diff --git a/src/constantinople_lab_to_nwb/fiber_photometry/tutorials/fiber_photometry_example_notebook.ipynb b/src/constantinople_lab_to_nwb/fiber_photometry/tutorials/fiber_photometry_example_notebook.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bb03dc16-4677-4334-813b-d8fc08f89e80",
+   "metadata": {},
+   "source": [
+    "# Fiber photometry demo\n",
+    "\n",
+    "This tutorial demonstrates how to access an NWB file from the [DANDI:001265](https://dandiarchive.org/dandiset/001265/draft) dataset using `pynwb`.\n",
+    "\n",
+    "This dataset contains fiber photometry recordings during decision-making behavioral task in rats. Deeplabcut software (v.2.2.3) was used for tracking the behavior ports (right port, central port, and left port) and 6 body parts (right ear, nose, left ear, mid-point along the right torso, mid-point along the left torso, and base of the tail). Video data were acquired using cameras attached to the ceiling of behavior rigs to capture the top-down view of the arena (Doric USB3 behavior camera, Sony IMX290, recorded with Doric Neuroscience Studio v6 software). The fluorescence from activity-dependent (GRAB-DA and GRAB-ACh) and activity-independent (isosbestic or mCherry) signals was acquired simultaneously via demodulation and downsampled on-the-fly by a factor of 25 to ~481.9 Hz. The behavioral tasks were conducted in a high-throughput facility where rats were trained in increasingly complex protocols. Trials were initiated by a nose-poke in a lit center port and required maintaining a center fixation for 0.8 to 1.2 seconds, during which a tone indicated the possible reward size. A subsequent side LED indicated the potential reward location, followed by a delay period drawn from an exponential distribution (mean = 2.5 s). Rats could opt out at any time by poking the unlit port, restarting the trial. Catch trials, where the delay period only ended if the rat opted out, constituted 15-25% of the trials. Rats received penalties for premature fixation breaks. Additionally, the tasks introduced semi-observable hidden states by varying reward statistics across uncued blocks (high, low, and mixed), structured hierarchically, with blocks transitioning after 40 successfully completed trials."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d2773ac-6671-4ff5-8af8-1a4f985d049e",
+   "metadata": {},
+   "source": [
+    "# Reading an NWB file\n",
+    "\n",
+    "This section demonstrates how to read an NWB file using `pynwb`.\n",
+    "Based on the [NWB File Basics](https://pynwb.readthedocs.io/en/stable/tutorials/general/plot_file.html#sphx-glr-tutorials-general-plot-file-py) tutorial from [PyNWB](https://pynwb.readthedocs.io/en/stable/#).\n",
+    "\n",
+    "An [NWBFile](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.NWBFile) represents a single session of an experiment. Each NWBFile must have a `session description`, `identifier`, and `session start time`.\n",
+    "\n",
+    "Reading is carried out using the [NWBHDF5IO](https://pynwb.readthedocs.io/en/stable/pynwb.html#pynwb.NWBHDF5IO) class. To read the NWB file use the read mode \"r\" to retrieve an NWBFile object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ae5552fe-d1f7-4cbe-bf53-3d7fd1bdb4c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pynwb import NWBHDF5IO\n",
+    "import ndx_structured_behavior\n",
+    "\n",
+    "nwbfile_path = \"/Users/weian/data/001265/sub-G026/sub-G026_ses-RWTautowait2-20210528-101504.nwb\"\n",
+    "io = NWBHDF5IO(nwbfile_path, load_namespaces=True)\n",
+    "nwbfile = io.read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ee4f8ff-42b9-463e-b4ee-cf79a5e5276a",
+   "metadata": {},
+   "source": [
+    "Importantly, the session start time is the reference time for all timestamps in the file. For instance, an event with a timestamp of 0 in the file means the event occurred exactly at the session start time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7b03772-f6a4-4b0b-8ce3-6220a1c6e691",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.datetime(2021, 5, 28, 0, 0, tzinfo=tzoffset(None, -14400))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.session_start_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0017838-4578-416e-bfb0-e9377f37a7e0",
+   "metadata": {},
+   "source": [
+    "This section demonstrates how to access the [Subject](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.Subject) field in an NWBFile.\n",
+    "\n",
+    "The [Subject](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.Subject) field can be accessed as `nwbfile.subject`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2690ec0a-94de-4beb-978e-0deaf3c81668",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>subject (Subject)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".age__reference\">age__reference: </span><span class=\"field-value\">birth</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".description\">description: </span><span class=\"field-value\">Vendor: OVR</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".sex\">sex: </span><span class=\"field-value\">F</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".species\">species: </span><span class=\"field-value\">Rattus norvegicus</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject_id\">subject_id: </span><span class=\"field-value\">G026</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".date_of_birth\"><b>date_of_birth</b></summary><span class=\"field-key\">2020-05-27 00:00:00+02:00</span></details></div>"
+      ],
+      "text/plain": [
+       "subject pynwb.file.Subject at 0x4685041440\n",
+       "Fields:\n",
+       "  age__reference: birth\n",
+       "  date_of_birth: 2020-05-27 00:00:00+02:00\n",
+       "  description: Vendor: OVR\n",
+       "  sex: F\n",
+       "  species: Rattus norvegicus\n",
+       "  subject_id: G026"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.subject"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33f9e287-8e54-4d48-9b8b-a3de4f7b2061",
+   "metadata": {},
+   "source": [
+    "# Access raw fiber photometry\n",
+    "\n",
+    "This section demonstrates how to access the raw fiber photometry data in the NWBFile.\n",
+    "\n",
+    "`NWB` organizes data into different groups depending on the type of data. Groups can be thought of as folders within the file. Here are some of the groups within an NWBFile and the types of data they are intended to store:\n",
+    "\n",
+    "- `acquisition`: raw, acquired data that should never change\n",
+    "- `processing`: processed data, typically the results of preprocessing algorithms and could change\n",
+    "\n",
+    "The raw fluorescence traces from the optical fiber(s) are added to `nwbfile.acquisition` and are stored in a `FiberPhotometryResponseSeries` object using [`ndx-fiber-photometry`](https://github.com/catalystneuro/ndx-fiber-photometry). The raw fluorescence data can be accessed as `nwbfile.acquisition[\"estimated_fiber_photometry_response_series\"]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4ae63b1e-5f40-4f25-87e1-35ffa23ac5ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fiber_photometry_response_series = nwbfile.acquisition[\"estimated_fiber_photometry_response_series\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ab058ffa-59df-463c-a9ec-d2551c8a71e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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DACBbCEoBAABwUqsbClq6dGkkk8km/7RE3759U8e7d+9u9HVXX311rF+/PrZt2xYLFiyIv/7rv46CgoLU58lkMpYsWRLDhw+PsrKyBser+9Cl7pwAADixVFVVxZIlS9LOnUjb7i1atChtK+e8vLy44oorMl6nLe9DS1fI+sMf/pDhGbX8hY/6evToEV/72tfSzr344osZrQEAkA1svQcAAMBJLS8vL3X83nvvtXn9ulu0bN++vcnXn3XWWXHDDTfEDTfcEBERlZWV8dhjj8W//Mu/RHV1dfzhD3+IO+64I/7jP/7juOPs2LEjdZyfn9/keQAAZIO77747rr/++kb1LS0tjbvuuivVfvzxx9N+tzyec889t1nzi2i7IFJz1Q8VzZw5M2077Ew5mQJZe/fuzdyE/r/a2tqMjzl+/Ph46KGHUu3f/va3Ga8BAHCyE5QCAADgpDZq1KjUm9Jr166NOXPmtGn9oUOHpo4z8SCif//+UVhYGGeffXbqId+LL74YBw4ciM6dOx/zus2bN6eOL7jgghbPAwDgZFRQUJC2Sufx1A//jBkzpk0C5/W3m2utIFJzlJeXx+rVq9POtdW2e611HxYvXtziQNbpp5+e1i4oKIjvf//7LZrXaaed1qLrj6Z///5p7bqrzgIA8IkT4zdvAAAAaKbJkyfHvHnzIuKTrfd27tzZ6JUAMuGiiy6KRCIRyWQyfvOb38ShQ4cy8oDnyiuvTB3X1NTE+++/H/369Ttm/9LS0tTxyJEjW1wfAIDMKy8vj1dffTXt3OzZs9tpNkdasGBB2tbUI0aMiPPPPz/jddryPtRfIev6669v8u/rvXv3Tmsnk8m47LLLWjq1jOvYsWNau6ampp1mAgBw4spp7wkAAABAS4wcOTLGjRsXERH79++Pv/qrv4qDBw826tqDBw/Gnj17WlS/d+/eMWzYsFT9jRs3Hrf/7t27GzVuZWVl6jgnJyd69ep13DHffPPNiIjIzc0VlAIAOEEdLYh03nnnteOM/k8ymYyioqK0c625mlRb3IejBbKa85369u2btgLUtm3bTsgQUv2tyM8444x2mgkAwIlLUAoAAICT3sMPPxxdu3aNiIiVK1fGl770pVi/fv0x+5eVlcW8efMiPz8/1q5d2+L6U6ZMSR2/8sorx+178cUXx4wZM+L5558/ZqCrrKwsZs2alWqPHz8+OnXqdMwxV61alXrQNGHChBNm6xYAAP5PWwaRmuPVV1+N8vLyVLtTp04xY8aMjNdpy/tQVFSUkUBWx44dY8yYMal2dXX1cf/eaC9r1qxJa9ffig8AAFvvAQAAkAXOO++8eOqpp2L69OmphxajR4+Os88+O4YPHx49e/aMjz/+OHbt2hVvvPFG7NixI6P1Z86cGffdd19ERCxfvjzuueeeY/atqamJp556Kp566qk47bTTYujQoTFo0KDo3r177NmzJ7Zu3RqvvfZaqv9pp50WDzzwwHHrL1u2LG0uAACceNoqiNRc9beomzJlSvTs2TPjdU7WQNbEiRPjpZdeSrUffvjhGDt2bLPHy7RDhw7FggUL0s6NHz++nWYDAHDiEpQCAAAgK0yePDn+8z//M2666abU9ndbtmyJLVu2HPOa/Pz8OPPMM1tce8iQITF27NhYs2ZNbNiwIcrLy2PgwIFH7dutW7fU8f79+2P9+vXHfBt94MCB8fjjj8fQoUOPWXv//v3xq1/9KiI+2RLkiiuuaME3AQCgtdQPsbRWEKk5Pvroo1i8eHHaudbcdq+u1roPq1evjq1bt6baLQ1k3XzzzfFP//RPsXfv3oiIWLx4cfzqV7+Kv/iLv2jpVDPiO9/5Trz99ttp56666qp2mg0AwInL1nsAAABkjQsuuCBee+21WLFiRcyZMyeGDh0avXv3jg4dOkSXLl0iPz8/JkyYEIWFhbF27drYunVrDBs2LCO1b7/99oj45M31n//858fs9/rrr8eaNWti3rx5MWXKlBgyZEh07do1cnJyomvXrjF48OC45pproqioKDZv3hxf/OIXj1v36aefjg8//DAiIr75zW9Gx44dM/J9AADInOrq6lYJIs2fPz8SiUTqJz8/v1njLFmyJKqqqlLtvn37xsSJE1s8v/pa6z4cTaZXyMrNzY1vf/vbqXZtbW1cd9118cwzzzRpnI0bN8b06dOP+fkjjzzS4Hbe9T300ENRWFiYdu7KK6+MESNGNGkcAIBPg0Sy7ubMAAAAQLPU1tbGueeeG2+++Wbk5eVFRUVFnHrqqa1ed9SoUVFSUhLdunWL8vLy6NWrV6vXBADIBsXFxXHJJZek2uXl5c0OGjWkqKgoZs2alWr37ds3Kisro0OHlm38MX/+/Jg9e3aqPWDAgKioqGjyOJdeemmsWrUq1b7zzjsb3P65OVrrPtRXXV0dffv2jX379qXOPfvsszF58uQWjVtbWxtTpkyJ5557LnUukUjE1KlT49Zbb40xY8ZE586d0675+OOP44033oiXXnoplixZEv/1X/8VEZ+8YHE0N954YyxYsCAuuOCCmDZtWkycODHOPffcI8atrq6Ol19+OR544IF49dVX0z7r1atXrF+/Ps4+++wWfV8AgGxk6z0AAADIgJycnPjHf/zHmD59euzcuTPmz58ft9xyS6vWLC4ujpKSkoiIuOOOO4SkAABOUPW3m5s5c2bGw0HNtW3btiguLk4711bb7rXWfViyZElaSCpTK2Tl5OTEk08+GVOnTk3ds2QyGcuWLYtly5ZF586dY8CAAdGjR4/4+OOPY+/evbF9+/Y4fPhwk2uVlpZGaWlp3HvvvdGhQ4fo379/5ObmRqdOnWLPnj1RUVERNTU1R1zXrVu3ePbZZ4WkAACOwdZ7AAAAkCHXXnttjB49OiIivvOd78SBAwdatd59990XERH9+vWLO++8s1VrAQDQPL/73e/SVmuKaL0gUnMUFRWlrW40YsSIOO+88zJepy3vQ/1t9zIZyMrNzY2VK1fGHXfcccSYBw4ciLKysli/fn2UlpbGtm3bjhqS6t+/f5NqHjp0KMrLy+P111+PkpKSeOutt44akho1alT893//d1x88cVN+1IAAJ8iglIAAACQIYlEIn7wgx9ETk5ObNu2LX74wx+2Wq0VK1akHjTdf//90a1bt1arBQBA87VVEKm5ioqK0tqtFV7KpkBWhw4d4sEHH4zf/va38Y1vfCP69OnT4DX5+fnxjW98I1588cXjbo84Z86cuP3222P48OHRsWPHBsc99dRTY+LEifHss8/GunXrrCQFANCARPJYmyADAAAAAAAAx5VMJmPTpk2xadOm2L17d+zduzc6d+4cubm5MXDgwCgoKIjPfvazTR734MGDsXnz5igvL4933nkn9u3bFzU1NdG9e/fo0aNHDBkyJC688MLo1KlTK3wrAIDsJCgFAAAAAAAAAABkPVvvAQAAAAAAAAAAWU9QCgAAAAAAAAAAyHqCUgAAAAAAAAAAQNYTlAIAAAAAAAAAALKeoBQAAAAAAAAAAJD1BKUAAAAAAAAAAICsJygFAAAAAAAAAABkPUEpAAAAAAAAAAAg6wlKAQAAAAAAAAAAWU9QCgAAAAAAAAAAyHqCUgAAAAAAAAAAQNYTlAIAAAAAAAAAALKeoBQAAAAAAAAAAJD1BKUAAAAAAAAAAICsJygFAAAAAAAAAABkPUEpAAAAAAAAAAAg6wlKAQAAAAAAAAAAWU9QCgAAAAAAAAAAyHqCUgAAAAAAAAAAQNYTlAIAAAAAAAAAALKeoBQAAAAAAAAAAJD1BKUAAAAAAAAAAICsJygFAAAAAAAAAABkPUEpAAAAAAAAAAAg6wlKAQAAAAAAAAAAWU9QCgAAAAAAAAAAyHqCUgAAAAAAAAAAQNYTlAIAAAAAAAAAALKeoBQAAAAAAAAAAJD1BKUAAAAAAAAAAICsJygFAAAAAAAAAABkPUEpAAAAAAAAAAAg6wlKAQAAAAAAAAAAWU9QCgAAAAAAAAAAyHqCUgAAAAAAAAAAQNYTlAIAAAAAAAAAALLe/wOEOy3cszVZ7AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2400x900 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "# Prepare data for plotting\n",
+    "data = fiber_photometry_response_series.data[100:5000, :]\n",
+    "timestamps = fiber_photometry_response_series.get_timestamps()[100:5000]\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=data.shape[1], ncols=1, figsize=(8, 3), sharey=True, sharex=True, dpi=300)\n",
+    "\n",
+    "legend = [\"estimated signal for mCherry\", \"estimated signal for GRAB-DA\"]\n",
+    "color = [\"red\", \"green\"]\n",
+    "for i, ax in enumerate(axes):\n",
+    "    ax.plot(timestamps, data[:, i], linewidth=0.5, color=color[i])\n",
+    "\n",
+    "    ax.tick_params(axis='y', labelsize=8)\n",
+    "    ax.tick_params(axis='x', labelsize=8)\n",
+    "\n",
+    "    ax.legend([legend[i]], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})\n",
+    "\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "\n",
+    "axes[0].spines['bottom'].set_visible(False)\n",
+    "axes[0].set_title(\"Raw fluorescence traces\", fontsize=8)\n",
+    "plt.xlabel('Time (s)', fontsize=8)\n",
+    "plt.tick_params(axis='x', labelsize=8)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1253df2-12ee-460f-9f83-019c988b57fb",
+   "metadata": {},
+   "source": [
+    "## Fiber photometry metadata\n",
+    "\n",
+    "The fiber photometry metadata includes the type of indicator(s), optical fiber(s), excitation source(s), photodector(s), dichroic mirror(s), and optical filter(s) that were used to construct a single fluorescence signal.\n",
+    "\n",
+    "The metadata is stored in a `FiberPhotometryTable` object using [`ndx-fiber-photometry`](https://github.com/catalystneuro/ndx-fiber-photometry) and is added to `nwbfile.lab_meta_data`. It can be accessed as `nwbfile.lab_meta_data[\"FiberPhotometry\"].fiber_photometry_table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e7462740-84f5-49f0-8f8c-09d39c3b569e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>optical_fiber</th>\n",
+       "      <th>excitation_source</th>\n",
+       "      <th>photodetector</th>\n",
+       "      <th>dichroic_mirror</th>\n",
+       "      <th>coordinates</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NAcc</td>\n",
+       "      <td>grab abc.Indicator at 0x4904706240\\nFields:\\n ...</td>\n",
+       "      <td>optical_fiber abc.OpticalFiber at 0x4904706192...</td>\n",
+       "      <td>excitation_source_grab_isosbestic abc.Excitati...</td>\n",
+       "      <td>photodetector abc.Photodetector at 0x490470950...</td>\n",
+       "      <td>dichroic_mirror abc.DichroicMirror at 0x490402...</td>\n",
+       "      <td>[nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NAcc</td>\n",
+       "      <td>grab abc.Indicator at 0x4904706240\\nFields:\\n ...</td>\n",
+       "      <td>optical_fiber abc.OpticalFiber at 0x4904706192...</td>\n",
+       "      <td>excitation_source_grab abc.ExcitationSource at...</td>\n",
+       "      <td>photodetector abc.Photodetector at 0x490470950...</td>\n",
+       "      <td>dichroic_mirror abc.DichroicMirror at 0x490402...</td>\n",
+       "      <td>[nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NAcc</td>\n",
+       "      <td>mcherry abc.Indicator at 0x4904705616\\nFields:...</td>\n",
+       "      <td>optical_fiber abc.OpticalFiber at 0x4904706192...</td>\n",
+       "      <td>excitation_source_mcherry abc.ExcitationSource...</td>\n",
+       "      <td>photodetector abc.Photodetector at 0x490470950...</td>\n",
+       "      <td>dichroic_mirror abc.DichroicMirror at 0x490402...</td>\n",
+       "      <td>[nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   location                                          indicator  \\\n",
+       "id                                                               \n",
+       "0      NAcc  grab abc.Indicator at 0x4904706240\\nFields:\\n ...   \n",
+       "1      NAcc  grab abc.Indicator at 0x4904706240\\nFields:\\n ...   \n",
+       "2      NAcc  mcherry abc.Indicator at 0x4904705616\\nFields:...   \n",
+       "\n",
+       "                                        optical_fiber  \\\n",
+       "id                                                      \n",
+       "0   optical_fiber abc.OpticalFiber at 0x4904706192...   \n",
+       "1   optical_fiber abc.OpticalFiber at 0x4904706192...   \n",
+       "2   optical_fiber abc.OpticalFiber at 0x4904706192...   \n",
+       "\n",
+       "                                    excitation_source  \\\n",
+       "id                                                      \n",
+       "0   excitation_source_grab_isosbestic abc.Excitati...   \n",
+       "1   excitation_source_grab abc.ExcitationSource at...   \n",
+       "2   excitation_source_mcherry abc.ExcitationSource...   \n",
+       "\n",
+       "                                        photodetector  \\\n",
+       "id                                                      \n",
+       "0   photodetector abc.Photodetector at 0x490470950...   \n",
+       "1   photodetector abc.Photodetector at 0x490470950...   \n",
+       "2   photodetector abc.Photodetector at 0x490470950...   \n",
+       "\n",
+       "                                      dichroic_mirror      coordinates  \n",
+       "id                                                                      \n",
+       "0   dichroic_mirror abc.DichroicMirror at 0x490402...  [nan, nan, nan]  \n",
+       "1   dichroic_mirror abc.DichroicMirror at 0x490402...  [nan, nan, nan]  \n",
+       "2   dichroic_mirror abc.DichroicMirror at 0x490402...  [nan, nan, nan]  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"FiberPhotometry\"].fiber_photometry_table[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf2596a-ae7b-4884-bdd1-ecbdbf7c722e",
