vega-datasets

2.11.0 | GitHub | 2025-02-07 20:36:42 [UTC]

Common repository for example datasets used by Vega related projects. BSD-3-Clause license applies only to package code and infrastructure. Users should verify their use of datasets complies with the license terms of the original sources. Dataset license information, where included, is a reference starting point only and is provided without any warranty of accuracy or completeness.

licenses

name	path	title
BSD-3-Clause	https://opensource.org/license/bsd-3-clause	The 3-Clause BSD License

contributors

title	path
UW Interactive Data Lab	http://idl.cs.washington.edu
vega-datasets contributors	https://github.com/vega/vega-datasets/graphs/contributors

resources

`7zip.png`

path

7zip.png

description

Application icon from open-source software project. Used in Image-based Scatter Plot example.

sources

title	path
7-Zip	https://www.7-zip.org/

licenses

title	path
GNU Lesser General Public License	https://www.7-zip.org/license.txt

`airports.csv`

path

airports.csv

description

Airports in the United States and its territories, including major commercial, regional, and municipal airports. Contains information about each airport's location (latitude/longitude coordinates), identification codes, name, city, state, and country.

schema

name	type
iata	string
name	string
city	string
state	string
country	string
latitude	number
longitude	number

sources

title	path
Airports (Data.gov)	https://catalog.data.gov/dataset/airports-5e97a

licenses

name	title
other-open	https://www.usa.gov/government-works

`annual-precip.json`

path

annual-precip.json

description

A raster grid of global annual precipitation for the year 2016 at a resolution 1 degree of lon/lat per cell.

sources

title	path
Climate Forecast System Version 2	https://www.cpc.ncep.noaa.gov/products/people/wwang/cfsv2_fcst_history/

licenses

title	path
Public Domain	https://www.weather.gov/disclaimer/

`anscombe.json`

path

anscombe.json

description

Eleven (x,y) pairs of numbers, with means x̄=9.0 and ȳ=7.5, and identical linear regression lines (same slope and intercept) and correlation coefficients (approximately 0.816). When plotted, reveals starkly different patterns: one shows a linear relationship, another a non-linear curve, the third a near-perfect linear relationship disrupted by a single outlier, and the fourth a near-vertical line of points where a single outlier entirely dictates the regression.

In his 1973 paper "Graphs in Statistical Analysis" Yale Professor Francis Anscombe uses these four datasets to argue that visualization is essential to good statistical work, not merely an optional supplement. This was a radical position at a time when most statistical analysis was done through batch processing on mainframes with no graphical output. Serves as a powerful demonstration that identical summary statistics can mask radically different patterns in data, making the case that statistical analysis should combine both numerical calculations and graphical examination.

schema

name	type
Series	string
X	integer
Y	number

sources

title	path
Anscombe's quartet (Wikipedia)	https://en.wikipedia.org/wiki/Anscombe%27s_quartet#Data
Anscombe, F. J. (1973). Graphs in Statistical Analysis. The American Statistician, 27(1):17-21.	https://www.jstor.org/stable/2682899

`barley.json`

path

barley.json

description

Yields of barley varieties from experiments conducted by the Minnesota Agricultural Experiment Station (MAES) across six sites in Minnesota. The USDA Technical Bulletin No. 735 (December 1940) republished these yields data with explicit credit to MAES as the source.

It was analyzed by agronomists F.R. Immer, H.K. Hayes, and L. Powers in the 1934 paper "Statistical Determination of Barley Varietal Adaption".

R.A. Fisher popularized its use in the field of statistics when he included it in his book "The Design of Experiments".

Since then it has been used to demonstrate new visualization techniques, including the trellis charts developed by Richard Becker, William Cleveland and others in the 1990s.

schema

name	type
yield	number
variety	string
year	integer
site	string

sources

title	path
The Design of Experiments Reference	https://en.wikipedia.org/wiki/The_Design_of_Experiments
Wiebe, G. A., Reinbach-Welch, L., Cowan, P. R. (1940). Yields of Barley Varieties in the United States and Canada, 1932-36. United States: U.S. Department of Agriculture.	https://books.google.com/books?id=OUfxLocnpKkC&pg=PA19

licenses

name
Dataset collected by Minnesota Agricultural Experiment Station - license status unspecified

`birdstrikes.csv`

path

birdstrikes.csv

description

Records of reported wildlife strikes received by the U.S. FAA

schema

name	type
Airport Name	string
Aircraft Make Model	string
Effect Amount of damage	string
Flight Date	date
Aircraft Airline Operator	string
Origin State	string
Phase of flight	string
Wildlife Size	string
Wildlife Species	string
Time of day	string
Cost Other	integer
Cost Repair	integer
Cost Total $	integer
Speed IAS in knots	integer

sources

title	path
FAA Wildlife Strike Database	http://wildlife.faa.gov

licenses

title	path
U.S. Government Dataset	https://resources.data.gov/open-licenses/

`budget.json`

path

budget.json

description

Historical and forecasted federal revenue/receipts produced in 2016 by the U.S. Office of Management and Budget.

schema

name	type
Source Category Code	integer
Source category name	string
Source subcategory	integer
Source subcategory name	string
Agency code	integer
Agency name	string
Bureau code	integer
Bureau name	string
Account code	integer
Account name	string
Treasury Agency code	integer
On- or off-budget	string
1962	string
1963	string
1964	string
1965	string
1966	string
1967	string
1968	string
1969	string
1970	string
1971	string
1972	string
1973	string
1974	string
1975	string
1976	string
TQ	string
1977	string
1978	string
1979	string
1980	string
1981	string
1982	string
1983	string
1984	string
1985	string
1986	string
1987	string
1988	string
1989	string
1990	string
1991	string
1992	string
1993	string
1994	string
1995	string
1996	string
1997	string
1998	string
1999	string
2000	string
2001	string
2002	string
2003	string
2004	string
2005	string
2006	string
2007	string
2008	string
2009	string
2010	string
2011	string
2012	string
2013	string
2014	string
2015	string
2016	string
2017	string
2018	string
2019	string
2020	string

sources

title	path
Office of Management and Budget - Budget FY 2016 - Receipts	https://www.govinfo.gov/app/details/BUDGET-2016-DB/BUDGET-2016-DB-3

licenses

title	path
U.S. Government Dataset	https://resources.data.gov/open-licenses/

`budgets.json`

path

budgets.json

description

U.S. federal budget projections and actual outcomes from 1980 through 2010. Originally analyzed by The New York Times in 2010. Reveals how budget forecasts made in any given year compared to what actually happened, with positive values indicating surpluses (briefly seen around 2000) and negative values representing deficits (reaching a particularly large value of -$1.78 trillion during the 2008-2009 financial crisis).

schema

name	type	description
budgetYear	integer	The year for which the budget outcome is being reported
forecastYear	integer	The year for which the budget was forecast
value	number	The budget outcome or projection value (in trillions of 2010 dollars)

sources

title	path
Office of Management and Budget	https://www.whitehouse.gov/omb/

licenses

title	path
U.S. Government Dataset	https://resources.data.gov/open-licenses/

`burtin.json`

path

burtin.json

description

Compares the performance of three antibiotics against 16 different bacteria. Based on graphic designer Will Burtin's 1951 visualization of antibiotic effectiveness, originally published in Scope Magazine and featured as an example in the Protovis project, a precursor to D3.js.

