Sources of open data for statistics, data science, and machine learning

How To
Open Data
Data Science
Machine Learning
Even though the default data included in R and other languages are carefully selected and valuable. They are old, limited in sample size, and may not be more than toy examples in the current context.

Rohit Farmer


October 25, 2022

2022-10-26 Added sections for Kaggle and other platforms
2022-10-25 Initial uncomplete post and Kaggle notebook


R and Python, the two most popular data science and machine learning programming languages, come with a default data set for demonstration and educational purposes. Moreover, many popular data science libraries such as tidyverse, lme4, nlme, MASS, survival, Bioconductor, and sklearn, amongst others, also contain example datasets for unit testing and demonstration. However, even though the included data are carefully selected, most of them are old (from the 1970s and ’80s) and hence limited in sample size (due to the limitation in the then available computing power) and may not be more than a toy example in the current context. Therefore, in a learning path after utilizing the default datasets for initial testing of a statistical function or a machine learning model, it’s both desired and recommended to practice on a real-life dataset. Working on a real-life dataset that is apt in the current context of data science and computing resources would not only teach the desired statistical/machine learning technique but also expose the learner to challenges that are usually not associated with toy datasets—for example, class imbalance, missing values, wrongly labeled datatypes, statistical noise, etc.

For a real-life dataset, I recommend using open data released by government agencies and other non-government organizations as part of their openness in operations. These datasets are not only of adequate size but also represent what is happening around us in a field of interest. Many countries around the world release data to inform citizens about their operations and fair practices. However, I do not think, other than the established data analytics-based companies and research groups, ordinary citizens ever look into such resources. I bet many would not even know that their government agencies are making ample amounts of data available for scrutiny. Therefore, in my opinion, budding data scientists should take it upon themselves to utilize these datasets in place of toy examples to not only justify the existence of such a resource but also, through analysis, gain first-hand insight and transfer it to their friends, family, and broader audience. Such a practice by amateur data scientists may have far-reaching implications than what I can convey here.

In the sections below, for completeness, I will briefly discuss how to access default datasets available in R and then move on to other resources, including open government data mentioned earlier.


Please check out this notebook on Kaggle to work with the code presented in this post Some of the code is changed slightly to match Kaggle’s notebook environment.


Default datasets in R

In R (v4.1.3), there are 104 datasets for various statistical and machine-learning tasks. The commands in the cell below list all the datasets available by default (Table 1) and across all the installed packages, respectively. This article summarizes some of R’s popular datasets, namely mtcars, iris, etc.

