diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb
index 80c3f01..c1b4953 100644
--- a/notebooks/tutorial.ipynb
+++ b/notebooks/tutorial.ipynb
@@ -13,7 +13,7 @@
    "source": [
     "**Open-source Data Pipeline for Markerless Pose Estimation in Neurophysiology**\n",
     "\n",
-    "This tutorial focuses on providing a comprehensive understanding of the open-source data pipeline offered by `Element-DeepLabCut`. "
+    "This tutorial aims to provide a comprehensive understanding of the open-source data pipeline by `Element-DeepLabCut`."
    ]
   },
   {
@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The package is designed to facilitate pose estimation analyses and streamline the organization of data using `DataJoint`. "
+    "The package is designed to simplify pose estimation analyses and streamline data organization using `DataJoint`. "
    ]
   },
   {
@@ -41,7 +41,7 @@
    "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, participants will have a clear grasp of how to set up and apply the `Element DeepLabCut` for their specific pose estimation projects. "
    ]
   },
   {
@@ -50,17 +50,17 @@
    "source": [
     "**Key Components and Objectives**\n",
     "\n",
-    "- Setup\n",
+    "**- Setup**\n",
     "\n",
-    "- Design the DataJoint Pipeline\n",
+    "**- Designing the DataJoint Pipeline**\n",
     "\n",
-    "- Step 1 - Register an existing model in DataJoint pipeline\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 Subject, Session, and Behavior Videos**\n",
     "\n",
-    "- Step 3 - DLC inference task\n",
+    "**- Step 3: DeepLabCut Inference Task**\n",
     "\n",
-    "- Step 4 - Visualization of results"
+    "**- Step 4: Visualization of Results**"
    ]
   },
   {
@@ -69,7 +69,7 @@
    "source": [
     "For detailed documentation and tutorials on general DataJoint principles that support collaboration, automation, reproducibility, and visualizations:\n",
     "\n",
-    "[`DataJoint for Python - Interactive Tutorials`](https://github.com/datajoint/datajoint-tutorials) - Fundamentals including table tiers, query operations, fetch operations, automated computations with the make function, etc.\n",
+    "[`DataJoint for Python - Interactive Tutorials`](https://github.com/datajoint/datajoint-tutorials) covers fundamentals, including table tiers, query operations, fetch operations, automated computations with the make function, and more.\n",
     "\n",
     "[`DataJoint for Python - Documentation`](https://datajoint.com/docs/core/datajoint-python/0.14/)\n",
     "\n",
@@ -87,41 +87,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following tutorial consists of studying 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 objective is to extract pose estimations of the animal's head and tail base from video footage. \n",
+    "The goal is to extract pose estimations of the animal's head and tail base from video footage. \n",
     "\n",
-    "This information can provide valuable insights into the animal's movements, postures, and interactions within the environment. \n",
+    "This information offers 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",
     "\n",
-    "The results of this Element example could be combined with other modalities to assemble a complete pipeline for your particular lab/study.\n",
+    "#### Steps to Run the Element-DeepLabCut\n",
     "\n",
-    "#### Steps to run the Element\n",
+    "To run the Element, ensure that you have:\n",
     "\n",
-    "The Element assumes you:\n",
+    "- A DeepLabCut (DLC) project folder on your machine.\n",
     "\n",
-    "- Have a DLC project folder on your machine\n",
-    "\n",
-    "- Have labeled data in your DLC project folder\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`. "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Upon completing this tutorial, you will have acquired practical proficiency in employing the `Element-DeepLabCut` package to effectively tackle the complexities of pose estimation. \n",
-    "\n",
-    "This tutorial and sample dataset will serve as a practical foundation for your learning journey with the Element package, enabling you to apply these techniques to your own research projects. \n",
-    "\n",
-    "By integrating this element package with other Elements of DataJoint, you unlock a powerful data pipeline that provides numerous benefits for your research workflow. "
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,33 +140,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's connect to the database server. "
+    "This codespace provides a local database private to you for experimentation. Let's connect to the database server:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2023-10-14 07:16:40,528][INFO]: Connecting root@fakeservices.datajoint.io:3306\n",
-      "[2023-10-14 07:16:40,535][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()"
    ]
@@ -195,804 +163,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This tutorial is setup so that the element-deeplabcut is already configured, and instantiated, connected downstream from subject and session.\n",
-    "And that's what we're doing here, importing the schemas for subject, session, train, model, etc."