+   "metadata": {},
+   "source": [
+    "The `fiber_photometry_table_region` attribute in the `FiberPhotometryResponseSeries` object references row(s) of this table:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c813a52f-bbd9-4ba4-8865-d26159f7d056",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>optical_fiber</th>\n",
+       "      <th>excitation_source</th>\n",
+       "      <th>photodetector</th>\n",
+       "      <th>dichroic_mirror</th>\n",
+       "      <th>coordinates</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NAcc</td>\n",
+       "      <td>grab abc.Indicator at 0x4904706240\\nFields:\\n ...</td>\n",
+       "      <td>optical_fiber abc.OpticalFiber at 0x4904706192...</td>\n",
+       "      <td>excitation_source_grab abc.ExcitationSource at...</td>\n",
+       "      <td>photodetector abc.Photodetector at 0x490470950...</td>\n",
+       "      <td>dichroic_mirror abc.DichroicMirror at 0x490402...</td>\n",
+       "      <td>[nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NAcc</td>\n",
+       "      <td>mcherry abc.Indicator at 0x4904705616\\nFields:...</td>\n",
+       "      <td>optical_fiber abc.OpticalFiber at 0x4904706192...</td>\n",
+       "      <td>excitation_source_mcherry abc.ExcitationSource...</td>\n",
+       "      <td>photodetector abc.Photodetector at 0x490470950...</td>\n",
+       "      <td>dichroic_mirror abc.DichroicMirror at 0x490402...</td>\n",
+       "      <td>[nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   location                                          indicator  \\\n",
+       "id                                                               \n",
+       "1      NAcc  grab abc.Indicator at 0x4904706240\\nFields:\\n ...   \n",
+       "2      NAcc  mcherry abc.Indicator at 0x4904705616\\nFields:...   \n",
+       "\n",
+       "                                        optical_fiber  \\\n",
+       "id                                                      \n",
+       "1   optical_fiber abc.OpticalFiber at 0x4904706192...   \n",
+       "2   optical_fiber abc.OpticalFiber at 0x4904706192...   \n",
+       "\n",
+       "                                    excitation_source  \\\n",
+       "id                                                      \n",
+       "1   excitation_source_grab abc.ExcitationSource at...   \n",
+       "2   excitation_source_mcherry abc.ExcitationSource...   \n",
+       "\n",
+       "                                        photodetector  \\\n",
+       "id                                                      \n",
+       "1   photodetector abc.Photodetector at 0x490470950...   \n",
+       "2   photodetector abc.Photodetector at 0x490470950...   \n",
+       "\n",
+       "                                      dichroic_mirror      coordinates  \n",
+       "id                                                                      \n",
+       "1   dichroic_mirror abc.DichroicMirror at 0x490402...  [nan, nan, nan]  \n",
+       "2   dichroic_mirror abc.DichroicMirror at 0x490402...  [nan, nan, nan]  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fiber_photometry_table_region = nwbfile.acquisition[\"estimated_fiber_photometry_response_series\"].fiber_photometry_table_region[:]\n",
+    "fiber_photometry_table_region.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db4b85d7-592e-446f-b92e-704da9002739",
+   "metadata": {},
+   "source": [
+    "The metadata on the optical fiber is added to `nwbfile.devices` and can be accessed as `nwbfile.devices[\"optical_fiber\"]` or can be accessed from the referenced optical fiber in the `fiber_photometry_table_region` of the `FiberPhotometryResponseSeries`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3d61b67a-ee48-446b-8c89-0f5319bf1e06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>optical_fiber (OpticalFiber)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".description\">description: </span><span class=\"field-value\">Chronically implantable optic fibers (Thor labs) with 400 µm core, 0.5 NA fiber optics were implanted unilaterally over the injection site (DV -6.7). Doric Lenses hardware and software (Doric Neuroscience Studio) were used to record fluorescence. Two-channel motion artifact correction (TMAC) was used to correct for movement artifacts, with mCherry as the activity-independent channel.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".manufacturer\">manufacturer: </span><span class=\"field-value\">Thor labs</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".numerical_aperture\">numerical_aperture: </span><span class=\"field-value\">0.5</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".core_diameter_in_um\">core_diameter_in_um: </span><span class=\"field-value\">400.0</span></div></div>"
+      ],
+      "text/plain": [
+       "optical_fiber abc.OpticalFiber at 0x4904706192\n",
+       "Fields:\n",
+       "  core_diameter_in_um: 400.0\n",
+       "  description: Chronically implantable optic fibers (Thor labs) with 400 µm core, 0.5 NA fiber optics were implanted unilaterally over the injection site (DV -6.7). Doric Lenses hardware and software (Doric Neuroscience Studio) were used to record fluorescence. Two-channel motion artifact correction (TMAC) was used to correct for movement artifacts, with mCherry as the activity-independent channel.\n",
+       "  manufacturer: Thor labs\n",
+       "  numerical_aperture: 0.5"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fiber_photometry_table_region[\"optical_fiber\"][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "78af1517-fc29-4a4b-b094-6bec3344ce8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>grab (Indicator)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".description\">description: </span><span class=\"field-value\">To measure dopamine activity, AAV9-hsyn-GRAB-DA2h (AddGene #140554) was injected into the NAcc.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".manufacturer\">manufacturer: </span><span class=\"field-value\">Addgene</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".label\">label: </span><span class=\"field-value\">AAV9-hsyn-GRAB-DA2h</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".injection_location\">injection_location: </span><span class=\"field-value\">NAcc</span></div></div>"
+      ],
+      "text/plain": [
+       "grab abc.Indicator at 0x4904706240\n",
+       "Fields:\n",
+       "  description: To measure dopamine activity, AAV9-hsyn-GRAB-DA2h (AddGene #140554) was injected into the NAcc.\n",
+       "  injection_location: NAcc\n",
+       "  label: AAV9-hsyn-GRAB-DA2h\n",
+       "  manufacturer: Addgene"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fiber_photometry_table_region[\"indicator\"][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f71e7210-0230-4bac-a132-4a311350d9ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>excitation_source_grab (ExcitationSource)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".description\">description: </span><span class=\"field-value\">The excitation wavelength for GRAB indicator.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".manufacturer\">manufacturer: </span><span class=\"field-value\">Doric Lenses</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".illumination_type\">illumination_type: </span><span class=\"field-value\">LED</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".excitation_wavelength_in_nm\">excitation_wavelength_in_nm: </span><span class=\"field-value\">470.0</span></div></div>"
+      ],
+      "text/plain": [
+       "excitation_source_grab abc.ExcitationSource at 0x4904704752\n",
+       "Fields:\n",
+       "  description: The excitation wavelength for GRAB indicator.\n",
+       "  excitation_wavelength_in_nm: 470.0\n",
+       "  illumination_type: LED\n",
+       "  manufacturer: Doric Lenses"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fiber_photometry_table_region[\"excitation_source\"][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "940e052e-e2d5-473b-af93-9d0fdda909b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>photodetector (Photodetector)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".manufacturer\">manufacturer: </span><span class=\"field-value\">Doric Lenses</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".model\">model: </span><span class=\"field-value\">TBD</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".detector_type\">detector_type: </span><span class=\"field-value\">photodiode</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".detected_wavelength_in_nm\">detected_wavelength_in_nm: </span><span class=\"field-value\">470.0</span></div></div>"
+      ],
+      "text/plain": [
+       "photodetector abc.Photodetector at 0x4904709504\n",
+       "Fields:\n",
+       "  detected_wavelength_in_nm: 470.0\n",
+       "  detector_type: photodiode\n",
+       "  manufacturer: Doric Lenses\n",
+       "  model: TBD"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fiber_photometry_table_region[\"photodetector\"][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5f6fe5b9-964e-42c9-960f-8a35fb54f8e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>dichroic_mirror (DichroicMirror)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".manufacturer\">manufacturer: </span><span class=\"field-value\">Doric Lenses</span></div></div>"
+      ],
+      "text/plain": [
+       "dichroic_mirror abc.DichroicMirror at 0x4904029360\n",
+       "Fields:\n",
+       "  manufacturer: Doric Lenses"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fiber_photometry_table_region[\"dichroic_mirror\"][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a6e7950-c5cd-48e8-a1b5-8c7829a35850",
+   "metadata": {},
+   "source": [
+    "# Access raw behavior data\n",
+    "\n",
+    "This section demonstrates how to access the Bpod data in the NWBFile.\n",
+    "\n",
+    "## Accessing the task metadata\n",
+    "\n",
+    "The task-related general metadata is stored in a `Task` object which can be accessed as `nwbfile.lab_meta_data[\"task\"]`.\n",
+    "\n",
+    "The `EventTypesTable` is a column-based table to store the type of events that occur during the task (e.g. port poke from the animal), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].event_types`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "26569fa8-c306-4ea3-823f-e1559057c189",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>event_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>left_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>right_port_poke</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          event_name\n",
+       "id                  \n",
+       "0        state_timer\n",
+       "1     left_port_poke\n",
+       "2   center_port_poke\n",
+       "3    right_port_poke"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].event_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05907a65-0416-41af-b6ef-1ee798d2f7d8",
+   "metadata": {},
+   "source": [
+    "The `ActionTypesTable` is a column-based table to store the type of actions that occur during the task (e.g. sound output from the acquisition system), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].action_types`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "769a4b0a-0103-4822-af65-f2b67ca005c0",
+   "metadata": {},
+   "source": [
+    "The `ActionTypesTable` is a column-based table to store the type of actions that occur during the task (e.g. sound output from the acquisition system), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].action_types`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f571980e-52e9-4c39-ba60-226987b5fb49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>action_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     action_name\n",
+       "id              \n",
+       "0   sound_output"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].action_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a9fb369-11e7-43e0-a636-d9dea9145704",
+   "metadata": {},
+   "source": [
+    "The `StateTypesTable` is a column-based table to store the type of states that occur during the task (e.g. while the animal is waiting for reward), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].state_types`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "26ebc676-46e4-4dc9-8f5b-7d32f7464cc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>opt_out</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>stop_sound</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>go_cue</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>wait_for_side_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>punish_violation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>announce_reward</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>reward</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            state_name\n",
+       "id                    \n",
+       "0              opt_out\n",
+       "1           stop_sound\n",
+       "2               go_cue\n",
+       "3   wait_for_side_poke\n",
+       "4     punish_violation\n",
+       "5        wait_for_poke\n",
+       "6       nose_in_center\n",
+       "7      announce_reward\n",
+       "8               reward"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].state_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7156cbb-5f37-4cf7-aa7a-3713071e1e4d",
+   "metadata": {},
+   "source": [
+    "The arguments for the task is stored in a `TaskArgumentsTable` which can be accessed as `nwbfile.lab_meta_data[\"task\"].task_arguments`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cf6a1b1d-a44f-4d1f-ab05-c80145e2f1a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>argument_name</th>\n",
+       "      <th>argument_description</th>\n",
+       "      <th>expression</th>\n",
+       "      <th>expression_type</th>\n",
+       "      <th>output_type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>reward_volume_ul</td>\n",
+       "      <td>The volume of reward in microliters.</td>\n",
+       "      <td>40</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nose_in_center</td>\n",
+       "      <td>The time in seconds when the animal is require...</td>\n",
+       "      <td>1.09128201436924</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>time_increment_for_nose_in_center</td>\n",
+       "      <td>The time increment for nose in center in seconds.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>target_duration_for_nose_in_center</td>\n",
+       "      <td>The goal for how long the animal must poke cen...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>training_stage</td>\n",
+       "      <td>The stage of the training.</td>\n",
+       "      <td>9</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>reward_delay</td>\n",
+       "      <td>The delay in seconds to receive reward, drawn ...</td>\n",
+       "      <td>0.03789609238432629</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>target_reward_delay</td>\n",
+       "      <td>The target delay in seconds to receive reward.</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>time_increment_for_reward_delay</td>\n",
+       "      <td>The time increment during monotonic increase o...</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>violation_time_out</td>\n",
+       "      <td>The time-out if nose is center is not satisfie...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>block_type</td>\n",
+       "      <td>The block type (High, Low or Mixed). High and ...</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>string</td>\n",
+       "      <td>string</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>num_trials_in_mixed_blocks</td>\n",
+       "      <td>The number of trials in each mixed blocks.</td>\n",
+       "      <td>40</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>num_trials_in_adaptation_blocks</td>\n",
+       "      <td>The number of trials in each high reward (20, ...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>punish_sound_enabled</td>\n",
+       "      <td>Whether to play a white noise pulse on error.</td>\n",
+       "      <td>True</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>catch_percentage</td>\n",
+       "      <td>The percentage of catch trials.</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>is_catch</td>\n",
+       "      <td>Whether the trial is a catch trial.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>current_trial</td>\n",
+       "      <td>The current trial number.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>cumulative_reward_volume_ul</td>\n",
+       "      <td>The cumulative volume received during session ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>is_warm_up</td>\n",
+       "      <td>Whether the trial is warm-up.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>override_nose_in_center</td>\n",
+       "      <td>Whether the required time for maintaining cent...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>trials_in_stage</td>\n",
+       "      <td>The cumulative number of trials in the stages.</td>\n",
+       "      <td>42622</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>min_reward_volume_ul</td>\n",
