diff --git a/zarrtraj/benchmarking.ipynb b/notebooks/initial_benchmark.ipynb
similarity index 100%
rename from zarrtraj/benchmarking.ipynb
rename to notebooks/initial_benchmark.ipynb
diff --git a/notebooks/yiip_benchmark.ipynb b/notebooks/yiip_benchmark.ipynb
new file mode 100644
index 0000000..94c0e76
--- /dev/null
+++ b/notebooks/yiip_benchmark.ipynb
@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import MDAnalysis as mda\n",
+    "import MDAnalysisData\n",
+    "\n",
+    "yiip = MDAnalysisData.yiip_equilibrium.fetch_yiip_equilibrium_short()\n",
+    "u = mda.Universe(yiip.topology, 5 * [yiip.trajectory], in_memory=True)\n",
+    "positions = u.trajectory.get_array()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "too many values to unpack (expected 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[20], line 26\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m h5py\u001b[38;5;241m.\u001b[39mFile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myiip_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcompression\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcompression_opts\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_hdf5.h5\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m     22\u001b[0m         root\u001b[38;5;241m.\u001b[39mcreate_dataset(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpositions\u001b[39m\u001b[38;5;124m'\u001b[39m, data\u001b[38;5;241m=\u001b[39mpositions, \n\u001b[1;32m     23\u001b[0m                             chunks \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m, np\u001b[38;5;241m.\u001b[39mshape(positions)[\u001b[38;5;241m1\u001b[39m], \u001b[38;5;241m3\u001b[39m), \n\u001b[1;32m     24\u001b[0m                             compression\u001b[38;5;241m=\u001b[39mcompression, compression_opts\u001b[38;5;241m=\u001b[39mcompression_opts)\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m zarr_compressors:\n\u001b[1;32m     27\u001b[0m     compression \u001b[38;5;241m=\u001b[39m k\n\u001b[1;32m     28\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(v[\u001b[38;5;241m0\u001b[39m], v[\u001b[38;5;241m1\u001b[39m]):\n",
+      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import zarr\n",
+    "import h5py\n",
+    "\n",
+    "zarr_compressors = {'blosc': (0,9), 'zlib': (0, 9), 'bz2': (1,9), 'lzma': (0,9)}\n",
+    "# For lzma, pass in compression opts as dict(preset=<int>)\n",
+    "\n",
+    "hdf5_compressors = {'gzip':(0,9)}\n",
+    "\n",
+    "\n",
+    "# Load yiip data into minimal zarr file and hdf5 files of different compressors\n",
+    "def create_zarr_traj(compression, compression_opts):\n",
+    "    root = zarr.open(f'yiip_{compression}_{compression_opts}_zarr.zarr', mode='a')\n",
+    "    if compression == 'lzma':\n",
+    "            compression_opts = dict(preset=compression_opts)\n",
+    "    root.create_dataset('positions', data=positions, \n",
+    "                        chunks = (1, np.shape(positions)[1], 3), \n",
+    "                        compression=compression, compression_opts=compression_opts)\n",
+    "    \n",
+    "def create_hdf5_traj(compression, compression_opts):\n",
+    "    with h5py.File(f'yiip_{compression}_{compression_opts}_hdf5.h5', 'w') as root:\n",
+    "        root.create_dataset('positions', data=positions, \n",
+    "                            chunks = (1, np.shape(positions)[1], 3), \n",
+    "                            compression=compression, compression_opts=compression_opts)\n",
+    "\n",
+    "for k, v in zarr_compressors:\n",
+    "    compression = k\n",
+    "    for i in range(v[0], v[1]):\n",
+    "        create_zarr_traj(compression, i)\n",
+    "\n",
+    "for k, v in hdf5_compressors:\n",
+    "    compression = k\n",
+    "    for i in range(v[0], v[1]):\n",
+    "        create_zarr_traj(compression, i)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "111815"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(positions)[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "zarrtraj",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}