Numerical values represent the minimum inhibitory concentration (MIC) of each antibiotic, measured in units per milliliter, with lower values indicating higher antibiotic effectiveness.

As noted in the Protovis example, "Recreating this display revealed some minor errors in the original: a missing grid line at 0.01 μg/ml, and an exaggeration of some values for penicillin".

The vega-datsets version is largely consistent with the Protovis version, with one correction (changing 'Brucella antracis' to the correct 'Bacillus anthracis') and the addition of a new column, 'Genus', to group related bacterial species together.

The caption of the original 1951 visualization reads as follows:

Antibacterial ranges of Neomycin, Penicillin and Streptomycin

The chart compares the in vitro sensitivities to neomycin of some of the common pathogens (gram+ in red and gram- in blue) with their sensitivities to penicillin, and streptomycin.

The effectiveness of the antibiotics is expressed as the highest dilution in μ/ml. which inhibits the test organism.

High dilutions are toward the periphery; consequently the length of the colored bar is proportional to the effectiveness.

It is apparent that neomycin is especially effective against Staph. albus and aureus, Streph. fecalis, A. aerogenes, S. typhosa, E. coli, Ps. aeruginosa, Br. abortus, K. pneumoniae, Pr. vulgaris, S. schottmuelleri and M. tuberculosis.

Unfortunately, some strains of proteus, pseudomonas and hemolytic streptococcus are resistant to neomycin, although the majority of these are sensitive to neomycin.

It also inhibits actinomycetes, but is inactive against viruses and fungi. Its mode of action is not understood.

schema

name	type
Bacteria	string
Penicillin	number
Streptomycin	number
Neomycin	number
Gram_Staining	string
Genus	string

sources

title	path
Scope Magazine	https://graphicdesignarchives.org/projects/scope-magazine-vol-iii-5/
Protovis Antibiotics Example	https://mbostock.github.io/protovis/ex/antibiotics-burtin.html

licenses

title	path
BSD License (via Protovis)	https://mbostock.github.io/protovis/

`cars.json`

path

cars.json

description

Collection of car specifications and performance metrics from various automobile manufacturers.

schema

name	type
Name	string
Miles_per_Gallon	integer
Cylinders	integer
Displacement	number
Horsepower	integer
Weight_in_lbs	integer
Acceleration	number
Year	date
Origin	string

sources

title	path
StatLib Datasets Archive	http://lib.stat.cmu.edu/datasets/

licenses

title	path
The original was distributed in 1982 for educational and scientific purposes.	http://lib.stat.cmu.edu/datasets/cars.desc

`co2-concentration.csv`

path

co2-concentration.csv

description

Atmospheric CO2 concentration measurements from Mauna Loa Observatory, Hawaii. Contains monthly readings from 1958-2020 with two key measurements:

CO2 concentrations in millionths of a mole of CO2 per mole of air (parts per million), reported on the 2012 SIO manometric mole fraction scale
Seasonally adjusted values where a 4-harmonic fit with linear gain factor has been subtracted to remove the quasi-regular seasonal cycle Values are adjusted to 24:00 hours on the 15th of each month. Only includes rows with valid data.

schema

name	type
Date	date
CO2	number
adjusted CO2	number

sources

title	path
Scripps CO2 Program	https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record
In-situ CO2 Data	https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv

licenses

title	path
Creative Commons Attribution 4.0	https://creativecommons.org/licenses/by/4.0/

`countries.json`

path

countries.json

description

Key demographic indicators (life expectancy at birth and fertility rate measured as babies per woman) for various countries from 1955 to 2000 at 5-year intervals. Includes both current values and adjacent time period values (previous and next) for each indicator. Gapminder's data documentation notes that its philosophy is to fill data gaps with estimates and use current geographic boundaries for historical data. Gapminder states that it aims to "show people the big picture" rather than support detailed numeric analysis.

schema

name	type	description
_comment	string
year	integer	Years from 1955 to 2000 at 5-year intervals
fertility	number	Fertility rate (average number of children per woman) for the given year
life_expect	number	Life expectancy in years for the given year
n_fertility	number	Fertility rate for the next 5-year interval
n_life_expect	number	Life expectancy for the next 5-year interval
country	string	Name of the country

sources

title	path	version
Gapminder Foundation - Life Expectancy	https://docs.google.com/spreadsheets/d/1RehxZjXd7_rG8v2pJYV6aY0J3LAsgUPDQnbY4dRdiSs/edit?gid=176703676#gid=176703676	14
Gapminder Foundation - Fertility	https://docs.google.com/spreadsheets/d/1aLtIpAWvDGGa9k2XXEz6hZugWn0wCd5nmzaRPPjbYNA/edit?gid=176703676#gid=176703676	14

licenses

title	path
Creative Commons Attribution 4.0 International	https://www.gapminder.org/free-material/

`crimea.json`

path

crimea.json

description

Monthly mortality rates from British military hospitals during the Crimean War (1854-1856), which informed Florence Nightingale's groundbreaking work in public health. Nightingale credits Dr. William Farr for compiling the data from the 1858 Medical and Surgical History of the British Army. Categorizes deaths into "zymotic" diseases (preventable infectious diseases), wounds/injuries, and other causes. Covering the period from April 1854 to March 1856, it includes monthly army strength alongside mortality figures. Transformed by Nightingale into her now-famous polar area diagrams.

The annual mortality rates plotted in the chart can be calculated using the formula

(Deaths × 1000 × 12) ÷ Army Size.

As The Lancet argued in 2020, Nightingale's innovative visualizations proved that "far more men died of disease, infection, and exposure than in battle—a fact that shocked the British nation." Her work also vividly illustrated the dramatic impact of sanitary reforms, particularly in reducing preventable deaths.

schema

name	type	description
date	date	First day of each month during the observation period, in ISO 8601 format (YYYY-MM-DD)
wounds	integer	Deaths from "Wounds and Injuries" which comprised: Luxatio (dislocation), Sub-Luxatio (partial dislocation), Vulnus Sclopitorum (gunshot wounds), Vulnus Incisum (incised wounds), Contusio (bruising), Fractura (fractures), Ambustio (burns) and Concussio-Cerebri (brain concussion)
other	integer	Deaths from All Other Causes
disease	integer	Deaths from Zymotic Diseases (preventable infectious diseases)
army_size	integer	Estimated Average Monthly Strength of the Army

sources

title	path
Nightingale, Florence. A contribution to the sanitary history of the British army during the late war with Russia. London : John W. Parker and Son, 1859. Table II. Table showing the Estimated Average Monthly Strength of the Army; and the Deaths and Annual Rate of Mortality per 1,000 in each month, from April 1854, to March 1856 (inclusive), in the Hospitals of the Army in the East.	https://nrs.lib.harvard.edu/urn-3:hms.count:1177146?n=21

licenses

title	path
Harvard Library - Digitized Content Copyright & Viewer Terms of Use	https://library.harvard.edu/privacy-terms-use-copyright-information#digitizedcontent

`disasters.csv`

path

disasters.csv

description

Annual number of deaths from disasters, sourced from EM-DAT (Emergency Events Database) maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at UCLouvain, Belgium. Processed by Our World in Data to standardize country names and world region definitions, converting units, calculating derived indicators, and adapting metadata. Deaths are reported as absolute numbers.