+       "      <td>The minimum volume of reward in microliters. (...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>high_ITI</td>\n",
+       "      <td>Task parameter.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>auto_change_catch_probability</td>\n",
+       "      <td>Whether to change the probability automaticall...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>previous_was_violation</td>\n",
+       "      <td>Whether the previous trial was a violation.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>changed</td>\n",
+       "      <td>Whether a block transition occurred for the tr...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>num_trials_in_stage_3</td>\n",
+       "      <td>Determines how many trials occur in stage 3 be...</td>\n",
+       "      <td>400</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>num_trials_in_stage_8</td>\n",
+       "      <td>Determines how many trials occur in stage 8 be...</td>\n",
+       "      <td>250</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>center_port_cue</td>\n",
+       "      <td>Task parameter.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         argument_name  \\\n",
+       "id                                       \n",
+       "0                     reward_volume_ul   \n",
+       "1                       nose_in_center   \n",
+       "2    time_increment_for_nose_in_center   \n",
+       "3   target_duration_for_nose_in_center   \n",
+       "4                       training_stage   \n",
+       "5                         reward_delay   \n",
+       "6                  target_reward_delay   \n",
+       "7      time_increment_for_reward_delay   \n",
+       "8                   violation_time_out   \n",
+       "9                           block_type   \n",
+       "10          num_trials_in_mixed_blocks   \n",
+       "11     num_trials_in_adaptation_blocks   \n",
+       "12                punish_sound_enabled   \n",
+       "13                    catch_percentage   \n",
+       "14                            is_catch   \n",
+       "15                       current_trial   \n",
+       "16         cumulative_reward_volume_ul   \n",
+       "17                          is_warm_up   \n",
+       "18             override_nose_in_center   \n",
+       "19                     trials_in_stage   \n",
+       "20                min_reward_volume_ul   \n",
+       "21                            high_ITI   \n",
+       "22       auto_change_catch_probability   \n",
+       "23              previous_was_violation   \n",
+       "24                             changed   \n",
+       "25               num_trials_in_stage_3   \n",
+       "26               num_trials_in_stage_8   \n",
+       "27                     center_port_cue   \n",
+       "\n",
+       "                                 argument_description           expression  \\\n",
+       "id                                                                           \n",
+       "0                The volume of reward in microliters.                   40   \n",
+       "1   The time in seconds when the animal is require...     1.09128201436924   \n",
+       "2   The time increment for nose in center in seconds.                    0   \n",
+       "3   The goal for how long the animal must poke cen...                    1   \n",
+       "4                          The stage of the training.                    9   \n",
+       "5   The delay in seconds to receive reward, drawn ...  0.03789609238432629   \n",
+       "6      The target delay in seconds to receive reward.                  1.5   \n",
+       "7   The time increment during monotonic increase o...                0.025   \n",
+       "8   The time-out if nose is center is not satisfie...                    1   \n",
+       "9   The block type (High, Low or Mixed). High and ...                Mixed   \n",
+       "10         The number of trials in each mixed blocks.                   40   \n",
+       "11  The number of trials in each high reward (20, ...                   40   \n",
+       "12      Whether to play a white noise pulse on error.                 True   \n",
+       "13                    The percentage of catch trials.                 0.25   \n",
+       "14                Whether the trial is a catch trial.                False   \n",
+       "15                          The current trial number.                    0   \n",
+       "16  The cumulative volume received during session ...                    0   \n",
+       "17                      Whether the trial is warm-up.                False   \n",
+       "18  Whether the required time for maintaining cent...                False   \n",
+       "19     The cumulative number of trials in the stages.                42622   \n",
+       "20  The minimum volume of reward in microliters. (...                    5   \n",
+       "21                                    Task parameter.                    0   \n",
+       "22  Whether to change the probability automaticall...                False   \n",
+       "23        Whether the previous trial was a violation.                False   \n",
+       "24  Whether a block transition occurred for the tr...                False   \n",
+       "25  Determines how many trials occur in stage 3 be...                  400   \n",
+       "26  Determines how many trials occur in stage 8 be...                  250   \n",
+       "27                                    Task parameter.                False   \n",
+       "\n",
+       "   expression_type output_type  \n",
+       "id                              \n",
+       "0          integer     numeric  \n",
+       "1           double     numeric  \n",
+       "2           double     numeric  \n",
+       "3           double     numeric  \n",
+       "4          integer     numeric  \n",
+       "5           double     numeric  \n",
+       "6           double     numeric  \n",
+       "7           double     numeric  \n",
+       "8           double     numeric  \n",
+       "9           string      string  \n",
+       "10         integer     numeric  \n",
+       "11         integer     numeric  \n",
+       "12         boolean     boolean  \n",
+       "13          double     numeric  \n",
+       "14         boolean     boolean  \n",
+       "15         integer     numeric  \n",
+       "16          double     numeric  \n",
+       "17         boolean     boolean  \n",
+       "18         boolean     boolean  \n",
+       "19         integer     numeric  \n",
+       "20          double     numeric  \n",
+       "21          double     numeric  \n",
+       "22         boolean     boolean  \n",
+       "23         boolean     boolean  \n",
+       "24         boolean     boolean  \n",
+       "25         integer     numeric  \n",
+       "26         integer     numeric  \n",
+       "27         boolean     boolean  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].task_arguments[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "438202ef-1152-4d2a-a37b-568a0854149e",
+   "metadata": {},
+   "source": [
+    "The `TaskRecording` object stores the data for events, states, and actions that occured during the task. The `TaskRecording` is added as acquisition which can be accessed as `nwbfile.acquisition[\"task_recording\"]`.\n",
+    "\n",
+    "The `EventsTable` is a column-based table to store the information about the events (e.g. poke times), one event per row. This table can be accessed as `nwbfile.acquisition[\"task_recording\"].events`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9e1e3601-c025-4019-860e-389802e1b384",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>value</th>\n",
+       "      <th>event_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>103.3331</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>103.4485</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>103.4420</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>103.5575</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>104.4420</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12596</th>\n",
+       "      <td>4785.5592</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12597</th>\n",
+       "      <td>4785.9513</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12598</th>\n",
+       "      <td>4785.9286</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12599</th>\n",
+       "      <td>4786.1974</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12600</th>\n",
+       "      <td>4786.9286</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>12601 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       timestamp  event_type    value        event_name\n",
+       "0       103.3331           2       In  center_port_poke\n",
+       "1       103.4485           2       In  center_port_poke\n",
+       "2       103.4420           2      Out  center_port_poke\n",
+       "3       103.5575           2      Out  center_port_poke\n",
+       "4       104.4420           0  Expired       state_timer\n",
+       "...          ...         ...      ...               ...\n",
+       "12596  4785.5592           2       In  center_port_poke\n",
+       "12597  4785.9513           2       In  center_port_poke\n",
+       "12598  4785.9286           2      Out  center_port_poke\n",
+       "12599  4786.1974           2      Out  center_port_poke\n",
+       "12600  4786.9286           0  Expired       state_timer\n",
+       "\n",
+       "[12601 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].events[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].event_types[:],\n",
+    "    left_on=\"event_type\",\n",
+    "    right_on=\"id\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f9ff294-acf3-4537-925b-6067736b0e04",
+   "metadata": {},
+   "source": [
+    "The `ActionsTable` is a column-based table to store the information about the actions (e.g. sound onset times), one action per row. This table can be accessed as `nwbfile.acquisition[\"task_recording\"].actions`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50581517-f2d6-4ef3-a5f5-443e1e554cd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].actions[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].action_types[:],\n",
+    "    left_on=\"action_type\",\n",
+    "    right_on=\"id\",\n",
+    ").head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6d624a2a-7f23-43a4-8610-1d6c36dd0f9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>action_type</th>\n",
+       "      <th>value</th>\n",
+       "      <th>action_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11.4262</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11.6163</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>103.3332</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>103.4421</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>103.9423</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestamp  action_type value   action_name\n",
+       "0    11.4262            0    On  sound_output\n",
+       "1    11.6163            0    On  sound_output\n",
+       "2   103.3332            0    On  sound_output\n",
+       "3   103.4421            0    On  sound_output\n",
+       "4   103.9423            0    On  sound_output"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].actions[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].action_types[:],\n",
+    "    left_on=\"action_type\",\n",
+    "    right_on=\"id\",\n",
+    ").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ef8e491-f716-4f31-a690-0909fc109aa6",
+   "metadata": {},
+   "source": [
+    "The `StatesTable` is a column-based table to store the information about the states (e.g. the duration while nose is in center port). This table can be accessed as `nwbfile.acquisition[\"task_recording\"].states`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4f6be669-3486-4cd5-ae24-701bd225030e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>state_type</th>\n",
+       "      <th>state_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11.4261</td>\n",
+       "      <td>103.3331</td>\n",
+       "      <td>5</td>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>103.3331</td>\n",
+       "      <td>103.4420</td>\n",
+       "      <td>6</td>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>103.4420</td>\n",
+       "      <td>104.4420</td>\n",
+       "      <td>4</td>\n",
+       "      <td>punish_violation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>104.5276</td>\n",
+       "      <td>144.9311</td>\n",
+       "      <td>5</td>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>144.9311</td>\n",
+       "      <td>144.9435</td>\n",
+       "      <td>6</td>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   start_time  stop_time  state_type        state_name\n",
+       "0     11.4261   103.3331           5     wait_for_poke\n",
+       "1    103.3331   103.4420           6    nose_in_center\n",
+       "2    103.4420   104.4420           4  punish_violation\n",
+       "3    104.5276   144.9311           5     wait_for_poke\n",
+       "4    144.9311   144.9435           6    nose_in_center"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].states[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].state_types[:],\n",
+    "    left_on=\"state_type\",\n",
+    "    right_on=\"id\",\n",
+    ").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6560d9fe-8f12-425a-807d-ada473c64f52",
+   "metadata": {},
+   "source": [
+    "### Plot the events, actions, and states\n",
+    "\n",
+    "The ``plot_events``, ``plot_actions``, and ``plot_states`` functions can consume both the raw table as well as a subset of the table as a pandas DataFrame created through slicing, e.g., via ``events[:100]`` will plot only the first 100 rows from the events table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7ba4fc9b-55dd-4f12-9b0a-b6e2c2ae3882",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "from ndx_structured_behavior.plot import plot_events, plot_actions, plot_states, plot_trials\n",
+    "\n",
+    "# Get the events from file\n",
+    "events = nwbfile.get_acquisition(\"task_recording\").events\n",
+    "event_types = nwbfile.get_lab_meta_data(\"task\").event_types\n",
+    "\n",
+    "# Plot the data\n",
+    "fig = plot_events(\n",
+    "    events=events[20:100],\n",
+    "    event_types=event_types,\n",
+    "    show_event_values=True,\n",
+    "    figsize=(18,4),\n",
+    "    marker_size=500,\n",
+    ")\n",
+    "plt.title(\"Events\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6ac8adfe-a036-4297-a625-496edd23fc83",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the actions from file\n",
+    "actions = nwbfile.get_acquisition(\"task_recording\").actions\n",
+    "action_types = nwbfile.get_lab_meta_data(\"task\").action_types\n",
+    "\n",
+    "# Plot the data\n",
+    "fig = plot_actions(\n",
+    "    actions=actions[20:100],\n",
+    "    action_types=action_types,\n",
+    "    figsize=(18,4),\n",
+    "    marker_size=500,\n",
+    ")\n",
+    "plt.title(\"Actions\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "75738369-5aa3-4e88-afc2-9ac014827c8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the states from file\n",
+    "states = nwbfile.get_acquisition(\"task_recording\").states\n",
+    "state_types = nwbfile.get_lab_meta_data(\"task\").state_types\n",
+    "\n",
+    "# Plot the data\n",
+    "plot_states(states=states[20:50],\n",
+    "            state_types=state_types,\n",
+    "            marker_size=500)\n",
+    "plt.title(\"States\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e25976b1-298d-455a-9a0c-2fbf578c7408",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>states</th>\n",
+       "      <th>events</th>\n",
+       "      <th>actions</th>\n",
+       "      <th>high_ITI</th>\n",
+       "      <th>target_duration_for_nose_in_center</th>\n",
+       "      <th>target_reward_delay</th>\n",
+       "      <th>is_catch</th>\n",
+       "      <th>catch_percentage</th>\n",
+       "      <th>...</th>\n",
+       "      <th>training_stage</th>\n",
+       "      <th>block_type</th>\n",
+       "      <th>trials_in_stage</th>\n",
+       "      <th>reward_delay</th>\n",
+       "      <th>cumulative_reward_volume_ul</th>\n",
+       "      <th>center_port_cue</th>\n",
+       "      <th>is_warm_up</th>\n",
+       "      <th>changed</th>\n",
+       "      <th>previous_was_violation</th>\n",
+       "      <th>auto_change_catch_probability</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11.4261</td>\n",
+       "      <td>104.4420</td>\n",
+       "      <td>[0, 1]</td>\n",
+       "      <td>[0, 1, 2, 3]</td>\n",
+       "      <td>[0, 1, 2, 3, 4]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>42622</td>\n",
+       "      <td>0.037896</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>104.5276</td>\n",
+       "      <td>145.9435</td>\n",
+       "      <td>[3, 4]</td>\n",
+       "      <td>[5, 6, 7, 8, 9, 10, 11, 12]</td>\n",
+       "      <td>[5, 6, 7, 8, 9]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>42624</td>\n",
+       "      <td>1.852722</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>146.0112</td>\n",
+       "      <td>203.4972</td>\n",