+    "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.:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2023-10-14 07:16:40,699][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": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "<g id=\"node7\" class=\"node\">\n",
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-       "<!-- session.Session&#45;&gt;session.ProjectSession -->\n",
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-       "<g id=\"node25\" class=\"node\">\n",
-       "<title>session.Session.Attribute</title>\n",
-       "<g id=\"a_node25\"><a xlink:title=\"→ session.Session\rattribute_name       \r------------------------------\rattribute_value=&quot;&quot;   \rattribute_blob=null  \r\">\n",
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-       "<!-- session.Session&#45;&gt;session.Session.Attribute -->\n",
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-       "<title>session.SessionNote</title>\n",
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-       "<!-- session.Session&#45;&gt;session.SessionNote -->\n",
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-       "</g>\n",
-       "<!-- session.SessionExperimenter -->\n",
-       "<g id=\"node31\" class=\"node\">\n",
-       "<title>session.SessionExperimenter</title>\n",
-       "<g id=\"a_node31\"><a xlink:title=\"→ session.Session\r→ lab.User\r\">\n",
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-       "</g>\n",
-       "<!-- session.Session&#45;&gt;session.SessionExperimenter -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>session.Session-&gt;session.SessionExperimenter</title>\n",
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-       "</g>\n",
-       "<!-- session.SessionDirectory -->\n",
-       "<g id=\"node37\" class=\"node\">\n",
-       "<title>session.SessionDirectory</title>\n",
-       "<g id=\"a_node37\"><a xlink:title=\"→ session.Session\r------------------------------\rsession_dir          \r\">\n",
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-       "</g>\n",
-       "</g>\n",
-       "<!-- session.Session&#45;&gt;session.SessionDirectory -->\n",
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-       "<title>session.Session-&gt;session.SessionDirectory</title>\n",
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-       "<g id=\"node42\" class=\"node\">\n",
-       "<title>model.VideoRecording</title>\n",
-       "<g id=\"a_node42\"><a xlink:title=\"→ session.Session\rrecording_id         \r------------------------------\r→ lab.Device\r\">\n",
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-       "<!-- session.Session&#45;&gt;model.VideoRecording -->\n",
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-       "<title>session.Session-&gt;model.VideoRecording</title>\n",
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-       "<title>subject.Subject</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"subject              \r------------------------------\rsubject_nickname=&quot;&quot;  \rsex                  \rsubject_birth_date   \rsubject_description=&quot;&quot; \r\">\n",
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-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;session.Session -->\n",
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-       "<title>subject.Subject-&gt;session.Session</title>\n",
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-       "<!-- subject.Subject&#45;&gt;subject.Subject.Source -->\n",
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-       "<!-- subject.Subject&#45;&gt;subject.Subject.User -->\n",
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-       "<g id=\"node16\" class=\"node\">\n",
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-       "<title>subject.Zygosity</title>\n",
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-       "<!-- subject.Subject&#45;&gt;subject.Subject.Strain -->\n",
-       "<g id=\"edge13\" 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=\"M1251.51,-338.11C1195.82,-328.4 1102.41,-311.49 1022.65,-294 1011.69,-291.6 999.77,-288.71 989.12,-286.02\"/>\n",
-       "</g>\n",
-       "<!-- subject.Subject.Lab -->\n",
-       "<g id=\"node48\" class=\"node\">\n",
-       "<title>subject.Subject.Lab</title>\n",
-       "<g id=\"a_node48\"><a xlink:title=\"→ subject.Subject\r→ lab.Lab\r------------------------------\rsubject_alias=&quot;&quot;     \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"2229.15,-286 2126.15,-286 2126.15,-267 2229.15,-267 2229.15,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2177.65\" 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=\"edge14\" 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=\"M1349.85,-342.19C1390.95,-338.66 1451.09,-333.68 1503.65,-330 1776.34,-310.93 1847.94,-338.49 2117.65,-294 2128.84,-292.15 2140.95,-289.05 2151.33,-286.02\"/>\n",
-       "</g>\n",
-       "<!-- subject.SubjectDeath -->\n",
-       "<g id=\"node51\" class=\"node\">\n",
-       "<title>subject.SubjectDeath</title>\n",
-       "<g id=\"a_node51\"><a xlink:title=\"→ subject.Subject\r------------------------------\rdeath_date           \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"1366.15,-294 1235.15,-294 1235.15,-259 1366.15,-259 1366.15,-294\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1300.65\" y=\"-273.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">subject.SubjectDeath</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;subject.SubjectDeath -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;subject.SubjectDeath</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1300.65,-329.8C1300.65,-318.95 1300.65,-304.87 1300.65,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- model.ModelEvaluation -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>model.ModelEvaluation</title>\n",
-       "<g id=\"a_node3\"><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=\"1933.65\" cy=\"-134.5\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1933.65\" 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.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=\"1873.65\" cy=\"-68\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1873.65\" 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=\"node27\" class=\"node\">\n",
-       "<title>model.PoseEstimation.BodyPartPosition</title>\n",
-       "<g id=\"a_node27\"><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=\"1970.65,-19 1776.65,-19 1776.65,0 1970.65,0 1970.65,-19\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1873.65\" 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=\"edge16\" 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=\"M1873.65,-54.74C1873.65,-43.97 1873.65,-28.56 1873.65,-19.06\"/>\n",
-       "</g>\n",
-       "<!-- lab.User -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>lab.User</title>\n",
-       "<g id=\"a_node5\"><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=\"672.15,-365 619.15,-365 619.15,-330 672.15,-330 672.15,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"627.15\" 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;subject.Subject.User -->\n",
-       "<g id=\"edge17\" 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=\"M655.57,-329.8C663.73,-316.08 674.95,-297.19 681.44,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- lab.ProjectUser -->\n",
