diff --git a/ReadMe.md b/ReadMe.md
index 29988cf..e5d44c9 100644
--- a/ReadMe.md
+++ b/ReadMe.md
@@ -9,6 +9,11 @@ in Python, with added test coverage.
 
 Find the full [documentation on Read-the-Docs][docs].
 
+[![citation](https://zenodo.org/badge/263433787.svg)](https://zenodo.org/badge/latestdoi/263433787)
+[![license](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[![release](https://img.shields.io/pypi/v/kde-diffusion.svg)](https://pypi.python.org/pypi/kde-diffusion)
+[![downloads](https://pepy.tech/badge/kde-diffusion)](https://pepy.tech/project/kde-diffusion)
+[![documentation](https://readthedocs.org/projects/kde-diffusion/badge/?version=latest)](https://kde-diffusion.readthedocs.io/en/latest/?badge=latest)
 
 [paper]: https://dx.doi.org/10.1214/10-AOS799
 [kde1d]: https://mathworks.com/matlabcentral/fileexchange/14034
@@ -16,8 +21,3 @@ Find the full [documentation on Read-the-Docs][docs].
 [docs]:  https://kde-diffusion.readthedocs.io
 
 
-[![release](https://img.shields.io/pypi/v/kde-diffusion.svg)](https://pypi.python.org/pypi/kde-diffusion)
-[![downloads](https://pepy.tech/badge/kde-diffusion)](https://pepy.tech/project/kde-diffusion)
-[![citation](https://zenodo.org/badge/263433787.svg)](https://zenodo.org/badge/latestdoi/263433787)
-[![license](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
-[![documentation](https://readthedocs.org/projects/kde-diffusion/badge/?version=latest)](https://kde-diffusion.readthedocs.io/en/latest/?badge=latest)
diff --git a/docs/index.md b/docs/index.md
index 2e570a0..6032898 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -43,15 +43,9 @@ The bandwidth selection is what makes kernel density estimation a
 non-parametric method, so that we avoid making — possibly misguided —
 assumptions about the nature of the data.
 
-
-[1]: https://dx.doi.org/10.1214/10-AOS799
-[2]: https://mathworks.com/matlabcentral/fileexchange/14034
-[3]: https://mathworks.com/matlabcentral/fileexchange/17204
-[4]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html
-[5]: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
-[6]: https://kdepy.readthedocs.io
-[7]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html
-
+[![citation](https://zenodo.org/badge/263433787.svg)](https://zenodo.org/badge/latestdoi/263433787)
+[![license](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[![source](https://img.shields.io/github/stars/John-Hennig/KDE-diffusion?style=social)](https://github.com/John-Hennig/KDE-diffusion)
 
 ```{toctree}
 :hidden:
@@ -61,3 +55,11 @@ usage
 implementation
 api
 ```
+
+[1]: https://dx.doi.org/10.1214/10-AOS799
+[2]: https://mathworks.com/matlabcentral/fileexchange/14034
+[3]: https://mathworks.com/matlabcentral/fileexchange/17204
+[4]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html
+[5]: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
+[6]: https://kdepy.readthedocs.io
+[7]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html
diff --git a/pypi.md b/pypi.md
new file mode 100644
index 0000000..a0dcc65
--- /dev/null
+++ b/pypi.md
@@ -0,0 +1,19 @@
+Kernel density estimation via diffusion in 1d and 2d.
+
+Provides the fast, adaptive kernel density estimator based on linear
+diffusion processes for one-dimensional and two-dimensional input data
+as outlined in the [2010 paper by Botev et al.][paper] The reference
+implementation for [1d][kde1d] and [2d][kde2d], in Matlab, was provided
+by the paper's first author, Zdravko Botev. This is a re-implementation
+in Python, with added test coverage.
+
+Find the full [documentation on Read-the-Docs][docs].
+
+[![citation](https://zenodo.org/badge/263433787.svg)](https://zenodo.org/badge/latestdoi/263433787)
+[![license](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[![source](https://img.shields.io/github/stars/John-Hennig/KDE-diffusion?style=social)](https://github.com/John-Hennig/KDE-diffusion)
+
+[paper]: https://dx.doi.org/10.1214/10-AOS799
+[kde1d]: https://mathworks.com/matlabcentral/fileexchange/14034
+[kde2d]: https://mathworks.com/matlabcentral/fileexchange/17204
+[docs]:  https://kde-diffusion.readthedocs.io
diff --git a/pyproject.toml b/pyproject.toml
index 136dd0b..e213a8b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ requires = ['NumPy', 'SciPy']
 requires-python = '>=3.6'
 license = 'MIT'
 home-page = 'https://pypi.org/project/KDE-diffusion'
-description-file = 'ReadMe.md'
+description-file = 'pypi.md'
 keywords = 'kernel density estimation, statistics'
 classifiers = [
     'Development Status :: 5 - Production/Stable',