variables

This table contains all the variables of all the datasets we have at Our World In Data. Note that this includes only the information about the variables like name and default display settings, not the data itself.

Data license: CC-BY

23 rows where sourceId = 29574 sorted by id descending

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id ▲ name unit description createdAt updatedAt code coverage timespan datasetId sourceId shortUnit display columnOrder originalMetadata grapherConfigAdmin shortName catalogPath dimensions schemaVersion processingLevel processingLog titlePublic titleVariant attributionShort attribution descriptionShort descriptionFromProducer descriptionKey descriptionProcessing licenses license grapherConfigETL type sort dataChecksum metadataChecksum
736545 Average score on Atari games relative to humans (100%) % Average performance across 57 Atari 2600 games, such as Frogger and Pac-Man. Measured relative to human performance. 2023-07-03 14:54:48 2024-07-08 15:20:31       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
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 0     performance_atari grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_atari   1                 
[]
         float 
[]
 debbb42b8e0418e91d54651ef2936daf 600894df7ff422d688a1765249bba1f1
736544 Average score on Atari games relative to humans - state of the art color code     2023-07-03 14:54:48 2024-07-08 15:20:32       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{
    "zeroDay": "2019-01-01",
    "yearIsDay": true
}
 0     performance_atari_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_atari_improved   1                 
[]
         string 
[]
 686ff21987bb60f088eb68224744ff0e 0ec21035225b9b60867b29b72dce261f
736543 Top-5 accuracy - state of the art color code %   2023-07-03 14:54:48 2024-07-08 15:20:32       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
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 0     papers_with_code_imagenet_top5_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#papers_with_code_imagenet_top5_improved   1                 
[]
         string 
[]
 f42064b3b9a6c49fb5f9142dd026d002 df3cbc0fca766a6c39ffb7029b9e57ee
736542 Top-1 accuracy - state of the art color code %   2023-07-03 14:54:48 2024-07-08 15:20:32       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
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 0     papers_with_code_imagenet_top1_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#papers_with_code_imagenet_top1_improved   1                 
[]
         string 
[]
 0ddc701cb29eabe7a6b287dac24a3f23 86d7b79e8fd5ee68d2a42d7cbd7d61d5
736541 Top-5 accuracy % The top-5 accuracy measure is used to assess how frequently a model's top five predictions include the correct answer from a list of 1000 options. Here's an example to illustrate what this benchmark tests: When an image classification model is presented with an image of an animal, it will assign probabilities to each possible label. Based on these probabilities, the model generates its top five predictions out of a total of 1000 animal labels. For instance, the model might output the following predictions as its top five guesses: * Cat * Dog * Elephant * Lion * Tiger Suppose the correct label for the image is "dog." If "dog" appears among the model's top five predictions, then the model's prediction is considered correct according to the top-5 accuracy metric. On the other hand, if the correct label is "giraffe" and "giraffe" is not included in the model's top five predictions, then the model's prediction would be considered incorrect based on the top-5 accuracy measure. To calculate the top-5 accuracy, researchers evaluate the model's performance on a large dataset with known labels. They compute the percentage of examples in the dataset where the correct label is present within the model's top five predictions out of the 1000 possible options. This measure provides a broader perspective on the model's performance by considering whether the correct answer is among its top guesses, even if it's not the model's absolute top prediction. 2023-07-03 14:54:48 2024-07-08 15:20:31       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
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 0     papers_with_code_imagenet_top5 grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#papers_with_code_imagenet_top5   1                 
[]
         float 
[]
 87596d558e2e33107e675da91d259c0c 6dda9259f7ebf5d6eeba088e2c7b7d34
736540 Accuracy on Social Sciences knowledge tests - state of the art- state of the art color code     2023-07-03 14:54:48 2024-07-08 15:20:31       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{
    "zeroDay": "2019-01-01",
    "yearIsDay": true
}
 0     performance_social_sciences_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_social_sciences_improved   1                 
[]
         string 
[]
 b992ab3e8f86d8d221143acabd9c2938 dcd398383d6aeddbb86fc35c7df6db5c