-       "<g id=\"node23\" class=\"node\">\n",
-       "<title>lab.ProjectUser</title>\n",
-       "<g id=\"a_node23\"><a xlink:title=\"→ lab.Project\r→ lab.User\r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"576.15,-294 477.15,-294 477.15,-259 576.15,-259 576.15,-294\"/>\n",
-       "<text text-anchor=\"middle\" x=\"526.65\" y=\"-273.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">lab.ProjectUser</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.User&#45;&gt;lab.ProjectUser -->\n",
-       "<g id=\"edge18\" 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=\"M618.94,-331.02C600.03,-320.05 574.69,-305.35 555.34,-294.14\"/>\n",
-       "</g>\n",
-       "<!-- lab.User&#45;&gt;session.SessionExperimenter -->\n",
-       "<g id=\"edge19\" 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=\"M635.47,-329.96C625.82,-312.05 614.11,-282.85 623.65,-259 629.31,-244.86 640.78,-232.36 651.32,-223.1\"/>\n",
-       "</g>\n",
-       "<!-- lab.LabMembership -->\n",
-       "<g id=\"node44\" class=\"node\">\n",
-       "<title>lab.LabMembership</title>\n",
-       "<g id=\"a_node44\"><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=\"2108.15,-294 2007.15,-294 2007.15,-259 2108.15,-259 2108.15,-294\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2057.65\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">lab.LabMembership</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.User&#45;&gt;lab.LabMembership -->\n",
-       "<g id=\"edge20\" 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=\"M672.37,-342.4C698.98,-338.5 741.04,-332.82 777.65,-330 1318.5,-288.31 1461.2,-374.49 1997.65,-294 2000.75,-293.54 2003.91,-292.96 2007.09,-292.32\"/>\n",
-       "</g>\n",
-       "<!-- lab.ProtocolType -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>lab.ProtocolType</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"protocol_type        \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"865.15,-436 776.15,-436 776.15,-401 865.15,-401 865.15,-436\"/>\n",
-       "<text text-anchor=\"start\" x=\"784.15\" 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=\"854.65,-365 786.65,-365 786.65,-330 854.65,-330 854.65,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"794.65\" 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=\"edge21\" 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=\"M820.65,-400.8C820.65,-389.95 820.65,-375.87 820.65,-365.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.Source -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>lab.Source</title>\n",
-       "<g id=\"a_node8\"><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=\"1576.15,-365 1513.15,-365 1513.15,-330 1576.15,-330 1576.15,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"1521.15\" 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.Subject.Source -->\n",
-       "<g id=\"edge22\" 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=\"M1520.46,-329.97C1500.26,-316.18 1472.29,-297.06 1456.27,-286.12\"/>\n",
-       "</g>\n",
-       "<!-- subject.Allele.Source -->\n",
-       "<g id=\"node32\" class=\"node\">\n",
-       "<title>subject.Allele.Source</title>\n",
-       "<g id=\"a_node32\"><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=\"1874.15,-286 1765.15,-286 1765.15,-267 1874.15,-267 1874.15,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1819.65\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Allele.Source</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Source&#45;&gt;subject.Allele.Source -->\n",
-       "<g id=\"edge23\" 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=\"M1576.29,-338.8C1617.75,-328.69 1692.71,-310.29 1756.65,-294 1766.62,-291.46 1777.46,-288.64 1787.3,-286.06\"/>\n",
-       "</g>\n",
-       "<!-- lab.Lab -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>lab.Lab</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"lab                  \r------------------------------\rlab_name             \raddress              \rtime_zone            \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"2311.65,-365 2263.65,-365 2263.65,-330 2311.65,-330 2311.65,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"2271.65\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.Lab</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Location -->\n",
-       "<g id=\"node41\" class=\"node\">\n",
-       "<title>lab.Location</title>\n",
-       "<g id=\"a_node41\"><a xlink:title=\"→ lab.Lab\rlocation             \r------------------------------\rlocation_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"2316.15,-294 2247.15,-294 2247.15,-259 2316.15,-259 2316.15,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"2255.15\" y=\"-275\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.Location</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Lab&#45;&gt;lab.Location -->\n",
-       "<g id=\"edge24\" 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=\"M2286.2,-329.8C2285.25,-318.95 2284.03,-304.87 2283.09,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.Lab&#45;&gt;lab.LabMembership -->\n",
-       "<g id=\"edge25\" 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=\"M2263.47,-339.25C2226.91,-328.28 2156.72,-307.22 2108.56,-292.77\"/>\n",
-       "</g>\n",
-       "<!-- lab.Lab&#45;&gt;subject.Subject.Lab -->\n",
-       "<g id=\"edge26\" 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=\"M2263.51,-331.36C2241.37,-317.47 2209.38,-297.4 2191.35,-286.09\"/>\n",
-       "</g>\n",
-       "<!-- lab.Lab.Organization -->\n",
-       "<g id=\"node50\" class=\"node\">\n",
-       "<title>lab.Lab.Organization</title>\n",
-       "<g id=\"a_node50\"><a xlink:title=\"→ lab.Lab\r→ lab.Organization\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"2440.65,-286 2334.65,-286 2334.65,-267 2440.65,-267 2440.65,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2387.65\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">lab.Lab.Organization</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Lab&#45;&gt;lab.Lab.Organization -->\n",
-       "<g id=\"edge27\" 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=\"M2311.6,-329.97C2331.59,-316.18 2359.29,-297.06 2375.16,-286.12\"/>\n",
-       "</g>\n",
-       "<!-- model.Model.BodyPart -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>model.Model.BodyPart</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"→ model.Model\r→ model.BodyPart\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"2139.65,-144 2023.65,-144 2023.65,-125 2139.65,-125 2139.65,-144\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2081.65\" y=\"-132\" font-family=\"arial\" font-size=\"10.00\">model.Model.BodyPart</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.Model.BodyPart&#45;&gt;model.PoseEstimation.BodyPartPosition -->\n",
-       "<g id=\"edge28\" 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=\"M2067.67,-124.91C2043.93,-110.34 1994.17,-79.96 1951.65,-55 1930,-42.29 1904.73,-27.97 1888.96,-19.09\"/>\n",
-       "</g>\n",
-       "<!-- subject.Line.Allele -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>subject.Line.Allele</title>\n",
