diff --git a/Dimension Estimation.ipynb b/Dimension Estimation.ipynb
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+++ b/Dimension Estimation.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# requirements:\n",
+    "\n",
+    "%pip install -q scikit-dimension pandas numba matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data shape: (32, 100)\n",
+      "estimated IDs (DANCo, lPCA): 6.6024110947192325 5.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import skdim\n",
+    "import numpy as np\n",
+    "\n",
+    "#generate data : np.array (n_points x n_dim). Here a uniformly sampled 5-ball embedded in 10 dimensions\n",
+    "n_rows = 32\n",
+    "n_features = 100\n",
+    "n_components = 5\n",
+    "\n",
+    "data = np.zeros((n_rows, n_features))\n",
+    "\n",
+    "data[:,:n_components] = skdim.datasets.hyperBall(n=n_rows, d=n_components, radius=2)\n",
+    "\n",
+    "# add some correlation\n",
+    "data[:,8] = data[:, 1] + np.random.rand(n_rows)\n",
+    "data[:,12] = data[:, 1] + np.random.rand(n_rows)\n",
+    "data[:,20] = data[:, 2] + np.random.rand(n_rows)\n",
+    "\n",
+    "print('data shape:', data.shape)\n",
+    "\n",
+    "#estimate global intrinsic dimension\n",
+    "danco = skdim.id.DANCo().fit(data)\n",
+    "\n",
+    "#estimate local intrinsic dimension (dimension in k-nearest-neighborhoods around each point):\n",
+    "lpca = skdim.id.lPCA().fit_pw(data,\n",
+    "                              n_neighbors=n_rows-1,\n",
+    "                              n_jobs=1)\n",
+    "\n",
+    "#get estimated intrinsic dimension\n",
+    "print('estimated IDs (DANCo, lPCA):', danco.dimension_, np.mean(lpca.dimension_pw_))"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
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+  "kernelspec": {
+   "display_name": "Python 3.9.9 ('py3')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "pygments_lexer": "ipython3",
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+}