+       "      <td>[6, 7, 8, 9, 10, 11]</td>\n",
+       "      <td>[14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 2...</td>\n",
+       "      <td>[10, 11, 12, 13, 14, 15, 16]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>42625</td>\n",
+       "      <td>1.822595</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>203.5646</td>\n",
+       "      <td>211.6551</td>\n",
+       "      <td>[13, 14]</td>\n",
+       "      <td>[71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 8...</td>\n",
+       "      <td>[17, 18, 19, 20, 21]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>42626</td>\n",
+       "      <td>2.707947</td>\n",
+       "      <td>20</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>211.7232</td>\n",
+       "      <td>215.2550</td>\n",
+       "      <td>[16, 17, 18, 19, 20, 21]</td>\n",
+       "      <td>[143, 144, 145, 146, 148, 149, 150, 151, 152, ...</td>\n",
+       "      <td>[22, 23, 24, 25, 26, 27, 28]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>42627</td>\n",
+       "      <td>2.286669</td>\n",
+       "      <td>20</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    start_time  stop_time                    states  \\\n",
+       "id                                                    \n",
+       "0      11.4261   104.4420                    [0, 1]   \n",
+       "1     104.5276   145.9435                    [3, 4]   \n",
+       "2     146.0112   203.4972      [6, 7, 8, 9, 10, 11]   \n",
+       "3     203.5646   211.6551                  [13, 14]   \n",
+       "4     211.7232   215.2550  [16, 17, 18, 19, 20, 21]   \n",
+       "\n",
+       "                                               events  \\\n",
+       "id                                                      \n",
+       "0                                        [0, 1, 2, 3]   \n",
+       "1                         [5, 6, 7, 8, 9, 10, 11, 12]   \n",
+       "2   [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 2...   \n",
+       "3   [71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 8...   \n",
+       "4   [143, 144, 145, 146, 148, 149, 150, 151, 152, ...   \n",
+       "\n",
+       "                         actions  high_ITI  \\\n",
+       "id                                           \n",
+       "0                [0, 1, 2, 3, 4]         0   \n",
+       "1                [5, 6, 7, 8, 9]         0   \n",
+       "2   [10, 11, 12, 13, 14, 15, 16]         0   \n",
+       "3           [17, 18, 19, 20, 21]         0   \n",
+       "4   [22, 23, 24, 25, 26, 27, 28]         0   \n",
+       "\n",
+       "    target_duration_for_nose_in_center  target_reward_delay  is_catch  \\\n",
+       "id                                                                      \n",
+       "0                                    1                  1.5     False   \n",
+       "1                                    1                  1.5     False   \n",
+       "2                                    1                  1.5     False   \n",
+       "3                                    1                  1.5     False   \n",
+       "4                                    1                  1.5     False   \n",
+       "\n",
+       "    catch_percentage  ...  training_stage  block_type  trials_in_stage  \\\n",
+       "id                    ...                                                \n",
+       "0               0.25  ...               9       Mixed            42622   \n",
+       "1               0.25  ...               9       Mixed            42624   \n",
+       "2               0.25  ...               9       Mixed            42625   \n",
+       "3               0.25  ...               9       Mixed            42626   \n",
+       "4               0.25  ...               9       Mixed            42627   \n",
+       "\n",
+       "    reward_delay  cumulative_reward_volume_ul  center_port_cue  is_warm_up  \\\n",
+       "id                                                                           \n",
+       "0       0.037896                            0            False       False   \n",
+       "1       1.852722                            0            False       False   \n",
+       "2       1.822595                            0            False       False   \n",
+       "3       2.707947                           20            False       False   \n",
+       "4       2.286669                           20            False       False   \n",
+       "\n",
+       "    changed  previous_was_violation auto_change_catch_probability  \n",
+       "id                                                                 \n",
+       "0     False                   False                         False  \n",
+       "1     False                    True                         False  \n",
+       "2     False                    True                         False  \n",
+       "3     False                   False                         False  \n",
+       "4     False                    True                         False  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trials = nwbfile.trials\n",
+    "\n",
+    "trials[:].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "81c0bc70-ab61-4864-8fbf-8bd4916fefa0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot trial 4 - 7\n",
+    "plot_trials(\n",
+    "    trials=trials[4:8],\n",
+    "    states=states, state_types=state_types,\n",
+    "    actions=actions, action_types=action_types,\n",
+    "    events=events, event_types=event_types,\n",
+    "    figsize=None,\n",
+    "    fontsize=18,\n",
+    "    rectangle_height=1,\n",
+    "    marker_size=500)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7f1638d-219f-4afe-af4c-576e1230e5f7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e053f49-c591-4bfd-b92d-ddc03bd38040",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/constantinople_lab_to_nwb/fiber_photometry/tutorials/fiber_photometry_run_session_to_nwb.ipynb b/src/constantinople_lab_to_nwb/fiber_photometry/tutorials/fiber_photometry_run_session_to_nwb.ipynb
new file mode 100644
index 0000000..fa599d0
--- /dev/null
+++ b/src/constantinople_lab_to_nwb/fiber_photometry/tutorials/fiber_photometry_run_session_to_nwb.ipynb
@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "90ae0edb-2fab-47dc-ad31-e79cf2905733",
+   "metadata": {},
+   "source": [
+    "# Fiber photometry data conversion to NWB\n",
+    "\n",
+    "This notebook demonstrates how to convert an example session from the Fiber photometry dataset to NWB.\n",
+    "\n",
+    "## Fiber photometry dataset\n",
+    "\n",
+    "This dataset contains fiber photometry recordings during decision-making behavioral task in rats. Deeplabcut software (v.2.2.3) was used for tracking the behavior ports (right port, central port, and left port) and 6 body parts (right ear, nose, left ear, mid-point along the right torso, mid-point along the left torso, and base of the tail). Video data were acquired using cameras attached to the ceiling of behavior rigs to capture the top-down view of the arena (Doric USB3 behavior camera, Sony IMX290, recorded with Doric Neuroscience Studio v6 software). The fluorescence from activity-dependent (GRAB-DA and GRAB-ACh) and activity-independent (isosbestic or mCherry) signals was acquired simultaneously via demodulation and downsampled on-the-fly by a factor of 25 to ~481.9 Hz. The recorded demodulated fluorescence was corrected for photobleaching and motion using Two-channel motion artifact correction (3) with mCherry or isosbestic signal as the activity-independent channel. The behavioral tasks were conducted in a high-throughput facility where rats were trained in increasingly complex protocols. Trials were initiated by a nose-poke in a lit center port and required maintaining a center fixation for 0.8 to 1.2 seconds, during which a tone indicated the possible reward size. A subsequent side LED indicated the potential reward location, followed by a delay period drawn from an exponential distribution (mean = 2.5 s). Rats could opt out at any time by poking the unlit port, restarting the trial. Catch trials, where the delay period only ended if the rat opted out, constituted 15-25% of the trials. Rats received penalties for premature fixation breaks. Additionally, the tasks introduced semi-observable hidden states by varying reward statistics across uncued blocks (high, low, and mixed), structured hierarchically, with blocks transitioning after 40 successfully completed trials.\n",
+    "\n",
+    "\n",
+    "This notebook demonstrates how to convert an example session to NWB.\n",
+    "\n",
+    "This dataset have the following data streams:\n",
+    "- Raw behavior: Bpod output (.mat), Video (.mp4)\n",
+    "- Processed behavior: DeepLabCut output (.h5)\n",
+    "- Fiber photometry: Doric (.doric or .csv)\n",
+    "\n",
+    "## Notes on the conversion\n",
+    "\n",
+    "The conversion notes is located in `src/constantinople_lab_to_nwb/fiber_photometry/fiber_photometry_notes.md`. This file contains information about the expected file structure and the conversion process.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9309ffc0-aedb-4c21-9d20-8442813c29e2",
+   "metadata": {},
+   "source": [
+    "## Convert a single session to NWB\n",
+    "\n",
+    "The `fiber_photometry_convert_session.py` script defines the `session_to_nwb` function that converts a session of fiber photometry data to NWB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca3883d4-c9df-4024-83fa-fb2c1082cc1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from constantinople_lab_to_nwb.fiber_photometry.fiber_photometry_convert_session import session_to_nwb\n",
+    "from neuroconv.utils import load_dict_from_file, dict_deep_update\n",
+    "\n",
+    "from constantinople_lab_to_nwb.fiber_photometry import FiberPhotometryNWBConverter\n",
+    "from ndx_pose import PoseEstimation\n",
+    "\n",
+    "from constantinople_lab_to_nwb.utils import get_subject_metadata_from_rat_info_folder\n",
+    "\n",
+    "# Parameters for conversion\n",
+    "\n",
+    "# The path to the raw fiber photometry file (.doric or .csv).\n",
+    "doric_file_path=\"/Volumes/T9/Constantinople/Preprocessed_data/J069/Raw/J069_ACh_20230809_HJJ_0002.doric\"\n",
+    "# The path to the NWB file to write.\n",
+    "nwbfile_path=\"/Volumes/T9/Constantinople/nwbfiles/J069_ACh_20230809_HJJ_0002.nwb\"\n",
+    "# The path to the DLC output (.h5), optional\n",
+    "dlc_file_path=\"/Volumes/T9/Constantinople/DeepLabCut/J069/J069-2023-08-09_rig104cam01_0002compDLC_resnet50_GRAB_DA_DMS_RIG104DoricCamera_J029May12shuffle1_500000.h5\"\n",
+    "# The path to the behavior camera recording (.mp4), optional\n",
+    "video_file_path=\"/Volumes/T9/Constantinople/Compressed Videos/J069/J069-2023-08-09_rig104cam01_0002comp.mp4\"\n",
+    "# The raw behavior data from Bpod (contains data for a single session)\n",
+    "bpod_file_path=\"/Volumes/T9/Constantinople/raw_Bpod/J069/DataFiles/J069_RWTautowait2_20230809_131216.mat\"\n",
+    "\n",
+    "# Load the default fiber photometry metadata from the yaml file\n",
+    "# For .doric files use \"doric_fiber_photometry_metadata.yaml\"\n",
+    "# For .csv files use \"doric_csv_fiber_photometry_metadata.yaml\"\n",
+    "current_dir = Path(__file__).parent if '__file__' in globals() else Path.cwd().parent\n",
+    "fiber_photometry_metadata_file_path = current_dir / \"metadata\" / \"doric_fiber_photometry_metadata.yaml\"\n",
+    "fiber_photometry_metadata = load_dict_from_file(fiber_photometry_metadata_file_path)\n",
+    "\n",
+    "# Get subject metadata from rat registry\n",
+    "rat_registry_folder_path = \"/Volumes/T9/Constantinople/Rat_info\"\n",
+    "subject_metadata = get_subject_metadata_from_rat_info_folder(\n",
+    "    folder_path=rat_registry_folder_path,\n",
+    "    subject_id=\"J069\",\n",
+    "    date=\"2023-08-09\",\n",
+    ")\n",
+    "\n",
+    "# Optional parameters\n",
+    "\n",
+    "# Whether to overwrite an existing NWB file.\n",
+    "overwrite = True\n",
+    "# Whether to print verbose output.\n",
+    "verbose = True\n",
+    "\n",
+    "session_to_nwb(\n",
+    "    nwbfile_path=nwbfile_path,\n",
+    "    raw_fiber_photometry_file_path=doric_file_path,\n",
+    "    fiber_photometry_metadata=fiber_photometry_metadata,\n",
+    "    raw_behavior_file_path=bpod_file_path,\n",
+    "    subject_metadata=subject_metadata,\n",
+    "    dlc_file_path=dlc_file_path,\n",
+    "    video_file_path=video_file_path,\n",
+    "    overwrite=overwrite,\n",
+    "    verbose=verbose,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "879cd669-a5a5-4111-9aef-ca13fb9ed2b8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_example_notebook.ipynb b/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_example_notebook.ipynb
new file mode 100644
index 0000000..0f5f56f
--- /dev/null
+++ b/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_example_notebook.ipynb
@@ -0,0 +1,2642 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "90ae0edb-2fab-47dc-ad31-e79cf2905733",
+   "metadata": {},
+   "source": [
+    "# Electrophysiology demo\n",
+    "\n",
+    "This tutorial demonstrates how to access an NWB file from the [DANDI:001264](https://dandiarchive.org/dandiset/001264/draft) dataset using `pynwb`.\n",
+    "\n",
+    "This dataset contains extracellular electrophysiology recordings from rats performing a value-based decision-making task. \n",
+    "\n",
+    "Neural data were acquired using Neuropixels probes (384 channels, 30 kHz sampling rate) with Neuropix-PXI hardware and OpenEphys, and preprocessed using Kilosort 2.5 with manual curation in Phy. \n",
+    "\n",
+    "Trials were initiated by a nose-poke in a lit center port and required maintaining a center fixation for 0.8 to 1.2 seconds, during which a tone indicated the possible reward size. A subsequent side LED indicated the potential reward location, followed by a delay period drawn from an exponential distribution (mean = 2.5 s). Rats could opt out at any time by poking the unlit port, restarting the trial. Catch trials, where the delay period only ended if the rat opted out, constituted 15-25% of the trials. Rats received penalties for premature fixation breaks. Additionally, the tasks introduced semi-observable hidden states by varying reward statistics across uncued blocks (high, low, and mixed), structured hierarchically, with blocks transitioning after 40 successfully completed trials."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f58b841c-573c-4b9f-92d3-8c6d0cad2e08",
+   "metadata": {},
+   "source": [
+    "# Reading an NWB file\n",
+    "\n",
+    "This section demonstrates how to read an NWB file using `pynwb`.\n",
+    "Based on the [NWB File Basics](https://pynwb.readthedocs.io/en/stable/tutorials/general/plot_file.html#sphx-glr-tutorials-general-plot-file-py) tutorial from [PyNWB](https://pynwb.readthedocs.io/en/stable/#).\n",
+    "\n",
+    "An [NWBFile](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.NWBFile) represents a single session of an experiment. Each NWBFile must have a `session description`, `identifier`, and `session start time`.\n",
+    "\n",
+    "Reading is carried out using the [NWBHDF5IO](https://pynwb.readthedocs.io/en/stable/pynwb.html#pynwb.NWBHDF5IO) class. To read the NWB file use the read mode \"r\" to retrieve an NWBFile object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "55f744a2-8657-4bcc-927e-61be5c1b84c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pynwb import NWBHDF5IO\n",
+    "import ndx_structured_behavior\n",
+    "\n",
+    "nwbfile_path = \"/Users/weian/data/001264/sub-J076/sub-J076_ses-RWTautowait2-20231212-145250_ecephys.nwb\"\n",
+    "io = NWBHDF5IO(nwbfile_path, load_namespaces=True)\n",
+    "nwbfile = io.read()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25a5bb56-7dd1-4d4e-b56c-a65543811c89",
+   "metadata": {},
+   "source": [
+    "Importantly, the session start time is the reference time for all timestamps in the file. For instance, an event with a timestamp of 0 in the file means the event occurred exactly at the session start time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6562ea81-123b-41d4-acc3-c25b8434a1ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.datetime(2023, 12, 12, 14, 53, 12, tzinfo=tzoffset(None, -18000))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.session_start_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fcae1c6-d866-4bd1-897d-563323d83e28",
+   "metadata": {},
+   "source": [
+    "This section demonstrates how to access the [Subject](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.Subject) field in an NWBFile.\n",
+    "\n",