-       "<g id=\"a_node12\"><a xlink:title=\"→ subject.Line\r→ subject.Allele\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"1988.65,-286 1892.65,-286 1892.65,-267 1988.65,-267 1988.65,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1940.65\" y=\"-274\" font-family=\"arial\" font-size=\"10.00\">subject.Line.Allele</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.BodyPart -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>model.BodyPart</title>\n",
-       "<g id=\"a_node13\"><a xlink:title=\"body_part            \r------------------------------\rbody_part_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"2228.65,-223 2142.65,-223 2142.65,-188 2228.65,-188 2228.65,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"2150.65\" 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=\"edge29\" 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=\"M2160.74,-187.97C2139.95,-174.18 2111.14,-155.06 2094.64,-144.12\"/>\n",
-       "</g>\n",
-       "<!-- lab.ProjectSourceCode -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>lab.ProjectSourceCode</title>\n",
-       "<g id=\"a_node15\"><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=\"161.15,-294 20.15,-294 20.15,-259 161.15,-259 161.15,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"28.15\" 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.PoseEstimationTask -->\n",
-       "<g id=\"node18\" class=\"node\">\n",
-       "<title>model.PoseEstimationTask</title>\n",
-       "<g id=\"a_node18\"><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=\"1844.15,-152 1683.15,-152 1683.15,-117 1844.15,-117 1844.15,-152\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1763.65\" y=\"-131.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">model.PoseEstimationTask</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimationTask&#45;&gt;model.PoseEstimation -->\n",
-       "<g id=\"edge30\" class=\"edge\">\n",
-       "<title>model.PoseEstimationTask-&gt;model.PoseEstimation</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1791.98,-116.89C1815.02,-103.38 1846.31,-85.03 1862.66,-75.44\"/>\n",
-       "</g>\n",
-       "<!-- subject.Allele -->\n",
-       "<g id=\"node19\" class=\"node\">\n",
-       "<title>subject.Allele</title>\n",
-       "<g id=\"a_node19\"><a xlink:title=\"allele               \r------------------------------\rallele_standard_name=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"1857.15,-365 1782.15,-365 1782.15,-330 1857.15,-330 1857.15,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"1790.15\" 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.Line.Allele -->\n",
-       "<g id=\"edge31\" 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=\"M1848.63,-329.97C1872.82,-316.18 1906.34,-297.06 1925.53,-286.12\"/>\n",
-       "</g>\n",
-       "<!-- subject.Allele&#45;&gt;subject.Allele.Source -->\n",
-       "<g id=\"edge32\" 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=\"M1819.65,-329.8C1819.65,-316.08 1819.65,-297.19 1819.65,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- subject.Allele&#45;&gt;subject.Zygosity -->\n",
-       "<g id=\"edge33\" 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=\"M1789.95,-329.97C1770.46,-319.13 1745.06,-305 1725.54,-294.13\"/>\n",
-       "</g>\n",
-       "<!-- train.TrainingParamSet -->\n",
-       "<g id=\"node20\" class=\"node\">\n",
-       "<title>train.TrainingParamSet</title>\n",
-       "<g id=\"a_node20\"><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=\"2574.65,-294 2458.65,-294 2458.65,-259 2574.65,-259 2574.65,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"2466.65\" 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=\"node49\" class=\"node\">\n",
-       "<title>train.TrainingTask</title>\n",
-       "<g id=\"a_node49\"><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=\"2572.15,-223 2461.15,-223 2461.15,-188 2572.15,-188 2572.15,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"2469.15\" 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=\"edge34\" 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=\"M2516.65,-258.8C2516.65,-247.95 2516.65,-233.87 2516.65,-223.05\"/>\n",
-       "</g>\n",
-       "<!-- model.Model -->\n",
-       "<g id=\"node52\" class=\"node\">\n",
-       "<title>model.Model</title>\n",
-       "<g id=\"a_node52\"><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=\"2124.65,-223 2038.65,-223 2038.65,-188 2124.65,-188 2124.65,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"2046.65\" 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=\"edge35\" 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=\"M2458.64,-260.73C2455.61,-260.11 2452.6,-259.53 2449.65,-259 2310.52,-234.05 2271.89,-252.51 2133.65,-223 2130.78,-222.39 2127.84,-221.68 2124.89,-220.91\"/>\n",
-       "</g>\n",
-       "<!-- train.ModelTraining -->\n",
-       "<g id=\"node21\" class=\"node\">\n",
-       "<title>train.ModelTraining</title>\n",
-       "<g id=\"a_node21\"><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=\"2516.65\" cy=\"-134.5\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2516.65\" 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",
-       "<!-- subject.SubjectCull -->\n",
-       "<g id=\"node22\" class=\"node\">\n",
-       "<title>subject.SubjectCull</title>\n",
-       "<g id=\"a_node22\"><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=\"1591.65,-223 1471.65,-223 1471.65,-188 1591.65,-188 1591.65,-223\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1531.65\" y=\"-202.4\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">subject.SubjectCull</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet -->\n",
-       "<g id=\"node24\" class=\"node\">\n",
-       "<title>train.VideoSet</title>\n",
-       "<g id=\"a_node24\"><a xlink:title=\"video_set_id         \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"2684.65,-294 2592.65,-294 2592.65,-259 2684.65,-259 2684.65,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"2600.65\" 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.File -->\n",
-       "<g id=\"node28\" class=\"node\">\n",
-       "<title>train.VideoSet.File</title>\n",
-       "<g id=\"a_node28\"><a xlink:title=\"→ train.VideoSet\rfile_id              \r------------------------------\rfile_path            \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"2687.15,-215 2590.15,-215 2590.15,-196 2687.15,-196 2687.15,-215\"/>\n",
-       "<text text-anchor=\"start\" x=\"2598.15\" y=\"-204\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.VideoSet.File</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet&#45;&gt;train.VideoSet.File -->\n",
-       "<g id=\"edge36\" 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=\"M2638.65,-258.8C2638.65,-245.08 2638.65,-226.19 2638.65,-215.27\"/>\n",
-       "</g>\n",
-       "<!-- train.VideoSet&#45;&gt;train.TrainingTask -->\n",
-       "<g id=\"edge37\" 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=\"M2609.43,-258.97C2590.25,-248.13 2565.27,-234 2546.06,-223.13\"/>\n",
-       "</g>\n",
-       "<!-- model.RecordingInfo -->\n",
-       "<g id=\"node29\" class=\"node\">\n",
-       "<title>model.RecordingInfo</title>\n",
-       "<g id=\"a_node29\"><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=\"1218.65\" cy=\"-134.5\" rx=\"80\" ry=\"17.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1218.65\" 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",