736539 Accuracy on other knowledge tests - state of the art color code     2023-07-03 14:54:48 2024-07-08 15:20:31       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{
    "zeroDay": "2019-01-01",
    "yearIsDay": true
}
 0     performance_other_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_other_improved   1                 
[]
         string 
[]
 39850a6eb1bff77861a1d84d342db5ab d7ce4bd85ecb2cc3459df565224c8d30
736538 Accuracy on STEM subjects knowledge tests - state of the art color code     2023-07-03 14:54:48 2024-07-08 15:20:31       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{
    "zeroDay": "2019-01-01",
    "yearIsDay": true
}
 0     performance_stem_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_stem_improved   1                 
[]
         string 
[]
 b0654fd329f56dfa613289ba85e9871d a17db9c89960c16ccb919fd4c0b2f9ba
736537 Top-1 accuracy % The top-1 accuracy measure is used to assess how frequently a model's absolute top prediction matches the correct answer from a given set of options. Here's an example to illustrate what this benchmark tests: Imagine an image classification model that is presented with an image of an animal. The model assigns probabilities to each potential label and generates its highest-confidence prediction. For instance, when analyzing an image, the model might predict "Cat" as the most probable label. To evaluate the model's accuracy using the top-1 measure, researchers compare this prediction with the correct label. If the model's top prediction matches the correct label (e.g., if the actual animal in the image is indeed a cat), then the model's prediction is considered correct according to the top-1 accuracy metric. On the other hand, if the model's top prediction does not match the correct label (e.g., if the image shows a dog, but the model predicts a cat), then the model's prediction is considered incorrect based on the top-1 accuracy measure. To calculate the top-1 accuracy, researchers analyze the model's performance on a large dataset where the correct labels are known. They determine the percentage of examples in the dataset where the model's highest-confidence prediction matches the actual label. This measure provides a focused evaluation of the model's ability to make accurate predictions by considering only its absolute top guess. 2023-07-03 14:54:48 2024-07-08 15:20:32       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
{
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    "zeroDay": "2019-01-01",
    "shortUnit": "%",
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 0     papers_with_code_imagenet_top1 grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#papers_with_code_imagenet_top1   1                 
[]
         float 
[]
 469596c8ce37b6a7a480eee72e11ffa6 ed95d530167cd069ffe70e1aea3b8639
736536 With/without extra training data (ImageNet)     2023-07-03 14:54:48 2024-07-08 15:20:31     -2222-1612 Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{}
 0     training_data grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#training_data   1                 
[]
         string 
[]
 16ebe6290f249f34d13b6720fab1bbb6 706500cc1f1a1b578abf42d96708f963
736535 Performance on math and problem-solving tasks - state of the art color code     2023-07-03 14:54:48 2024-07-08 15:20:32       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{
    "zeroDay": "2019-01-01",
    "yearIsDay": true
}
 0     performance_math_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_math_improved   1                 
[]
         string 
[]
 d2339daf1dc31d6450499d42ce3cd586 afccc5e233fdbdbc24cf0564a33be5dc
736534 Average accuracy on all knowledge tests - state of the art color code     2023-07-03 14:54:48 2024-07-08 15:20:31       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{
    "zeroDay": "2019-01-01",
    "yearIsDay": true
}
 0     performance_language_average_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_language_average_improved   1                 
[]
         string 
[]
 4403269846ee7c80f5af373bb56dbf5b 0797550ec27d98778d0213ad896d0a4f
736533 Coding performance on interviews - state of the art color code     2023-07-03 14:54:48 2024-07-08 15:20:31       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{
    "zeroDay": "2019-01-01",
    "yearIsDay": true
}
 0     performance_code_any_interview_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_code_any_interview_improved   1                 
[]
         string 
[]
 c0a0be512d226d8b4cc9ff795927bc66 badc9080ee8d8f301752ae06edcda06c
736532 Accuracy on Humanities subjects - state of the art color code     2023-07-03 14:54:48 2024-07-08 15:20:32       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{
    "zeroDay": "2019-01-01",
    "yearIsDay": true
}
 0     performance_humanities_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_humanities_improved   1                 
[]
         string 
[]
 b992ab3e8f86d8d221143acabd9c2938 65347d31a418002f0d207d7db7b8ae11
736531 Coding performance on competitions - state of the art color code     2023-07-03 14:54:48 2024-07-08 15:20:31       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574   