# Default datasets

# Datasets across all the installed packages
data(package = .packages(all.available = TRUE))
dat <- data()
dat <- as_tibble(dat$results) %>% dplyr::select(-LibPath) %>%
  dplyr::filter(Package == "datasets")
knitr::kable(dat) %>%
    kable_styling(bootstrap_options = c("striped", "hover")) %>%
    scroll_box(width = "100%", height = "300px")
Table 1: Default datasets in R
Package Item Title
datasets AirPassengers Monthly Airline Passenger Numbers 1949-1960
datasets BJsales Sales Data with Leading Indicator
datasets BJsales.lead (BJsales) Sales Data with Leading Indicator
datasets BOD Biochemical Oxygen Demand
datasets CO2 Carbon Dioxide Uptake in Grass Plants
datasets ChickWeight Weight versus age of chicks on different diets
datasets DNase Elisa assay of DNase
datasets EuStockMarkets Daily Closing Prices of Major European Stock Indices, 1991-1998
datasets Formaldehyde Determination of Formaldehyde
datasets HairEyeColor Hair and Eye Color of Statistics Students
datasets Harman23.cor Harman Example 2.3
datasets Harman74.cor Harman Example 7.4
datasets Indometh Pharmacokinetics of Indomethacin
datasets InsectSprays Effectiveness of Insect Sprays
datasets JohnsonJohnson Quarterly Earnings per Johnson & Johnson Share
datasets LakeHuron Level of Lake Huron 1875-1972
datasets LifeCycleSavings Intercountry Life-Cycle Savings Data
datasets Loblolly Growth of Loblolly pine trees
datasets Nile Flow of the River Nile
datasets Orange Growth of Orange Trees
datasets OrchardSprays Potency of Orchard Sprays
datasets PlantGrowth Results from an Experiment on Plant Growth
datasets Puromycin Reaction Velocity of an Enzymatic Reaction
datasets Seatbelts Road Casualties in Great Britain 1969-84
datasets Theoph Pharmacokinetics of Theophylline
datasets Titanic Survival of passengers on the Titanic
datasets ToothGrowth The Effect of Vitamin C on Tooth Growth in Guinea Pigs
datasets UCBAdmissions Student Admissions at UC Berkeley
datasets UKDriverDeaths Road Casualties in Great Britain 1969-84
datasets UKgas UK Quarterly Gas Consumption
datasets USAccDeaths Accidental Deaths in the US 1973-1978
datasets USArrests Violent Crime Rates by US State
datasets USJudgeRatings Lawyers' Ratings of State Judges in the US Superior Court
datasets USPersonalExpenditure Personal Expenditure Data
datasets UScitiesD Distances Between European Cities and Between US Cities
datasets VADeaths Death Rates in Virginia (1940)
datasets WWWusage Internet Usage per Minute
datasets WorldPhones The World's Telephones
datasets ability.cov Ability and Intelligence Tests
datasets airmiles Passenger Miles on Commercial US Airlines, 1937-1960
datasets airquality New York Air Quality Measurements
datasets anscombe Anscombe's Quartet of 'Identical' Simple Linear Regressions
datasets attenu The Joyner-Boore Attenuation Data
datasets attitude The Chatterjee-Price Attitude Data
datasets austres Quarterly Time Series of the Number of Australian Residents
datasets beaver1 (beavers) Body Temperature Series of Two Beavers
datasets beaver2 (beavers) Body Temperature Series of Two Beavers
datasets cars Speed and Stopping Distances of Cars
datasets chickwts Chicken Weights by Feed Type
datasets co2 Mauna Loa Atmospheric CO2 Concentration
datasets crimtab Student's 3000 Criminals Data
datasets discoveries Yearly Numbers of Important Discoveries
datasets esoph Smoking, Alcohol and (O)esophageal Cancer
datasets euro Conversion Rates of Euro Currencies
datasets euro.cross (euro) Conversion Rates of Euro Currencies
datasets eurodist Distances Between European Cities and Between US Cities
datasets faithful Old Faithful Geyser Data
datasets fdeaths (UKLungDeaths) Monthly Deaths from Lung Diseases in the UK
datasets freeny Freeny's Revenue Data
datasets freeny.x (freeny) Freeny's Revenue Data
datasets freeny.y (freeny) Freeny's Revenue Data
datasets infert Infertility after Spontaneous and Induced Abortion
datasets iris Edgar Anderson's Iris Data
datasets iris3 Edgar Anderson's Iris Data
datasets islands Areas of the World's Major Landmasses
datasets ldeaths (UKLungDeaths) Monthly Deaths from Lung Diseases in the UK
datasets lh Luteinizing Hormone in Blood Samples
datasets longley Longley's Economic Regression Data
datasets lynx Annual Canadian Lynx trappings 1821-1934
datasets mdeaths (UKLungDeaths) Monthly Deaths from Lung Diseases in the UK
datasets morley Michelson Speed of Light Data
datasets mtcars Motor Trend Car Road Tests
datasets nhtemp Average Yearly Temperatures in New Haven
datasets nottem Average Monthly Temperatures at Nottingham, 1920-1939
datasets npk Classical N, P, K Factorial Experiment
datasets occupationalStatus Occupational Status of Fathers and their Sons
datasets precip Annual Precipitation in US Cities
datasets presidents Quarterly Approval Ratings of US Presidents
datasets pressure Vapor Pressure of Mercury as a Function of Temperature
datasets quakes Locations of Earthquakes off Fiji
datasets randu Random Numbers from Congruential Generator RANDU
datasets rivers Lengths of Major North American Rivers
datasets rock Measurements on Petroleum Rock Samples
datasets sleep Student's Sleep Data
datasets stack.loss (stackloss) Brownlee's Stack Loss Plant Data
datasets stack.x (stackloss) Brownlee's Stack Loss Plant Data
datasets stackloss Brownlee's Stack Loss Plant Data
datasets (state) US State Facts and Figures
datasets state.area (state) US State Facts and Figures
datasets (state) US State Facts and Figures
datasets state.division (state) US State Facts and Figures
datasets (state) US State Facts and Figures
datasets state.region (state) US State Facts and Figures
datasets state.x77 (state) US State Facts and Figures
datasets sunspot.month Monthly Sunspot Data, from 1749 to "Present"
datasets sunspot.year Yearly Sunspot Data, 1700-1988
datasets sunspots Monthly Sunspot Numbers, 1749-1983
datasets swiss Swiss Fertility and Socioeconomic Indicators (1888) Data
datasets treering Yearly Treering Data, -6000-1979
datasets trees Diameter, Height and Volume for Black Cherry Trees
datasets uspop Populations Recorded by the US Census
datasets volcano Topographic Information on Auckland's Maunga Whau Volcano
datasets warpbreaks The Number of Breaks in Yarn during Weaving
datasets women Average Heights and Weights for American Women

