diff --git a/docs/tutorials/notebooks b/docs/tutorials/notebooks
index 7ae61d53..ba197060 160000
--- a/docs/tutorials/notebooks
+++ b/docs/tutorials/notebooks
@@ -1 +1 @@
-Subproject commit 7ae61d535361c00e845c39217d513c049f340241
+Subproject commit ba197060e7e195e8efe1d02e2f2e6e7c02abe0e9
diff --git a/pertpy/data/__init__.py b/pertpy/data/__init__.py
index d8ccfec5..9a23f0a7 100644
--- a/pertpy/data/__init__.py
+++ b/pertpy/data/__init__.py
@@ -25,6 +25,7 @@
     gehring_2019,
     haber_2017_regions,
     hagai_2018,
+    human_cytokine_dict,
     kang_2018,
     mcfarland_2020,
     norman_2019,
@@ -84,6 +85,7 @@
     "gehring_2019",
     "haber_2017_regions",
     "hagai_2018",
+    "human_cytokine_dict",
     "kang_2018",
     "mcfarland_2020",
     "norman_2019",
diff --git a/pertpy/data/_datasets.py b/pertpy/data/_datasets.py
index 49bd41bf..a4e94ac0 100644
--- a/pertpy/data/_datasets.py
+++ b/pertpy/data/_datasets.py
@@ -1,5 +1,6 @@
 from pathlib import Path
 
+import pandas as pd
 import scanpy as sc
 from anndata import AnnData
 from mudata import MuData
@@ -1598,3 +1599,43 @@ def hagai_2018() -> AnnData:  # pragma: no cover
     adata = sc.read_h5ad(output_file_path)
 
     return adata
+
+
+def human_cytokine_dict(exclude_well_biased_genes: bool = True) -> pd.DataFrame:
+    """Human Cytokine Dictionary curated from PBMC allows you to infer differential cytokine activity.
+
+    The Human Cytokine Dictionary was created from single-cell RNA-seq of 9,697,974 human peripheral blood mononuclear cells (PBMC)
+    from 12 donors stimulated in vitro with 87 different cytokines.
+    Genes with a mean-to-stddev-ratio above 1 across all 6 wells for >10 cytokines in a given cell type and for >5 cell types are "well-biased".
+
+    Args:
+        exclude_well_biased_genes: Whether to exclude well-biased genes from the returned dataframe.
+
+    References:
+        Oesinghaus, L., Becker, S., Vornholz, L., Papalexi, E. et al.
+        A single-cell cytokine dictionary of human peripheral blood.
+        bioRxiv (2025). https://doi.org/10.64898/2025.12.12.693897
+
+    Returns:
+        :class:`~ pandas.DataFrame` object of differentially expressed genes after cytokine perturbation.
+
+    """
+    output_file_name = "human_cytokine_dict.csv"
+    output_file_path = settings.datasetdir / output_file_name
+    if not Path(output_file_path).exists():
+        _download(
+            url="https://cdn.parsebiosciences.com/gigalab/10m/DEGs.csv",
+            output_file_name=output_file_name,
+            output_path=settings.datasetdir,
+            is_zip=False,
+        )
+
+    cytokine_dict = pd.read_csv(output_file_path, index_col=0)
+    revision_cytokines = ["TGF-beta1", "IL-18", "C3a"]
+    cytokine_dict = cytokine_dict[~cytokine_dict["cytokine"].isin(revision_cytokines)]
+    cytokine_dict = cytokine_dict.reset_index(drop=True)
+
+    if exclude_well_biased_genes:
+        cytokine_dict = cytokine_dict.loc[~cytokine_dict.well_biased]
+
+    return cytokine_dict