+    "The [Subject](https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.Subject) field can be accessed as `nwbfile.subject`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4c98c8ab-dd37-448d-a28c-a7f8a61a76da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>subject (Subject)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".age__reference\">age__reference: </span><span class=\"field-value\">birth</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".description\">description: </span><span class=\"field-value\">Vendor: BRED</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".sex\">sex: </span><span class=\"field-value\">M</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".species\">species: </span><span class=\"field-value\">Rattus norvegicus</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject_id\">subject_id: </span><span class=\"field-value\">J076</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".weight\">weight: </span><span class=\"field-value\">0.495</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".date_of_birth\"><b>date_of_birth</b></summary><span class=\"field-key\">2023-01-10 00:00:00+01:00</span></details></div>"
+      ],
+      "text/plain": [
+       "subject pynwb.file.Subject at 0x4978951616\n",
+       "Fields:\n",
+       "  age__reference: birth\n",
+       "  date_of_birth: 2023-01-10 00:00:00+01:00\n",
+       "  description: Vendor: BRED\n",
+       "  sex: M\n",
+       "  species: Rattus norvegicus\n",
+       "  subject_id: J076\n",
+       "  weight: 0.495"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.subject"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12b5b6aa-9a44-4de3-a8e2-a50b5d538f6f",
+   "metadata": {},
+   "source": [
+    "# Access Recording\n",
+    "\n",
+    "This section demonstrates how to access the raw `ElectricalSeries` data.\n",
+    "\n",
+    "`NWB` organizes data into different groups depending on the type of data. Groups can be thought of as folders within the file. Here are some of the groups within an NWBFile and the types of data they are intended to store:\n",
+    "\n",
+    "- `acquisition`: raw, acquired data that should never change\n",
+    "- `processing`: processed data, typically the results of preprocessing algorithms and could change\n",
+    "\n",
+    "## Raw ElectricalSeries\n",
+    "\n",
+    "The raw ElectricalSeries data is stored in an [pynwb.ecephys.ElectricalSeries](https://pynwb.readthedocs.io/en/stable/pynwb.ecephys.html#pynwb.ecephys.ElectricalSeries) object which is added to `nwbfile.acquisition`. The data can be accessed as `nwbfile.acquisition[\"electrical_series\"]`.\n",
+    "\n",
+    "The data in `ElectricalSeries` is stored as a two dimensional array: the first dimension is time, the second dimension represents electrodes/channels.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1c694cd3-33e9-46b8-a87e-a34d4d4eaf64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "electrical_series = nwbfile.acquisition[\"electrical_series\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b83afa53-8f41-4dac-b812-607161e5ea13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1280x960 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "# Prepare data for plotting\n",
+    "data = electrical_series.data[:1000, :10]\n",
+    "timestamps = electrical_series.get_timestamps()[:1000]\n",
+    "df = pd.DataFrame(data)\n",
+    "df[\"Time (s)\"] = timestamps\n",
+    "df.set_index(\"Time (s)\", inplace=True)\n",
+    "df.columns.name = \"electrodes\"\n",
+    "channel_name_mapper = dict(zip(df.columns, electrical_series.electrodes[\"channel_name\"][:]))\n",
+    "df.rename(channel_name_mapper, axis=1, inplace=True)\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=len(df.columns), sharex=True, sharey=True, dpi=200)\n",
+    "lines = df.plot(subplots=True, ax=axes, legend=False, linewidth=0.8)\n",
+    "\n",
+    "# Hide y-axis labels\n",
+    "for ax in axes:\n",
+    "    ax.yaxis.set_visible(False)\n",
+    "\n",
+    "# Remove box around the plots\n",
+    "for ax in axes:\n",
+    "    ax.set_frame_on(False)\n",
+    "\n",
+    "# Get handles and labels for all lines\n",
+    "handles, labels = [], []\n",
+    "for line in lines:\n",
+    "    h, l = line.get_legend_handles_labels()\n",
+    "    handles.extend(h)\n",
+    "    labels.extend(l)\n",
+    "\n",
+    "# Create a single legend box\n",
+    "fig.legend(handles, labels, loc='upper right', bbox_to_anchor=(1.2, 0.8), frameon=False)\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4cdef31-3032-4abd-8c31-107653a6b54a",
+   "metadata": {},
+   "source": [
+    "The electrodes table describe the electrodes that generated this data. Extracellular electrodes are stored in an \"electrodes\" table, which is a [DynamicTable](https://hdmf.readthedocs.io/en/stable/hdmf.common.table.html#hdmf.common.table.DynamicTable) and can be can be converted to a pandas DataFrame for convenient analysis using `nwbfile.electrodes.to_dataframe()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "53e37829-e7ea-4ac6-a47a-cf6ac70f205b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>group</th>\n",
+       "      <th>group_name</th>\n",
+       "      <th>channel_name</th>\n",
+       "      <th>channel_depth_um</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   location                                              group  \\\n",
+       "id                                                               \n",
+       "0   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "1   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "2   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "3   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "4   unknown  ElectrodeGroup pynwb.ecephys.ElectrodeGroup at...   \n",
+       "\n",
+       "        group_name channel_name  channel_depth_um  \n",
+       "id                                                 \n",
+       "0   ElectrodeGroup          AP1               NaN  \n",
+       "1   ElectrodeGroup          AP2               NaN  \n",
+       "2   ElectrodeGroup          AP3               NaN  \n",
+       "3   ElectrodeGroup          AP4               NaN  \n",
+       "4   ElectrodeGroup          AP5               NaN  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.electrodes.to_dataframe().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9f114f1-5e02-4382-96ef-53a9a5189260",
+   "metadata": {},
+   "source": [
+    "## Filtered ElectricalSeries\n",
+    "\n",
+    "\n",
+    "The processed ecephys data is stored in \"processing/ecephys\" which can be accessed as `nwbfile.processing[\"ecephys\"]`.\n",
+    "Within this processing module we can access the container of filtered traces as `nwbfile.processing[\"ecephys\"][\"LFP\"]` which can hold multiple processed `ElectricalSeries` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "281be40d-1a73-4b97-ad78-cf6ddbb8e0e7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-04T12:41:53.499609Z",
+     "start_time": "2024-12-04T12:41:53.497410Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nwbfile.processing[\"ecephys\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7ab8c6d2-06ca-4c31-93b8-fb5556e07c60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>lfp_electrical_series (ElectricalSeries)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".starting_time\">starting_time: </span><span class=\"field-value\">1.1578276308196287</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".rate\">rate: </span><span class=\"field-value\">2500.0</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".description\">description: </span><span class=\"field-value\">The processed traces from the Neuropixels probe (384 channels, 1 kHz sampling rate) using Neuropix-PXI hardware and OpenEphys.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".conversion\">conversion: </span><span class=\"field-value\">1.949999928474426e-07</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".unit\">unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".data\"><b>data</b></summary></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".starting_time_unit\">starting_time_unit: </span><span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrodes.description\">description: </span><span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".electrodes.table\"><b>table</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrodes.table\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".electrodes.table\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>group</th>\n",
+       "      <th>group_name</th>\n",
+       "      <th>channel_name</th>\n",
+       "      <th>channel_depth_um</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5245904624\\nFields:\\n  description: The electrode group on the Neuropixels probe.\\n  device: DeviceEcephys pynwb.device.Device at 0x4966573376\\nFields:\\n  description: OpenEphys recording\\n\\n  location: LO\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5245904624\\nFields:\\n  description: The electrode group on the Neuropixels probe.\\n  device: DeviceEcephys pynwb.device.Device at 0x4966573376\\nFields:\\n  description: OpenEphys recording\\n\\n  location: LO\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5245904624\\nFields:\\n  description: The electrode group on the Neuropixels probe.\\n  device: DeviceEcephys pynwb.device.Device at 0x4966573376\\nFields:\\n  description: OpenEphys recording\\n\\n  location: LO\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5245904624\\nFields:\\n  description: The electrode group on the Neuropixels probe.\\n  device: DeviceEcephys pynwb.device.Device at 0x4966573376\\nFields:\\n  description: OpenEphys recording\\n\\n  location: LO\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>AP4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 764 more rows.</p></details></details></details></div>"
+      ],
+      "text/plain": [
+       "lfp_electrical_series pynwb.ecephys.ElectricalSeries at 0x4978372352\n",
+       "Fields:\n",
+       "  comments: no comments\n",
+       "  conversion: 1.949999928474426e-07\n",
+       "  data: <HDF5 dataset \"data\": shape (16639319, 384), type \"<i2\">\n",
+       "  description: The processed traces from the Neuropixels probe (384 channels, 1 kHz sampling rate) using Neuropix-PXI hardware and OpenEphys.\n",
+       "  electrodes: electrodes <class 'hdmf.common.table.DynamicTableRegion'>\n",
+       "  offset: 0.0\n",
+       "  rate: 2500.0\n",
+       "  resolution: -1.0\n",
+       "  starting_time: 1.1578276308196287\n",
+       "  starting_time_unit: seconds\n",
+       "  unit: volts"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "processed_ecephys = nwbfile.processing[\"ecephys\"][\"LFP\"]\n",
+    "\n",
+    "filtered_electrical_series = processed_ecephys[\"lfp_electrical_series\"]\n",
+    "filtered_electrical_series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "462de2e0-d78f-409f-9527-ed386778e0c1",
+   "metadata": {},
+   "source": [
+    "# Access Units\n",
+    "\n",
+    "Spike times are stored in the `Units` table, which is a DynamicTable and can be can be converted to a pandas DataFrame for convenient analysis using `nwbfile.units.to_dataframe()`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fecc7acc-9288-4c07-abd2-f9391a3f89e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>spike_times</th>\n",
+       "      <th>unit_name</th>\n",
+       "      <th>ContamPct</th>\n",
+       "      <th>original_cluster_id</th>\n",
+       "      <th>amp</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>Amplitude</th>\n",
+       "      <th>ch</th>\n",
+       "      <th>sh</th>\n",
+       "      <th>depth</th>\n",
+       "      <th>fr</th>\n",
+       "      <th>n_spikes</th>\n",
+       "      <th>KSLabel</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[21.66726096415296, 21.75022763081963, 22.0087...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>335.4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66.992470</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>2031.3</td>\n",
+       "      <td>74</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1600.0</td>\n",
+       "      <td>0.103813</td>\n",
+       "      <td>691</td>\n",
+       "      <td>mua</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[6.692660964152964, 12.883760964152962, 16.011...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>152.4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>75.382370</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>940.9</td>\n",
+       "      <td>105</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1900.0</td>\n",
+       "      <td>0.262612</td>\n",
+       "      <td>1748</td>\n",
+       "      <td>mua</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[21.750294297486292, 22.093794297486298, 22.43...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>495.3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>82.178314</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>5098.0</td>\n",
+       "      <td>105</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1900.0</td>\n",
+       "      <td>0.041765</td>\n",
+       "      <td>278</td>\n",
+       "      <td>mua</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[176.8220942974863, 238.27396096415296, 424.92...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>3</td>\n",
+       "      <td>59.803703</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>680.2</td>\n",
+       "      <td>125</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2100.0</td>\n",
+       "      <td>0.005108</td>\n",
+       "      <td>34</td>\n",
+       "      <td>mua</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[2.7437276308196274, 4.940194297486293, 5.3296...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>99.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>36.706387</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>476.2</td>\n",
+       "      <td>133</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2180.0</td>\n",
+       "      <td>2.682606</td>\n",
+       "      <td>17856</td>\n",
+       "      <td>mua</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          spike_times unit_name  ContamPct  \\\n",
+       "id                                                                           \n",
+       "0   [21.66726096415296, 21.75022763081963, 22.0087...         0      335.4   \n",
+       "1   [6.692660964152964, 12.883760964152962, 16.011...         1      152.4   \n",
+       "2   [21.750294297486292, 22.093794297486298, 22.43...         2      495.3   \n",
+       "3   [176.8220942974863, 238.27396096415296, 424.92...         3        inf   \n",
+       "4   [2.7437276308196274, 4.940194297486293, 5.3296...         4       99.4   \n",
+       "\n",
+       "    original_cluster_id        amp quality  Amplitude   ch  sh   depth  \\\n",
+       "id                                                                       \n",
+       "0                     0  66.992470     nan     2031.3   74   0  1600.0   \n",
+       "1                     1  75.382370     nan      940.9  105   0  1900.0   \n",
+       "2                     2  82.178314     nan     5098.0  105   0  1900.0   \n",
+       "3                     3  59.803703     nan      680.2  125   0  2100.0   \n",
+       "4                     4  36.706387     nan      476.2  133   0  2180.0   \n",
+       "\n",
+       "          fr  n_spikes KSLabel  \n",
+       "id                              \n",
+       "0   0.103813       691     mua  \n",
+       "1   0.262612      1748     mua  \n",
+       "2   0.041765       278     mua  \n",
+       "3   0.005108        34     mua  \n",
+       "4   2.682606     17856     mua  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.units[:].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1c15222-a974-4453-af2b-19bc93b414d3",
+   "metadata": {},
+   "source": [
+    "# Access raw behavior data\n",
+    "\n",
+    "This section demonstrates how to access the Bpod data in the NWBFile.\n",
+    "\n",
+    "## Accessing the task metadata\n",
+    "\n",
+    "The task-related general metadata is stored in a `Task` object which can be accessed as `nwbfile.lab_meta_data[\"task\"]`.\n",
+    "\n",
+    "The `EventTypesTable` is a column-based table to store the type of events that occur during the task (e.g. port poke from the animal), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].event_types`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e3dc8745-ee19-43ad-924b-7e4ac2d89f71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>event_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>left_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>right_port_poke</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          event_name\n",
+       "id                  \n",
+       "0        state_timer\n",
+       "1     left_port_poke\n",
+       "2   center_port_poke\n",
+       "3    right_port_poke"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].event_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2b6be4b-26b5-45e1-b498-8367209cf0dd",
+   "metadata": {},
+   "source": [
+    "The `ActionTypesTable` is a column-based table to store the type of actions that occur during the task (e.g. sound output from the acquisition system), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].action_types`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "be87f496-6e1c-41c2-836d-2834eea6734d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>action_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     action_name\n",
+       "id              \n",
+       "0   sound_output"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].action_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4e00955-134e-4ff6-9c7d-a2fbfba7a015",
