diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb
index 5a6b16e..a7f842a 100644
--- a/notebooks/tutorial.ipynb
+++ b/notebooks/tutorial.ipynb
@@ -11,7 +11,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Open-source Data Pipeline for Markerless Pose Estimation in Neurophysiology**\n",
+    "### **Open-source Data Pipeline for Markerless Pose Estimation in Neurophysiology**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "\n",
     "This tutorial aims to provide a comprehensive understanding of the open-source data pipeline by `Element-DeepLabCut`."
    ]
@@ -27,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The package is designed to simplify pose estimation analyses and streamline data organization using `DataJoint`. "
+    "The package is designed to integrate the **model training** and **pose estimation analyses** into a data pipeline and streamline model and video management using DataJoint. "
    ]
   },
   {
@@ -41,14 +47,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By the end of this tutorial, participants will have a clear grasp of how to set up and apply the `Element DeepLabCut` for their specific pose estimation projects. "
+    "By the end of this tutorial, you will have a clear grasp of how to set up and integrate the `Element DeepLabCut` into your specific research projects and your lab. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### **Key Components and Objectives**\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Key Components and Objectives**\n",
     "\n",
     "**- Setup**\n",
     "\n",
@@ -56,11 +68,18 @@
     "\n",
     "**- Step 1: Register an Existing Model in the DataJoint Pipeline**\n",
     "\n",
-    "**- Step 2: Insert Subject, Session, and Behavior Videos**\n",
+    "**- Step 2: Insert Example Data into Subject and Session tables**\n",
     "\n",
-    "**- Step 3: DeepLabCut Inference Task**\n",
+    "**- Step 3: Run the DeepLabCut Inference Task**\n",
     "\n",
-    "**- Step 4: Visualization of Results**"
+    "**- Step 4: Visualize the Results**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### **Documentation and tutorials**"
    ]
   },
   {
@@ -80,35 +99,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup"
+    "### **Setup**"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This tutorial examines the **behavior of a freely moving mouse** in an open-field environment. \n",
+    "This tutorial examines the **behavior of a freely moving mouse** in an open-field environment.\n",
     "\n",
     "The goal is to extract pose estimations of the animal's **head and tail base** from video footage. \n",
     "\n",
-    "This information offers valuable insights into the **animal's movements, postures, and interactions** within the environment. \n",
+    "The **resulting x and y coordinates** offer valuable insights into the **animal's movements, postures, and interactions** within the environment. \n",
     "\n",
-    "The results of this Element example can be **combined with other modalities** to create a complete data pipeline for your specific lab or study.\n",
+    "The results of this Element example can be **combined with other modalities** to create a complete customizable data pipeline for your specific lab or study. For instance, you can combine `element-deeplabcut` with `element-calcium-imaging` and `element-array-ephys` to characterize the neural activity.\n",
     "\n",
     "#### Steps to Run the Element-DeepLabCut\n",
     "\n",
-    "To run the Element, ensure that you have:\n",
+    "The input data for this data pipeline is as follows:\n",
+    "\n",
+    "- A DeepLabCut (DLC) project folder.\n",
     "\n",
-    "- A DeepLabCut (DLC) project folder on your machine.\n",
+    "- The labeled training data in your DLC project folder.\n",
     "\n",
-    "- Labeled data in your DLC project folder.\n",
     "\n",
-    "This tutorial includes a DLC project folder with example data and its results in `example_data`. "
+    "This tutorial includes this DLC project folder with example data and the results as well in `example_data` directory. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start this tutorial by importing the packages necessary to run the data pipeline."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,16 +145,9 @@
     "                                                              + \"element directory\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First start by importing the packages necessary to run this pipeline."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,28 +165,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2023-10-17 15:33:12,400][INFO]: Connecting root@fakeservices.datajoint.io:3306\n",
-      "[2023-10-17 15:33:12,407][INFO]: Connected root@fakeservices.datajoint.io:3306\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DataJoint connection (connected) root@fakeservices.datajoint.io:3306"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "dj.conn()"
    ]
@@ -175,810 +176,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Design the DataJoint Pipeline"
+    "### **Design the DataJoint Pipeline**"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This tutorial assumes that `element-deeplabcut` is already configured and instantiated, with the database connected downstream from existing subject and session tables. Import schemas for subject, session, train, model, etc.:"
+    "This tutorial presumes that the `element-deeplabcut` has been pre-configured and instantiated, with the database linked downstream to pre-existing `subject` and `session` tables. \n",
+    "\n",
+    "Now, we will proceed to import the essential schemas required to construct this data pipeline, with particular attention to the primary components: `train` and `model`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2023-10-17 15:33:14,915][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from tutorial_pipeline import lab, subject, session, train, model  "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can represent a diagram of some of the upstream and downstream dependencies connected to these `model` and `train` schemas:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"2754pt\" height=\"444pt\" viewBox=\"0.00 0.00 2753.50 444.00\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 440)\">\n",
-       "<title>%3</title>\n",
-       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-440 2749.5,-440 2749.5,4 -4,4\"/>\n",
-       "<!-- lab.UserRole -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>lab.UserRole</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"user_role            \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"373.5,-365 299.5,-365 299.5,-330 373.5,-330 373.5,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"307.5\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.UserRole</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.LabMembership -->\n",
-       "<g id=\"node42\" class=\"node\">\n",
-       "<title>lab.LabMembership</title>\n",
-       "<g id=\"a_node42\"><a xlink:title=\"→ lab.Lab\r→ lab.User\r------------------------------\r→ [nullable] lab.UserRole\r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"387,-294 286,-294 286,-259 387,-259 387,-294\"/>\n",
-       "<text text-anchor=\"middle\" x=\"336.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">lab.LabMembership</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.UserRole&#45;&gt;lab.LabMembership -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>lab.UserRole-&gt;lab.LabMembership</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M336.5,-329.8C336.5,-318.95 336.5,-304.87 336.5,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- session.SessionDirectory -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>session.SessionDirectory</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ session.Session\r------------------------------\rsession_dir          \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"2006.5,-223 1854.5,-223 1854.5,-188 2006.5,-188 2006.5,-223\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1930.5\" y=\"-202.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">session.SessionDirectory</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording.File -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>model.VideoRecording.File</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ model.VideoRecording\rfile_id              \r------------------------------\rfile_path            \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"899.5,-144 765.5,-144 765.5,-125 899.5,-125 899.5,-144\"/>\n",
-       "<text text-anchor=\"start\" x=\"773.5\" y=\"-133\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.VideoRecording.File</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Strain -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>subject.Strain</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"strain               \r------------------------------\rstrain_standard_name \rstrain_desc=&quot;&quot;       \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"2105,-365 2028,-365 2028,-330 2105,-330 2105,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"2036\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">subject.Strain</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject.Strain -->\n",
-       "<g id=\"node38\" class=\"node\">\n",
-       "<title>subject.Subject.Strain</title>\n",
-       "<g id=\"a_node38\"><a xlink:title=\"→ subject.Subject\r------------------------------\r→ subject.Strain\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"2123,-286 2010,-286 2010,-267 2123,-267 2123,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2066.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Subject.Strain</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Strain&#45;&gt;subject.Subject.Strain -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>subject.Strain-&gt;subject.Subject.Strain</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M2066.5,-329.8C2066.5,-316.08 2066.5,-297.19 2066.5,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- lab.ProtocolType -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>lab.ProtocolType</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"protocol_type        \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"1703,-436 1614,-436 1614,-401 1703,-401 1703,-436\"/>\n",
-       "<text text-anchor=\"start\" x=\"1622\" y=\"-417\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.ProtocolType</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Protocol -->\n",
-       "<g id=\"node34\" class=\"node\">\n",
-       "<title>lab.Protocol</title>\n",
-       "<g id=\"a_node34\"><a xlink:title=\"protocol             \r------------------------------\r→ lab.ProtocolType\rprotocol_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"1692.5,-365 1624.5,-365 1624.5,-330 1692.5,-330 1692.5,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"1632.5\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.Protocol</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.ProtocolType&#45;&gt;lab.Protocol -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>lab.ProtocolType-&gt;lab.Protocol</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M1658.5,-400.8C1658.5,-389.95 1658.5,-375.87 1658.5,-365.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.ProjectPublication -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>lab.ProjectPublication</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"→ lab.Project\rpublication          \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"1540.5,-294 1406.5,-294 1406.5,-259 1540.5,-259 1540.5,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"1414.5\" y=\"-274.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">lab.ProjectPublication</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.SubjectDeath -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>subject.SubjectDeath</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ subject.Subject\r------------------------------\rdeath_date           \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"1992,-294 1861,-294 1861,-259 1992,-259 1992,-294\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1926.5\" y=\"-273.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">subject.SubjectDeath</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.SubjectCull -->\n",
-       "<g id=\"node28\" class=\"node\">\n",
-       "<title>subject.SubjectCull</title>\n",