{
    "zeroDay": "2019-01-01",
    "yearIsDay": true
}
 0     performance_code_any_competition_improved grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_code_any_competition_improved   1                 
[]
         string 
[]
 ac9e342e1ebeedaf101000a573fead9c ad72379039ff7138e494137582c8b6e5
736530 Accuracy on STEM subjects knowledge tests % This benchmark assesses the accuracy of models in STEM subjects knowledge based on the MMLU benchmark. The MMLU benchmark covers a wide range of 57 subjects, including STEM, humanities, social sciences, and more. It encompasses subjects of varying difficulty levels, spanning from elementary concepts to advanced professional topics. This comprehensive benchmark assesses not only world knowledge but also problem-solving abilities. 2023-07-03 14:54:48 2024-07-08 15:20:30       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
{
    "unit": "%",
    "zeroDay": "2019-01-01",
    "shortUnit": "%",
    "yearIsDay": true,
    "numDecimalPlaces": 1
}
 0     performance_stem grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_stem   1                 
[]
         float 
[]
 513861990b890431367eccbc6ec71d7e 5c9842d32397d9bd8ce9d2998fed12f7
736529 Accuracy on other knowledge tests % This benchmark assesses the average accuracy of models across subjects other than STEM, humanities, social sciences based on the MMLU benchmark. The MMLU benchmark covers a wide range of 57 subjects, including STEM, humanities, social sciences, and more. It encompasses subjects of varying difficulty levels, spanning from elementary concepts to advanced professional topics. This comprehensive benchmark assesses not only world knowledge but also problem-solving abilities. 2023-07-03 14:54:48 2024-07-08 15:20:31       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
{
    "unit": "%",
    "zeroDay": "2019-01-01",
    "shortUnit": "%",
    "yearIsDay": true,
    "numDecimalPlaces": 1
}
 0     performance_other grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_other   1                 
[]
         float 
[]
 774883813cce879e7ac5d7a67fdc1df0 6432122c3d15ef28b79e639d497dbf8c
736528 Accuracy on Social Sciences knowledge tests % This benchmark assesses the accuracy of models in social sciences knowledge based on the MMLU benchmark. The MMLU benchmark covers a wide range of 57 subjects, including STEM, humanities, social sciences, and more. It encompasses subjects of varying difficulty levels, spanning from elementary concepts to advanced professional topics. This comprehensive benchmark assesses not only world knowledge but also problem-solving abilities. 2023-07-03 14:54:48 2024-07-08 15:20:30       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
{
    "unit": "%",
    "zeroDay": "2019-01-01",
    "shortUnit": "%",
    "yearIsDay": true,
    "numDecimalPlaces": 1
}
 0     performance_social_sciences grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_social_sciences   1                 
[]
         float 
[]
 a1e53397b333244f23dfd6be697bf314 0e7cfd6ae1bdc23802beb32679306b20
736527 Performance on math and problem-solving tasks % This benchmark assesses the accuracy of models on math and problem solving tasks based on the MATH benchmark. The MATH benchmark consists of 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations. 2023-07-03 14:54:48 2024-07-08 15:20:30       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
{
    "unit": "%",
    "zeroDay": "2019-01-01",
    "shortUnit": "%",
    "yearIsDay": true,
    "numDecimalPlaces": 1
}
 0     performance_math grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_math   1                 
[]
         float 
[]
 a4f2238d9420d82691a35fdb80133198 4c3dffb26fcfc27eaf7a989b1a2fe3ba
736526 Coding performance on interviews % This benchmark assesses the accuracy of models in coding interviews based on the APPS benchmark. The APPS benchmark focuses on coding ability and problem-solving in a natural language context, simulating the evaluation process employed during human programmer interviews. It presents coding problems in unrestricted natural language and evaluates the correctness of solutions. The coding tasks within this benchmark are sourced from open-access coding websites such as Codeforces and Kattis. These tasks cover a spectrum of difficulty levels, ranging from introductory to collegiate competition level. The benchmark measures the accuracy of models in solving programming tasks specifically tailored for coding interviews. 2023-07-03 14:54:48 2024-07-08 15:20:30       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
{
    "unit": "%",
    "zeroDay": "2019-01-01",
    "shortUnit": "%",
    "yearIsDay": true,
    "numDecimalPlaces": 1
}
 0     performance_code_any_interview grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_code_any_interview   1                 
[]
         float 
[]
 fe03953458ce64c74fe18dc40fc1035c a0f205e9c914f0a51b0d8df8ebf7c0a8