+   "metadata": {},
+   "source": [
+    "The `StateTypesTable` is a column-based table to store the type of states that occur during the task (e.g. while the animal is waiting for reward), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].state_types`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "18e31ed3-7e60-4381-b0eb-9f83ecc273a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>go_cue</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>announce_reward</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>stop_sound</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>wait_for_side_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>opt_out</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>reward</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>punish_violation</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            state_name\n",
+       "id                    \n",
+       "0               go_cue\n",
+       "1       nose_in_center\n",
+       "2      announce_reward\n",
+       "3        wait_for_poke\n",
+       "4           stop_sound\n",
+       "5   wait_for_side_poke\n",
+       "6              opt_out\n",
+       "7               reward\n",
+       "8     punish_violation"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].state_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d599112f-80f4-44c5-9815-e2b81919ee26",
+   "metadata": {},
+   "source": [
+    "The arguments for the task is stored in a `TaskArgumentsTable` which can be accessed as `nwbfile.lab_meta_data[\"task\"].task_arguments`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0cb976d4-ba8e-4f4f-90c2-637f66999dc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>argument_name</th>\n",
+       "      <th>argument_description</th>\n",
+       "      <th>expression</th>\n",
+       "      <th>expression_type</th>\n",
+       "      <th>output_type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>reward_volume_ul</td>\n",
+       "      <td>The volume of reward in microliters.</td>\n",
+       "      <td>5</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nose_in_center</td>\n",
+       "      <td>The time in seconds when the animal is require...</td>\n",
+       "      <td>1.0953925215757918</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>time_increment_for_nose_in_center</td>\n",
+       "      <td>The time increment for nose in center in seconds.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>target_duration_for_nose_in_center</td>\n",
+       "      <td>The goal for how long the animal must poke cen...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>training_stage</td>\n",
+       "      <td>The stage of the training.</td>\n",
+       "      <td>9</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>reward_delay</td>\n",
+       "      <td>The delay in seconds to receive reward, drawn ...</td>\n",
+       "      <td>100</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>target_reward_delay</td>\n",
+       "      <td>The target delay in seconds to receive reward.</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>time_increment_for_reward_delay</td>\n",
+       "      <td>The time increment during monotonic increase o...</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>violation_time_out</td>\n",
+       "      <td>The time-out if nose is center is not satisfie...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>block_type</td>\n",
+       "      <td>The block type (High, Low or Test). High and L...</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>string</td>\n",
+       "      <td>string</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>num_trials_in_test_blocks</td>\n",
+       "      <td>The number of trials in each mixed blocks.</td>\n",
+       "      <td>40</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>num_trials_in_adaptation_blocks</td>\n",
+       "      <td>The number of trials in each high reward (20, ...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>punish_sound_enabled</td>\n",
+       "      <td>Whether to play a white noise pulse on error.</td>\n",
+       "      <td>True</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>catch_percentage</td>\n",
+       "      <td>The percentage of catch trials.</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>is_catch</td>\n",
+       "      <td>Whether the trial is a catch trial.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>current_trial</td>\n",
+       "      <td>The current trial number.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>cumulative_reward_volume_ul</td>\n",
+       "      <td>The cumulative volume received during session ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>is_warm_up</td>\n",
+       "      <td>Whether the trial is warm-up.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>override_nose_in_center</td>\n",
+       "      <td>Whether the required time for maintaining cent...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>trials_in_stage</td>\n",
+       "      <td>The cumulative number of trials in the stages.</td>\n",
+       "      <td>129054</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>min_reward_volume_ul</td>\n",
+       "      <td>The minimum volume of reward in microliters. (...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>auto_change_catch_probability</td>\n",
+       "      <td>Whether to change the probability automaticall...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>previous_was_violation</td>\n",
+       "      <td>Whether the previous trial was a violation.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>changed</td>\n",
+       "      <td>Whether a block transition occurred for the tr...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>num_trials_in_stage_3</td>\n",
+       "      <td>Determines how many trials occur in stage 3 be...</td>\n",
+       "      <td>400</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>num_trials_in_stage_8</td>\n",
+       "      <td>Determines how many trials occur in stage 8 be...</td>\n",
+       "      <td>250</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>center_port_cue</td>\n",
+       "      <td>Task parameter.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>cycle_blocks</td>\n",
+       "      <td>Task parameter.</td>\n",
+       "      <td>True</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         argument_name  \\\n",
+       "id                                       \n",
+       "0                     reward_volume_ul   \n",
+       "1                       nose_in_center   \n",
+       "2    time_increment_for_nose_in_center   \n",
+       "3   target_duration_for_nose_in_center   \n",
+       "4                       training_stage   \n",
+       "5                         reward_delay   \n",
+       "6                  target_reward_delay   \n",
+       "7      time_increment_for_reward_delay   \n",
+       "8                   violation_time_out   \n",
+       "9                           block_type   \n",
+       "10           num_trials_in_test_blocks   \n",
+       "11     num_trials_in_adaptation_blocks   \n",
+       "12                punish_sound_enabled   \n",
+       "13                    catch_percentage   \n",
+       "14                            is_catch   \n",
+       "15                       current_trial   \n",
+       "16         cumulative_reward_volume_ul   \n",
+       "17                          is_warm_up   \n",
+       "18             override_nose_in_center   \n",
+       "19                     trials_in_stage   \n",
+       "20                min_reward_volume_ul   \n",
+       "21       auto_change_catch_probability   \n",
+       "22              previous_was_violation   \n",
+       "23                             changed   \n",
+       "24               num_trials_in_stage_3   \n",
+       "25               num_trials_in_stage_8   \n",
+       "26                     center_port_cue   \n",
+       "27                        cycle_blocks   \n",
+       "\n",
+       "                                 argument_description          expression  \\\n",
+       "id                                                                          \n",
+       "0                The volume of reward in microliters.                   5   \n",
+       "1   The time in seconds when the animal is require...  1.0953925215757918   \n",
+       "2   The time increment for nose in center in seconds.                   0   \n",
+       "3   The goal for how long the animal must poke cen...                   1   \n",
+       "4                          The stage of the training.                   9   \n",
+       "5   The delay in seconds to receive reward, drawn ...                 100   \n",
+       "6      The target delay in seconds to receive reward.                 1.5   \n",
+       "7   The time increment during monotonic increase o...               0.025   \n",
+       "8   The time-out if nose is center is not satisfie...                   1   \n",
+       "9   The block type (High, Low or Test). High and L...               Mixed   \n",
+       "10         The number of trials in each mixed blocks.                  40   \n",
+       "11  The number of trials in each high reward (20, ...                  40   \n",
+       "12      Whether to play a white noise pulse on error.                True   \n",
+       "13                    The percentage of catch trials.                0.25   \n",
+       "14                Whether the trial is a catch trial.               False   \n",
+       "15                          The current trial number.                   0   \n",
+       "16  The cumulative volume received during session ...                   0   \n",
+       "17                      Whether the trial is warm-up.               False   \n",
+       "18  Whether the required time for maintaining cent...               False   \n",
+       "19     The cumulative number of trials in the stages.              129054   \n",
+       "20  The minimum volume of reward in microliters. (...                   5   \n",
+       "21  Whether to change the probability automaticall...               False   \n",
+       "22        Whether the previous trial was a violation.               False   \n",
+       "23  Whether a block transition occurred for the tr...               False   \n",
+       "24  Determines how many trials occur in stage 3 be...                 400   \n",
+       "25  Determines how many trials occur in stage 8 be...                 250   \n",
+       "26                                    Task parameter.               False   \n",
+       "27                                    Task parameter.                True   \n",
+       "\n",
+       "   expression_type output_type  \n",
+       "id                              \n",
+       "0          integer     numeric  \n",
+       "1           double     numeric  \n",
+       "2           double     numeric  \n",
+       "3           double     numeric  \n",
+       "4          integer     numeric  \n",
+       "5           double     numeric  \n",
+       "6           double     numeric  \n",
+       "7           double     numeric  \n",
+       "8           double     numeric  \n",
+       "9           string      string  \n",
+       "10         integer     numeric  \n",
+       "11         integer     numeric  \n",
+       "12         boolean     boolean  \n",
+       "13          double     numeric  \n",
+       "14         boolean     boolean  \n",
+       "15         integer     numeric  \n",
+       "16          double     numeric  \n",
+       "17         boolean     boolean  \n",
+       "18         boolean     boolean  \n",
+       "19         integer     numeric  \n",
+       "20          double     numeric  \n",
+       "21         boolean     boolean  \n",
+       "22         boolean     boolean  \n",
+       "23         boolean     boolean  \n",
+       "24         integer     numeric  \n",
+       "25         integer     numeric  \n",
+       "26         boolean     boolean  \n",
+       "27         boolean     boolean  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].task_arguments[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d052694b-7783-4929-8ae2-f948810d61ba",
+   "metadata": {},
+   "source": [
+    "The `TaskRecording` object stores the data for events, states, and actions that occured during the task. The `TaskRecording` is added as acquisition which can be accessed as `nwbfile.acquisition[\"task_recording\"]`.\n",
+    "\n",
+    "The `EventsTable` is a column-based table to store the information about the events (e.g. poke times), one event per row. This table can be accessed as `nwbfile.acquisition[\"task_recording\"].events`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7a007082-90eb-4949-9253-7a050965c030",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>value</th>\n",
+       "      <th>event_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>38.5442</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>38.6237</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>39.6237</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>39.8822</td>\n",
+       "      <td>1</td>\n",
+       "      <td>In</td>\n",
+       "      <td>left_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.0315</td>\n",
+       "      <td>1</td>\n",
+       "      <td>In</td>\n",
+       "      <td>left_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8682</th>\n",
+       "      <td>6313.5514</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8683</th>\n",
+       "      <td>6314.3740</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8684</th>\n",
+       "      <td>6315.0374</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8685</th>\n",
+       "      <td>6315.4097</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8686</th>\n",
+       "      <td>6316.4097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8687 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      timestamp  event_type    value        event_name\n",
+       "0       38.5442           2       In  center_port_poke\n",
+       "1       38.6237           2      Out  center_port_poke\n",
+       "2       39.6237           0  Expired       state_timer\n",
+       "3       39.8822           1       In    left_port_poke\n",
+       "4       40.0315           1       In    left_port_poke\n",
+       "...         ...         ...      ...               ...\n",
+       "8682  6313.5514           2      Out  center_port_poke\n",
+       "8683  6314.3740           0  Expired       state_timer\n",
+       "8684  6315.0374           2       In  center_port_poke\n",
+       "8685  6315.4097           2      Out  center_port_poke\n",
+       "8686  6316.4097           0  Expired       state_timer\n",
+       "\n",
+       "[8687 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].events[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].event_types[:],\n",
+    "    left_on=\"event_type\",\n",
+    "    right_on=\"id\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcef31e2-52b9-4772-8ca5-c5ffcf6545b5",
+   "metadata": {},
+   "source": [
+    "The `ActionsTable` is a column-based table to store the information about the actions (e.g. sound onset times), one action per row. This table can be accessed as `nwbfile.acquisition[\"task_recording\"].actions`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "405d4add-b42d-49c8-93a5-1d47f7f7a2b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>action_type</th>\n",
+       "      <th>value</th>\n",
+       "      <th>action_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19.9881</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.1782</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38.5443</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.6238</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>39.1240</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestamp  action_type value   action_name\n",
+       "0    19.9881            0    On  sound_output\n",
+       "1    20.1782            0    On  sound_output\n",
+       "2    38.5443            0    On  sound_output\n",
+       "3    38.6238            0    On  sound_output\n",
+       "4    39.1240            0    On  sound_output"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].actions[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].action_types[:],\n",
+    "    left_on=\"action_type\",\n",
+    "    right_on=\"id\",\n",
+    ").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04ecb784-f7ba-4de6-8da5-2e824bb7e28e",
+   "metadata": {},
+   "source": [
+    "The `StatesTable` is a column-based table to store the information about the states (e.g. the duration while nose is in center port). This table can be accessed as `nwbfile.acquisition[\"task_recording\"].states`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cf25d54a-9e06-4127-af67-429c2ae6c48b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>state_type</th>\n",
+       "      <th>state_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19.9880</td>\n",
+       "      <td>38.5442</td>\n",