-       "<g id=\"a_node28\"><a xlink:title=\"→ subject.SubjectDeath\r------------------------------\rcull_method=&quot;&quot;       \rcull_reason=&quot;&quot;       \rcull_notes=&quot;&quot;        \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"2292.5,-223 2172.5,-223 2172.5,-188 2292.5,-188 2292.5,-223\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2232.5\" y=\"-202.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">subject.SubjectCull</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.SubjectDeath&#45;&gt;subject.SubjectCull -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>subject.SubjectDeath-&gt;subject.SubjectCull</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1992.17,-261.01C2056.38,-246.84 2146.87,-226.83 2163.5,-223 2166.38,-222.34 2169.33,-221.65 2172.3,-220.96\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject.Protocol -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>subject.Subject.Protocol</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"→ subject.Subject\r→ lab.Protocol\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"1720,-286 1597,-286 1597,-267 1720,-267 1720,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1658.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Subject.Protocol</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.TrainingParamSet -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>train.TrainingParamSet</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"paramset_idx         \r------------------------------\rparamset_desc        \rparam_set_hash       \rparams               \rUNIQUE INDEX (param_set_hash)\r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"255.5,-294 139.5,-294 139.5,-259 255.5,-259 255.5,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"147.5\" y=\"-275\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.TrainingParamSet</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.TrainingTask -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
-       "<title>train.TrainingTask</title>\n",
-       "<g id=\"a_node23\"><a xlink:title=\"→ train.VideoSet\r→ train.TrainingParamSet\rtraining_id          \r------------------------------\rmodel_prefix=&quot;&quot;      \rproject_path=&quot;&quot;      \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"226,-223 115,-223 115,-188 226,-188 226,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"123\" y=\"-203.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.TrainingTask</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.TrainingParamSet&#45;&gt;train.TrainingTask -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>train.TrainingParamSet-&gt;train.TrainingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M190.96,-258.8C186.72,-247.95 181.21,-233.87 176.98,-223.05\"/>\n",
-       "</g>\n",
-       "<!-- model.Model -->\n",
-       "<g id=\"node39\" class=\"node\">\n",
-       "<title>model.Model</title>\n",
-       "<g id=\"a_node39\"><a xlink:title=\"model_name           \r------------------------------\rtask                 \rdate                 \riteration            \rsnapshotindex        \rshuffle              \rtrainingsetindex     \rscorer               \rconfig_template      \rproject_path         \rmodel_prefix=&quot;&quot;      \rmodel_description=&quot;&quot; \r→ [nullable] train.TrainingParamSet\rUNIQUE INDEX (task, date, iteration, shuffle, snapshotindex, trainingsetindex)\r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"434.5,-223 348.5,-223 348.5,-188 434.5,-188 434.5,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"356.5\" y=\"-203.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">model.Model</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.TrainingParamSet&#45;&gt;model.Model -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>train.TrainingParamSet-&gt;model.Model</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M243.97,-258.97C275.69,-247.69 317.42,-232.85 348.4,-221.83\"/>\n",
-       "</g>\n",
-       "<!-- subject.Allele.Source -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>subject.Allele.Source</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ subject.Allele\r------------------------------\r→ lab.Source\rsource_identifier=&quot;&quot; \rsource_url=&quot;&quot;        \rexpression_data_url=&quot;&quot; \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"2631,-286 2522,-286 2522,-267 2631,-267 2631,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2576.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Allele.Source</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Device -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>lab.Device</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"device               \r------------------------------\rmodality             \rdescription=&quot;&quot;       \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"836,-294 773,-294 773,-259 836,-259 836,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"781\" y=\"-275\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.Device</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording -->\n",
-       "<g id=\"node36\" class=\"node\">\n",
-       "<title>model.VideoRecording</title>\n",
-       "<g id=\"a_node36\"><a xlink:title=\"→ session.Session\rrecording_id         \r------------------------------\r→ lab.Device\r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"902,-223 763,-223 763,-188 902,-188 902,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"771\" y=\"-203.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">model.VideoRecording</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Device&#45;&gt;model.VideoRecording -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>lab.Device-&gt;model.VideoRecording</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M811.28,-258.8C815.68,-247.95 821.39,-233.87 825.78,-223.05\"/>\n",
-       "</g>\n",
-       "<!-- session.ProjectSession -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>session.ProjectSession</title>\n",
-       "<g id=\"a_node12\"><a xlink:title=\"→ lab.Project\r→ session.Session\r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"1656.5,-223 1514.5,-223 1514.5,-188 1656.5,-188 1656.5,-223\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1585.5\" y=\"-202.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">session.ProjectSession</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Line.Allele -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>subject.Line.Allele</title>\n",
-       "<g id=\"a_node13\"><a xlink:title=\"→ subject.Line\r→ subject.Allele\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"2745.5,-286 2649.5,-286 2649.5,-267 2745.5,-267 2745.5,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2697.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Line.Allele</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet.File -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>train.VideoSet.File</title>\n",
-       "<g id=\"a_node14\"><a xlink:title=\"→ train.VideoSet\rfile_id              \r------------------------------\rfile_path            \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"97,-215 0,-215 0,-196 97,-196 97,-215\"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-204\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.VideoSet.File</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Allele -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>subject.Allele</title>\n",
-       "<g id=\"a_node15\"><a xlink:title=\"allele               \r------------------------------\rallele_standard_name=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"2614,-365 2539,-365 2539,-330 2614,-330 2614,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"2547\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">subject.Allele</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Allele&#45;&gt;subject.Allele.Source -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>subject.Allele-&gt;subject.Allele.Source</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M2576.5,-329.8C2576.5,-316.08 2576.5,-297.19 2576.5,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- subject.Allele&#45;&gt;subject.Line.Allele -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>subject.Allele-&gt;subject.Line.Allele</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M2605.48,-329.97C2629.67,-316.18 2663.19,-297.06 2682.38,-286.12\"/>\n",
-       "</g>\n",
-       "<!-- subject.Zygosity -->\n",
-       "<g id=\"node37\" class=\"node\">\n",
-       "<title>subject.Zygosity</title>\n",
-       "<g id=\"a_node37\"><a xlink:title=\"→ subject.Subject\r→ subject.Allele\r------------------------------\rzygosity             \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"2381,-294 2278,-294 2278,-259 2381,-259 2381,-294\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2329.5\" y=\"-273.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">subject.Zygosity</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Allele&#45;&gt;subject.Zygosity -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>subject.Allele-&gt;subject.Zygosity</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M2538.99,-336.02C2496.88,-324.26 2428.3,-305.1 2381.15,-291.93\"/>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimationTask -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
-       "<title>model.PoseEstimationTask</title>\n",
-       "<g id=\"a_node16\"><a xlink:title=\"→ model.VideoRecording\r→ model.Model\r------------------------------\rtask_mode=&quot;load&quot;     \rpose_estimation_output_dir=&quot;&quot; \rpose_estimation_params=null \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"747,-152 586,-152 586,-117 747,-117 747,-152\"/>\n",
-       "<text text-anchor=\"middle\" x=\"666.5\" y=\"-131.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">model.PoseEstimationTask</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimation -->\n",
-       "<g id=\"node29\" class=\"node\">\n",
-       "<title>model.PoseEstimation</title>\n",
-       "<g id=\"a_node29\"><a xlink:title=\"→ model.PoseEstimationTask\r------------------------------\rpose_estimation_time \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"610.5\" cy=\"-68\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"610.5\" y=\"-64.9\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.PoseEstimation</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimationTask&#45;&gt;model.PoseEstimation -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>model.PoseEstimationTask-&gt;model.PoseEstimation</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M652.08,-116.89C641.45,-104.65 627.37,-88.44 618.63,-78.37\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject -->\n",
-       "<g id=\"node17\" class=\"node\">\n",
-       "<title>subject.Subject</title>\n",
-       "<g id=\"a_node17\"><a xlink:title=\"subject              \r------------------------------\rsubject_nickname=&quot;&quot;  \rsex                  \rsubject_birth_date   \rsubject_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"1975.5,-365 1877.5,-365 1877.5,-330 1975.5,-330 1975.5,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"1885.5\" y=\"-345.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">subject.Subject</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;subject.SubjectDeath -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;subject.SubjectDeath</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1926.5,-329.8C1926.5,-318.95 1926.5,-304.87 1926.5,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;subject.Subject.Protocol -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;subject.Subject.Protocol</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1877.34,-333.84C1823.4,-319.96 1738.28,-298.04 1691.69,-286.05\"/>\n",
-       "</g>\n",
-       "<!-- session.Session -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>session.Session</title>\n",
-       "<g id=\"a_node22\"><a xlink:title=\"→ subject.Subject\rsession_datetime     \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"1842.5,-294 1738.5,-294 1738.5,-259 1842.5,-259 1842.5,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"1746.5\" y=\"-274.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">session.Session</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;session.Session -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;session.Session</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1893.92,-329.97C1872.55,-319.13 1844.7,-305 1823.28,-294.13\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject.Source -->\n",