schema

name	type
Entity	string
Year	integer
Deaths	integer

sources

title	path
EM-DAT: The Emergency Events Database	https://www.emdat.be
Hannah Ritchie, Pablo Rosado and Max Roser (2022) - Natural Disasters	https://ourworldindata.org/natural-catastrophes

licenses

title	path
EM-DAT terms of use	https://doc.emdat.be/docs/legal/terms-of-use/
Creative Commons BY license (Our World in Data)	https://creativecommons.org/licenses/by/4.0/

`driving.json`

path

driving.json

description

Tracks the relationship between driving habits and gasoline prices in the United States during a period spanning multiple significant events, including the cheap gas era, Arab oil embargo, energy crisis, record low prices, and the "swing backward" from 1956 to 2010.

schema

name	type	description	categories
side	string	Label positioning indicator used in the original visualization to optimize	['left', 'right', 'top', 'bottom']
		readability and prevent overlap
year	integer	Year of observation from 1956 to 2010
miles	integer	Miles driven per capita per year, ranging from approximately 4,000 to 10,000 miles
gas	number	Price of a gallon of regular grade gasoline, adjusted for inflation

sources

title	path
New York Times (citing U.S. Energy Information Administration, Federal Highway Administration, and Brookings Institution)	https://archive.nytimes.com/www.nytimes.com/imagepages/2010/05/02/business/02metrics.html

`earthquakes.json`

path

earthquakes.json

description

Represents approximately one week of continuous monitoring from USGS's "all earthquakes" real-time feed, which includes 1,703 seismic events of all magnitudes recorded by the USGS Earthquake Hazards Program from January 31 to February 7, 2018 (UTC).

sources

title	path
USGS Earthquake Feed	https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_week.geojson

licenses

title	path
U.S. Public Domain	https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits

`ffox.png`

path

ffox.png

description

Application icon from open-source software project. Used in Image-based Scatter Plot example.

sources

title	path
Mozilla Firefox	https://www.mozilla.org/firefox/

licenses

title	path
Mozilla Trademark License	https://www.mozilla.org/en-US/foundation/trademarks/policy/

`flare-dependencies.json`

path

flare-dependencies.json

description

Indicates, with flare.json, relationships among classes in a software hierarchy.

schema

name	type
source	integer
target	integer

`flare.json`

path

flare.json

description

Indicates, with flare-dependencies.json, relationships among classes in a software hierarchy.

schema

name	type
id	integer
name	string

`flights-10k.json`

path

flights-10k.json

description

Flight delay statistics (10,000 rows) from U.S. Bureau of Transportation Statistics. Collected under regulatory reporting requirements (14 CFR Part 234), which mandate that qualifying airlines report on-time performance data to BTS. Transformed using /scripts/flights.py

schema

name	type
date	string
delay	integer
distance	integer
origin	string
destination	string

sources

title	path
U.S. Bureau of Transportation Statistics	https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr

licenses

name	path	title
other-open	https://www.ecfr.gov/current/title-14/chapter-II/subchapter-A/part-234	Data Collected Under U.S. DOT Regulatory Requirements - License Terms Not Explicitly Specified

`flights-200k.arrow`

path

flights-200k.arrow

description

Flight delay statistics (200,000 rows) from U.S. Bureau of Transportation Statistics. Collected under regulatory reporting requirements (14 CFR Part 234), which mandate that qualifying airlines report on-time performance data to BTS. Transformed using /scripts/flights.py

schema

name	type
delay	integer
distance	integer
time	number

sources

title	path
U.S. Bureau of Transportation Statistics	https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr

licenses

name	path	title
other-open	https://www.ecfr.gov/current/title-14/chapter-II/subchapter-A/part-234	Data Collected Under U.S. DOT Regulatory Requirements - License Terms Not Explicitly Specified

`flights-200k.json`

path

flights-200k.json

description

Flight delay statistics (200,000 rows) from U.S. Bureau of Transportation Statistics. Collected under regulatory reporting requirements (14 CFR Part 234), which mandate that qualifying airlines report on-time performance data to BTS. Transformed using /scripts/flights.py

schema

name	type
delay	integer
distance	integer
time	number

sources

title	path
U.S. Bureau of Transportation Statistics	https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr

licenses

name	path	title
other-open	https://www.ecfr.gov/current/title-14/chapter-II/subchapter-A/part-234	Data Collected Under U.S. DOT Regulatory Requirements - License Terms Not Explicitly Specified

`flights-20k.json`

path

flights-20k.json

description

Flight delay statistics (20,000 rows) from U.S. Bureau of Transportation Statistics. Collected under regulatory reporting requirements (14 CFR Part 234), which mandate that qualifying airlines report on-time performance data to BTS. Transformed using /scripts/flights.py

schema

name	type
date	string
delay	integer
distance	integer
origin	string
destination	string

sources

title	path
U.S. Bureau of Transportation Statistics	https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr

licenses

name	path	title
other-open	https://www.ecfr.gov/current/title-14/chapter-II/subchapter-A/part-234	Data Collected Under DOT Regulatory Requirements - License Terms Not Explicitly Specified

`flights-2k.json`

path

flights-2k.json

description

Flight delay statistics (2,000 rows) from U.S. Bureau of Transportation Statistics. Collected under regulatory reporting requirements (14 CFR Part 234), which mandate that qualifying airlines report on-time performance data to BTS. Transformed using /scripts/flights.py

schema

name	type
date	string
delay	integer
distance	integer
origin	string
destination	string

sources

title	path
U.S. Bureau of Transportation Statistics	https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr

licenses

name	path	title
other-open	https://www.ecfr.gov/current/title-14/chapter-II/subchapter-A/part-234	Data Collected Under U.S. DOT Regulatory Requirements - License Terms Not Explicitly Specified

`flights-3m.parquet`

path

flights-3m.parquet

description

Flight delay statistics (3 million rows) from U.S. Bureau of Transportation Statistics. Collected under regulatory reporting requirements (14 CFR Part 234), which mandate that qualifying airlines report on-time performance data to BTS. Transformed using /scripts/flights.py

schema

name	type
date	datetime
delay	integer
distance	integer
origin	string
destination	string

sources

title	path
U.S. Bureau of Transportation Statistics	https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr

licenses

name	path	title
other-open	https://www.ecfr.gov/current/title-14/chapter-II/subchapter-A/part-234	Data Collected Under U.S. DOT Regulatory Requirements - License Terms Not Explicitly Specified

`flights-5k.json`

path

flights-5k.json

description

Flight delay statistics (5,000 rows) from U.S. Bureau of Transportation Statistics. Collected under regulatory reporting requirements (14 CFR Part 234), which mandate that qualifying airlines report on-time performance data to BTS. Transformed using /scripts/flights.py

schema

name	type
date	string
delay	integer
distance	integer
origin	string
destination	string

sources

title	path
U.S. Bureau of Transportation Statistics	https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr

licenses

name	path	title
other-open	https://www.ecfr.gov/current/title-14/chapter-II/subchapter-A/part-234	Data Collected Under U.S. DOT Regulatory Requirements - License Terms Not Explicitly Specified

`flights-airport.csv`

path

flights-airport.csv

description

Flight information for the year 2008. Each record consists of an origin airport (identified by IATA id), a destination airport, and the count of flights along this route.

schema

name	type
origin	string
destination	string
count	integer

sources

title	path
U.S. Bureau of Transportation Statistics
U.S. Bureau of Transportation Statistics	https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`football.json`

path

football.json

description

Football match outcomes across multiple divisions from 2013 to 2017, part of a larger dataset from OpenFootball. The subset was made such that there are records for all five chosen divisions over the time period.

schema

name	type
date	date
division	string
home_team	string
away_team	string
home_score	integer
away_score	integer

sources

title	path
OpenFootball	https://github.com/openfootball/football.json

licenses

path
https://github.com/openfootball/football.json?tab=readme-ov-file#license

`gapminder-health-income.csv`

path

gapminder-health-income.csv

description

Per-capita income, life expectancy, population and regional grouping. Reference year for the data is not specified. Gapminder historical data is subject to revisions.