736525 Accuracy on Humanities knowledge tests % This benchmark assesses the accuracy of models in humanities knowledge based on the MMLU benchmark. The MMLU benchmark covers a wide range of 57 subjects, including STEM, humanities, social sciences, and more. It encompasses subjects of varying difficulty levels, spanning from elementary concepts to advanced professional topics. This comprehensive benchmark assesses not only world knowledge but also problem-solving abilities. 2023-07-03 14:54:48 2024-07-08 15:20:30       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
{
    "unit": "%",
    "zeroDay": "2019-01-01",
    "shortUnit": "%",
    "yearIsDay": true,
    "numDecimalPlaces": 1
}
 0     performance_humanities grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_humanities   1                 
[]
         float 
[]
 2b39edfe3d2594d0629d9ceab4df6ccd 33cc4c0d6b20bf33ea4f7fc9e31aae0e
736524 Average accuracy on all knowledge tests % This benchmark assesses the average accuracy of models across all subjects based on the MMLU benchmark. The MMLU benchmark covers a wide range of 57 subjects, including STEM, humanities, social sciences, and more. It encompasses subjects of varying difficulty levels, spanning from elementary concepts to advanced professional topics. This comprehensive benchmark assesses not only world knowledge but also problem-solving abilities. 2023-07-03 14:54:48 2024-07-08 15:20:30       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
{
    "unit": "%",
    "zeroDay": "2019-01-01",
    "shortUnit": "%",
    "yearIsDay": true,
    "numDecimalPlaces": 1
}
 0     performance_language_average grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_language_average   1                 
[]
         float 
[]
 350d88ac6e544f9a30fdfc2d60f9ca9f a4e7bac7e8e71ce4627973ea44b60302
736523 Coding performance on competitions % This benchmark measures the accuracy of models in coding competitions based on the APPS benchmark. The APPS benchmark focuses on coding ability and problem-solving in a natural language context. It aims to replicate the evaluation process used for human programmers by presenting coding problems in unrestricted natural language and assessing the correctness of solutions. The coding tasks included in this benchmark are sourced from open-access coding websites such as Codeforces and Kattis. These tasks span a range of difficulty levels, from introductory to collegiate competition level. The benchmark evaluates the accuracy of models in solving programming tasks specifically designed for coding competitions. 2023-07-03 14:54:48 2024-07-08 15:20:30       Performance on Coding, Math, Language, Image Classification and Atari tasks (Papers With Code, 2023) 6102 Papers With Code (2023) 29574 % 
{
    "unit": "%",
    "zeroDay": "2019-01-01",
    "shortUnit": "%",
    "yearIsDay": true,
    "numDecimalPlaces": 1
}
 0     performance_code_any_competition grapher/artificial_intelligence/2023-06-14/papers_with_code_benchmarks/papers_with_code_benchmarks#performance_code_any_competition   1                 
[]
         float 
[]
 477f6de5c6607f577f8c7992350adfa7 bc2253ceace36d8dbcc5d84fe34cda70

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CREATE TABLE "variables" (
	"id" INTEGER PRIMARY KEY AUTOINCREMENT,
	"name" VARCHAR(750) NULL  ,
	"unit" VARCHAR(255) NOT NULL  ,
	"description" TEXT NULL  ,
	"createdAt" DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ,
	"updatedAt" DATETIME NULL  ,
	"code" VARCHAR(255) NULL  ,
	"coverage" VARCHAR(255) NOT NULL  ,
	"timespan" VARCHAR(255) NOT NULL  ,
	"datasetId" INTEGER NOT NULL  ,
	"sourceId" INTEGER NULL  ,
	"shortUnit" VARCHAR(255) NULL  ,
	"display" TEXT NOT NULL  ,
	"columnOrder" INTEGER NOT NULL DEFAULT '0' ,
	"originalMetadata" TEXT NULL  ,
	"grapherConfigAdmin" TEXT NULL  ,
	"shortName" VARCHAR(255) NULL  ,
	"catalogPath" VARCHAR(767) NULL  ,
	"dimensions" TEXT NULL  ,
	"schemaVersion" INTEGER NOT NULL DEFAULT '1' ,
	"processingLevel" VARCHAR(30) NULL  ,
	"processingLog" TEXT NULL  ,
	"titlePublic" VARCHAR(512) NULL  ,
	"titleVariant" VARCHAR(255) NULL  ,
	"attributionShort" VARCHAR(512) NULL  ,
	"attribution" TEXT NULL  ,
	"descriptionShort" TEXT NULL  ,
	"descriptionFromProducer" TEXT NULL  ,
	"descriptionKey" TEXT NULL  ,
	"descriptionProcessing" TEXT NULL  ,
	"licenses" TEXT NULL  ,
	"license" TEXT NULL  ,
	"grapherConfigETL" TEXT NULL  ,
	"type" TEXT NULL  ,
	"sort" TEXT NULL  ,
	"dataChecksum" VARCHAR(64) NULL  ,
	"metadataChecksum" VARCHAR(64) NULL,
	FOREIGN KEY("datasetId") REFERENCES "datasets" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT,
	FOREIGN KEY("sourceId") REFERENCES "sources" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT
);
CREATE UNIQUE INDEX "idx_catalogPath" ON "variables" ("catalogPath");
CREATE UNIQUE INDEX "unique_short_name_per_dataset" ON "variables" ("shortName", "datasetId");
CREATE UNIQUE INDEX "variables_code_fk_dst_id_7bde8c2a_uniq" ON "variables" ("code", "datasetId");
CREATE INDEX "variables_datasetId_50a98bfd_fk_datasets_id" ON "variables" ("datasetId");
CREATE UNIQUE INDEX "variables_name_fk_dst_id_f7453c33_uniq" ON "variables" ("name", "datasetId");
CREATE INDEX "variables_sourceId_31fce80a_fk_sources_id" ON "variables" ("sourceId");