diff --git a/DESCRIPTION b/DESCRIPTION
index 6f69390..62432fb 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -12,8 +12,9 @@ Description: In the spirit of Anscombe's quartet, this package includes datasets
    assumptions about the data generating mechanism are needed when estimating 
    causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>,
    D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.1080/26939169.2023.2276446>, 
-   "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and
-   Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.
+   "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>,
+   Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>, and 
+   an AUROC quartet for binary classification problems.
 License: MIT + file LICENSE
 URL: https://github.com/r-causal/quartets,
     https://r-causal.github.io/quartets/
diff --git a/R/auroc_quartet.R b/R/auroc_quartet.R
new file mode 100644
index 0000000..2a5a892
--- /dev/null
+++ b/R/auroc_quartet.R
@@ -0,0 +1,20 @@
+#' Area under the ROC Curve (AUROC) Quartet Data
+#'
+#' This dataset contains 4 datasets, each with equivalent
+#' values for the AUROC, a popular model evaluation metric for
+#' classification problems. Each curve features a substantially
+#' different AUROC curve shape with implications for other evaluation
+#' methods such as lift, precision, and recall. 
+#' 
+#' AUROC curves are parameterized as one of two functional forms
+#' defined by `method` (piecewise linear or the Beta distribution CDF). 
+#'
+#' @references Riederer E (2026). _roctet_: . Python package version 0.1.0. 
+#'
+#' @format A dataframe with 4,000 rows and 4 variables:
+#'
+#' * `id`: The data id number within each method
+#' * `method`: The data generating mechanism ("beta" or "piecewise")
+#' * `score`: The numerical prediction score
+#' * `target`: The true binary outcome being predicted
+"auroc_quartet"
diff --git a/README.qmd b/README.qmd
index 526516b..75ec8e8 100644
--- a/README.qmd
+++ b/README.qmd
@@ -30,6 +30,7 @@ The quartets package is a collection of datasets aimed to help data analysis pra
 * [Interaction Triptych](#interaction-triptych)
 * [Rashomon Quartet](#rashomon-quartet)
 * [Gelman Variation and Heterogeneity Causal Quartets](#gelman-variation-and-heterogenity-causal-quartets)
+* [AUROC Quartet](#auroc-quartet)
 
 ## Installation 
 
@@ -284,6 +285,50 @@ heterogeneous_causal_quartet |>
   knitr::kable()
 ```
 
+## AUROC Quartet 
+
+Similar to the Rashomon Quartet, this dataset demonstrates that the popular AUROC model evaluation metric for predictive classification models does not tell the full story. The `auroc_quartet` dataset contains four datasets consisting of a predictive score and true target. Each score-target pair has the same AUC but the shapes of the ROC plots are materially different, leading to large differences in other fit-for-purpose evaluation metrics like precision and recall. 
+
+Data is generated from the [`roctet`](https://github.com/emilyriederer/roctet) python package.