Gapminder (v30, 2023) defines per-capita income as follows:

"This is real GDP per capita (gross domestic product per person adjusted for inflation) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States."

schema

name	type
country	string
income	integer
health	number
population	integer
region	string

sources

title	path
Gapminder Foundation	https://www.gapminder.org
Gapminder GDP Per Capita Data	https://docs.google.com/spreadsheets/d/1i5AEui3WZNZqh7MQ4AKkJuCz4rRxGR_pw_9gtbcBOqQ/edit?gid=501532268#gid=501532268

licenses

title	path
Creative Commons Attribution 4.0 International	https://www.gapminder.org/free-material/

`gapminder.json`

path

gapminder.json

description

Combines key demographic indicators (life expectancy at birth, population, and fertility rate measured as babies per woman) for various countries from 1955 to 2005 at 5-year intervals. Includes a 'cluster' column, a categorical variable grouping countries. Gapminder's data documentation notes that its philosophy is to fill data gaps with estimates and use current geographic boundaries for historical data. Gapminder states that it aims to "show people the big picture" rather than support detailed numeric analysis.

Notes:

Country Selection: The set of countries matches the version of this dataset originally added to this collection in 2015. The specific criteria for country selection in that version are not known. Data for Aruba are no longer available in the new version. Hong Kong has been revised to Hong Kong, China in the new version.
Data Precision: The precision of float values may have changed from the original version. These changes reflect the most recent source data used for each indicator.
Regional Groupings: To preserve continuity with previous versions of this dataset, we have retained the column name 'cluster' instead of renaming it to 'six_regions'.

schema

name	type	description	categories
year	integer	Years from 1955 to 2005 at 5-year intervals
country	string	Name of the country
cluster	integer	A categorical variable grouping countries by region	[{'value': 0, 'label': 'south_asia'}, {'value': 1, 'label': 'europe_central_asia'}, {'value': 2, 'label': 'sub_saharan_africa'}, {'value': 3, 'label': 'america'}, {'value': 4, 'label': 'east_asia_pacific'}, {'value': 5, 'label': 'middle_east_north_africa'}]
pop	integer	Population of the country
life_expect	number	Life expectancy in years
fertility	number	Fertility rate (average number of children per woman

sources

title	path	version
Gapminder Foundation - Life Expectancy (Data)	https://docs.google.com/spreadsheets/d/1RehxZjXd7_rG8v2pJYV6aY0J3LAsgUPDQnbY4dRdiSs/edit?gid=176703676#gid=176703676	14
Gapminder Foundation - Life Expectancy (Documentation)	https://www.gapminder.org/data/documentation/gd004/
Gapminder Foundation - Population (Data)	https://docs.google.com/spreadsheets/d/1c1luQNdpH90tNbMIeU7jD__59wQ0bdIGRFpbMm8ZBTk/edit?gid=176703676#gid=176703676	7
Gapminder Foundation - Population (Documentation)	https://www.gapminder.org/data/documentation/gd003/
Gapminder Foundation - Fertility (Data)	https://docs.google.com/spreadsheets/d/1aLtIpAWvDGGa9k2XXEz6hZugWn0wCd5nmzaRPPjbYNA/edit?gid=176703676#gid=176703676	14
Gapminder Foundation - Fertility Documentation (Documentation)	https://www.gapminder.org/data/documentation/gd008/
Gapminder Foundation - Data Geographies (Data)	https://docs.google.com/spreadsheets/d/1qHalit8sXC0R8oVXibc2wa2gY7bkwGzOybEMTWp-08o/edit?gid=1597424158#gid=1597424158	2
Gapminder Foundation - Data Geographies (Documentation)	https://www.gapminder.org/data/geo/
Gapminder Data Documentation	https://www.gapminder.org/data/documentation/

licenses

title	path
Creative Commons Attribution 4.0 International	https://www.gapminder.org/free-material/

`gimp.png`

path

gimp.png

description

Application icon from open-source software project. Used in Image-based Scatter Plot example.

sources

title	path
GIMP - About GIMP	https://www.gimp.org/about/

licenses

path
https://www.gimp.org/docs/userfaq.html#whats-the-gimps-license-and-how-do-i-comply-with-it

`github.csv`

path

github.csv

description

Simulated GitHub contribution data showing hourly commit counts across different times of day. Designed to demonstrate typical patterns of developer activity in a GitHub-style punchcard visualization format.

schema

name	type	description
time	string	Hourly timestamp from January 1st to May 30th, 2015
count	integer	Simulated hourly commit counts

sources

title
Generated using `/scripts/github.py`.

licenses

path
https://github.com/vega/vega-datasets/blob/main/scripts/LICENSE

`global-temp.csv`

path

global-temp.csv

description

Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies (Land-Ocean Temperature Index, L-OTI), 1880-2023.

schema

name	type
year	integer
temp	number

sources

title	path
NASA Goddard Institute for Space Studies	https://data.giss.nasa.gov/gistemp/

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`income.json`

path

income.json

description

Household income distribution by US state, derived from the Census Bureau's American Community Survey 3-Year Data (2013). The dataset shows the percentage of households within different income brackets for each state. Generated using /scripts/income.py. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

schema

name	type
name	string
region	string
id	integer
pct	number
total	integer
group	string

sources

title	path
U.S. Census Bureau American Community Survey 3-Year Data (2013)	https://www.census.gov/data/developers/data-sets/acs-3year/2013.html
Census Bureau Data API User Guide	https://www.census.gov/data/developers/guidance/api-user-guide.html

licenses

title	path	name
U.S. Census Bureau API Terms of Service	https://www.census.gov/data/developers/about/terms-of-service.html	Census Bureau Terms of Use

`iowa-electricity.csv`

path

iowa-electricity.csv

description

Annual net generation of electricity in Iowa by source, in thousand megawatthours. U.S. EIA data downloaded on May 6, 2018. Useful for illustrating stacked area charts. Demonstrates dramatic increase in wind power production.

schema

name	type
year	date
source	string
net_generation	integer

sources

title

path

U.S. Energy Information Administration

https://www.eia.gov/beta/electricity/data/browser/#/topic/0?agg=2,0,1&fuel=vvg&geo=00000g&sec=g&linechart=ELEC.GEN.OTH-IA-99.A~ELEC.GEN.COW-IA-99.A~ELEC.GEN.PEL-IA-99.A~ELEC.GEN.PC-IA-99.A~ELEC.GEN.NG-IA-99.A~~ELEC.GEN.NUC-IA-99.A~ELEC.GEN.HYC-IA-99.A~ELEC.GEN.AOR-IA-99.A~ELEC.GEN.HPS-IA-99.A~&columnchart=ELEC.GEN.ALL-IA-99.A&map=ELEC.GEN.ALL-IA-99.A&freq=A&start=2001&end=2017&ctype=linechart&ltype=pin&tab=overview&maptype=0&rse=0&pin=

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`jobs.json`

path

jobs.json

description

U.S. census data on occupations by sex and year across decades between 1850 and 2000. Obtained from IPUMS USA, which "collects, preserves and harmonizes U.S. census microdata" from as early as 1790.