-       "<!-- subject.Line -->\n",
-       "<g id=\"node30\" class=\"node\">\n",
-       "<title>subject.Line</title>\n",
-       "<g id=\"a_node30\"><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=\"1764.15,-365 1695.15,-365 1695.15,-330 1764.15,-330 1764.15,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"1703.15\" 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=\"edge38\" 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=\"M1764.17,-333.35C1767.37,-332.2 1770.56,-331.06 1773.65,-330 1821.78,-313.42 1878.42,-296.06 1911.83,-286.04\"/>\n",
-       "</g>\n",
-       "<!-- subject.Line&#45;&gt;subject.Subject.Line -->\n",
-       "<g id=\"edge39\" 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=\"M1695.03,-331.19C1663.61,-317.29 1618.43,-297.31 1592.98,-286.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.ProjectKeywords -->\n",
-       "<g id=\"node33\" class=\"node\">\n",
-       "<title>lab.ProjectKeywords</title>\n",
-       "<g id=\"a_node33\"><a xlink:title=\"→ lab.Project\rkeyword              \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"306.15,-294 179.15,-294 179.15,-259 306.15,-259 306.15,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"187.15\" 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.Protocol&#45;&gt;subject.Subject.Protocol -->\n",
-       "<g id=\"edge40\" 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=\"M820.65,-329.8C820.65,-316.08 820.65,-297.19 820.65,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording.File -->\n",
-       "<g id=\"node35\" class=\"node\">\n",
-       "<title>model.VideoRecording.File</title>\n",
-       "<g id=\"a_node35\"><a xlink:title=\"→ model.VideoRecording\rfile_id              \r------------------------------\rfile_path            \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"1450.65,-144 1316.65,-144 1316.65,-125 1450.65,-125 1450.65,-144\"/>\n",
-       "<text text-anchor=\"start\" x=\"1324.65\" y=\"-133\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.VideoRecording.File</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.ProjectPublication -->\n",
-       "<g id=\"node36\" class=\"node\">\n",
-       "<title>lab.ProjectPublication</title>\n",
-       "<g id=\"a_node36\"><a xlink:title=\"→ lab.Project\rpublication          \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"458.65,-294 324.65,-294 324.65,-259 458.65,-259 458.65,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"332.65\" 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",
-       "<!-- lab.Organization -->\n",
-       "<g id=\"node38\" class=\"node\">\n",
-       "<title>lab.Organization</title>\n",
-       "<g id=\"a_node38\"><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=\"2439.65,-365 2335.65,-365 2335.65,-330 2439.65,-330 2439.65,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"2343.65\" 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=\"edge41\" 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=\"M2387.65,-329.8C2387.65,-316.08 2387.65,-297.19 2387.65,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- lab.Project -->\n",
-       "<g id=\"node39\" class=\"node\">\n",
-       "<title>lab.Project</title>\n",
-       "<g id=\"a_node39\"><a xlink:title=\"project              \r------------------------------\rproject_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"274.15,-365 211.15,-365 211.15,-330 274.15,-330 274.15,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"219.15\" 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;session.ProjectSession -->\n",
-       "<g id=\"edge42\" 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=\"M210.91,-345.53C135.3,-342.11 -45.16,-326.8 10.65,-259 35.17,-229.21 136.83,-216.08 207.64,-210.47\"/>\n",
-       "</g>\n",
-       "<!-- lab.Project&#45;&gt;lab.ProjectSourceCode -->\n",
-       "<g id=\"edge43\" 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=\"M210.78,-332.04C186.32,-320.93 152.53,-305.59 127.01,-294\"/>\n",
-       "</g>\n",
-       "<!-- lab.Project&#45;&gt;lab.ProjectUser -->\n",
-       "<g id=\"edge44\" 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=\"M274.43,-339.54C318.1,-329.88 399.07,-311.58 467.65,-294 470.74,-293.21 473.92,-292.37 477.12,-291.52\"/>\n",
-       "</g>\n",
-       "<!-- lab.Project&#45;&gt;lab.ProjectKeywords -->\n",
-       "<g id=\"edge45\" 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=\"M242.65,-329.8C242.65,-318.95 242.65,-304.87 242.65,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.Project&#45;&gt;lab.ProjectPublication -->\n",
-       "<g id=\"edge46\" 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=\"M274.25,-331.87C298.17,-320.79 331.04,-305.57 355.91,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- lab.UserRole -->\n",
-       "<g id=\"node40\" class=\"node\">\n",
-       "<title>lab.UserRole</title>\n",
-       "<g id=\"a_node40\"><a xlink:title=\"user_role            \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"2094.65,-365 2020.65,-365 2020.65,-330 2094.65,-330 2094.65,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"2028.65\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.UserRole</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.UserRole&#45;&gt;lab.LabMembership -->\n",
-       "<g id=\"edge47\" 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=\"M2057.65,-329.8C2057.65,-318.95 2057.65,-304.87 2057.65,-294.05\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.PoseEstimationTask -->\n",
-       "<g id=\"edge48\" 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=\"M1453.41,-189.8C1456.53,-189.18 1459.62,-188.58 1462.65,-188 1537.15,-173.73 1622.34,-159.02 1683.03,-148.81\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.RecordingInfo -->\n",
-       "<g id=\"edge49\" class=\"edge\">\n",
-       "<title>model.VideoRecording-&gt;model.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1344.13,-187.97C1316.67,-176.49 1280.4,-161.32 1253.92,-150.25\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.VideoRecording.File -->\n",
-       "<g id=\"edge50\" 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=\"M1383.65,-187.8C1383.65,-174.08 1383.65,-155.19 1383.65,-144.27\"/>\n",
-       "</g>\n",
-       "<!-- subject.Strain -->\n",
-       "<g id=\"node45\" class=\"node\">\n",
-       "<title>subject.Strain</title>\n",
-       "<g id=\"a_node45\"><a xlink:title=\"strain               \r------------------------------\rstrain_standard_name \rstrain_desc=&quot;&quot;       \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"995.15,-365 918.15,-365 918.15,-330 995.15,-330 995.15,-365\"/>\n",
-       "<text text-anchor=\"start\" x=\"926.15\" y=\"-346\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">subject.Strain</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Strain&#45;&gt;subject.Subject.Strain -->\n",
-       "<g id=\"edge51\" 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=\"M956.65,-329.8C956.65,-316.08 956.65,-297.19 956.65,-286.27\"/>\n",
-       "</g>\n",
-       "<!-- lab.Device -->\n",
-       "<g id=\"node47\" class=\"node\">\n",
-       "<title>lab.Device</title>\n",