-       "<g id=\"node32\" class=\"node\">\n",
-       "<title>subject.Subject.Source</title>\n",
-       "<g id=\"a_node32\"><a xlink:title=\"→ subject.Subject\r------------------------------\r→ lab.Source\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"2259.5,-286 2141.5,-286 2141.5,-267 2259.5,-267 2259.5,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2200.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Subject.Source</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;subject.Subject.Source -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;subject.Subject.Source</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1975.8,-334.08C2030.96,-320.19 2118.84,-298.06 2166.71,-286.01\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;subject.Zygosity -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;subject.Zygosity</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1975.68,-337.47C1989.81,-334.96 2005.25,-332.29 2019.5,-330 2129.9,-312.23 2159,-316.64 2268.5,-294 2271.51,-293.38 2274.59,-292.69 2277.69,-291.97\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;subject.Subject.Strain -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;subject.Subject.Strain</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1960.03,-329.97C1988.02,-316.18 2026.8,-297.06 2049.01,-286.12\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject.Line -->\n",
-       "<g id=\"node40\" class=\"node\">\n",
-       "<title>subject.Subject.Line</title>\n",
-       "<g id=\"a_node40\"><a xlink:title=\"→ subject.Subject\r------------------------------\r→ subject.Line\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"2504,-286 2399,-286 2399,-267 2504,-267 2504,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2451.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Subject.Line</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;subject.Subject.Line -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;subject.Subject.Line</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1975.62,-337.04C1989.75,-334.52 2005.2,-331.96 2019.5,-330 2183.63,-307.51 2227.53,-323.73 2390.5,-294 2401.51,-291.99 2413.43,-288.96 2423.79,-286.05\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject.User -->\n",
-       "<g id=\"node45\" class=\"node\">\n",
-       "<title>subject.Subject.User</title>\n",
-       "<g id=\"a_node45\"><a xlink:title=\"→ subject.Subject\r→ lab.User\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"962.5,-286 854.5,-286 854.5,-267 962.5,-267 962.5,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"908.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Subject.User</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;subject.Subject.User -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;subject.Subject.User</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1877.36,-342.56C1831.8,-338.98 1762.1,-333.72 1701.5,-330 1377.71,-310.14 1293.21,-342.74 972.5,-294 960.59,-292.19 947.68,-289.09 936.6,-286.06\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject.Lab -->\n",
-       "<g id=\"node50\" class=\"node\">\n",
-       "<title>subject.Subject.Lab</title>\n",
-       "<g id=\"a_node50\"><a xlink:title=\"→ subject.Subject\r→ lab.Lab\r------------------------------\rsubject_alias=&quot;&quot;     \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"1243,-286 1140,-286 1140,-267 1243,-267 1243,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1191.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Subject.Lab</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;subject.Subject.Lab -->\n",
-       "<g id=\"edge20\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;subject.Subject.Lab</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1877.39,-342.93C1727.35,-331.98 1283.22,-299.3 1252.5,-294 1241.36,-292.08 1229.32,-289.03 1218.89,-286.08\"/>\n",
-       "</g>\n",
-       "<!-- train.ModelTraining -->\n",
-       "<g id=\"node18\" class=\"node\">\n",
-       "<title>train.ModelTraining</title>\n",
-       "<g id=\"a_node18\"><a xlink:title=\"→ train.TrainingTask\r------------------------------\rlatest_snapshot      \rconfig_template      \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"170.5\" cy=\"-134.5\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"170.5\" y=\"-131.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">train.ModelTraining</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.ProjectSourceCode -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
-       "<title>lab.ProjectSourceCode</title>\n",
-       "<g id=\"a_node19\"><a xlink:title=\"→ lab.Project\rrepository_url       \r------------------------------\rrepository_name=&quot;&quot;   \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"1122,-294 981,-294 981,-259 1122,-259 1122,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"989\" y=\"-274.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">lab.ProjectSourceCode</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.ModelEvaluation -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>model.ModelEvaluation</title>\n",
-       "<g id=\"a_node20\"><a xlink:title=\"→ model.Model\r------------------------------\rtrain_iterations     \rtrain_error=null     \rtest_error=null      \rp_cutoff=null        \rtrain_error_p=null   \rtest_error_p=null    \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"496.5\" cy=\"-134.5\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"496.5\" y=\"-131.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.ModelEvaluation</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.RecordingInfo -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>model.RecordingInfo</title>\n",
-       "<g id=\"a_node21\"><a xlink:title=\"→ model.VideoRecording\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"997.5\" cy=\"-134.5\" rx=\"80\" ry=\"17.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"997.5\" y=\"-131.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">model.RecordingInfo</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- session.Session&#45;&gt;session.SessionDirectory -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>session.Session-&gt;session.SessionDirectory</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1824.03,-258.97C1846.04,-248.13 1874.71,-234 1896.75,-223.13\"/>\n",
-       "</g>\n",
-       "<!-- session.Session&#45;&gt;session.ProjectSession -->\n",
-       "<g id=\"edge22\" class=\"edge\">\n",
-       "<title>session.Session-&gt;session.ProjectSession</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1741.4,-258.97C1709.06,-248.09 1666.88,-233.89 1634.56,-223.01\"/>\n",
-       "</g>\n",
-       "<!-- session.SessionNote -->\n",
-       "<g id=\"node31\" class=\"node\">\n",
-       "<title>session.SessionNote</title>\n",
-       "<g id=\"a_node31\"><a xlink:title=\"→ session.Session\r------------------------------\rsession_note         \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"2154.5,-223 2024.5,-223 2024.5,-188 2154.5,-188 2154.5,-223\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2089.5\" y=\"-202.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">session.SessionNote</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- session.Session&#45;&gt;session.SessionNote -->\n",
-       "<g id=\"edge23\" class=\"edge\">\n",
-       "<title>session.Session-&gt;session.SessionNote</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1842.55,-261.47C1845.92,-260.62 1849.25,-259.79 1852.5,-259 1924.59,-241.46 1943.07,-239.07 2015.5,-223 2018.33,-222.37 2021.21,-221.73 2024.13,-221.08\"/>\n",
-       "</g>\n",
-       "<!-- session.Session&#45;&gt;model.VideoRecording -->\n",
-       "<g id=\"edge24\" class=\"edge\">\n",
-       "<title>session.Session-&gt;model.VideoRecording</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1738.41,-260.65C1735.41,-260.03 1732.42,-259.48 1729.5,-259 1424.25,-209.33 1053.84,-205.1 902.25,-205.73\"/>\n",
-       "</g>\n",
-       "<!-- session.Session.Attribute -->\n",
-       "<g id=\"node41\" class=\"node\">\n",
-       "<title>session.Session.Attribute</title>\n",
-       "<g id=\"a_node41\"><a xlink:title=\"→ session.Session\rattribute_name       \r------------------------------\rattribute_value=&quot;&quot;   \rattribute_blob=null  \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"1836.5,-215 1708.5,-215 1708.5,-196 1836.5,-196 1836.5,-215\"/>\n",
-       "<text text-anchor=\"start\" x=\"1716.5\" y=\"-204\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">session.Session.Attribute</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- session.Session&#45;&gt;session.Session.Attribute -->\n",
-       "<g id=\"edge25\" class=\"edge\">\n",
-       "<title>session.Session-&gt;session.Session.Attribute</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1786.14,-258.8C1782.56,-245.08 1777.64,-226.19 1774.79,-215.27\"/>\n",
-       "</g>\n",
-       "<!-- session.SessionExperimenter -->\n",
-       "<g id=\"node48\" class=\"node\">\n",
-       "<title>session.SessionExperimenter</title>\n",
-       "<g id=\"a_node48\"><a xlink:title=\"→ session.Session\r→ lab.User\r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"707.5,-223 531.5,-223 531.5,-188 707.5,-188 707.5,-223\"/>\n",
-       "<text text-anchor=\"middle\" x=\"619.5\" y=\"-202.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">session.SessionExperimenter</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- session.Session&#45;&gt;session.SessionExperimenter -->\n",
-       "<g id=\"edge26\" class=\"edge\">\n",
-       "<title>session.Session-&gt;session.SessionExperimenter</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1738.42,-260.58C1735.42,-259.98 1732.43,-259.44 1729.5,-259 1300.78,-193.97 1186.77,-257.33 754.5,-223 739.32,-221.79 723.22,-220.15 707.62,-218.36\"/>\n",
-       "</g>\n",
-       "<!-- train.TrainingTask&#45;&gt;train.ModelTraining -->\n",
-       "<g id=\"edge27\" class=\"edge\">\n",
-       "<title>train.TrainingTask-&gt;train.ModelTraining</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M170.5,-187.8C170.5,-175.35 170.5,-158.66 170.5,-147.51\"/>\n",
-       "</g>\n",
-       "<!-- lab.Project -->\n",
-       "<g id=\"node24\" class=\"node\">\n",
-       "<title>lab.Project</title>\n",
-       "<g id=\"a_node24\"><a xlink:title=\"project              \r------------------------------\rproject_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"1356,-365 1293,-365 1293,-330 1356,-330 1356,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"1301\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.Project</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Project&#45;&gt;lab.ProjectPublication -->\n",
-       "<g id=\"edge28\" class=\"edge\">\n",
-       "<title>lab.Project-&gt;lab.ProjectPublication</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1356.1,-331.87C1380.02,-320.79 1412.89,-305.57 1437.76,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.Project&#45;&gt;session.ProjectSession -->\n",
-       "<g id=\"edge29\" class=\"edge\">\n",
-       "<title>lab.Project-&gt;session.ProjectSession</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1356.3,-342.7C1411.17,-335.56 1520.55,-318.62 1549.5,-294 1570.9,-275.8 1579.77,-242.87 1583.31,-223.01\"/>\n",
-       "</g>\n",
-       "<!-- lab.Project&#45;&gt;lab.ProjectSourceCode -->\n",
-       "<g id=\"edge30\" class=\"edge\">\n",
-       "<title>lab.Project-&gt;lab.ProjectSourceCode</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1292.92,-338.52C1250.11,-327.7 1172.63,-308.12 1116.98,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.ProjectKeywords -->\n",
-       "<g id=\"node25\" class=\"node\">\n",
-       "<title>lab.ProjectKeywords</title>\n",
-       "<g id=\"a_node25\"><a xlink:title=\"→ lab.Project\rkeyword              \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"1388,-294 1261,-294 1261,-259 1388,-259 1388,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"1269\" y=\"-274.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">lab.ProjectKeywords</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Project&#45;&gt;lab.ProjectKeywords -->\n",
-       "<g id=\"edge31\" class=\"edge\">\n",
-       "<title>lab.Project-&gt;lab.ProjectKeywords</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1324.5,-329.8C1324.5,-318.95 1324.5,-304.87 1324.5,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.ProjectUser -->\n",
-       "<g id=\"node30\" class=\"node\">\n",
-       "<title>lab.ProjectUser</title>\n",