Originally created for a 2006 data visualization project called sense.us by IBM Research (Jeff Heer, Martin Wattenberg and Fernanda Viégas), described here. The dataset is also referenced in this vega example.

Based on a tabulation of the OCC1950 variable by sex across IPUMS USA samples. Appears to be derived from Version 6.0 (2015) of IPUMS USA, according to 2024 correspondence with the IPUMS Project. IPUMS has made improvements to occupation coding since version 6, particularly for 19th-century samples, which may result in discrepancies between this dataset and current IPUMS data. Details on data revisions are available here.

IPUMS USA confirmed in 2024 correspondence that hosting this dataset on vega-datasets is permissible, stating:

We're excited to hear that this dataset made its way to this repository and is being used by students for data visualization. We allow for these types of redistributions of summary data so long as the underlying microdata records are not shared.

Represents summary data. Underlying microdata records are not included.
Users attempting to replicate or extend this data should use the PERWT (person weight) variable as an expansion factor when working with IPUMS USA extracts.
Due to coding revisions, figures for earlier years (particularly 19th century) may not match current IPUMS USA data exactly.

When using this dataset, please refer to IPUMS USA terms of use. The organization requests use of the following citation for this json file:

Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0

schema

name	type	description
job	string	The occupation title
sex	string	Sex (men/women)
year	integer	Census year
count	integer	Number of individuals in the occupation
perc	number	Percentage of the workforce in the occupation

sources

title	path	version
IPUMS USA	https://usa.ipums.org/usa/	6

licenses

title	path
IPUMS Terms of Use	https://www.ipums.org/about/terms

`la-riots.csv`

path

la-riots.csv

description

A comprehensive record of fatalities during the five days of civil unrest in Los Angeles beginning April 29, 1992, documenting over 60 deaths with associated geographic coordinates where each person lost their life. Originally compiled and published by the Los Angeles Times Data Desk.

schema

name	type
first_name	string
last_name	string
age	integer
gender	string
race	string
death_date	date
address	string
neighborhood	string
type	string
longitude	number
latitude	number

sources

title	path
LA Riots Deaths, Los Angeles Times Data Desk	http://spreadsheets.latimes.com/la-riots-deaths/

`londonboroughs.json`

path

londonBoroughs.json

description

Boundaries of London boroughs reprojected and simplified from London_Borough_Excluding_MHW shapefile. Original data "contains National Statistics data © Crown copyright and database right (2015)" and "Contains Ordnance Survey data © Crown copyright and database right [2015].

sources

title	path
Statistical GIS Boundary Files, London Datastore	https://data.london.gov.uk/dataset/statistical-gis-boundary-files-london

licenses

title	path
UK Open Government License	https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

`londoncentroids.json`

path

londonCentroids.json

description

Calculated from londonBoroughs.json using d3.geoCentroid.

schema

name	type
name	string
cx	number
cy	number

sources

title	path
londonBoroughs.json from the vega-datasets repository	https://github.com/vega/vega-datasets/blob/main/data/londonBoroughs.json

licenses

title	path
UK Open Government License	https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

`londontubelines.json`

path

londonTubeLines.json

description

A topologically-encoded representation of select London Underground rail lines, derived from OpenStreetMap data. These 394 LineString geometries, encoded using 406 arcs, depict transport paths between stations with stations marked as nodes along the lines. Originally transformed from a GeoJSON intermediary tfl_lines.json into TopoJSON format, this network configuration reflects the system as of February 4, 2018, and may not incorporate subsequent modifications or expansions.

sources

title	path
OpenStreetMap Data (processed by oobrien/vis)	https://github.com/oobrien/vis/blob/master/tubecreature/data/tfl_lines.json

licenses

title	path
Open Data Commons Open Database License (ODbL)	https://opendatacommons.org/licenses/odbl/

`lookup_groups.csv`

path

lookup_groups.csv

description

A nine-row lookup table for the lookup_people.csv dataset, mapping people to groups. Used to demonstrate lookup transforms.

schema

name	type
group	integer
person	string

sources

title
Generated Data

licenses

path
https://github.com/vega/vega-datasets/blob/main/scripts/LICENSE

`lookup_people.csv`

path

lookup_people.csv

description

A synthetic list of nine people and their associated name, age, and height in centimeters. Used in conjunction with lookup_groups.csv to demonstrate lookup transforms.

schema

name	type
name	string
age	integer
height	integer

sources

title
Generated Data

licenses

path
https://github.com/vega/vega-datasets/blob/main/scripts/LICENSE

`miserables.json`

path

miserables.json

description

A weighted network of coappearances of characters in Victor Hugo's novel "Les Miserables". Nodes represent characters as indicated by the labels and edges connect any pair of characters that appear in the same chapter of the book. The values on the edges are the number of such coappearances.

sources

title	path
D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993).	https://www-cs-faculty.stanford.edu/~knuth/sgb.html

licenses

path
https://websites.umich.edu/~mejn/netdata/

`monarchs.json`

path

monarchs.json

description

A chronological list of English and British monarchs from Elizabeth I through George IV.

Contains two intentional inaccuracies to maintain compatibility with the Wheat and Wages example visualization:

the start date for the reign of Elizabeth I is shown as 1565, instead of 1558;
the end date for the reign of George IV is shown as 1820, instead of 1830. These discrepancies align the monarchs.json dataset with the start and end dates of the wheat.json dataset used in the visualization. The entry "W&M" represents the joint reign of William III and Mary II. While the dataset shows their reign as 1689-1702, the official Web site of the British royal family indicates that Mary II's reign ended in 1694, though William III continued to rule until 1702. The commonwealth field is used to flag the period from 1649 to 1660, which includes the Commonwealth of England, the Protectorate, and the period leading to the Restoration. While historically more accurate to call this the "interregnum," the field name of commonwealth from the original dataset is retained for backwards compatibility.

Important

Revised in Aug. 2024 to show James II's reign now ends in 1688 (previously 1689).