+       "      <td>3</td>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>38.5442</td>\n",
+       "      <td>38.6237</td>\n",
+       "      <td>1</td>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38.6237</td>\n",
+       "      <td>39.6237</td>\n",
+       "      <td>8</td>\n",
+       "      <td>punish_violation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>39.7154</td>\n",
+       "      <td>42.2451</td>\n",
+       "      <td>3</td>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>42.2451</td>\n",
+       "      <td>42.5430</td>\n",
+       "      <td>1</td>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   start_time  stop_time  state_type        state_name\n",
+       "0     19.9880    38.5442           3     wait_for_poke\n",
+       "1     38.5442    38.6237           1    nose_in_center\n",
+       "2     38.6237    39.6237           8  punish_violation\n",
+       "3     39.7154    42.2451           3     wait_for_poke\n",
+       "4     42.2451    42.5430           1    nose_in_center"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].states[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].state_types[:],\n",
+    "    left_on=\"state_type\",\n",
+    "    right_on=\"id\",\n",
+    ").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "536cc673-a323-4fde-80a4-dbd52c1573bd",
+   "metadata": {},
+   "source": [
+    "### Plot the events, actions, and states\n",
+    "\n",
+    "The ``plot_events``, ``plot_actions``, and ``plot_states`` functions can consume both the raw table as well as a subset of the table as a pandas DataFrame created through slicing, e.g., via ``events[:100]`` will plot only the first 100 rows from the events table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5e0b1ea3-bf38-405e-935d-6159a8de81e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "from ndx_structured_behavior.plot import plot_events, plot_actions, plot_states, plot_trials\n",
+    "\n",
+    "# Get the events from file\n",
+    "events = nwbfile.get_acquisition(\"task_recording\").events\n",
+    "event_types = nwbfile.get_lab_meta_data(\"task\").event_types\n",
+    "\n",
+    "# Plot the data\n",
+    "fig = plot_events(\n",
+    "    events=events[20:100],\n",
+    "    event_types=event_types,\n",
+    "    show_event_values=True,\n",
+    "    figsize=(18,4),\n",
+    "    marker_size=500,\n",
+    ")\n",
+    "plt.title(\"Events\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3133c5a4-163b-4f1c-b3f1-0596c98f1e55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the actions from file\n",
+    "actions = nwbfile.get_acquisition(\"task_recording\").actions\n",
+    "action_types = nwbfile.get_lab_meta_data(\"task\").action_types\n",
+    "\n",
+    "# Plot the data\n",
+    "fig = plot_actions(\n",
+    "    actions=actions[20:100],\n",
+    "    action_types=action_types,\n",
+    "    figsize=(18,4),\n",
+    "    marker_size=500,\n",
+    ")\n",
+    "plt.title(\"Actions\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5f90f877-5ecb-40fb-9c8d-50ee870f7198",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the states from file\n",
+    "states = nwbfile.get_acquisition(\"task_recording\").states\n",
+    "state_types = nwbfile.get_lab_meta_data(\"task\").state_types\n",
+    "\n",
+    "# Plot the data\n",
+    "plot_states(states=states[20:100],\n",
+    "            state_types=state_types,\n",
+    "            marker_size=500)\n",
+    "plt.title(\"States\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a761eef8-db30-445e-8f43-7ff4149e4f52",
+   "metadata": {},
+   "source": [
+    "## Accessing the trials\n",
+    "\n",
+    "The `TrialsTable` is a column-based table to store information about trials, one trial per row.\n",
+    "The table can be accessed from the file as `nwbfile.trials`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "547c05e1-e5ba-4144-a95d-c54b5ef49899",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>states</th>\n",
+       "      <th>events</th>\n",
+       "      <th>actions</th>\n",
+       "      <th>nose_in_center</th>\n",
+       "      <th>catch_percentage</th>\n",
+       "      <th>violation_time_out</th>\n",
+       "      <th>changed</th>\n",
+       "      <th>trials_in_stage</th>\n",
+       "      <th>...</th>\n",
+       "      <th>is_warm_up</th>\n",
+       "      <th>block_type</th>\n",
+       "      <th>override_nose_in_center</th>\n",
+       "      <th>cumulative_reward_volume_ul</th>\n",
+       "      <th>target_reward_delay</th>\n",
+       "      <th>reward_delay</th>\n",
+       "      <th>target_duration_for_nose_in_center</th>\n",
+       "      <th>reward_volume_ul</th>\n",
+       "      <th>punish_sound_enabled</th>\n",
+       "      <th>num_trials_in_test_blocks</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19.9880</td>\n",
+       "      <td>39.6237</td>\n",
+       "      <td>[0, 1]</td>\n",
+       "      <td>[0, 1]</td>\n",
+       "      <td>[0, 1, 2, 3, 4]</td>\n",
+       "      <td>1.095400</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>129054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.847489</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>True</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39.7154</td>\n",
+       "      <td>43.5430</td>\n",
+       "      <td>[3, 4]</td>\n",
+       "      <td>[3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ...</td>\n",
+       "      <td>[5, 6, 7, 8, 9]</td>\n",
+       "      <td>1.188173</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>129056</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.847489</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>True</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>43.6313</td>\n",
+       "      <td>46.8007</td>\n",
+       "      <td>[6, 7]</td>\n",
+       "      <td>[26, 27, 28, 29, 30, 31, 32, 33]</td>\n",
+       "      <td>[10, 11, 12, 13, 14]</td>\n",
+       "      <td>1.188173</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>129057</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>8.223894</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>True</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>46.8732</td>\n",
+       "      <td>59.3249</td>\n",
+       "      <td>[9, 10]</td>\n",
+       "      <td>[35, 36, 37, 38]</td>\n",
+       "      <td>[15, 16, 17, 18, 19]</td>\n",
+       "      <td>1.109749</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>129058</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>True</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>59.4011</td>\n",
+       "      <td>77.6604</td>\n",
+       "      <td>[12, 13, 14, 15, 16]</td>\n",
+       "      <td>[40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 5...</td>\n",
+       "      <td>[20, 21, 22, 23, 24, 25, 26, 27]</td>\n",
+       "      <td>1.018144</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>129059</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>True</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    start_time  stop_time                states  \\\n",
+       "id                                                \n",
+       "0      19.9880    39.6237                [0, 1]   \n",
+       "1      39.7154    43.5430                [3, 4]   \n",
+       "2      43.6313    46.8007                [6, 7]   \n",
+       "3      46.8732    59.3249               [9, 10]   \n",
+       "4      59.4011    77.6604  [12, 13, 14, 15, 16]   \n",
+       "\n",
+       "                                               events  \\\n",
+       "id                                                      \n",
+       "0                                              [0, 1]   \n",
+       "1   [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ...   \n",
+       "2                    [26, 27, 28, 29, 30, 31, 32, 33]   \n",
+       "3                                    [35, 36, 37, 38]   \n",
+       "4   [40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 5...   \n",
+       "\n",
+       "                             actions  nose_in_center  catch_percentage  \\\n",
+       "id                                                                       \n",
+       "0                    [0, 1, 2, 3, 4]        1.095400              0.25   \n",
+       "1                    [5, 6, 7, 8, 9]        1.188173              0.25   \n",
+       "2               [10, 11, 12, 13, 14]        1.188173              0.25   \n",
+       "3               [15, 16, 17, 18, 19]        1.109749              0.25   \n",
+       "4   [20, 21, 22, 23, 24, 25, 26, 27]        1.018144              0.25   \n",
+       "\n",
+       "    violation_time_out  changed  trials_in_stage  ...  is_warm_up  block_type  \\\n",
+       "id                                                ...                           \n",
+       "0                    1    False           129054  ...       False       Mixed   \n",
+       "1                    1    False           129056  ...       False       Mixed   \n",
+       "2                    1    False           129057  ...       False       Mixed   \n",
+       "3                    1    False           129058  ...       False       Mixed   \n",
+       "4                    1    False           129059  ...       False       Mixed   \n",
+       "\n",
+       "    override_nose_in_center  cumulative_reward_volume_ul  target_reward_delay  \\\n",
+       "id                                                                              \n",
+       "0                     False                            0                  1.5   \n",
+       "1                     False                            0                  1.5   \n",
+       "2                     False                            0                  1.5   \n",
+       "3                     False                            0                  1.5   \n",
+       "4                     False                            0                  1.5   \n",
+       "\n",
+       "    reward_delay  target_duration_for_nose_in_center  reward_volume_ul  \\\n",
+       "id                                                                       \n",
+       "0       0.847489                                   1                20   \n",
+       "1       0.847489                                   1                20   \n",
+       "2       8.223894                                   1                20   \n",
+       "3     100.000000                                   1                20   \n",
+       "4     100.000000                                   1                20   \n",
+       "\n",
+       "    punish_sound_enabled  num_trials_in_test_blocks  \n",
+       "id                                                   \n",
+       "0                   True                         40  \n",
+       "1                   True                         40  \n",
+       "2                   True                         40  \n",
+       "3                   True                         40  \n",
+       "4                   True                         40  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trials = nwbfile.trials\n",
+    "\n",
+    "trials[:].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f55fdd71-2069-4468-a605-788e250095c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot trial 7 - 9\n",
+    "plot_trials(\n",
+    "    trials=trials[7:10],\n",
+    "    states=states, state_types=state_types,\n",
+    "    actions=actions, action_types=action_types,\n",
+    "    events=events, event_types=event_types,\n",
+    "    figsize=None,\n",
+    "    fontsize=18,\n",
+    "    rectangle_height=1,\n",
+    "    marker_size=500)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8e5a9f8-d927-45fb-acc4-7017ddc2f874",
+   "metadata": {},
+   "source": [
+    "# Access processed behavior data\n",
+    "\n",
+    "This section demonstrates how to access the processed behavior data in the NWBFile.\n",
+    "The table can be accessed from the file as `nwbfile.intervals[\"processed_trials\"]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b13df93c-8de1-49d6-a40e-50967ed875a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>center_port_onset_time</th>\n",
+       "      <th>center_port_offset_time</th>\n",
+       "      <th>rewarded_port_onset_time</th>\n",
+       "      <th>rewarded_port_offset_time</th>\n",
+       "      <th>rewarded_port_flash_onset_time</th>\n",
+       "      <th>rewarded_port_flash_offset_time</th>\n",
+       "      <th>opt_out_port_onset_time</th>\n",
+       "      <th>opt_out_port_offset_time</th>\n",
+       "      <th>...</th>\n",
+       "      <th>reward_volume_ul</th>\n",
+       "      <th>wait_for_center_poke</th>\n",
+       "      <th>is_rewarded</th>\n",
+       "      <th>is_violation</th>\n",
+       "      <th>is_opt_out</th>\n",
+       "      <th>wait_time</th>\n",
+       "      <th>rewarded_port</th>\n",
+       "      <th>reaction_time</th>\n",
+       "      <th>short_latency_reaction_time</th>\n",
+       "      <th>inter_trial_interval</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19.9880</td>\n",
+       "      <td>39.6237</td>\n",
+       "      <td>19.988000</td>\n",
+       "      <td>38.544471</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20</td>\n",
+       "      <td>18.5562</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18.5562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39.7154</td>\n",
+       "      <td>43.5430</td>\n",
+       "      <td>39.715700</td>\n",
+       "      <td>42.245437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2.5297</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.5297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>43.6313</td>\n",
+       "      <td>46.8007</td>\n",
+       "      <td>43.631651</td>\n",
+       "      <td>45.152297</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1.5206</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.5206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>46.8732</td>\n",
+       "      <td>59.3249</td>\n",
+       "      <td>46.873609</td>\n",
+       "      <td>58.205011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20</td>\n",
+       "      <td>11.3312</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.3312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>59.4011</td>\n",
+       "      <td>77.6604</td>\n",
+       "      <td>59.401748</td>\n",
+       "      <td>60.846570</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>61.866683</td>\n",
+       "      <td>77.461225</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1.4448</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>15.5943</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.8094</td>\n",
+       "      <td>1.4448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>6237.8301</td>\n",
+       "      <td>6265.6915</td>\n",
+       "      <td>6237.931018</td>\n",
+       "      <td>6264.549141</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.6177</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>26.6177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>6266.2179</td>\n",
+       "      <td>6268.1972</td>\n",
+       "      <td>6266.319266</td>\n",
+       "      <td>6266.787763</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.4685</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.4685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>6268.7188</td>\n",
+       "      <td>6311.9109</td>\n",
+       "      <td>6268.820197</td>\n",
+       "      <td>6310.892692</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>42.0718</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>42.0718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>6312.4316</td>\n",
+       "      <td>6314.3740</td>\n",
+       "      <td>6312.533718</td>\n",
+       "      <td>6313.397255</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.8635</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.8635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>6314.8954</td>\n",
+       "      <td>6316.4097</td>\n",
+       "      <td>6314.997554</td>\n",
+       "      <td>6315.139579</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.1420</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.1420</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>484 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     start_time  stop_time  center_port_onset_time  center_port_offset_time  \\\n",
+       "id                                                                            \n",
+       "0       19.9880    39.6237               19.988000                38.544471   \n",
+       "1       39.7154    43.5430               39.715700                42.245437   \n",
+       "2       43.6313    46.8007               43.631651                45.152297   \n",
+       "3       46.8732    59.3249               46.873609                58.205011   \n",
+       "4       59.4011    77.6604               59.401748                60.846570   \n",
+       "..          ...        ...                     ...                      ...   \n",
+       "479   6237.8301  6265.6915             6237.931018              6264.549141   \n",