-       "<g id=\"a_node30\"><a xlink:title=\"→ lab.Project\r→ lab.User\r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"630,-294 531,-294 531,-259 630,-259 630,-294\"/>\n",
-       "<text text-anchor=\"middle\" x=\"580.5\" y=\"-273.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">lab.ProjectUser</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Project&#45;&gt;lab.ProjectUser -->\n",
-       "<g id=\"edge32\" class=\"edge\">\n",
-       "<title>lab.Project-&gt;lab.ProjectUser</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1292.82,-346.27C1194.87,-345.1 888.51,-337.92 639.5,-294 636.46,-293.46 633.36,-292.84 630.24,-292.15\"/>\n",
-       "</g>\n",
-       "<!-- lab.Location -->\n",
-       "<g id=\"node26\" class=\"node\">\n",
-       "<title>lab.Location</title>\n",
-       "<g id=\"a_node26\"><a xlink:title=\"→ lab.Lab\rlocation             \r------------------------------\rlocation_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"513,-294 444,-294 444,-259 513,-259 513,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"452\" y=\"-275\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.Location</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Source -->\n",
-       "<g id=\"node27\" class=\"node\">\n",
-       "<title>lab.Source</title>\n",
-       "<g id=\"a_node27\"><a xlink:title=\"source               \r------------------------------\rsource_name          \rcontact_details=&quot;&quot;   \rsource_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"2376,-365 2313,-365 2313,-330 2376,-330 2376,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"2321\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.Source</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Source&#45;&gt;subject.Allele.Source -->\n",
-       "<g id=\"edge33\" class=\"edge\">\n",
-       "<title>lab.Source-&gt;subject.Allele.Source</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M2376.27,-337.05C2421.57,-323.58 2504.04,-299.05 2547.66,-286.08\"/>\n",
-       "</g>\n",
-       "<!-- lab.Source&#45;&gt;subject.Subject.Source -->\n",
-       "<g id=\"edge34\" class=\"edge\">\n",
-       "<title>lab.Source-&gt;subject.Subject.Source</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M2312.9,-331.36C2283.92,-317.47 2242.04,-297.4 2218.43,-286.09\"/>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimation.BodyPartPosition -->\n",
-       "<g id=\"node44\" class=\"node\">\n",
-       "<title>model.PoseEstimation.BodyPartPosition</title>\n",
-       "<g id=\"a_node44\"><a xlink:title=\"→ model.PoseEstimation\r→ model.Model.BodyPart\r------------------------------\rframe_index          \rx_pos                \ry_pos                \rz_pos=null           \rlikelihood           \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"707.5,-19 513.5,-19 513.5,0 707.5,0 707.5,-19\"/>\n",
-       "<text text-anchor=\"middle\" x=\"610.5\" y=\"-7\" font-family=\"arial\" font-size=\"10.00\">model.PoseEstimation.BodyPartPosition</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimation&#45;&gt;model.PoseEstimation.BodyPartPosition -->\n",
-       "<g id=\"edge35\" class=\"edge\">\n",
-       "<title>model.PoseEstimation-&gt;model.PoseEstimation.BodyPartPosition</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M610.5,-54.74C610.5,-43.97 610.5,-28.56 610.5,-19.06\"/>\n",
-       "</g>\n",
-       "<!-- lab.Lab.Organization -->\n",
-       "<g id=\"node33\" class=\"node\">\n",
-       "<title>lab.Lab.Organization</title>\n",
-       "<g id=\"a_node33\"><a xlink:title=\"→ lab.Lab\r→ lab.Organization\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"754.5,-286 648.5,-286 648.5,-267 754.5,-267 754.5,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"701.5\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">lab.Lab.Organization</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Protocol&#45;&gt;subject.Subject.Protocol -->\n",
-       "<g id=\"edge36\" class=\"edge\">\n",
-       "<title>lab.Protocol-&gt;subject.Subject.Protocol</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M1658.5,-329.8C1658.5,-316.08 1658.5,-297.19 1658.5,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- lab.User -->\n",
-       "<g id=\"node35\" class=\"node\">\n",
-       "<title>lab.User</title>\n",
-       "<g id=\"a_node35\"><a xlink:title=\"user                 \r------------------------------\ruser_email=&quot;&quot;        \ruser_cellphone=&quot;&quot;    \ruser_fullname=&quot;&quot;     \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"526,-365 473,-365 473,-330 526,-330 526,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"481\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.User</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.User&#45;&gt;lab.ProjectUser -->\n",
-       "<g id=\"edge37\" class=\"edge\">\n",
-       "<title>lab.User-&gt;lab.ProjectUser</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M519.11,-329.8C531.84,-318.95 548.37,-304.87 561.07,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.User&#45;&gt;lab.LabMembership -->\n",
-       "<g id=\"edge38\" class=\"edge\">\n",
-       "<title>lab.User-&gt;lab.LabMembership</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M472.95,-335.26C446.52,-324.07 405.65,-306.77 375.53,-294.02\"/>\n",
-       "</g>\n",
-       "<!-- lab.User&#45;&gt;subject.Subject.User -->\n",
-       "<g id=\"edge39\" class=\"edge\">\n",
-       "<title>lab.User-&gt;subject.Subject.User</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M526.15,-343.43C584.41,-336.51 727.38,-318.36 845.5,-294 856.35,-291.76 868.13,-288.82 878.53,-286.04\"/>\n",
-       "</g>\n",
-       "<!-- lab.User&#45;&gt;session.SessionExperimenter -->\n",
-       "<g id=\"edge40\" class=\"edge\">\n",
-       "<title>lab.User-&gt;session.SessionExperimenter</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M472.67,-332.61C458.5,-323.67 442.44,-310.62 434.5,-294 427.79,-279.96 425.12,-271.41 434.5,-259 446.85,-242.65 489.89,-229.91 531.35,-221.08\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.VideoRecording.File -->\n",
-       "<g id=\"edge41\" class=\"edge\">\n",
-       "<title>model.VideoRecording-&gt;model.VideoRecording.File</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M832.5,-187.8C832.5,-174.08 832.5,-155.19 832.5,-144.27\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.PoseEstimationTask -->\n",
-       "<g id=\"edge42\" class=\"edge\">\n",
-       "<title>model.VideoRecording-&gt;model.PoseEstimationTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M792.74,-187.97C766.55,-177.09 732.4,-162.89 706.22,-152.01\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.RecordingInfo -->\n",
-       "<g id=\"edge43\" class=\"edge\">\n",
-       "<title>model.VideoRecording-&gt;model.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M872.02,-187.97C899.48,-176.49 935.75,-161.32 962.23,-150.25\"/>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.PoseEstimationTask -->\n",
-       "<g id=\"edge44\" class=\"edge\">\n",
-       "<title>model.Model-&gt;model.PoseEstimationTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M434.77,-193.64C479.32,-182.47 549.28,-164.91 600.69,-152.01\"/>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.ModelEvaluation -->\n",
-       "<g id=\"edge45\" class=\"edge\">\n",
-       "<title>model.Model-&gt;model.ModelEvaluation</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M416.65,-187.97C438.73,-173.46 469.78,-153.06 485.88,-142.48\"/>\n",
-       "</g>\n",
-       "<!-- model.Model.BodyPart -->\n",
-       "<g id=\"node52\" class=\"node\">\n",
-       "<title>model.Model.BodyPart</title>\n",
-       "<g id=\"a_node52\"><a xlink:title=\"→ model.Model\r→ model.BodyPart\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"406.5,-144 290.5,-144 290.5,-125 406.5,-125 406.5,-144\"/>\n",
-       "<text text-anchor=\"middle\" x=\"348.5\" y=\"-132\" font-family=\"arial\" font-size=\"10.00\">model.Model.BodyPart</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.Model.BodyPart -->\n",
-       "<g id=\"edge46\" class=\"edge\">\n",
-       "<title>model.Model-&gt;model.Model.BodyPart</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M381.09,-187.8C372.54,-174.08 360.77,-155.19 353.96,-144.27\"/>\n",
-       "</g>\n",
-       "<!-- subject.Line -->\n",
-       "<g id=\"node43\" class=\"node\">\n",
-       "<title>subject.Line</title>\n",
-       "<g id=\"a_node43\"><a xlink:title=\"line                 \r------------------------------\rspecies=&quot;&quot;           \rline_description=&quot;&quot;  \rtarget_phenotype=&quot;&quot;  \ris_active            \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"2732,-365 2663,-365 2663,-330 2732,-330 2732,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"2671\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">subject.Line</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Line&#45;&gt;subject.Line.Allele -->\n",
-       "<g id=\"edge47\" class=\"edge\">\n",
-       "<title>subject.Line-&gt;subject.Line.Allele</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M2697.5,-329.8C2697.5,-316.08 2697.5,-297.19 2697.5,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- subject.Line&#45;&gt;subject.Subject.Line -->\n",
-       "<g id=\"edge48\" class=\"edge\">\n",
-       "<title>subject.Line-&gt;subject.Subject.Line</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M2662.78,-336.76C2614.57,-323.24 2528.12,-298.99 2482.17,-286.1\"/>\n",
-       "</g>\n",
-       "<!-- model.BodyPart -->\n",
-       "<g id=\"node46\" class=\"node\">\n",
-       "<title>model.BodyPart</title>\n",
-       "<g id=\"a_node46\"><a xlink:title=\"body_part            \r------------------------------\rbody_part_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"330.5,-223 244.5,-223 244.5,-188 330.5,-188 330.5,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"252.5\" y=\"-204\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.BodyPart</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.BodyPart&#45;&gt;model.Model.BodyPart -->\n",
-       "<g id=\"edge49\" class=\"edge\">\n",
-       "<title>model.BodyPart-&gt;model.Model.BodyPart</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M302.27,-187.8C314.4,-174.08 331.1,-155.19 340.75,-144.27\"/>\n",
-       "</g>\n",
-       "<!-- lab.Lab -->\n",
-       "<g id=\"node47\" class=\"node\">\n",
-       "<title>lab.Lab</title>\n",
-       "<g id=\"a_node47\"><a xlink:title=\"lab                  \r------------------------------\rlab_name             \raddress              \rtime_zone            \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"725.5,-365 677.5,-365 677.5,-330 725.5,-330 725.5,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"685.5\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.Lab</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Lab&#45;&gt;lab.Location -->\n",
-       "<g id=\"edge50\" class=\"edge\">\n",
-       "<title>lab.Lab-&gt;lab.Location</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M677.43,-340.16C642.72,-330.86 576.72,-312.61 521.5,-294 518.77,-293.08 515.96,-292.09 513.14,-291.08\"/>\n",
-       "</g>\n",
-       "<!-- lab.Lab&#45;&gt;lab.Lab.Organization -->\n",
-       "<g id=\"edge51\" class=\"edge\">\n",
-       "<title>lab.Lab-&gt;lab.Lab.Organization</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M701.5,-329.8C701.5,-316.08 701.5,-297.19 701.5,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- lab.Lab&#45;&gt;lab.LabMembership -->\n",
-       "<g id=\"edge52\" class=\"edge\">\n",
-       "<title>lab.Lab-&gt;lab.LabMembership</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M677.27,-341.92C618.36,-330.78 466.63,-302.1 387,-287.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.Lab&#45;&gt;subject.Subject.Lab -->\n",
-       "<g id=\"edge53\" class=\"edge\">\n",
-       "<title>lab.Lab-&gt;subject.Subject.Lab</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M725.6,-333.19C728.53,-331.95 731.54,-330.84 734.5,-330 904.91,-281.54 957.17,-325.61 1131.5,-294 1142.45,-292.01 1154.3,-288.96 1164.56,-286.01\"/>\n",
-       "</g>\n",
-       "<!-- train.VideoSet -->\n",
-       "<g id=\"node49\" class=\"node\">\n",
-       "<title>train.VideoSet</title>\n",
-       "<g id=\"a_node49\"><a xlink:title=\"video_set_id         \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"94.5,-294 2.5,-294 2.5,-259 94.5,-259 94.5,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"10.5\" y=\"-274.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.VideoSet</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet&#45;&gt;train.VideoSet.File -->\n",
-       "<g id=\"edge54\" class=\"edge\">\n",
