplete and was last updated 03 November 2024.
? ^^
+ + Describes the specific area, application, or field in which a large-scale AI model is designed to operate. The 2024 data is incomplete and was last updated 6 November 2024.
? ^
- - Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models'
? ^^^
+ + Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/tracking-compute-intensive-ai-models'
? ^^^^^^
- - url_main: https://epoch.ai/blog/tracking-compute-intensive-ai-models
? -
+ + url_main: https://epochai.org/blog/tracking-compute-intensive-ai-models
? ++++
- - url_download: https://epoch.ai/data/epochdb/large_scale_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/large_scale_ai_models.csv
? ++++
- - url: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
~ Column yearly_count (changed metadata)
- - Describes the specific area, application, or field in which a large-scale AI model is designed to operate. The 2024 data is incomplete and was last updated 03 November 2024.
? ^^
+ + Describes the specific area, application, or field in which a large-scale AI model is designed to operate. The 2024 data is incomplete and was last updated 6 November 2024.
? ^
- - Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models'
? ^^^
+ + Robi Rahman, David Owen and Josh You (2024), "Tracking Compute-Intensive AI Models". Published online at epochai.org. Retrieved from: 'https://epochai.org/blog/tracking-compute-intensive-ai-models'
? ^^^^^^
- - url_main: https://epoch.ai/blog/tracking-compute-intensive-ai-models
? -
+ + url_main: https://epochai.org/blog/tracking-compute-intensive-ai-models
? ++++
- - url_download: https://epoch.ai/data/epochdb/large_scale_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/large_scale_ai_models.csv
? ++++
- - url: https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
? -
+ + url: https://epochai.org/blog/how-much-does-it-cost-to-train-frontier-ai-models
? ++++
= Dataset garden/artificial_intelligence/2024-11-03/epoch_regressions
= Table epoch_regressions
~ Column domain (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column organization_categorization (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column parameters (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column publication_date (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column training_computation_petaflop (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
~ Column training_dataset_size__datapoints (changed metadata)
- - Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epoch.ai/data/epochdb/visualization’
? -
+ + Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’
? ++++
- - url_main: https://epoch.ai/mlinputs/visualization
? -
+ + url_main: https://epochai.org/mlinputs/visualization
? ++++
- - url_download: https://epoch.ai/data/epochdb/notable_ai_models.csv
? -
+ + url_download: https://epochai.org/data/epochdb/notable_ai_models.csv
? ++++
= Dataset garden/education/2023-07-17/education_barro_lee_projections
= Table education_barro_lee_projections
~ Dim country
- - Removed values: 446 / 11802 (3.78%)
year country
2014 Cook Islands
1986 Oceania
2008 South and West Asia (UIS)
2014 South and West Asia (UIS)
2007 Sub-Saharan Africa (UIS)
~ Dim year
- - Removed values: 446 / 11802 (3.78%)
country year
Cook Islands 2014
Oceania 1986
South and West Asia (UIS) 2008
South and West Asia (UIS) 2014
Sub-Saharan Africa (UIS) 2007
~ Dataset garden/education/2023-07-17/education_lee_lee
- - title: Human Capital in the Long Run (Lee and Lee 2016), WDI (World Bank) and UNESCO
? ^ -----------
+ + title: Human Capital in the Long Run (Lee and Lee 2016) and WDI (World Bank)
? ^^^^
= Table education_lee_lee
~ Dim country
- - Removed values: 701 / 12737 (5.50%)
year country
2022 Cook Islands
2007 North America and Western Europe (UIS)
2007 Oceania
2003 South and West Asia (UIS)
2019 Sub-Saharan Africa (UIS)
~ Dim year
- - Removed values: 701 / 12737 (5.50%)
country year
Cook Islands 2022
North America and Western Europe (UIS) 2007
Oceania 2007
South and West Asia (UIS) 2003
Sub-Saharan Africa (UIS) 2019
~ Column f_primary_enrollment_rates_combined_wb (changed metadata, changed data)
- - Total number of female students of the official age group for primary education who are enrolled in any level of education, expressed as a percentage of the corresponding female population. Divide the total number of female students in the official school age range for primary education who are enrolled in any level of education by the female population of the same age group and multiply the result by 100. The difference between the total NER and the adjusted NER provides a measure of the proportion of children in the official relevant school age group who are enrolled in levels of education below the one intended for their age. The difference between the total NER and the adjusted NER for primary education is due to enrolment in pre-primary education. The total NER should be based on total enrolment of the official relevant school age group in any level of education for all types of schools and education institutions, including public, private and all other institutions that provide organized educational programmes.
+ + Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics s
...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: 2024-11-25 10:56:08 UTC |
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This PR was created to explore a new population time series.
The PR is left open so that some external actors can see our analysis
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