owid: variables: 4 rows where datasetId = 6640 sorted by id descending

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

4 rows where datasetId = 6640 sorted by id descending

descending

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id ▲	name	unit	createdAt	updatedAt	datasetId	shortUnit	display	shortName	catalogPath	schemaVersion	processingLevel	descriptionShort	descriptionKey	descriptionProcessing	grapherConfigETL	type	sort	dataChecksum	metadataChecksum
959817	GPU computational performance per dollar	FLOP/s/$	2024-07-29 11:40:03	2024-07-29 11:40:03	Trends in Machine Learning Hardware 6640		{ "unit": "FLOP/s/$", "zeroDay": "2000-01-01", "yearIsDay": true, "numDecimalPlaces": 0 }	comp_performance_per_dollar	grapher/artificial_intelligence/2024-07-11/epoch_gpus/epoch_gpus#comp_performance_per_dollar	2	major	Graphics processing units (GPUs) are the dominant computing hardware for artificial intelligence systems. GPU performance is shown in [floating-point operations](#dod:flop) operations/second (FLOP/s) per US dollar, adjusted for inflation.	[]	- Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation). - It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team. - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price. - 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.	{ "note": "FLOP/s values refer to 32-bit (full) precision. Data is expressed in constant 2023 US$. Inflation adjustment is based on the US Consumer Price Index (CPI)." }	float	[]	a990e808fcd1d74c964fcbdff7fab745	b290c84bf93a0134e786844b5c08517e
959816	Release price	US$	2024-07-29 11:40:03	2024-07-29 11:40:03	Trends in Machine Learning Hardware 6640	$	{ "unit": "US$", "zeroDay": "2000-01-01", "shortUnit": "$", "yearIsDay": true, "numDecimalPlaces": 0 }	release_price__usd	grapher/artificial_intelligence/2024-07-11/epoch_gpus/epoch_gpus#release_price__usd	2	major	The price of the GPU at the time of its release.Data is expressed in constant 2023 US$. Inflation adjustment is based on the US Consumer Price Index (CPI).	[]	- Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation). - It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team. - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price. - 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.		int	[]	a2082affefd26aa10d81db1fc4d5524c	076035cc78638e25af0908d7f9989309
959815	Manufacturer		2024-07-29 11:40:03	2024-07-29 11:40:03	Trends in Machine Learning Hardware 6640		{ "zeroDay": "2000-01-01", "yearIsDay": true, "numDecimalPlaces": 0 }	manufacturer	grapher/artificial_intelligence/2024-07-11/epoch_gpus/epoch_gpus#manufacturer	2	major	The company that produced the GPU.	[]	- Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation). - It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team. - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price. - 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.		string	[]	0635c0e58e2cfe7db603d785304d18d4	c74e617fe1e507e10ed1ff5ee0b5ba0c
959814	FP32 performance	FLOP/s	2024-07-29 11:40:03	2024-07-29 11:40:03	Trends in Machine Learning Hardware 6640		{ "unit": "FLOP/s", "zeroDay": "2000-01-01", "yearIsDay": true, "numDecimalPlaces": 0 }	fp32__single_precision__performance__flop_s	grapher/artificial_intelligence/2024-07-11/epoch_gpus/epoch_gpus#fp32__single_precision__performance__flop_s	2	major	The number of floating-point operations per second that can be performed by the GPU. FLOP/s values refer to 32-bit (full) precision.	[]	- Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation). - It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team. - It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price. - 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.		float	[]	f14ca0efd229c3761a7f4f0055c22963	fcb1ac2ca868e0b1592079c442b547f2

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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");