-       "<title>train.VideoSet-&gt;train.VideoSet.File</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M48.5,-258.8C48.5,-245.08 48.5,-226.19 48.5,-215.27\"/>\n",
-       "</g>\n",
-       "<!-- train.VideoSet&#45;&gt;train.TrainingTask -->\n",
-       "<g id=\"edge55\" class=\"edge\">\n",
-       "<title>train.VideoSet-&gt;train.TrainingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M77.72,-258.97C96.9,-248.13 121.88,-234 141.09,-223.13\"/>\n",
-       "</g>\n",
-       "<!-- lab.Organization -->\n",
-       "<g id=\"node51\" class=\"node\">\n",
-       "<title>lab.Organization</title>\n",
-       "<g id=\"a_node51\"><a xlink:title=\"organization         \r------------------------------\rorg_name             \rorg_address=&quot;&quot;       \rorg_comment=&quot;&quot;       \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"847.5,-365 743.5,-365 743.5,-330 847.5,-330 847.5,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"751.5\" y=\"-345.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">lab.Organization</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Organization&#45;&gt;lab.Lab.Organization -->\n",
-       "<g id=\"edge56\" class=\"edge\">\n",
-       "<title>lab.Organization-&gt;lab.Lab.Organization</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M772.75,-329.8C753.95,-316 728.02,-296.97 713.18,-286.08\"/>\n",
-       "</g>\n",
-       "<!-- model.Model.BodyPart&#45;&gt;model.PoseEstimation.BodyPartPosition -->\n",
-       "<g id=\"edge57\" class=\"edge\">\n",
-       "<title>model.Model.BodyPart-&gt;model.PoseEstimation.BodyPartPosition</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M366.92,-124.85C415.03,-102.27 544.01,-41.71 592.1,-19.14\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>"
-      ],
-      "text/plain": [
-       "<datajoint.diagram.Diagram at 0x7f7369ff39d0>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "(\n",
     "    dj.Diagram(subject) \n",
@@ -993,243 +223,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you can see, this data pipeline is quite extensive, with various tables related to other components like models, training, and evaluation in DLC. Some, such as the `Subject` table, are not relevant to this tutorial and are upstream."
+    "As evident, this data pipeline is fairly comprehensive, encompassing several tables associated with different DeepLabCut components like model, train, and evaluation. A few tables, such as `Subject` or `Lab`, while integral to the pipeline, fall outside the scope of the scope of element-deeplabcut tutorial as they are upstream. \n",
+    "\n",
+    "Our focus in this tutorial will be primarily on the two core schemas:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"1086pt\" height=\"302pt\" viewBox=\"0.00 0.00 1085.50 302.00\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 298)\">\n",
-       "<title>%3</title>\n",
-       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-298 1081.5,-298 1081.5,4 -4,4\"/>\n",
-       "<!-- train.VideoSet.File -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>train.VideoSet.File</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"→ train.VideoSet\rfile_id              \r------------------------------\rfile_path            \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"97,-215 0,-215 0,-196 97,-196 97,-215\"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-204\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.VideoSet.File</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.Model -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>model.Model</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"model_name           \r------------------------------\rtask                 \rdate                 \riteration            \rsnapshotindex        \rshuffle              \rtrainingsetindex     \rscorer               \rconfig_template      \rproject_path         \rmodel_prefix=&quot;&quot;      \rmodel_description=&quot;&quot; \r→ [nullable] train.TrainingParamSet\rUNIQUE INDEX (task, date, iteration, shuffle, snapshotindex, trainingsetindex)\r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"434.5,-223 348.5,-223 348.5,-188 434.5,-188 434.5,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"356.5\" y=\"-203.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">model.Model</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimationTask -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>model.PoseEstimationTask</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"→ model.VideoRecording\r→ model.Model\r------------------------------\rtask_mode=&quot;load&quot;     \rpose_estimation_output_dir=&quot;&quot; \rpose_estimation_params=null \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"747,-152 586,-152 586,-117 747,-117 747,-152\"/>\n",
-       "<text text-anchor=\"middle\" x=\"666.5\" y=\"-131.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">model.PoseEstimationTask</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.PoseEstimationTask -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>model.Model-&gt;model.PoseEstimationTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M434.77,-193.64C479.32,-182.47 549.28,-164.91 600.69,-152.01\"/>\n",
-       "</g>\n",
-       "<!-- model.ModelEvaluation -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>model.ModelEvaluation</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ model.Model\r------------------------------\rtrain_iterations     \rtrain_error=null     \rtest_error=null      \rp_cutoff=null        \rtrain_error_p=null   \rtest_error_p=null    \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"496.5\" cy=\"-134.5\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"496.5\" y=\"-131.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.ModelEvaluation</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.ModelEvaluation -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>model.Model-&gt;model.ModelEvaluation</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M416.65,-187.97C438.73,-173.46 469.78,-153.06 485.88,-142.48\"/>\n",
-       "</g>\n",
-       "<!-- model.Model.BodyPart -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>model.Model.BodyPart</title>\n",
-       "<g id=\"a_node15\"><a xlink:title=\"→ model.Model\r→ model.BodyPart\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"406.5,-144 290.5,-144 290.5,-125 406.5,-125 406.5,-144\"/>\n",
-       "<text text-anchor=\"middle\" x=\"348.5\" y=\"-132\" font-family=\"arial\" font-size=\"10.00\">model.Model.BodyPart</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.Model.BodyPart -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>model.Model-&gt;model.Model.BodyPart</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M381.09,-187.8C372.54,-174.08 360.77,-155.19 353.96,-144.27\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording.File -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>model.VideoRecording.File</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ model.VideoRecording\rfile_id              \r------------------------------\rfile_path            \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"899.5,-144 765.5,-144 765.5,-125 899.5,-125 899.5,-144\"/>\n",
-       "<text text-anchor=\"start\" x=\"773.5\" y=\"-133\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.VideoRecording.File</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimation -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>model.PoseEstimation</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"→ model.PoseEstimationTask\r------------------------------\rpose_estimation_time \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"610.5\" cy=\"-68\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"610.5\" y=\"-64.9\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.PoseEstimation</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimation.BodyPartPosition -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>model.PoseEstimation.BodyPartPosition</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"→ model.PoseEstimation\r→ model.Model.BodyPart\r------------------------------\rframe_index          \rx_pos                \ry_pos                \rz_pos=null           \rlikelihood           \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"707.5,-19 513.5,-19 513.5,0 707.5,0 707.5,-19\"/>\n",
-       "<text text-anchor=\"middle\" x=\"610.5\" y=\"-7\" font-family=\"arial\" font-size=\"10.00\">model.PoseEstimation.BodyPartPosition</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimation&#45;&gt;model.PoseEstimation.BodyPartPosition -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>model.PoseEstimation-&gt;model.PoseEstimation.BodyPartPosition</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M610.5,-54.74C610.5,-43.97 610.5,-28.56 610.5,-19.06\"/>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimationTask&#45;&gt;model.PoseEstimation -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>model.PoseEstimationTask-&gt;model.PoseEstimation</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M652.08,-116.89C641.45,-104.65 627.37,-88.44 618.63,-78.37\"/>\n",
-       "</g>\n",
-       "<!-- train.ModelTraining -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>train.ModelTraining</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ train.TrainingTask\r------------------------------\rlatest_snapshot      \rconfig_template      \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"170.5\" cy=\"-134.5\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"170.5\" y=\"-131.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">train.ModelTraining</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.BodyPart -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>model.BodyPart</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"body_part            \r------------------------------\rbody_part_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"330.5,-223 244.5,-223 244.5,-188 330.5,-188 330.5,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"252.5\" y=\"-204\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.BodyPart</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.BodyPart&#45;&gt;model.Model.BodyPart -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>model.BodyPart-&gt;model.Model.BodyPart</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M302.27,-187.8C314.4,-174.08 331.1,-155.19 340.75,-144.27\"/>\n",
-       "</g>\n",
-       "<!-- train.VideoSet -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>train.VideoSet</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"video_set_id         \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"94.5,-294 2.5,-294 2.5,-259 94.5,-259 94.5,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"10.5\" y=\"-274.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.VideoSet</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet&#45;&gt;train.VideoSet.File -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>train.VideoSet-&gt;train.VideoSet.File</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M48.5,-258.8C48.5,-245.08 48.5,-226.19 48.5,-215.27\"/>\n",
-       "</g>\n",
-       "<!-- train.TrainingTask -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>train.TrainingTask</title>\n",
-       "<g id=\"a_node13\"><a xlink:title=\"→ train.VideoSet\r→ train.TrainingParamSet\rtraining_id          \r------------------------------\rmodel_prefix=&quot;&quot;      \rproject_path=&quot;&quot;      \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"226,-223 115,-223 115,-188 226,-188 226,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"123\" y=\"-203.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.TrainingTask</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet&#45;&gt;train.TrainingTask -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>train.VideoSet-&gt;train.TrainingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M77.72,-258.97C96.9,-248.13 121.88,-234 141.09,-223.13\"/>\n",
-       "</g>\n",
-       "<!-- model.RecordingInfo -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>model.RecordingInfo</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"→ model.VideoRecording\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"997.5\" cy=\"-134.5\" rx=\"80\" ry=\"17.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"997.5\" y=\"-131.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">model.RecordingInfo</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>model.VideoRecording</title>\n",
