diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..68f3e92be9c70519a1cea4e84c9433d22ea74673 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mat filter=lfs diff=lfs merge=lfs -text
diff --git a/88400423.json b/88400423.json
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/C90DFEBC.json b/C90DFEBC.json
new file mode 100644
index 0000000000000000000000000000000000000000..84991995aa70d8d887b123bc49eac328919528c1
--- /dev/null
+++ b/C90DFEBC.json
@@ -0,0 +1,6 @@
+{
+  "affiliated": true,
+  "ref_number": "C90DFEBC",
+  "headphone_type": "In-ear",
+  "sex": "Female"
+}
\ No newline at end of file
diff --git a/__pycache__/common.cpython-310.pyc b/__pycache__/common.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..97b7bfc862645abd96fb0bd193ef5b357d372d35
Binary files /dev/null and b/__pycache__/common.cpython-310.pyc differ
diff --git a/__pycache__/hrtf_vs_pitch_sonifier.cpython-310.pyc b/__pycache__/hrtf_vs_pitch_sonifier.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a3146839791ffdcdcb46831ae18440f8913c8c7c
Binary files /dev/null and b/__pycache__/hrtf_vs_pitch_sonifier.cpython-310.pyc differ
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..f01168bb4c238caab9ac78ade5f28eb9afa9cf8c
--- /dev/null
+++ b/app.py
@@ -0,0 +1,53 @@
+import json
+import os
+
+from pages.common import *
+
+hide_sidebar()
+# Initialize session state for page navigation
+st.session_state.page = 1  # Start at page 1
+st.session_state.user_data = {}  # Dictionary to store user responses
+st.session_state.inc_int = 0
+st.session_state.question_idx = 0
+st.session_state.example_idx = 0
+st.session_state.user_pref_idx = 0
+
+PLOT_PATH = "scatter_data"
+st.session_state.plots = {
+    int(ppath.split(".")[0]): json.load(open(os.path.join(PLOT_PATH, ppath), "r"))
+    for ppath in os.listdir(PLOT_PATH)
+}
+
+st.session_state.questions = json.load(open("spatial_questions.json", "r"))
+st.session_state.num_questions = len(
+    [1 for q in st.session_state.questions if q["ttype"] == "question"]
+)
+
+# Page 1 - Collect Input
+st.write(
+    """# Data Sonification Using Spatial Audio
+         """
+)
+st.image("./assets/images/tamu_logo.png", width=150)
+
+st.write(
+    """1. Our Research group is exploring the use of simulated spatial audio
+         for data representation.
+         """
+)
+st.write(
+    """2. In this study, you will listen to some audio files, and decide the chart that 
+         corresponds with the audio."""
+)
+st.write(
+    f"""3. This test contains {st.session_state.num_questions} questions and shouldn't take longer than 20 minutes."""
+)
+
+
+affiliated = st.checkbox("I am affiliated with the research team")
+
+if st.button("Continue", type="primary"):
+    # Save responses in session state and move to next page
+    st.session_state.user_data["affiliated"] = affiliated
+    st.session_state.user_data["ref_number"] = get_new_ref()
+    next_page()
diff --git a/assets/ding.wav b/assets/ding.wav
new file mode 100644
index 0000000000000000000000000000000000000000..03ab8ebfd8ec457e1ad2d3702e4166faf47ef7ad
Binary files /dev/null and b/assets/ding.wav differ
diff --git a/assets/images/headphone_types.png b/assets/images/headphone_types.png
new file mode 100644
index 0000000000000000000000000000000000000000..b49e835b7e27f1907cf437133f49959089271601
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diff --git a/assets/images/tamu_logo.png b/assets/images/tamu_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..f54b720d415f538b6e93f4587bf81db6fa21e459
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diff --git a/assets/snare.wav b/assets/snare.wav
new file mode 100644
index 0000000000000000000000000000000000000000..60587276e091cd10c589c41e87b3bb00b39d2967
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diff --git a/assets/snare2.wav b/assets/snare2.wav
new file mode 100644
index 0000000000000000000000000000000000000000..6e583912a4cd435eb307a7de8120ba127317dd88
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diff --git a/assets/snare3.wav b/assets/snare3.wav
new file mode 100644
index 0000000000000000000000000000000000000000..aee93fcdb8df8bb68fab56bda49d41d1630bb9d3
Binary files /dev/null and b/assets/snare3.wav differ
diff --git a/cipic_hrtf_database/female/15.mat b/cipic_hrtf_database/female/15.mat
new file mode 100755