Open government data

As I mentioned in the introduction, many governments worldwide release data for transparency and accountability; for example,, the US federal government’s open data site. also maintains a list of websites at pointing to data repositories related to US cities and counties, US states, and international countries and regions. The primary aim of these repositories is to publish information online as open data using standardized, machine-readable data formats with their metadata.

Depending upon the type of data requested, most of the data can be downloaded in multiple machine-readable formats either via the export option on the website or programmatically through APIs (see section Section 2.4). For example for tabular data popular formats include CSV, XML, RDF, JSON, and XML.

Interactive and exportable tables below show the list of websites at

open_gov <- read.csv("", header = FALSE)
colnames(open_gov) <- c("Item", "Website", "Type")
cat("Total number of entries: ", nrow(open_gov))
Total number of entries:  314

US cities and counties

city_county <- dplyr::filter(open_gov, Type == "US City or County")
DT::datatable(city_county, options = list(pageLength = 5))

US states

us_state <- dplyr::filter(open_gov, Type %in% c("US State", "Other State Related"))
DT::datatable(us_state, options = list(pageLength = 5))

International countries and regions

int_count <- dplyr::filter(open_gov, Type %in% c("International Country", "International Regional"))
DT::datatable(int_count, options = list(pageLength = 5))

An example of using Maryland state open data via an API

Since I live and work in Maryland, I want to see how wages in Maryland and its counties have changed over time. I also want to test if Montgomery county (where I live) has different wages compared to Frederick, Howard, and Prince George’s counties which borders Montgomery on the north, east, and south sides. Therefore, in this example, I will fetch Maryland Average Wage Per Job (Current Dollars): 2010-2020 data via API using RSocrata library in R and carry out some analysis.


See to learn more about how to work with open data APIs in various programming languages.

In Table 2, each row has an average wage for a year for Maryland, and each of its counties (columns) from 2010-2020 and Figure 1 shows the same data as a line graph depicting the change in wages (y-axis) over time (x-axis).

Table 3 lists the results of an unpaired two-sample t-test between wages from Montgomery and Frederick, Howard, and Prince George’s counties. As you can see from the t-test results, wages differ between Montgomery and Frederick, Howard, and Prince George’s counties, with Montogomery county residents earning higher than all its three bordering counties.