-       "<g id=\"a_node12\"><a xlink:title=\"→ session.Session\rrecording_id         \r------------------------------\r→ lab.Device\r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"902,-223 763,-223 763,-188 902,-188 902,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"771\" y=\"-203.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">model.VideoRecording</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.VideoRecording.File -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>model.VideoRecording-&gt;model.VideoRecording.File</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M832.5,-187.8C832.5,-174.08 832.5,-155.19 832.5,-144.27\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.PoseEstimationTask -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>model.VideoRecording-&gt;model.PoseEstimationTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M792.74,-187.97C766.55,-177.09 732.4,-162.89 706.22,-152.01\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.RecordingInfo -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>model.VideoRecording-&gt;model.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M872.02,-187.97C899.48,-176.49 935.75,-161.32 962.23,-150.25\"/>\n",
-       "</g>\n",
-       "<!-- train.TrainingTask&#45;&gt;train.ModelTraining -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>train.TrainingTask-&gt;train.ModelTraining</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M170.5,-187.8C170.5,-175.35 170.5,-158.66 170.5,-147.51\"/>\n",
-       "</g>\n",
-       "<!-- train.TrainingParamSet -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>train.TrainingParamSet</title>\n",
-       "<g id=\"a_node14\"><a xlink:title=\"paramset_idx         \r------------------------------\rparamset_desc        \rparam_set_hash       \rparams               \rUNIQUE INDEX (param_set_hash)\r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"338.5,-294 222.5,-294 222.5,-259 338.5,-259 338.5,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"230.5\" y=\"-275\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.TrainingParamSet</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.TrainingParamSet&#45;&gt;model.Model -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>train.TrainingParamSet-&gt;model.Model</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M307.09,-258.97C324.53,-248.13 347.27,-234 364.74,-223.13\"/>\n",
-       "</g>\n",
-       "<!-- train.TrainingParamSet&#45;&gt;train.TrainingTask -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>train.TrainingParamSet-&gt;train.TrainingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M254.15,-258.97C236.86,-248.13 214.34,-234 197.01,-223.13\"/>\n",
-       "</g>\n",
-       "<!-- model.Model.BodyPart&#45;&gt;model.PoseEstimation.BodyPartPosition -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>model.Model.BodyPart-&gt;model.PoseEstimation.BodyPartPosition</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M366.92,-124.85C415.03,-102.27 544.01,-41.71 592.1,-19.14\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>"
-      ],
-      "text/plain": [
-       "<datajoint.diagram.Diagram at 0x7f735f2dba30>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "dj.Diagram(train) + dj.Diagram(model)"
    ]
@@ -1238,26 +241,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This diagram represents the `element-deeplabcut` pipeline."
+    "This diagram represents an example of the `element-deeplabcut` pipeline."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 1 - Register an Existing Model in the DataJoint Pipeline"
+    "### **Step 1 - Register an Existing Model in the DataJoint Pipeline**"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A DeepLabCut model is defined in a specific folder structure with a `config.yaml` file that contains the model's specifications (see folder `example_data/inbox`). To \"register\" this DLC model with DataJoint, you can specify this config file:"
+    "A DeepLabCut project adheres to a specific folder structure, featuring a `config.yaml` file that outlines the model's specifications (located within the `example_data/inbox` directory). \n",
+    "\n",
+    "To \"register\" this DLC model into the pipeline, the initial step is to define the path to the configuration file. Subsequently, this path should be provided as an input to the function responsible for executing the model registration process."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1268,84 +273,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `insert_new_model` function is a helper function provided in `element-deeplacut` for convenient model registration.\n",
+    "The `insert_new_model` function serves as a convenient function within the `element-deeplacut` for simplifying the model registration process.\n",
     "\n",
-    "This function prints out the essential information, like the `model_name` and the `model_description`, together with other relevant information from the config file. \n",
-    "\n",
-    "If all the information is correct, you can confirm the insertion by typing 'yes,' which will insert the new model and its two body parts, `head` and `tailbase`:"
+    "Upon execution, this function generates a printout featuring vital details such as the `model_name` and `model_description`, along with pertinent information extracted from the configuration file. If all the information is accurate and in order, you can initiate the insertion process by typing 'yes', which will result in the registration of the new model, including its two associated body parts, `head` and `tailbase`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-10-17 15:35:14.274368: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
-      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "2023-10-17 15:35:18.481329: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n",
-      "2023-10-17 15:35:18.481376: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
-      "2023-10-17 15:35:18.859072: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2023-10-17 15:35:24.333497: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n",
-      "2023-10-17 15:35:24.333709: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n",
-      "2023-10-17 15:35:24.333725: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading DLC 2.3.7...\n",
-      "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n",
-      "--- DLC Model specification to be inserted ---\n",
-      "\tmodel_name: from_top_tracking_model_test\n",
-      "\tmodel_description: Model in example data: from_top_tracking model\n",
-      "\tscorer: DLCresnet50fromtoptrackingOct11shuffle1\n",
-      "\ttask: from_top_tracking\n",
-      "\tdate: Oct11\n",
-      "\titeration: 0\n",
-      "\tsnapshotindex: -1\n",
-      "\tshuffle: 1\n",
-      "\ttrainingsetindex: 0\n",
-      "\tproject_path: from_top_tracking-DataJoint-2023-10-11\n",
-      "\tparamset_idx: None\n",
-      "\t-- Template/Contents of config.yaml --\n",
-      "\t\tTask: from_top_tracking\n",
-      "\t\tscorer: DataJoint\n",
-      "\t\tdate: Oct11\n",
-      "\t\tmultianimalproject: False\n",
-      "\t\tidentity: None\n",
-      "\t\tproject_path: /workspaces/element-deeplabcut/example_data/inbox/from_top_tracking-DataJoint-2023-10-11\n",
-      "\t\tvideo_sets: {'/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/test.mp4': {'crop': '0, 500, 0, 500'}, '/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/train1.mp4': {'crop': '0, 500, 0, 500'}}\n",
-      "\t\tbodyparts: ['head', 'tailbase']\n",
-      "\t\tstart: 0\n",
-      "\t\tstop: 1\n",
-      "\t\tnumframes2pick: 40\n",
-      "\t\tskeleton: [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']]\n",
-      "\t\tskeleton_color: black\n",
-      "\t\tpcutoff: 0.6\n",
-      "\t\tdotsize: 12\n",
-      "\t\talphavalue: 0.7\n",
-      "\t\tcolormap: rainbow\n",
-      "\t\tTrainingFraction: [0.95]\n",
-      "\t\titeration: 0\n",
-      "\t\tdefault_net_type: resnet_50\n",
-      "\t\tdefault_augmenter: default\n",
-      "\t\tsnapshotindex: -1\n",
-      "\t\tbatch_size: 8\n",
-      "\t\tcropping: False\n",
-      "\t\tx1: 0\n",
-      "\t\tx2: 640\n",
-      "\t\ty1: 277\n",
-      "\t\ty2: 624\n",
-      "\t\tcorner2move2: [50, 50]\n",
-      "\t\tmove2corner: True\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.Model.insert_new_model(model_name='from_top_tracking_model_test',\n",
     "                             dlc_config=config_file_rel,\n",
@@ -1358,140 +295,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can check the `Model` table to confirm that the new model has been added:"
+    "You can check the `Model` table to confirm that the new model has been registered:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Relation{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Relation th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Relation td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Relation\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">model_name</p>\n",
-       "                            <span class=\"djtooltiptext\">User-friendly model name</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task</p>\n",
-       "                            <span class=\"djtooltiptext\">Task in the config yaml</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">date</p>\n",
-       "                            <span class=\"djtooltiptext\">Date in the config yaml</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">iteration</p>\n",
-       "                            <span class=\"djtooltiptext\">Iteration/version of this model</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">snapshotindex</p>\n",
-       "                            <span class=\"djtooltiptext\">which snapshot for prediction (if -1, latest)</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">shuffle</p>\n",
-       "                            <span class=\"djtooltiptext\">Shuffle (1) or not (0)</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">trainingsetindex</p>\n",
-       "                            <span class=\"djtooltiptext\">Index of training fraction list in config.yaml</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">scorer</p>\n",
-       "                            <span class=\"djtooltiptext\">Scorer/network name - DLC's GetScorerName()</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">config_template</p>\n",
-       "                            <span class=\"djtooltiptext\">Dictionary of the config for analyze_videos()</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">project_path</p>\n",
-       "                            <span class=\"djtooltiptext\">DLC's project_path in config relative to root</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">model_prefix</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">model_description</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">paramset_idx</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>from_top_tracking_model_test</td>\n",
-       "<td>from_top_tracking</td>\n",
-       "<td>Oct11</td>\n",
-       "<td>0</td>\n",
-       "<td>-1</td>\n",
-       "<td>1</td>\n",
-       "<td>0</td>\n",
-       "<td>DLCresnet50fromtoptrackingOct11shuffle1</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>from_top_tracking-DataJoint-2023-10-11</td>\n",
-       "<td></td>\n",
-       "<td>Model in example data: from_top_tracking model</td>\n",
-       "<td>None</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*model_name    task           date      iteration     snapshotindex  shuffle     trainingsetind scorer         config_tem project_path   model_prefix   model_descript paramset_idx  \n",
-       "+------------+ +------------+ +-------+ +-----------+ +------------+ +---------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+\n",
-       "from_top_track from_top_track Oct11     0             -1             1           0              DLCresnet50fro =BLOB=     from_top_track                Model in examp None          \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.Model()"
    ]
@@ -1500,120 +311,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Much of this information is directly sourced from the `config` file. However, it's worth noting that this model is currently distinct and singular. \n",
+    "A significant portion of this data is directly extracted from the `config` file. \n",
     "\n",
-    "If you wish to incorporate another model, you must specify a new `model_name`; duplication of an existing model is not permitted—it must be an entirely new model."