+
+## Example
+
+```{r}
+#| message: false
+#| warning: false
+#| cache: false
+library(tidyverse)
+library(tidymodels)
+library(patchwork)
+library(quartets)
+
+auroc_quartet_prep <-
+  auroc_quartet |>
+  group_by(id) |>
+  mutate(
+    target = factor(target, levels = c(0,1), labels = c(0,1)),
+    pred_at_10 = factor(as.integer(score > quantile(score, 0.9)))
+    )
+
+gg_auroc <- autoplot(
+  roc_curve(auroc_quartet_prep, target, score, event_level = "second")
+) + labs(title = "AUROC")
+
+gg_gain <- autoplot(
+  gain_curve(auroc_quartet_prep, target, score, event_level = "second")
+) + labs(title = "Gain Curve")
+
+(gg_auroc + gg_gain)
+
+auroc_quartet_prep |>
+  summarize(
+    auroc = roc_auc_vec(target, score,event_level = "second"),
+    precision_at_10 = precision_vec(target, pred_at_10, event_level = "second"),
+    recall_at_10 = recall_vec(target, pred_at_10, event_level = "second")
+  ) |>
+  knitr::kable(digits = 2)
+```
+
 
 ## References
 
diff --git a/data-raw/auroc-quartet.csv b/data-raw/auroc-quartet.csv
new file mode 100644
index 0000000..ceba1bd
--- /dev/null
+++ b/data-raw/auroc-quartet.csv
@@ -0,0 +1,4001 @@
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diff --git a/data-raw/generate-roctet.R b/data-raw/generate-roctet.R
new file mode 100644
index 0000000..7461e5b
--- /dev/null
+++ b/data-raw/generate-roctet.R
@@ -0,0 +1,6 @@
+# run this after `generate-roctet.py` to convert csv to rda
+library(readr)
+library(usethis)
+
+auroc_quartet <- readr::read_csv('data-raw/auroc-quartet.csv')
+usethis::use_data(auroc_quartet, overwrite = TRUE)
diff --git a/data-raw/generate-roctet.py b/data-raw/generate-roctet.py
new file mode 100644
index 0000000..5ad938d
--- /dev/null
+++ b/data-raw/generate-roctet.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.14"
+# dependencies = [
+#     "polars>=1.39.2",
+#     "roctet",
+# ]
+#
+# [tool.uv.sources]
+# roctet = { git = "https://github.com/emilyriederer/roctet",
+#            rev = "9a02057484d8b34d37159788771d897cf64145a2" }
+# ///
+
+# This python script is made reproducible with in-line `uv`` dependencies
+# To run this script as an executable file, install `uv` and run in terminal:
+# > cd data-raw
+# > chmod +x generate-roctet.py
+# > ./generate-roctet.py
+# Then run generate-roctet.R to convert the resulting csv to rda 
+
+from roctet import calc_roctet
+import polars as pl
+
+dfs_beta = calc_roctet(0.67, method="beta", n_sets=2, n_obsv=1_000)  
+dfs_pcws = calc_roctet(0.67, method="piecewise", n_sets=4, n_obsv=1_000)
+df = (
+  pl.concat(dfs_beta+dfs_pcws[1:3])
+    .with_columns(id = pl.col('id') + 1 + 2*(pl.col("method") == pl.lit('piecewise')))
+)
+df.write_csv('auroc-quartet.csv')
+    
diff --git a/data/auroc_quartet.rda b/data/auroc_quartet.rda
new file mode 100644
index 0000000..7e5abb6
Binary files /dev/null and b/data/auroc_quartet.rda differ
diff --git a/man/auroc_quartet.Rd b/man/auroc_quartet.Rd
new file mode 100644
index 0000000..c7e20bb
--- /dev/null
+++ b/man/auroc_quartet.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/auroc_quartet.R
+\docType{data}
+\name{auroc_quartet}
+\alias{auroc_quartet}
+\title{Area under the ROC Curve (AUROC) Quartet Data}
+\format{
+A dataframe with 4,000 rows and 4 variables:
+\itemize{
+\item \code{id}: The data id number within each method
+\item \code{method}: The data generating mechanism ("beta" or "piecewise")
+\item \code{score}: The numerical prediction score
+\item \code{target}: The true binary outcome being predicted
+}
+}
+\usage{
+auroc_quartet
+}
+\description{
+This dataset contains 4 datasets, each with equivalent
+values for the AUROC, a popular model evaluation metric for
+classification problems. Each curve features a substantially
+different AUROC curve shape with implications for other evaluation
+methods such as lift, precision, and recall.
+}
+\details{
+AUROC curves are parameterized as one of two functional forms
+defined by \code{method} (piecewise linear or the Beta distribution CDF).
+}
+\references{
+Riederer E (2026). \emph{roctet}: . Python package version 0.1.0.
+}
+\keyword{datasets}