# Fetch the data using the API endpoint
maw <- read.socrata("")
knitr::kable(dplyr::select(maw, -date_created)) %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    scroll_box(width = "100%", height = "400px")
Table 2: Maryland Average Wage Per Job (Current Dollars): 2010-2020
year maryland allegany_county anne_arundel_county baltimore_city baltimore_county calvert_county caroline_county carroll_county cecil_county charles_county dorchester_county frederick_county garrett_county harford_county howard_county kent_county montgomery_county prince_george_s_county queen_anne_s_county somerset_county st_mary_s_county talbot_county washington_county wicomico_county worcester_county
2010 53096 35771 56745 55640 49986 42726 34616 38027 42027 41290 35489 48018 31591 46741 58130 36334 65178 51808 36018 38228 60032 37845 38228 38472 30799
2011 54517 36677 58011 57027 50914 43431 35981 39039 42465 42200 35718 48794 32484 48558 60448 36815 67247 52844 36437 39652 63057 38462 39420 38915 31438
2012 55466 36983 58706 58876 51722 44239 37506 39919 43260 42888 37172 49972 32506 49772 62371 36622 68159 53292 37258 39853 63698 39807 39564 39066 31641
2013 55555 37827 59384 59318 51778 44126 38404 40736 44214 42909 37773 49570 33477 49624 62271 37572 67437 53441 36848 40744 63501 39901 40032 39714 32384
2014 56924 38449 60551 61112 52961 45162 39383 41607 45051 44260 39094 50747 34195 50205 64784 38411 68731 54985 37932 41802 64691 40118 41018 40863 33635
2015 58729 39888 62195 63389 54248 48825 41043 43325 46776 44919 40022 51510 35067 52418 66677 38741 71480 56456 38970 43397 65497 41313 42270 42599 34524
2016 59710 40708 63147 64481 55159 53657 40832 43815 47300 46958 40431 51630 34925 52862 67621 39504 72904 57251 39941 43575 65937 41740 42725 43875 35260
2017 61298 42143 64629 66365 56887 55922 42034 45576 48662 47673 41711 52270 35971 53775 68958 40446 74709 58829 42099 45988 67622 43105 44039 45491 35802
2018 62836 43197 66458 67005 58793 53557 43190 45690 49981 48225 41987 53624 37575 54921 71300 42422 76867 60383 43582 45381 68887 44670 45846 45567 37231
2019 64690 44692 68586 69930 60116 51598 45190 47189 52177 49193 43271 55621 38290 57349 74136 42575 78386 62096 44011 49234 70807 45115 46965 46620 38234
2020 70446 48294 74533 74483 65743 55903 49336 51470 55854 53404 47182 60646 40690 62395 82780 45891 86138 66777 48385 53880 77490 48338 50743 50556 41605
maw_gather <- maw %>% dplyr::select(-date_created) %>%
  gather(key = "county", value = "wage", -year ) %>% as_tibble()
ggplot(maw_gather, aes(x = year, y = as.numeric(wage), color = county)) +
  geom_line(aes(group = county)) + 
  labs(x = "Year", y = "Wage", color = "") +theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Figure 1: Maryland Average Wage Per Job (Current Dollars): 2010-2020

mft <- broom::tidy(t.test(as.numeric(maw$montgomery_county), 
                   as.numeric(maw$frederick_county))) %>% 
                   dplyr::mutate("test" = "Montgomery vs. Frederick")

mht <- broom::tidy(t.test(as.numeric(maw$montgomery_county), 
                   as.numeric(maw$howard_county))) %>% 
                   dplyr::mutate("test" = "Montgomery vs. Howard")

mpgt <- broom::tidy(t.test(as.numeric(maw$montgomery_county), 
                    as.numeric(maw$prince_george_s_county))) %>% 
                    dplyr::mutate("test" = "Montgomery vs. Prince George's")

all_t <- dplyr::bind_rows(mft, mht, mpgt) %>%
  dplyr::select(all_of(c("test", "estimate", "estimate1", 
  "estimate2", "statistic", "p.value")))

knitr::kable(all_t) %>%
    kable_styling(bootstrap_options = c("striped", "hover"))
Table 3: T-test results between wages from Montgomery and Frederick, Howard, and Prince George’s county
test estimate estimate1 estimate2 statistic p.value
Montgomery vs. Frederick 20439.455 72476 52036.55 9.452393 0.0000001
Montgomery vs. Howard 5250.909 72476 67225.09 1.858408 0.0781124
Montgomery vs. Prince George's 15370.364 72476 57105.64 6.597893 0.0000030


Kaggle is a website that hosts data science and machine learning competitions. Users can compete to win prizes, and the site also has a public dataset repository. In addition to hosting competitions and disseminating public datasets, Kaggle also hosts tutorials and Jupyter-like notebook environments for Python and R runtimes. Users can clone an existing notebook on Kaggle, create it from scratch or upload it from their local computer and provision free hardware resources, including GPUs, to execute their notebooks.