-       "<g id=\"a_node47\"><a xlink:title=\"device               \r------------------------------\rmodality             \rdescription=&quot;&quot;       \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"1217.15,-294 1154.15,-294 1154.15,-259 1217.15,-259 1217.15,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"1162.15\" y=\"-275\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">lab.Device</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- lab.Device&#45;&gt;model.VideoRecording -->\n",
-       "<g id=\"edge52\" 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=\"M1217.45,-262.15C1220.21,-261.06 1222.97,-259.99 1225.65,-259 1260.33,-246.1 1299.83,-232.98 1330.66,-223.08\"/>\n",
-       "</g>\n",
-       "<!-- train.TrainingTask&#45;&gt;train.ModelTraining -->\n",
-       "<g id=\"edge53\" class=\"edge\">\n",
-       "<title>train.TrainingTask-&gt;train.ModelTraining</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M2516.65,-187.8C2516.65,-175.35 2516.65,-158.66 2516.65,-147.51\"/>\n",
-       "</g>\n",
-       "<!-- subject.SubjectDeath&#45;&gt;subject.SubjectCull -->\n",
-       "<g id=\"edge54\" class=\"edge\">\n",
-       "<title>subject.SubjectDeath-&gt;subject.SubjectCull</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M1355.98,-258.97C1392.42,-248.09 1439.94,-233.89 1476.37,-223.01\"/>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.ModelEvaluation -->\n",
-       "<g id=\"edge55\" class=\"edge\">\n",
-       "<title>model.Model-&gt;model.ModelEvaluation</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M2046.2,-187.97C2013.36,-172.66 1966.47,-150.8 1945.08,-140.83\"/>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.Model.BodyPart -->\n",
-       "<g id=\"edge56\" 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=\"M2081.65,-187.8C2081.65,-174.08 2081.65,-155.19 2081.65,-144.27\"/>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.PoseEstimationTask -->\n",
-       "<g id=\"edge57\" 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=\"M2038.43,-195.12C1987.49,-184.07 1901.9,-165.5 1839.76,-152.02\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>"
-      ],
-      "text/plain": [
-       "<datajoint.diagram.Diagram at 0x7fae2c1fa490>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "(\n",
     "    dj.Diagram(subject) \n",
@@ -1003,240 +190,18 @@
     ")"
    ]
   },
+  {
+   "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."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
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-       "<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",
-       "<!-- model.RecordingInfo -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>model.RecordingInfo</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"→ model.VideoRecording\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
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-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.PoseEstimationTask -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>model.PoseEstimationTask</title>\n",
-       "<g id=\"a_node2\"><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=\"491.5,-152 330.5,-152 330.5,-117 491.5,-117 491.5,-152\"/>\n",
-       "<text text-anchor=\"middle\" x=\"411\" 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=\"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=\"521\" cy=\"-68\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"521\" 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=\"edge1\" class=\"edge\">\n",
-       "<title>model.PoseEstimationTask-&gt;model.PoseEstimation</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M439.33,-116.89C462.37,-103.38 493.66,-85.03 510.01,-75.44\"/>\n",
-       "</g>\n",
-       "<!-- model.ModelEvaluation -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>model.ModelEvaluation</title>\n",
-       "<g id=\"a_node3\"><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=\"581\" cy=\"-134.5\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"581\" 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.PoseEstimation.BodyPartPosition -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>model.PoseEstimation.BodyPartPosition</title>\n",
-       "<g id=\"a_node12\"><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=\"618,-19 424,-19 424,0 618,0 618,-19\"/>\n",
-       "<text text-anchor=\"middle\" x=\"521\" 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=\"edge2\" 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=\"M521,-54.74C521,-43.97 521,-28.56 521,-19.06\"/>\n",
-       "</g>\n",
-       "<!-- train.TrainingParamSet -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>train.TrainingParamSet</title>\n",
-       "<g id=\"a_node5\"><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=\"855,-294 739,-294 739,-259 855,-259 855,-294\"/>\n",
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-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.TrainingTask -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>train.TrainingTask</title>\n",
-       "<g id=\"a_node8\"><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=\"962.5,-223 851.5,-223 851.5,-188 962.5,-188 962.5,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"859.5\" 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=\"edge3\" 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=\"M823.35,-258.97C840.64,-248.13 863.16,-234 880.49,-223.13\"/>\n",
-       "</g>\n",
-       "<!-- model.Model -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>model.Model</title>\n",
-       "<g id=\"a_node14\"><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",
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-       "</g>\n",
-       "</g>\n",
-       "<!-- train.TrainingParamSet&#45;&gt;model.Model -->\n",
-       "<g id=\"edge4\" 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=\"M770.41,-258.97C752.97,-248.13 730.23,-234 712.76,-223.13\"/>\n",
-       "</g>\n",
-       "<!-- train.ModelTraining -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>train.ModelTraining</title>\n",
-       "<g id=\"a_node6\"><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=\"907\" cy=\"-134.5\" rx=\"13\" ry=\"13\"/>\n",
-       "<text text-anchor=\"middle\" x=\"907\" 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",
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-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>model.VideoRecording.File</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ model.VideoRecording\rfile_id              \r------------------------------\rfile_path            \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"312,-144 178,-144 178,-125 312,-125 312,-144\"/>\n",
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-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.TrainingTask&#45;&gt;train.ModelTraining -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>train.TrainingTask-&gt;train.ModelTraining</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M907,-187.8C907,-175.35 907,-158.66 907,-147.51\"/>\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=\"1075,-294 983,-294 983,-259 1075,-259 1075,-294\"/>\n",
-       "<text text-anchor=\"start\" x=\"991\" 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.TrainingTask -->\n",
-       "<g id=\"edge6\" 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=\"M999.78,-258.97C980.6,-248.13 955.62,-234 936.41,-223.13\"/>\n",