+    "It's important to highlight that every model added to the `model` table is unique and singular. When introducing a new model, it is mandatory to define a new `model_name`. Replicating an existing model is not permitted; the new model must be entirely distinct. Even if you attempt to employ a different `model_name` for a previously registered model, `DataJoint` will identify this duplication and trigger an error. This model management protocol is crucial for maintining data integrity."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 2 - Insert Subject, Session, and Behavior Videos"
+    "### **Step 2 - Insert Subject, Session, and Behavior Videos**"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Confirm the availability of data in the `Subject` and `Session` tables:"
+    "Now, let's delve into the `Subject` and `Session` tables and include some example data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Relation{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Relation th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Relation td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Relation\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">subject_nickname</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">sex</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">subject_birth_date</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">subject_description</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    subject_nickna sex     subject_birth_ subject_descri\n",
-       "+---------+ +------------+ +-----+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "subject.Subject()"
    ]
@@ -1622,12 +343,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Insert a subject into the `Subject` table:"
+    "Add a new entry for a subject in the `Subject` table:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1647,13 +368,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Define session keys and insert them into the `Session` table:\n",
+    "Create session keys and input them into the `Session` table: \n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1676,93 +397,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Relation{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Relation th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Relation td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Relation\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject6</td>\n",
-       "<td>2021-06-02 14:04:22</td></tr><tr><td>subject6</td>\n",
-       "<td>2021-06-03 14:43:10</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 2</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet\n",
-       "+----------+ +------------+\n",
-       "subject6     2021-06-02 14:\n",
-       "subject6     2021-06-03 14:\n",
-       " (Total: 2)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "session.Session()"
    ]
@@ -1771,12 +408,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Insert data into the `VideoRecording` table:"
+    "Insert data into the `VideoRecording` table as well:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1795,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1811,124 +448,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Populate the `RecordingInfo` table:"
+    "Now, populate the `RecordingInfo` table will extract the metadata from the video sets and storing it in the table. \n",
+    "\n",
+    "This metadata will serve as a valuable resource for subsequent analyses reliant on video characteristics and as a `lookup` table for accessing this video data whenever necessary."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Relation{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Relation th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Relation td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Relation\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">recording_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_height</p>\n",
-       "                            <span class=\"djtooltiptext\">height in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_width</p>\n",
-       "                            <span class=\"djtooltiptext\">width in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">nframes</p>\n",
-       "                            <span class=\"djtooltiptext\">number of frames</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">fps</p>\n",
-       "                            <span class=\"djtooltiptext\">(Hz) frames per second</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\">Datetime for the start of the recording</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_duration</p>\n",
-       "                            <span class=\"djtooltiptext\">video duration (s) from nframes / fps</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject6</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>500</td>\n",
-       "<td>500</td>\n",
-       "<td>60000</td>\n",
-       "<td>60</td>\n",
-       "<td>None</td>\n",
-       "<td>1000.0</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *recording_id  px_height     px_width     nframes     fps     recording_date recording_dura\n",
-       "+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n",
-       "subject6     2021-06-02 14: 1              500           500          60000       60      None           1000.0        \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.RecordingInfo.populate()\n",
     "model.RecordingInfo()"
@@ -1938,52 +467,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Recording info extracts metadata from the video and validates the number of frames (n_frames), which will correspond to the number of entries for each body part in the pose estimation results."
+    "We can confirm the accuracy of the inference analysis by cross-referencing it with the number of frames extracted (`nframes`). This count of frames should align with the number of entries for each body part in the pose estimation results."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 3 - DeepLabCut Inference Task"
+    "### **Step 3 - DeepLabCut Inference Task**"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `PoseEstimationTask` table is used for defining an inference task. Let's explore the table description:"
+    "The `PoseEstimationTask` table serves the purpose of specifying an inference task. Let's delve into the table description:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-> model.VideoRecording\n",
-      "-> model.Model\n",
-      "---\n",
-      "task_mode=\"load\"     : enum('load','trigger')       # load results or trigger computation\n",
-      "pose_estimation_output_dir=\"\" : varchar(255)                 # output dir relative to the root dir\n",
-      "pose_estimation_params=null : longblob                     # analyze_videos params, if not default\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'-> model.VideoRecording\\n-> model.Model\\n---\\ntask_mode=\"load\"     : enum(\\'load\\',\\'trigger\\')       # load results or trigger computation\\npose_estimation_output_dir=\"\" : varchar(255)                 # output dir relative to the root dir\\npose_estimation_params=null : longblob                     # analyze_videos params, if not default\\n'"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.PoseEstimationTask.describe()"
    ]
@@ -1992,52 +497,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To define and insert a task, you need to:\n",
+    "Defining and inserting a pose estimation task requires:\n",
     "\n",
     "1. Define a video recording.\n",
-    "2. Select a model.\n",
-    "3. Choose the task mode (load or trigger).\n",
-    "4. Specify the output directory and optional parameters.\n",
+    "2. Choose a model.\n",
+    "3. Select a task mode (\"load\" or \"trigger\").\n",
+    "4. Specify the output directory and any optional parameters.\n",
     "\n",
-    "When the task mode is \"trigger,\" DataJoint triggers the inference, running the DeepLabCut model. This might take a long time, depending on the hardware. If the hardware lacks GPU support, it's not recommended.\n",
+    "When utilizing the \"trigger\" task mode, DataJoint initiates the inference process, running the DeepLabCut model. The duration of this process can vary, depending on the available hardware. If your hardware lacks GPU support, it's advisable to avoid this mode for this tutorial.\n",
     "\n",
-    "For this exercise, we are choosing the **\"load\" task** mode because the server does not have the necessary GPU for inference. The results have already been prepared. The results of this inference are generated in `example_data\\outbox`. \n",
+    "For this particular exercise, we have opted for the **\"load\" task** mode since the server lacks the necessary GPU resources for inference. The inference results have already been prepared and can be found in the `example_data\\outbox` directory. \n",
     "\n",
-    "If you select the **\"trigger\" task**, DataJoint will perform the entire inference process and generate these file sets."
+    "If you choose the **\"trigger\" task** mode, DataJoint will handle the entire inference process and produce these sets of files."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's define the keys for recording and task:"
+    "Now, let's establish the keys for the pose estimation task: the recording identifiers, and the chosen model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'subject': 'subject6',\n",
-       " 'session_datetime': '2021-06-02 14:04:22',\n",
-       " 'recording_id': '1'}"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "recording_key"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2048,12 +540,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The results are located in the `pose_estimation_output_dir` location."
+    "The results can be found at the `pose_estimation_output_dir` location."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2068,123 +560,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Display the `PoseEstimationTask` table:"
+    "Show the contents of the `PoseEstimationTask` table."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Relation{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Relation th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Relation td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Relation\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">recording_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">model_name</p>\n",
-       "                            <span class=\"djtooltiptext\">User-friendly model name</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">load results or trigger computation</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pose_estimation_output_dir</p>\n",
-       "                            <span class=\"djtooltiptext\">output dir relative to the root dir</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pose_estimation_params</p>\n",
-       "                            <span class=\"djtooltiptext\">analyze_videos params, if not default</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject6</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>from_top_tracking_model_test</td>\n",
-       "<td>load</td>\n",
-       "<td>./example_data/outbox/from_top_tracking-DataJoint-2023-10-11/videos/device_1_recording_1_model_from_top_tracking_100000_maxiters</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *recording_id  *model_name    task_mode     pose_estimatio pose_estim\n",
-       "+----------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +--------+\n",
-       "subject6     2021-06-02 14: 1              from_top_track load          ./example_data =BLOB=    \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.PoseEstimationTask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2195,108 +585,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look into the `PoseEstimation` table."