index 0000000000000000000000000000000000000000..b0bbfb2b209ef1cb7aff1e55b098af4483470746
--- /dev/null
+++ b/cipic_hrtf_database/female/15.mat
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8e90491fec297bc831c7c18363d2e6de7d669961c7b06843ff8d25be45e4ae1
+size 4030520
diff --git a/cipic_hrtf_database/female/18.mat b/cipic_hrtf_database/female/18.mat
new file mode 100755
index 0000000000000000000000000000000000000000..00dfe1af091232e5cc0f2260853cce9d68b7dbe4
--- /dev/null
+++ b/cipic_hrtf_database/female/18.mat
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87001e762209aab9b74c3065dcbf2ef1e0f4245a74289547355a10f7e5fea706
+size 4030520
diff --git a/cipic_hrtf_database/female/60.mat b/cipic_hrtf_database/female/60.mat
new file mode 100755
index 0000000000000000000000000000000000000000..e6e83b71938a8f3af49c021dbc9f2549d69fa3a7
--- /dev/null
+++ b/cipic_hrtf_database/female/60.mat
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a0af86c1d65a66c8e054257eb197617ab2bd6319edc9dcdd285cf8541f8813a
+size 4030520
diff --git a/cipic_hrtf_database/male/147.mat b/cipic_hrtf_database/male/147.mat
new file mode 100755
index 0000000000000000000000000000000000000000..d0bdc264e0bfca7091f0d4f44342c917f95fd997
--- /dev/null
+++ b/cipic_hrtf_database/male/147.mat
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f55a06d302d90e25b465ca44d882ac1530ed5575d6e32d90a7c7fbc6bc7535d
+size 4030520
diff --git a/cipic_hrtf_database/male/152.mat b/cipic_hrtf_database/male/152.mat
new file mode 100755
index 0000000000000000000000000000000000000000..2d0e1bd9de2255c1207617c602068573b86012bf
--- /dev/null
+++ b/cipic_hrtf_database/male/152.mat
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1fd30b133af36a419a14a148c2da01b92900a69c2861fe6aa1ca3f07247651d8
+size 4030520
diff --git a/cipic_hrtf_database/male/27.mat b/cipic_hrtf_database/male/27.mat
new file mode 100755
index 0000000000000000000000000000000000000000..f21c85ab62612bcd1ccf908e8628e93e52f3f468
--- /dev/null
+++ b/cipic_hrtf_database/male/27.mat
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ef1f7c4c38d9915b4dc0f678864f59fb569401143b68dd9d78abc701e5d465e
+size 4030520
diff --git a/density.json b/density.json
new file mode 100644
index 0000000000000000000000000000000000000000..58d49b0abe6f7607a4f0a304a5571336b50873e0
--- /dev/null
+++ b/density.json
@@ -0,0 +1,35 @@
+[
+  {
+    "description": "Test if they can distinguish different densities",
+    "ttype": "question",
+    "plots": [
+      25,
+      29,
+      24,
+      26
+    ],
+    "answer": 26
+  },
+  {
+    "description": "Test if they can distinguish different densities",
+    "ttype": "question",
+    "plots": [
+      25,
+      22,
+      31,
+      24
+    ],
+    "answer": 31
+  },
+  {
+    "description": "Test if they can distinguish different densities",
+    "ttype": "question",
+    "plots": [
+      32,
+      26,
+      27,
+      33
+    ],
+    "answer": 33
+  }
+]
\ No newline at end of file
diff --git a/experiment_designer.ipynb b/experiment_designer.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e8abda083593247554989ccd1905ee17f0361c46
--- /dev/null
+++ b/experiment_designer.ipynb
@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1600 with 28 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "DATA_PATH = \"scatter_data\"\n",
+    "\n",
+    "plots = os.listdir(DATA_PATH)\n",
+    "COLS = 4\n",
+    "ROWS = len(plots) // 4 + 1\n",
+    "\n",
+    "fig, axs = plt.subplots(ROWS, COLS, figsize=(16,16))\n",
+    "\n",
+    "datas = {}\n",
+    "\n",
+    "for idx, plot in enumerate(plots):\n",
+    "    name = int(plot.split(\".\")[0])\n",
+    "    if name < 20:\n",
+    "        continue\n",
+    "    data = json.load(\n",
+    "            open(os.path.join(DATA_PATH , plot), \"r\"))\n",
+    "\n",
+    "    datas[name] = data \n",
+    "\n",
+    "    axs[idx // COLS][idx % COLS].scatter(data[\"x\"], data[\"y\"])\n",
+    "    axs[idx // COLS][idx % COLS].set_title(f\"Plot {name} {data['description']}\")\n",
+    "    \n",
+    "def plot_four(datasets, idxs, show_correct = None):\n",
+    "        fig, axes = plt.subplots(2, 2, figsize=(8, 8))\n",
+    "\n",
+    "        for i, ax in enumerate(axes.flat):\n",
+    "                ax.scatter(datasets[idxs[i]][\"x\"],datasets[idxs[i]][\"y\"])\n",
+    "                ax.set_title(f\"Plot {i + 1}\")\n",