-       "</g>\n",
-       "<!-- train.VideoSet.File -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>train.VideoSet.File</title>\n",
-       "<g id=\"a_node15\"><a xlink:title=\"→ train.VideoSet\rfile_id              \r------------------------------\rfile_path            \r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"1077.5,-215 980.5,-215 980.5,-196 1077.5,-196 1077.5,-215\"/>\n",
-       "<text text-anchor=\"start\" x=\"988.5\" y=\"-204\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.VideoSet.File</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=\"M1029,-258.8C1029,-245.08 1029,-226.19 1029,-215.27\"/>\n",
-       "</g>\n",
-       "<!-- model.Model.BodyPart -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>model.Model.BodyPart</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ model.Model\r→ model.BodyPart\r\">\n",
-       "<polygon fill=\"transparent\" stroke=\"transparent\" points=\"787,-144 671,-144 671,-125 787,-125 787,-144\"/>\n",
-       "<text text-anchor=\"middle\" x=\"729\" y=\"-132\" font-family=\"arial\" font-size=\"10.00\">model.Model.BodyPart</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.Model.BodyPart&#45;&gt;model.PoseEstimation.BodyPartPosition -->\n",
-       "<g id=\"edge8\" 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=\"M714.89,-124.95C690.93,-110.44 640.73,-80.15 598,-55 576.58,-42.39 551.64,-28.04 536.09,-19.13\"/>\n",
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-       "<!-- model.BodyPart -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>model.BodyPart</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"body_part            \r------------------------------\rbody_part_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"transparent\" points=\"833,-223 747,-223 747,-188 833,-188 833,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"755\" y=\"-204\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.BodyPart</text>\n",
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-       "</g>\n",
-       "</g>\n",
-       "<!-- model.BodyPart&#45;&gt;model.Model.BodyPart -->\n",
-       "<g id=\"edge9\" 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=\"M775.23,-187.8C763.1,-174.08 746.4,-155.19 736.75,-144.27\"/>\n",
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-       "<!-- model.VideoRecording -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>model.VideoRecording</title>\n",
-       "<g id=\"a_node13\"><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=\"314.5,-223 175.5,-223 175.5,-188 314.5,-188 314.5,-223\"/>\n",
-       "<text text-anchor=\"start\" x=\"183.5\" y=\"-203.4\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">model.VideoRecording</text>\n",
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-       "</g>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.RecordingInfo -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>model.VideoRecording-&gt;model.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M205.48,-187.97C178.02,-176.49 141.75,-161.32 115.27,-150.25\"/>\n",
-       "</g>\n",
-       "<!-- model.VideoRecording&#45;&gt;model.PoseEstimationTask -->\n",
-       "<g id=\"edge11\" 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=\"M284.76,-187.97C310.95,-177.09 345.1,-162.89 371.28,-152.01\"/>\n",
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-       "<!-- model.VideoRecording&#45;&gt;model.VideoRecording.File -->\n",
-       "<g id=\"edge12\" 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=\"M245,-187.8C245,-174.08 245,-155.19 245,-144.27\"/>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.PoseEstimationTask -->\n",
-       "<g id=\"edge13\" 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=\"M642.73,-193.64C598.18,-182.47 528.22,-164.91 476.81,-152.01\"/>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.ModelEvaluation -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>model.Model-&gt;model.ModelEvaluation</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M660.85,-187.97C638.77,-173.46 607.72,-153.06 591.62,-142.48\"/>\n",
-       "</g>\n",
-       "<!-- model.Model&#45;&gt;model.Model.BodyPart -->\n",
-       "<g id=\"edge15\" 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=\"M696.41,-187.8C704.96,-174.08 716.73,-155.19 723.54,-144.27\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>"
-      ],
-      "text/plain": [
-       "<datajoint.diagram.Diagram at 0x7fadf719ed00>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "dj.Diagram(model) + dj.Diagram(train)"
    ]
@@ -1245,98 +210,48 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 1 - Register an existing model in DataJoint pipeline"
+    "This diagram represents the `element-deeplabcut` pipeline."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A DeepLabCut model is defined in a DLC-specific folder structure with a file named `config.yaml` that contains the specifications of a DLC model.\n",
-    "\n",
-    "To \"register\" this DLC model with DataJoint, you can just specify this config file. See example below"
+    "## 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:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "config_file_rel = \"./example_data/inbox/from_top_tracking-DataJoint-2023-10-11/config.yaml\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `insert_new_model` function is a helper function provided in `element-deeplacut` for convenient model registration.\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`:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-10-14 07:16:42.318459: 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",
-      "2023-10-14 07:16:42.442151: 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",
-      "2023-10-14 07:16:42.442185: 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",
-      "2023-10-14 07:16:42.466555: 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",
-      "2023-10-14 07:16:43.231023: 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",
-      "2023-10-14 07:16:43.231135: 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",
-      "2023-10-14 07:16:43.231151: 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",
@@ -1345,141 +260,31 @@
     "                             model_description='Model in example data: from_top_tracking model')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can check the `Model` table to confirm that the new model has been added:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
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-       "        .Relation td{\n",
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-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
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-       "        .djtooltip .djtooltiptext {\n",
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-       "            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": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.Model()"
    ]
   },
+  {
+   "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",
+    "\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."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1487,112 +292,32 @@
     "## Step 2 - Insert Subject, Session, and Behavior Videos"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirm the availability of data in the `Subject` and `Session` tables:"