+       "480   6266.2179  6268.1972             6266.319266              6266.787763   \n",
+       "481   6268.7188  6311.9109             6268.820197              6310.892692   \n",
+       "482   6312.4316  6314.3740             6312.533718              6313.397255   \n",
+       "483   6314.8954  6316.4097             6314.997554              6315.139579   \n",
+       "\n",
+       "     rewarded_port_onset_time  rewarded_port_offset_time  \\\n",
+       "id                                                         \n",
+       "0                         NaN                        NaN   \n",
+       "1                         NaN                        NaN   \n",
+       "2                         NaN                        NaN   \n",
+       "3                         NaN                        NaN   \n",
+       "4                         NaN                        NaN   \n",
+       "..                        ...                        ...   \n",
+       "479                       NaN                        NaN   \n",
+       "480                       NaN                        NaN   \n",
+       "481                       NaN                        NaN   \n",
+       "482                       NaN                        NaN   \n",
+       "483                       NaN                        NaN   \n",
+       "\n",
+       "     rewarded_port_flash_onset_time  rewarded_port_flash_offset_time  \\\n",
+       "id                                                                     \n",
+       "0                               NaN                              NaN   \n",
+       "1                               NaN                              NaN   \n",
+       "2                               NaN                              NaN   \n",
+       "3                               NaN                              NaN   \n",
+       "4                               NaN                              NaN   \n",
+       "..                              ...                              ...   \n",
+       "479                             NaN                              NaN   \n",
+       "480                             NaN                              NaN   \n",
+       "481                             NaN                              NaN   \n",
+       "482                             NaN                              NaN   \n",
+       "483                             NaN                              NaN   \n",
+       "\n",
+       "     opt_out_port_onset_time  opt_out_port_offset_time  ...  reward_volume_ul  \\\n",
+       "id                                                      ...                     \n",
+       "0                        NaN                       NaN  ...                20   \n",
+       "1                        NaN                       NaN  ...                20   \n",
+       "2                        NaN                       NaN  ...                20   \n",
+       "3                        NaN                       NaN  ...                20   \n",
+       "4                  61.866683                 77.461225  ...                20   \n",
+       "..                       ...                       ...  ...               ...   \n",
+       "479                      NaN                       NaN  ...                40   \n",
+       "480                      NaN                       NaN  ...                40   \n",
+       "481                      NaN                       NaN  ...                40   \n",
+       "482                      NaN                       NaN  ...                40   \n",
+       "483                      NaN                       NaN  ...                40   \n",
+       "\n",
+       "     wait_for_center_poke  is_rewarded  is_violation is_opt_out  wait_time  \\\n",
+       "id                                                                           \n",
+       "0                 18.5562        False          True      False        NaN   \n",
+       "1                  2.5297        False          True      False        NaN   \n",
+       "2                  1.5206        False          True      False        NaN   \n",
+       "3                 11.3312        False          True      False        NaN   \n",
+       "4                  1.4448        False         False       True    15.5943   \n",
+       "..                    ...          ...           ...        ...        ...   \n",
+       "479               26.6177        False          True      False        NaN   \n",
+       "480                0.4685        False          True      False        NaN   \n",
+       "481               42.0718        False          True      False        NaN   \n",
+       "482                0.8635        False          True      False        NaN   \n",
+       "483                0.1420        False          True      False        NaN   \n",
+       "\n",
+       "     rewarded_port  reaction_time  short_latency_reaction_time  \\\n",
+       "id                                                               \n",
+       "0            Right            NaN                          NaN   \n",
+       "1             Left            NaN                          NaN   \n",
+       "2             Left            NaN                          NaN   \n",
+       "3             Left            NaN                          NaN   \n",
+       "4             Left            NaN                       0.8094   \n",
+       "..             ...            ...                          ...   \n",
+       "479           Left            NaN                          NaN   \n",
+       "480          Right            NaN                          NaN   \n",
+       "481          Right            NaN                          NaN   \n",
+       "482          Right            NaN                          NaN   \n",
+       "483          Right            NaN                          NaN   \n",
+       "\n",
+       "     inter_trial_interval  \n",
+       "id                         \n",
+       "0                 18.5562  \n",
+       "1                  2.5297  \n",
+       "2                  1.5206  \n",
+       "3                 11.3312  \n",
+       "4                  1.4448  \n",
+       "..                    ...  \n",
+       "479               26.6177  \n",
+       "480                0.4685  \n",
+       "481               42.0718  \n",
+       "482                0.8635  \n",
+       "483                0.1420  \n",
+       "\n",
+       "[484 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.intervals[\"processed_trials\"][:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d515db92-9631-4bb5-aeb2-5855a83fedab",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_run_session_to_nwb.ipynb b/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_run_session_to_nwb.ipynb
new file mode 100644
index 0000000..d67b35a
--- /dev/null
+++ b/src/constantinople_lab_to_nwb/schierek_embargo_2024/tutorials/ephys_run_session_to_nwb.ipynb
@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "352a2fb2-54fe-4418-ae34-eda86223c180",
+   "metadata": {},
+   "source": [
+    "# Neuropixels data conversion to NWB\n",
+    "\n",
+    "This notebook demonstrates how to convert an example session from the Neuropixels dataset to NWB.\n",
+    "\n",
+    "## Neuropixels dataset\n",
+    "\n",
+    "This dataset contains in vivo extracellular electrophysiology recordings from rats performing a value-based decision-making task. Neural data were acquired using Neuropixels probes (384 channels, 30 kHz sampling rate) with Neuropix-PXI hardware and OpenEphys, and preprocessed using Kilosort 2.5 with manual curation in Phy. Trials were initiated by a nose-poke in a lit center port and required maintaining a center fixation for 0.8 to 1.2 seconds, during which a tone indicated the possible reward size. A subsequent side LED indicated the potential reward location, followed by a delay period drawn from an exponential distribution (mean = 2.5 s). Rats could opt out at any time by poking the unlit port, restarting the trial. Catch trials, where the delay period only ended if the rat opted out, constituted 15-25% of the trials. Rats received penalties for premature fixation breaks. Additionally, the tasks introduced semi-observable hidden states by varying reward statistics across uncued blocks (high, low, and mixed), structured hierarchically, with blocks transitioning after 40 successfully completed trials.\n",
+    "This notebook demonstrates how to convert an example session to NWB.\n",
+    "\n",
+    "This dataset have the following data streams:\n",
+    "- Behavior: Bpod output (.mat)\n",
+    "- Recording AP, LFP: OpenEphys (binary format)\n",
+    "- Units: Phy output\n",
+    "\n",
+    "## Notes on the conversion\n",
+    "\n",
+    "The conversion notes is located in `src/constantinople_lab_to_nwb/schierek_embargo_2024/schierek_embargo_2024_notes.md`. This file contains information about the expected file structure and the conversion process.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b221cfff-c7e5-44e7-bd3a-024bf2f26f67",
+   "metadata": {},
+   "source": [
+    "## Convert a single session to NWB\n",
+    "\n",
+    "The `schierek_embargo_2024_convert_session.py` script defines the `session_to_nwb` function that converts a session of Neuropixels data to NWB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "695d7f2c-5b40-4e0d-b4b7-38546a4dce52",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-04T12:39:08.133836Z",
+     "start_time": "2024-12-04T12:39:08.128903Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from constantinople_lab_to_nwb.schierek_embargo_2024.schierek_embargo_2024_convert_session import session_to_nwb\n",
+    "from constantinople_lab_to_nwb.utils import get_subject_metadata_from_rat_info_folder\n",
+    "\n",
+    "# Parameters for conversion\n",
+    "\n",
+    "# The path to the NWB file to write.\n",
+    "nwbfile_path = \"/Volumes/T9/Constantinople/nwbfiles/sub-J076_ephys.nwb\"\n",
+    "# The OpenEphys recording folder path (make sure to include the 'Record Node #' in the folder path)\n",
+    "folder_path = \"/Volumes/T9/Constantinople/Ephys Data/J076_2023-12-12_14-52-04/Record Node 117\"\n",
+    "# The name of the *raw* recording stream (e.g. )\n",
+    "raw_stream_name = \"Neuropix-PXI-119.ProbeA-AP\"\n",
+    "# The name of the *LFP* recording stream (e.g. )\n",
+    "lfp_stream_name = \"Neuropix-PXI-119.ProbeA-LFP\"\n",
+    "# The path to the processed spike sorting file (.mat). This file contains the \"SU\" named struct that contains the processed spike data.\n",
+    "# This file also contains the \"S\" named struct containing the processed behavior data.\n",
+    "spike_sorting_mat_file_path = \"/Volumes/T9/Constantinople/Ephys Data/J076_2023-12-12.mat\"\n",
+    "# The path to the raw Bpod data\n",
+    "bpod_file_path = \"/Volumes/T9/Constantinople/raw_Bpod/J076/DataFiles/J076_RWTautowait2_20231212_145250.mat\"\n",
+    "\n",
+    "# Task specific parameters\n",
+    "\n",
+    "# The column name mapping is used to rename the columns in the processed data to more descriptive column names.\n",
+    "# New (unseen) task parameters should be added here.\n",
+    "column_name_mapping = dict(\n",
+    "    NoseInCenter=\"nose_in_center\",\n",
+    "    TrainingStage=\"training_stage\",\n",
+    "    Block=\"block_type\",\n",
+    "    BlockLengthAd=\"num_trials_in_adaptation_blocks\",\n",
+    "    BlockLengthTest=\"num_trials_in_test_blocks\",\n",
+    "    ProbCatch=\"catch_percentage\",\n",
+    "    RewardDelay=\"reward_delay\",\n",
+    "    RewardAmount=\"reward_volume_ul\",\n",
+    "    WaitForPoke=\"wait_for_center_poke\",\n",
+    "    hits=\"is_rewarded\",\n",
+    "    vios=\"is_violation\",\n",
+    "    optout=\"is_opt_out\",\n",
+    "    wait_time=\"wait_time\",\n",
+    "    wait_thresh=\"wait_time_threshold\",\n",
+    "    wait_for_cpoke=\"wait_for_center_poke\",\n",
+    "    zwait_for_cpoke=\"z_scored_wait_for_center_poke\",\n",
+    "    RewardedSide=\"rewarded_port\",\n",
+    "    Cled=\"center_poke_times\",\n",
+    "    Lled=\"left_poke_times\",\n",
+    "    Rled=\"right_poke_times\",\n",
+    "    l_opt=\"left_opt_out_times\",\n",
+    "    r_opt=\"right_opt_out_times\",\n",
+    "    ReactionTime=\"reaction_time\",\n",
+    "    slrt=\"short_latency_reaction_time\",\n",
+    "    iti=\"inter_trial_interval\",\n",
+    ")\n",
+    "# The column descriptions are used to add descriptions to the columns in the processed data.\n",
+    "# New (unseen) task parameter descriptions should be added here.\n",
+    "column_descriptions = dict(\n",
+    "    NoseInCenter=\"The time in seconds when the animal is required to maintain center port to initiate the trial (uniformly drawn from 0.8 - 1.2 seconds).\",\n",
+    "    TrainingStage=\"The stage of the training.\",\n",
+    "    Block=\"The block type (High, Low or Test). High and Low blocks are high reward (20, 40, or 80μL) or low reward (5, 10, or 20μL) blocks. Test blocks are mixed blocks.\",\n",
+    "    BlockLengthAd=\"The number of trials in each high reward (20, 40, or 80μL) or low reward (5, 10, or 20μL) blocks.\",\n",
+    "    BlockLengthTest=\"The number of trials in each mixed blocks.\",\n",
+    "    ProbCatch=\"The percentage of catch trials.\",\n",
+    "    RewardDelay=\"The delay in seconds to receive reward, drawn from exponential distribution with mean = 2.5 seconds.\",\n",
+    "    RewardAmount=\"The volume of reward in microliters.\",\n",
+    "    hits=\"Whether the subject received reward for each trial.\",\n",
+    "    vios=\"Whether the subject violated the trial by not maintaining center poke for the time required by 'nose_in_center'.\",\n",
+    "    optout=\"Whether the subject opted out for each trial.\",\n",
+    "    WaitForPoke=\"The time (s) between side port poke and center poke.\",\n",
+    "    wait_time=\"The wait time for the subject for for each trial in seconds, after removing outliers.\"\n",
+    "        \" For hit trials (when reward was delivered) the wait time is equal to the reward delay.\"\n",
+    "        \" For opt-out trials, the wait time is equal to the time waited from trial start to opting out.\",\n",
+    "    wait_for_cpoke=\"The time between side port poke and center poke in seconds, includes the time when the subject is consuming the reward.\",\n",
+    "    zwait_for_cpoke=\"The z-scored wait_for_cpoke using all trials.\",\n",
+    "    RewardedSide=\"The rewarded port (Left or Right) for each trial.\",\n",
+    "    Cled=\"The time of center port LED on/off for each trial (2 x ntrials).\",\n",
+    "    Lled=\"The time of left port LED on/off for each trial (2 x ntrials).\",\n",
+    "    Rled=\"The time of right port LED on/off for each trial (2 x ntrials).\",\n",
+    "    l_opt=\"The time of left port entered/exited for each trial (2 x ntrials).\",\n",
+    "    r_opt=\"The time of right port entered/exited for each trial (2 x ntrials).\",\n",
+    "    ReactionTime=\"The reaction time in seconds.\",\n",
+    "    slrt=\"The short-latency reaction time in seconds.\",\n",
+    "    iti=\"The time to initiate trial in seconds (the time between the end of the consummatory period and the time to initiate the next trial).\",\n",
+    "    wait_thresh=\"The threshold in seconds to remove wait-times (mean + 1*std of all cumulative wait-times).\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Get subject metadata from rat registry\n",
+    "rat_registry_folder_path = \"/Volumes/T9/Constantinople/Rat_info\"\n",
+    "subject_metadata = get_subject_metadata_from_rat_info_folder(\n",
+    "    folder_path=rat_registry_folder_path,\n",
+    "    subject_id=\"J076\",\n",
+    "    date=\"2023-12-12\",\n",
+    ")\n",
+    "\n",
+    "# Ephys registry file path (constains metadata for the neuropixels probe)\n",
+    "ephys_registry_file_path = \"/Volumes/T9/Constantinople/Ephys Data/Ephys_registry.mat\"\n",
+    "\n",
+    "\n",
+    "# Optional parameters\n",
+    "\n",
+    "# Whether to run a stub test conversion. The stubbed file will only contain a small portion of data.\n",
+    "# When running the full conversion stub_test should be disabled.\n",
+    "stub_test = True\n",
+    "# Whether to overwrite an existing NWB file.\n",
+    "overwrite = True\n",
+    "\n",
+    "# Run the `session_to_nwb` function with the parameters to convert to NWB\n",
+    "session_to_nwb(\n",
+    "    nwbfile_path=nwbfile_path,\n",
+    "    openephys_recording_folder_path=folder_path,\n",
+    "    ap_stream_name=raw_stream_name,\n",
+    "    lfp_stream_name=lfp_stream_name,\n",
+    "    processed_spike_sorting_file_path=spike_sorting_mat_file_path,\n",
+    "    raw_behavior_file_path=bpod_file_path,\n",
+    "    column_name_mapping=column_name_mapping,\n",
+    "    column_descriptions=column_descriptions,\n",
+    "    ephys_registry_file_path=ephys_registry_file_path,\n",
+    "    subject_metadata=subject_metadata,\n",
+    "    stub_test=stub_test,\n",
+    "    overwrite=overwrite,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e70b3e5-de74-4f15-bec0-688151d80ce4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37140fad-10d0-436d-a4a8-452083d33d10",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}