Source data has been verified against the kings & queens and interregnum pages of the official website of the British royal family (retrieved in Aug. 2024).

schema

name	type	description
name	string	The ruler's name or identifier (e.g., "W&M" for William and Mary, "Cromwell" for the period of interregnum)
start	integer	The year their rule began
end	integer	The year their rule ended
index	integer	A zero-based sequential number assigned to each entry, representing the chronological order of rulers

sources

title	path
The Royal Family - Kings & Queens	https://www.royal.uk/kings-and-queens-1066
The Royal Family - Interregnum	https://www.royal.uk/interregnum-1649-1660

licenses

title	path
Open Government Licence v3.0 (UK)	https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

`movies.json`

path

movies.json

description

A collection of films and their performance metrics, including box office earnings, budgets, and audience ratings. Contains known data quality issues and intentional errors, serving as a teaching resource for developing data cleaning and validation skills in real-world analysis workflows.

schema

name	type
Title	string
US Gross	integer
Worldwide Gross	integer
US DVD Sales	integer
Production Budget	integer
Release Date	string
MPAA Rating	string
Running Time min	integer
Distributor	string
Source	string
Major Genre	string
Creative Type	string
Director	string
Rotten Tomatoes Rating	integer
IMDB Rating	number
IMDB Votes	integer

`normal-2d.json`

path

normal-2d.json

description

Five hundred paired coordinates sampled from a bivariate normal distribution. The data is centered near the origin with standard deviations indicating a relatively equal spread in both dimensions. The variables exhibit negligible correlation (0.026), suggesting independence. Normality tests for each variable yield high p-values, supporting the normal distribution assumption. These characteristics make it well-suited for demonstrating statistical visualization techniques in Vega and Vega-Lite, including scatter plots, density plots, heatmaps, and marginal histograms/density curves. It can also serve as a clean baseline for testing new visualization methods or for educational purposes in data visualization and statistics. A contrast to uniformly distributed data in uniform-2d.json

schema

name	type	description
u	number	mean: 0.005, std: 0.192, range: [-0.578, 0.533], p-value: 0.680
v	number	mean: -0.011, std: 0.199, range: [-0.534, 0.606], p-value: 0.763

sources

title
Generated Data

licenses

path
https://github.com/vega/vega-datasets/blob/main/scripts/LICENSE

`obesity.json`

path

obesity.json

description

State-level obesity rates (BMI >= 30) for the U.S. in 1995. Originally Behavioral Risk Factor Surveillance System (BRFSS) statistics.

schema

name	type
id	integer
rate	number
state	string

sources

title	path
Protovis	https://mbostock.github.io/protovis/ex/us_stats.js

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`ohlc.json`

path

ohlc.json

description

Performance of the Chicago Board Options Exchange Volatility Index (VIX) in the summer of 2009.

The precise methodology used to derive the signal and calculate the ret columns is unclear.

schema

name	type
date	date
open	number
high	number
low	number
close	number
signal	string
ret	number

sources

title	path
Yahoo Finance VIX Data	https://finance.yahoo.com/chart/%5EVIX
CBOE - VIX Historical Data	https://www.cboe.com/tradable_products/vix/vix_historical_data/

`penguins.json`

path

penguins.json

description

Records of morphological measurements and demographic information from 344 Palmer Archipelago penguins across three species. Collected by Dr. Kristen Gorman and the Palmer Station Antarctica LTER. Data gathering occurred as part of Palmer Station's long-term ecological research, contributing to studies of Antarctic marine ecosystems and penguin biology. All measurements follow standardized units, enabling research into morphological variations between species and sexual dimorphism in Antarctic penguins.

schema

name	type	description
Species	string	Penguin species (Adelie, Gentoo, or Chinstrap)
Island	string	Island where the penguin was observed (Torgersen, Biscoe, or Dream)
Beak Length (mm)	number	Beak length in millimeters
Beak Depth (mm)	number	Beak depth in millimeters
Flipper Length (mm)	integer	Flipper length in millimeters
Body Mass (g)	integer	Body mass in grams
Sex	string	Sex of the penguin (MALE, FEMALE or null)

sources

title	path
Palmer Station Antarctica LTER	https://pal.lternet.edu/
Allison Horst's Penguins Repository	https://github.com/allisonhorst/penguins

licenses

name	path
CC0 1.0 Universal	https://github.com/allisonhorst/palmerpenguins?tab=CC0-1.0-1-ov-file#readme

`platformer-terrain.json`

path

platformer-terrain.json

description

Assets from the video game Celeste. Added in #376

schema

name	type
x	integer
y	integer
lumosity	number
saturation	integer
name	string
id	string
color	string
key	string

sources

title	path
Celeste Game	http://www.celestegame.com/

`political-contributions.json`

path

political-contributions.json

description

Summary financial information on contributions to candidates for U.S. elections. An updated version is available from the "all candidates" files (in pipe-delimited format) on the bulk data download page of the U.S. Federal Election Commission, or, alternatively, via OpenFEC. Information on each of the 25 columns is available from the FEC All Candidates File Description. The sample dataset in political-contributions.json contains 58 records with dates from 2015.

FEC data is subject to the commission's:

Sale or Use Policy
Privacy and Security Policy
Acceptable Use Policy

Additionally, the FEC's Github repository states:

This project is in the public domain within the United States, and we waive worldwide copyright and related rights through CC0 universal public domain dedication. Read more on our license page. A few restrictions limit the way you can use FEC data. For example, you can't use contributor lists for commercial purposes or to solicit donations. Learn more on FEC.gov.

schema

name	type
Candidate_Identification	string
Candidate_Name	string
Incumbent_Challenger_Status	string
Party_Code	integer
Party_Affiliation	string
Total_Receipts	number
Transfers_from_Authorized_Committees	integer
Total_Disbursements	number
Transfers_to_Authorized_Committees	number
Beginning_Cash	number
Ending_Cash	number
Contributions_from_Candidate	number
Loans_from_Candidate	integer
Other_Loans	integer
Candidate_Loan_Repayments	number
Other_Loan_Repayments	integer
Debts_Owed_By	number
Total_Individual_Contributions	integer
Candidate_State	string
Candidate_District	integer
Contributions_from_Other_Political_Committees	integer
Contributions_from_Party_Committees	integer
Coverage_End_Date	string
Refunds_to_Individuals	integer
Refunds_to_Committees	integer

sources

title	path
Federal Election Commission Bulk Data	https://www.fec.gov/data/browse-data/?tab=bulk-data
OpenFEC API	https://api.open.fec.gov/developers/

licenses

title	path
Creative Commons Zero 1.0 Universal	https://creativecommons.org/publicdomain/zero/1.0/

`population.json`

path

population.json

description

U.S. population counts by age group (0-90+ in 5-year intervals) and sex for each decade between 1850 and 2000, collected and harmonized from historical census records by IPUMS USA.

IPUMS updates and revises datasets over time, which may result in discrepancies with current IPUMS data.

When using this dataset, please refer to IPUMS USA terms of use. The organization requests the use of the following citation for this json file: Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0

schema

name	type	description
year	integer	Four-digit year of the survey
age	integer	Age group in 5-year intervals (0=0-4, 5=5-9, 10=10-14, ..., 90=90+)
sex	integer	Sex (1=men, 2=women)
people	integer	Number of individuals (IPUMS PERWT)

sources

title	path
IPUMS USA	https://usa.ipums.org/usa/

licenses

title	path
IPUMS Terms of Use	https://www.ipums.org/about/terms

`population_engineers_hurricanes.csv`

path

population_engineers_hurricanes.csv

description

Per-state population, number of engineers, and hurricanes. Used in Vega-Lite example, Three Choropleths Representing Disjoint Data from the Same Table

schema

name	type
state	string
id	integer
population	integer
engineers	number
hurricanes	integer

sources

title	path
Bureau of Labor Statistics	https://www.bls.gov/oes/tables.htm
American Community Survey	https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_07_3YR_S1901&prodType=table
NOAA National Climatic Data Center	https://www.ncdc.noaa.gov/cdo-web/datatools/records