+   ]
+  },
   {
    "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",
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-       "        .Relation td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
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-       "        .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> <td>subject6</td>\n",
-       "<td></td>\n",
-       "<td>F</td>\n",
-       "<td>2020-01-01</td>\n",
-       "<td>hneih_E105</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     subject_nickna sex     subject_birth_ subject_descri\n",
-       "+----------+ +------------+ +-----+ +------------+ +------------+\n",
-       "subject6                    F       2020-01-01     hneih_E105    \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "subject.Subject()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insert a subject into the `Subject` table:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1608,95 +333,19 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define session keys and insert them into the `Session` table:\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
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-       "        }\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": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "#Definition of the dictionary named \"session_keys\"\n",
     "session_keys = [\n",
@@ -1705,13 +354,35 @@
     "]\n",
     "\n",
     "#Insert this dictionary in the Session table\n",
-    "session.Session.insert(session_keys, skip_duplicates=True)\n",
+    "session.Session.insert(session_keys, skip_duplicates=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirm the inserted data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "session.Session()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insert data into the `VideoRecording` table:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1722,9 +393,16 @@
     "model.VideoRecording.insert1({**recording_key, 'device': 'Camera1'}, skip_duplicates=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insert video files into the `VideoRecording.File` table:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1738,121 +416,18 @@
     "    'file_path': Path(f)} for v_idx, f in enumerate(video_files))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Populate the `RecordingInfo` table:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "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": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "### RecordingInfo\n",
     "model.RecordingInfo.populate()\n",
@@ -1863,83 +438,85 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 3 - DLC inference task"
+    "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."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "{summary about next line}"
+    "## 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:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "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": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.PoseEstimationTask.describe()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To define and insert a task, you need to:\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",
+    "\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",
+    "\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",
+    "\n",
+    "If you select the **\"trigger\" task**, DataJoint will perform the entire inference process and generate these file sets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define the keys for recording and task:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'subject': 'subject6',\n",
-       " 'session_datetime': '2021-06-02 14:04:22',\n",
-       " 'recording_id': '1'}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "recording_key"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "task_key = {**recording_key, 'model_name': 'from_top_tracking_model_test'}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are located in the `pose_estimation_output_dir` location."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1950,120 +527,25 @@
     "     })"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display the `PoseEstimationTask` table:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "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": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.PoseEstimationTask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2071,246 +553,53 @@
     "model.PoseEstimation.populate()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look into the `PoseEstimation` 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\">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": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.PoseEstimation()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The most critical table is the `PoseEstimation.BodyPartPosition`. "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "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": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "### Results\n",
     "model.PoseEstimation.BodyPartPosition()"
    ]
   },
+  {
+   "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",
+    "\n",
+    "Entries contain `frame_index`, `x_pos` and `y_pos` positions, and `likelihood` (`z_pos` is zero). This structure is familiar to DeepLabCut users.\n",
+    "\n",
+    "These results can be fetched in a Pandas DataFrame structure: "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2319,256 +608,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>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": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use DataJoint `fetch` as a Pandas DataFrame and utilize the `explode` function to expand `x` and `y` positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df = df.explode(['frame_index', 'x_pos', 'y_pos', 'likelihood']).reset_index()\n",
     "df"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned earlier, you can confirm these results by the number of entries. There are 66000 frames for each body part, matching the `n_frames` from the `RecordingInfo` table."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2576,22 +653,18 @@
     "## Step 4 - Visualization of results"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, separate the data for the head and tailbase and then plot the head pose estimation and tailbase pose estimation."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -2609,20 +682,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.title('Tailbase pose estimation')\n",
     "plt.plot(tail_data['x_pos'],label='x_pos')\n",
@@ -2633,29 +695,25 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let's plot the head and tailbase positions on the same graph."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.plot(head_data['x_pos'], head_data['y_pos'], label='head')\n",
     "plt.plot(tail_data['x_pos'], tail_data['y_pos'], label='tailbase')\n",
     "plt.xlabel('x_pos (pixels)')\n",
     "plt.ylabel('y_pos (pixels)')\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   }
  ],
@@ -2675,7 +733,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,