While uploading public datasets, users can also generate a DOI for their dataset to give them a permanent identifier on the internet. For example, I am distributing “Classify the bitter or sweet taste of compounds” (Rohit Farmer 2022a) and “Tweets from heads of governments and states” (Rohit Farmer 2022b) datasets on Kaggle for classification and natural language processing analysis, respectively.

Datasets on Kaggle can be downloaded using a web browser or via its API, which also interacts with its competition environment. Users can find datasets for various statical and machine learning tasks available in various formats, including CSVs, MS Excel, images, SQLite databases, pandas pickle, etc.


I am still determining if Kaggle datasets similar to the ones from Zenodo, Figshare, and Dryad (mentioned in the section Section 4) are used in research/scientific studies or only in testing and learning data science. If you know the answer, then please put it in the comments.

Zenodo, Figshare, and Dryad

In contrast to Kaggle (mentioned in section Section 3) which is mainly a competition hosting and data science learning community Zenodo, Fighsare, and Dryad are data hosting platforms primarily used by scientists and researchers to serve data, manuscripts, and articles. The data hosted on these three platforms usually do not qualify for a domain-specific data respository or too large to attach as supplementary material to an article. They also aim to serve as a long-term archiving solution; therefore, even if the data/manuscript is published elsewhere under the correct licensing term, it can be redistributed here.

Zenodo is a digital repository that allows users to upload and share digital content such as datasets, software, and research papers. Zenodo is designed to preserve and provide long-term access to digital content. Zenodo is developed and operated by OpenAIRE, a European consortium that promotes open access to research. A unique feature of Zenodo is to archive releases from a GitHub repository and provide a DOI making it easier to cite a GitHub repository, for example, (Farmer 2022b). Another excellent feature is the ability to create communities to organize data and manuscripts of a similar kind. Anyone can create a community, and the owner can accept or reject a request for an item to be indexed in their community, for example,


Besides data and manuscripts, Zenodo can also be utilized to archive a blog post and generate a DOI, for example, (Farmer 2022a).

Like Zenodo, Figshare is a web-based platform for storing, sharing, and managing research data. Figshare also provides a unique identifier (DOI) for each data set, which can be used to cite the data set in publications. Data can be stored privately or publicly, and Figshare provides tools for managing data access and permissions. Data scientists and machine learning engineers often use Figshare to share data sets and models with collaborators or the public.

Dryad is also a digital repository primarily used for scientific and medical data associated with a publication in a scholarly journal. Dryad makes data discoverable, usable, and citable by integrating it into the scholarly publication process.

Zenodo, Figshare, and Dryad are available to anyone to upload data (download is always free and allowed); however, limitations may apply to the file size uploaded or the private vs. public data status. Additionally, many educational/research institutions may partner with one or all of these platforms, thus providing enhanced options.


Unlike open government data sources that are primarily used to download data, Kaggle, Zenodo, Figshare, and Dryad can be used to find the suitable dataset for your analysis and share your curated dataset publicly in a permanent citable manner.


Farmer, Rohit. 2022a. “A Case for Using Google Colab Notebooks as an Alternative to Web Servers for Scientific Software,” October.
———. 2022b. ColabHDStIM: A Google Colab Interface to HDStIM (High Dimensional Stimulation Immune Mapping). Zenodo.
Rohit Farmer. 2022a. “Classify the Bitter or Sweet Taste of Compounds.” Kaggle.
———. 2022b. “Tweets from Heads of Governments and States.” Kaggle.


BibTeX citation:
  author = {Rohit Farmer},
  title = {Sources of Open Data for Statistics, Data Science, and
    Machine Learning},
  date = {2022-10-25},
  url = {},
  langid = {en}
For attribution, please cite this work as:
Rohit Farmer. 2022. “Sources of Open Data for Statistics, Data Science, and Machine Learning.” October 25, 2022.