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`seattle-weather-hourly-normals.csv`

path

seattle-weather-hourly-normals.csv

description

Hourly weather normals with metric units. The 1981-2010 Climate Normals are NCDC's three-decade averages of climatological variables, including temperature and precipitation. Learn more in the documentation. We only included temperature, wind, and pressure and updated the format to be easier to parse.

schema

name	type
date	datetime
pressure	number
temperature	number
wind	number

sources

title	path
NOAA National Climatic Data Center (NCDC)	https://www.ncdc.noaa.gov/cdo-web/datatools/normals

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`seattle-weather.csv`

path

seattle-weather.csv

description

Daily weather in metric units. Transformed using /scripts/weather.py. The categorical "weather" field is synthesized from multiple fields in the original dataset. This data is intended for instructional purposes.

schema

name	type	description	categories
date	date	Date of the weather observation
precipitation	number	Amount of precipitation in millimeters
temp_max	number	Maximum daily temperature in degrees Celsius
temp_min	number	Minimum daily temperature in degrees Celsius
wind	number	Wind speed in kilometers per hour
weather	string	Categorical weather type synthesized from original NOAA data fields. Categories include: drizzle, rain, snow, sun, and fog.	['drizzle', 'rain', 'snow', 'sun', 'fog']

sources

title	path
NOAA National Climatic Data Center	https://www.ncdc.noaa.gov/cdo-web/datatools/records

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`sp500-2000.csv`

path

sp500-2000.csv

description

S&P 500 index values from 2000 to 2020.

schema

name	type
date	date
open	number
high	number
low	number
close	number
adjclose	number
volume	integer

sources

title	path
Yahoo Finance	https://finance.yahoo.com/quote/%5EDJI/history/

`sp500.csv`

path

sp500.csv

description

Monthly closing values of the S&P 500 stock market index from January 2000 to March 2010. Captures several significant market events including the dot-com bubble burst (2000-2002), the mid-2000s bull market, and the 2008 financial crisis.

schema

name	type	description
date	string	Date of monthly observation in the format 'MMM D YYYY'
price	number	Closing price of the S&P 500 index for the given month

`stocks.csv`

path

stocks.csv

description

Monthly stock prices for five companies from 2000 to 2010.

schema

name	type
symbol	string
date	string
price	number

`udistrict.json`

path

udistrict.json

description

Point locations of restaurants and cafes in Seattle's University District, categorized by cuisine type. Used to create a ridgeline plot example showing the prevalence of various food and beverage categories. The example graphic using this dataset states that it originally appeared in Alaska Airlines Beyond Magazine (Sep 2017, p. 120)

schema

name	type
key	string
lat	number

`unemployment-across-industries.json`

path

unemployment-across-industries.json

description

Industry-level unemployment from the Current Population Survey (CPS), published monthly by the U.S. Bureau of Labor Statistics. Includes unemployed persons and unemployment rate across 11 private industries, as well as agricultural, government, and self-employed workers. Covers January 2000 through February 2010. Industry classification follows format of CPS Table A-31. Transformed using scripts/make-unemployment-across-industries.py

The BLS Web site states:

"Users of the public API should cite the date that data were accessed or retrieved using the API. Users must clearly state that "BLS.gov cannot vouch for the data or analyses derived from these data after the data have been retrieved from BLS.gov." The BLS.gov logo may not be used by persons who are not BLS employees or on products (including web pages) that are not BLS-sponsored."

See full BLS terms of service.

schema

name	type	description
series	string	Industry name
year	integer	Year (2000-2010)
month	integer	Month (1-12)
count	integer	Number of unemployed persons (in thousands)
rate	number	Unemployment rate (percentage)
date	datetime	ISO 8601-formatted date string (e.g., "2000-01-01T08:00:00.000Z")

sources

title	path
U.S. Census Bureau Current Population Survey	https://www.census.gov/programs-surveys/cps.html
BLS LAUS Data Tools	https://www.bls.gov/lau/data.htm
Bureau of Labor Statistics Table A-31	https://www.bls.gov/web/empsit/cpseea31.htm

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`unemployment.tsv`

path

unemployment.tsv

description

County-level unemployment rates in the United States, with data generally consistent with levels reported in 2009. The dataset is structured as tab-separated values. The unemployment rate represents the number of unemployed persons as a percentage of the labor force. According to the Bureau of Labor Statistics (BLS) glossary:

Unemployed persons (Current Population Survey) [are] persons aged 16 years and older who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment sometime during the 4-week period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed.

Derived from the Local Area Unemployment Statistics (LAUS) program, a federal-state cooperative effort overseen by the Bureau of Labor Statistics (BLS). The LAUS program produces monthly and annual employment, unemployment, and labor force data for census regions and divisions, states, counties, metropolitan areas, and many cities and towns.

For the most up-to-date LAUS data:

Monthly and Annual Data Downloads:

Visit the LAUS Data Tools page for monthly and annual county data.

BLS Public Data API:

The BLS provides an API for developers to access various datasets, including LAUS data.
To use the API for LAUS data, refer to the LAUS Series ID Formats to construct your query.
API documentation and examples are available on the BLS Developers page.

When using BLS public data API and datasets, users should adhere to the BLS Terms of Service.

schema

name	type	description
id	integer	The combined state and county FIPS code
rate	number	The unemployment rate for the county

sources

title	path
BLS Developers API	https://www.bls.gov/developers/
BLS Handbook of Methods	https://www.bls.gov/opub/hom/lau/home.htm

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`uniform-2d.json`

path

uniform-2d.json

description

Five hundred paired coordinates (u, v) sampled from a bivariate uniform distribution. Centered near the origin with ranges spanning approximately [-0.5, 0.5] in both dimensions. The variables exhibit negligible correlation (-0.019), suggesting independence, as expected for a uniform distribution. A contrast to normally distributed data in normal-2d.json.

schema

name	type	description
u	number	mean: 0.015, std: 0.277, range: [-0.499, 0.500]
v	number	mean: -0.013, std: 0.276, range: [-0.500, 0.498]

sources

title
Generated Data

licenses

path
https://github.com/vega/vega-datasets/blob/main/scripts/LICENSE

`us-10m.json`

path

us-10m.json

description

US county boundaries represented at a 1:10,000,000 scale in TopoJSON format, which optimizes for smaller file sizes. Similar to offerings in the TopoJSON US Atlas collection, which in turn is a redistribution of the Census Bureau's cartographic boundary shapefiles.

sources

title	path
TopoJSON US Atlas	https://github.com/topojson/us-atlas
US Census Bureau Cartographic Boundary FIles	https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html

licenses

title	path
TopoJSON US Atlas ISC License	https://github.com/topojson/us-atlas/blob/master/LICENSE.md

`us-employment.csv`

path

us-employment.csv

description

Monthly employment total in a variety of job categories from January 2006 through December 2015, seasonally adjusted and reported in thousands. Downloaded and reformatted on Nov. 11, 2018.

In the mid 2000s the global economy was hit by a crippling recession. One result: Massive job losses across the United States. The downturn in employment, and the slow recovery in hiring that followed, was tracked each month by the Current Employment Statistics program at the U.S. Bureau of Labor Statistics.

Totals are included for the 22 "supersectors" tracked by the BLS. The "nonfarm" total is the category typically used by economists and journalists as a stand-in for the country's employment total.