+    "Now, let's examine the contents of the `PoseEstimation` table."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Relation{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Relation th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Relation td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Relation\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">recording_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">model_name</p>\n",
-       "                            <span class=\"djtooltiptext\">User-friendly model name</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pose_estimation_time</p>\n",
-       "                            <span class=\"djtooltiptext\">time of generation of this set of DLC results</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject6</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>from_top_tracking_model_test</td>\n",
-       "<td>2023-10-12 15:21:47</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *recording_id  *model_name    pose_estimatio\n",
-       "+----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject6     2021-06-02 14: 1              from_top_track 2023-10-12 15:\n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.PoseEstimation()"
    ]
@@ -2305,138 +601,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The most critical table is the `PoseEstimation.BodyPartPosition` because it will contain the results of the inference. "
+    "One of the most crucial table is `PoseEstimation.BodyPartPosition` as it will store the results of the inference: x and y coordinates, and likelihood of the pose estimation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Relation{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Relation th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Relation td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "        }\n",
-       "        .Relation tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b>uses DeepLabCut h5 output for body part position</b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Relation\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">recording_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">model_name</p>\n",
-       "                            <span class=\"djtooltiptext\">User-friendly model name</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">body_part</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">frame_index</p>\n",
-       "                            <span class=\"djtooltiptext\">frame index in model</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">x_pos</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">y_pos</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">z_pos</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">likelihood</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject6</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>from_top_tracking_model_test</td>\n",
-       "<td>head</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td></tr><tr><td>subject6</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>from_top_tracking_model_test</td>\n",
-       "<td>tailbase</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 2</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *recording_id  *model_name    *body_part    frame_inde x_pos      y_pos      z_pos      likelihood\n",
-       "+----------+ +------------+ +------------+ +------------+ +-----------+ +--------+ +--------+ +--------+ +--------+ +--------+\n",
-       "subject6     2021-06-02 14: 1              from_top_track head          =BLOB=     =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
-       "subject6     2021-06-02 14: 1              from_top_track tailbase      =BLOB=     =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
-       " (Total: 2)"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.PoseEstimation.BodyPartPosition()"
    ]
@@ -2445,16 +617,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "After pose estimation, entries related to the task include: `subject`, `session`, `recording_id`, `model name`, and each detected `body_part` (two entries in this case).\n",
+    "Following the pose estimation process, the task-related entries encompass: `subject`, `session`, `recording_id`, `model name`, and each detected `body_part` (in this case, two entries).\n",
     "\n",
-    "Entries also contain `frame_index`, `x_pos` and `y_pos` positions, and `likelihood` (`z_pos` is zero). This structure is familiar to DeepLabCut users.\n",
+    "Each entry includes `frame_index`, `x_pos` and `y_pos` positions, along with a `likelihood` value (`z_pos` is `None`). \n",
     "\n",
-    "Finally, these results can be fetched in a Pandas DataFrame structure: "
+    "These results can be retrieved in the form of a Pandas DataFrame."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2463,100 +635,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>subject</th>\n",
-       "      <th>session_datetime</th>\n",
-       "      <th>recording_id</th>\n",
-       "      <th>model_name</th>\n",
-       "      <th>body_part</th>\n",
-       "      <th>frame_index</th>\n",
-       "      <th>x_pos</th>\n",
-       "      <th>y_pos</th>\n",
-       "      <th>z_pos</th>\n",
-       "      <th>likelihood</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>head</td>\n",
-       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
-       "      <td>[273.9966125488281, 274.1033630371094, 274.032...</td>\n",
-       "      <td>[314.97100830078125, 315.1459655761719, 315.13...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.999998927116394, 0.999998927116394, 0.99999...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>tailbase</td>\n",
-       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
-       "      <td>[254.29002380371094, 254.2755584716797, 254.26...</td>\n",
-       "      <td>[275.48602294921875, 275.44000244140625, 275.4...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>[0.9999996423721313, 0.9999996423721313, 0.999...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    subject    session_datetime  recording_id                    model_name  \\\n",
-       "0  subject6 2021-06-02 14:04:22             1  from_top_tracking_model_test   \n",
-       "1  subject6 2021-06-02 14:04:22             1  from_top_tracking_model_test   \n",
-       "\n",
-       "  body_part                                        frame_index  \\\n",
-       "0      head  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
-       "1  tailbase  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
-       "\n",
-       "                                               x_pos  \\\n",
-       "0  [273.9966125488281, 274.1033630371094, 274.032...   \n",
-       "1  [254.29002380371094, 254.2755584716797, 254.26...   \n",
-       "\n",
-       "                                               y_pos z_pos  \\\n",
-       "0  [314.97100830078125, 315.1459655761719, 315.13...  None   \n",
-       "1  [275.48602294921875, 275.44000244140625, 275.4...  None   \n",
-       "\n",
-       "                                          likelihood  \n",
-       "0  [0.999998927116394, 0.999998927116394, 0.99999...  \n",
-       "1  [0.9999996423721313, 0.9999996423721313, 0.999...  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df"
    ]
@@ -2565,263 +646,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`frame_index` is an array of frame numbers, `x_pos` is a NumPy array of x positions, and `likelihood` is also a NumPy array.\n"
+    "`frame_index` consists of an array of frame numbers, `x_pos` is a NumPy array of x positions, and `likelihood` is a NumPy array as well.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Use DataJoint `fetch` as a Pandas DataFrame and utilize the `explode` function to expand `x` and `y` positions."
+    "Retrieve the data using the `fetch` method, store it as a Pandas DataFrame, and apply the `explode` function to expand the x and y positions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\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>index</th>\n",
-       "      <th>subject</th>\n",
-       "      <th>session_datetime</th>\n",
-       "      <th>recording_id</th>\n",
-       "      <th>model_name</th>\n",
-       "      <th>body_part</th>\n",
-       "      <th>frame_index</th>\n",
-       "      <th>x_pos</th>\n",
-       "      <th>y_pos</th>\n",
-       "      <th>z_pos</th>\n",
-       "      <th>likelihood</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>head</td>\n",
-       "      <td>0</td>\n",
-       "      <td>273.996613</td>\n",
-       "      <td>314.971008</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.999999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>head</td>\n",
-       "      <td>1</td>\n",
-       "      <td>274.103363</td>\n",
-       "      <td>315.145966</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.999999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>head</td>\n",
-       "      <td>2</td>\n",
-       "      <td>274.032654</td>\n",
-       "      <td>315.133331</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.999999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>head</td>\n",
-       "      <td>3</td>\n",
-       "      <td>274.025238</td>\n",
-       "      <td>315.152283</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.999999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>head</td>\n",
-       "      <td>4</td>\n",
-       "      <td>274.073181</td>\n",
-       "      <td>315.173248</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.999999</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>119995</th>\n",
-       "      <td>1</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>tailbase</td>\n",
-       "      <td>59995</td>\n",
-       "      <td>323.293884</td>\n",
-       "      <td>33.214066</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>119996</th>\n",
-       "      <td>1</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>tailbase</td>\n",
-       "      <td>59996</td>\n",
-       "      <td>321.602264</td>\n",
-       "      <td>32.794708</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>119997</th>\n",
-       "      <td>1</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>tailbase</td>\n",
-       "      <td>59997</td>\n",
-       "      <td>320.173981</td>\n",
-       "      <td>32.857304</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>119998</th>\n",
-       "      <td>1</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>tailbase</td>\n",
-       "      <td>59998</td>\n",
-       "      <td>318.708618</td>\n",
-       "      <td>33.147358</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.999999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>119999</th>\n",
-       "      <td>1</td>\n",
-       "      <td>subject6</td>\n",
-       "      <td>2021-06-02 14:04:22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>from_top_tracking_model_test</td>\n",
-       "      <td>tailbase</td>\n",
-       "      <td>59999</td>\n",
-       "      <td>317.674103</td>\n",
-       "      <td>33.861454</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>120000 rows × 11 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        index   subject    session_datetime  recording_id  \\\n",
-       "0           0  subject6 2021-06-02 14:04:22             1   \n",
-       "1           0  subject6 2021-06-02 14:04:22             1   \n",
-       "2           0  subject6 2021-06-02 14:04:22             1   \n",
-       "3           0  subject6 2021-06-02 14:04:22             1   \n",
-       "4           0  subject6 2021-06-02 14:04:22             1   \n",
-       "...       ...       ...                 ...           ...   \n",
-       "119995      1  subject6 2021-06-02 14:04:22             1   \n",
-       "119996      1  subject6 2021-06-02 14:04:22             1   \n",
-       "119997      1  subject6 2021-06-02 14:04:22             1   \n",
-       "119998      1  subject6 2021-06-02 14:04:22             1   \n",
-       "119999      1  subject6 2021-06-02 14:04:22             1   \n",
-       "\n",
-       "                          model_name body_part frame_index       x_pos  \\\n",
-       "0       from_top_tracking_model_test      head           0  273.996613   \n",
-       "1       from_top_tracking_model_test      head           1  274.103363   \n",
-       "2       from_top_tracking_model_test      head           2  274.032654   \n",
-       "3       from_top_tracking_model_test      head           3  274.025238   \n",
-       "4       from_top_tracking_model_test      head           4  274.073181   \n",
-       "...                              ...       ...         ...         ...   \n",
-       "119995  from_top_tracking_model_test  tailbase       59995  323.293884   \n",
-       "119996  from_top_tracking_model_test  tailbase       59996  321.602264   \n",
-       "119997  from_top_tracking_model_test  tailbase       59997  320.173981   \n",
-       "119998  from_top_tracking_model_test  tailbase       59998  318.708618   \n",
-       "119999  from_top_tracking_model_test  tailbase       59999  317.674103   \n",
-       "\n",
-       "             y_pos z_pos likelihood  \n",
-       "0       314.971008  None   0.999999  \n",
-       "1       315.145966  None   0.999999  \n",
-       "2       315.133331  None   0.999999  \n",
-       "3       315.152283  None   0.999999  \n",
-       "4       315.173248  None   0.999999  \n",
-       "...            ...   ...        ...  \n",
-       "119995   33.214066  None        1.0  \n",
-       "119996   32.794708  None        1.0  \n",
-       "119997   32.857304  None        1.0  \n",
-       "119998   33.147358  None   0.999999  \n",
-       "119999   33.861454  None        1.0  \n",
-       "\n",
-       "[120000 rows x 11 columns]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df = df.explode(['frame_index', 'x_pos', 'y_pos', 'likelihood']).reset_index()\n",
     "df"
@@ -2831,14 +670,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As mentioned earlier, you can confirm these results by the number of entries. There are 60000 frames for each body part, matching the `nframes` from the `RecordingInfo` table."
+    "As mentioned earlier, a validation step for these results involves verifying the number of entries. Each body part should have 60,000 frames, aligning with the `nframes` value stored in the `RecordingInfo` table."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 4 - Visualization of results"
+    "### **Step 4 - Visualization of results**"
    ]
   },
   {
@@ -2847,12 +686,12 @@
    "source": [
     "First, separate the data for the head and tailbase. \n",
     "\n",
-    "Then plot (1) the head pose estimation, and (2) the tailbase pose estimation."
+    "Following this, create two plots: (1) for the head pose estimation, and (2) for the tail base pose estimation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2864,20 +703,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots(2,1, figsize=(12, 4))\n",
     "\n",
@@ -2899,20 +727,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots(2,1, figsize=(12, 4))\n",
     "axs[0].set_title('x position - Tailbase pose estimation')\n",
@@ -2935,25 +752,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, let's plot the head and tailbase positions on the same graph."
+    "Lastly, let's generate a plot that displays the head and tailbase positions on the same graph."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots(2,1, figsize=(6,10))\n",
     "\n",
@@ -2977,13 +783,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we can plot the spatial mapping of both body parts, head and tail base."
+    "We have generated the spatial mapping plot for both body parts, head and tail base."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
@@ -3002,7 +803,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,