A calculated "nonfarm_change" column has been appended with the month-to-month change in that supersector's employment. It is useful for illustrating how to make bar charts that report both negative and positive values.

schema

name	type
month	date
nonfarm	integer
private	integer
goods_producing	integer
service_providing	integer
private_service_providing	integer
mining_and_logging	integer
construction	integer
manufacturing	integer
durable_goods	integer
nondurable_goods	integer
trade_transportation_utilties	integer
wholesale_trade	number
retail_trade	number
transportation_and_warehousing	number
utilities	number
information	integer
financial_activities	integer
professional_and_business_services	integer
education_and_health_services	integer
leisure_and_hospitality	integer
other_services	integer
government	integer
nonfarm_change	integer

sources

title	path
U.S. Bureau of Labor Statistics Current Employment Statistics	https://www.bls.gov/ces/

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`us-state-capitals.json`

path

us-state-capitals.json

description

Geographical coordinates and names of U.S. state capitals, transformed using scripts/us-state-capitals.py. Includes latitude, longitude, state name, and capital city name for all 50 U.S. states. Cities are represented as point locations of their capitol buildings using coordinates in the WGS84 geographic coordinate system.

According to USGS

"Map services and data downloaded from The National Map are free and in the public domain. There are no restrictions; however, we request that the following acknowledgment statement of the originating agency be included in products and data derived from our map services when citing, copying, or reprinting: Map services and data available from U.S. Geological Survey, National Geospatial Program."

schema

name	type
lon	number
lat	number
state	string
city	string

sources

title	path
U.S. Geological Survey National Geospatial Program - The National Map	https://www.usgs.gov/programs/national-geospatial-program/national-map

licenses

title	path
U.S. Public Domain	https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits
U.S. Government Dataset	https://www.usa.gov/government-works

`volcano.json`

path

volcano.json

description

Elevation measurements of Maunga Whau (Mt Eden), a volcano in Auckland, New Zealand, representing a rectangular area of 870 meters by 610 meters. Spacing between measurement points is approximately 10 meters in both directions. Digitized from a topographic map by Ross Ihaka and adapted from R datasets, Should not be regarded as accurate.

sources

title	path
R Datasets	https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/volcano.html

`weather.csv`

path

weather.csv

description

Daily weather observations from Seattle and New York. Transformed from NOAA data using the script /scripts/weather.py. The categorical "weather" field is synthesized from multiple fields in the original dataset. Intended for instructional purposes.

schema

name	type	description	categories
location	string	City location of the weather observation (Seattle or New York)
date	date	Date of the weather observation
precipitation	number	Amount of precipitation in millimeters
temp_max	number	Maximum daily temperature in degrees Celsius
temp_min	number	Minimum daily temperature in degrees Celsius
wind	number	Wind speed in kilometers per hour
weather	string	Categorical weather type synthesized from original NOAA data fields. Categories include: drizzle, rain, snow, sun, and fog.	['drizzle', 'rain', 'snow', 'sun', 'fog']

sources

title	path
NOAA Climate Data Online	http://www.ncdc.noaa.gov/cdo-web/datatools/findstation

licenses

title	path
U.S. Government Dataset	https://www.usa.gov/government-works

`weekly-weather.json`

path

weekly-weather.json

description

Instructional dataset showing actual and predicted temperature data.

Important

Named weather.json in previous versions (v1.4.0 - v2.11.0).

`wheat.json`

path

wheat.json

description

As noted by in this protovis example, "In an 1822 letter to Parliament, William Playfair, a Scottish engineer who is often credited as the founder of statistical graphics, published an elegant chart on the price of wheat. It plots 250 years of prices alongside weekly wages and the reigning monarch. He intended to demonstrate that:

'never at any former period was wheat so cheap, in proportion to mechanical labour, as it is at the present time.'"

schema

name	type
year	integer
wheat	number
wages	number

sources

title	path
1822 Playfair Chart	https://commons.wikimedia.org/wiki/File:Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat,_and_Wages_of_Labour_by_the_Week,_from_1565_to_1821.png

licenses

title	path
Public Domain	https://commons.wikimedia.org/wiki/Public_domain

`windvectors.csv`

path

windvectors.csv

description

Simulated wind patterns over northwestern Europe.

schema

name	type
longitude	number
latitude	number
dir	integer
dirCat	integer
speed	number

`world-110m.json`

path

world-110m.json

description

A 1:110,000,000-scale world map in TopoJSON format, optimized for web-based visualization. The simplified geographic boundaries focus on two key elements: land masses and country borders with their corresponding codes. The high level of generalization removes small geographic details while maintaining recognizable global features, making it ideal for overview maps and basic world visualizations. This format provides efficient compression compared to GeoJSON, reducing file size for web use. Part of the widely-used TopoJSON World Atlas collection, this has become a standard resource for creating web-based world maps where precise boundary detail isn't required.

sources

title	path
TopoJSON World Atlas (Likely original source, processed from Natural Earth data)	https://github.com/topojson/world-atlas
Natural Earth Data - Admin 0 Countries (1:110m)	https://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/

licenses

title	path
TopoJSON World Atlas ISC License	https://github.com/topojson/world-atlas/blob/master/LICENSE.md
Natural Earth Data Public Domain	https://www.naturalearthdata.com/about/terms-of-use/

`zipcodes.csv`

path

zipcodes.csv

description

Postal codes mapped to their geographical coordinates (latitude/longitude in WGS84) and administrative hierarchies, for the United States and Puerto Rico. The GeoNames geographical database provides worldwide postal code data with associated geographical and administrative information.

Historical snapshot first contributed to vega-datasets in 2017 and no longer current. Administrative boundaries have been redrawn, counties reorganized and renamed, and postal codes modified. Latitude/longitude coordinates have been updated by Geonames since this data was collected. For current postal code data, refer to the main GeoNames database.

schema

name	type
zip_code	integer
latitude	number
longitude	number
city	string
state	string
county	string

sources

title	path
GeoNames Postal Codes	https://download.geonames.org/export/zip/

licenses

title	path
Creative Commons Attribution 4.0 International	https://creativecommons.org/licenses/by/4.0/

Files

datapackage.md

Latest commit

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datapackage.md

File metadata and controls

vega-datasets

licenses

contributors

resources

7zip.png

path

description

sources

licenses

airports.csv

path

description

schema

sources

licenses

annual-precip.json

path

description

sources

licenses

anscombe.json

path

description

schema

sources

barley.json

path

description

schema

sources

licenses

birdstrikes.csv

path

description

schema

sources

licenses

budget.json

path

description

schema

sources

licenses

budgets.json

path

description

schema

sources

licenses

burtin.json

path

description

Antibacterial ranges of Neomycin, Penicillin and Streptomycin

schema

sources

licenses

cars.json

path

description

schema

sources

licenses

co2-concentration.csv

path

description

schema

sources

licenses

countries.json

path

description

schema

sources

licenses

`7zip.png`

`airports.csv`

`annual-precip.json`

`anscombe.json`

`barley.json`

`birdstrikes.csv`

`budget.json`

`budgets.json`

`burtin.json`

`cars.json`

`co2-concentration.csv`

`countries.json`

`crimea.json`

`disasters.csv`

`driving.json`

`earthquakes.json`

`ffox.png`

`flare-dependencies.json`

`flare.json`

`flights-10k.json`

`flights-200k.arrow`

`flights-200k.json`

`flights-20k.json`

`flights-2k.json`