+    "                ax.grid(True)\n",
+    "\n",
+    "                if show_correct is not None:\n",
+    "                        for spine in ax.spines.values():\n",
+    "                                if idxs[i] == show_correct:\n",
+    "                                        spine.set_linewidth(2)\n",
+    "                                        spine.set_color(\"green\")\n",
+    "                                else:\n",
+    "                                        spine.set_color(\"red\")\n",
+    "\n",
+    "\n",
+    "        plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "questions = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plots = [\n",
+    "    25,\n",
+    "    29,\n",
+    "    24,\n",
+    "    26 \n",
+    "]\n",
+    "\n",
+    "correct = 26 \n",
+    "\n",
+    "ttype = \"question\"\n",
+    "description = \"Test if they can distinguish different densities\"\n",
+    "\n",
+    "plot_four(datas , plots)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "questions.append({\n",
+    "    \"description\":description,\n",
+    "    \"ttype\":ttype,\n",
+    "    \"plots\":plots,\n",
+    "    \"answer\":correct\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'description': 'Can they distinguish the shape of a dip',\n",
+       "  'ttype': 'question',\n",
+       "  'plots': [18, 17, 19, 15],\n",
+       "  'answer': 17},\n",
+       " {'description': 'Can they distinguish the shape of a monotonous increase',\n",
+       "  'ttype': 'question',\n",
+       "  'plots': [9, 2, 4, 6],\n",
+       "  'answer': 6},\n",
+       " {'description': 'Test if they can distinguish vastly different shapes',\n",
+       "  'ttype': 'question',\n",
+       "  'plots': [14, 0, 3, 5],\n",
+       "  'answer': 5},\n",
+       " {'description': 'Test if they can distinguish the shape of a monotonous decrease',\n",
+       "  'ttype': 'question',\n",
+       "  'plots': [3, 15, 10, 17],\n",
+       "  'answer': 10},\n",
+       " {'description': 'Test if they can distinguish the shape of a step',\n",
+       "  'ttype': 'question',\n",
+       "  'plots': [6, 5, 14, 2],\n",
+       "  'answer': 5}]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "questions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "920"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "open(\n",
+    "    \"density.json\", \"w\"\n",
+    ").write(json.dumps(questions, indent=2))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/horizontal_gaps_vs_spatial.ipynb b/horizontal_gaps_vs_spatial.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bea1a17b6341a5fb556d02cdf6abbea2b51cb5a2
--- /dev/null
+++ b/horizontal_gaps_vs_spatial.ipynb
@@ -0,0 +1,252 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Time Dimension Gaps vs Spatial Experiment\n",
+    "\n",
+    "The goal of this experiment is to judge the efficiency of encoding the x-axis as a `spatial` dimension versus `time` dimension.\n",
+    "\n",
+    "By `time` dimension we mean leaving gaps (silences) behind sequential data points that correlate linearly in duration to the eucledian distance between the points.\n",
+    "\n",
+    "## Methodology\n",
+    "\n",
+    "- A set of graphs are sonified using `TOPH` in two distinct ways. The y-axis can either be `constant` or encoded using `pitch`.\n",
+    "\n",
+    "- The core idea is to encode the same graph (in y-dimension) with varying gaps between the points in the x-axis, and make the user choose from a random sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    from toph.audio.playable import Chain,SineWave, Silence, Wave\n",
+    "    from toph.audio.effect import SimplePanner\n",
+    "    from toph.audio.stage import AudioStage\n",
+    "\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    AUDIO_PATH = \"assets/ding.wav\"\n",
+    "\n",
+    "    def ax_gen(domain, range_):\n",
+    "\n",
+    "        do_scale = max(domain) - min(domain)\n",
+    "        ra_scale = max(range_) - min(range_)\n",
+    "\n",
+    "        def foo(dp):\n",
+    "\n",
+    "            dx = dp - min(domain) \n",
+    "            sdx = dx / do_scale \n",
+    "            return min(range_) + sdx * ra_scale \n",
+    "\n",
+    "        return foo\n",
+    "\n",
+    "    def sonify(xs, ys, x_mode, y_mode, gaps=0.5, save=None):\n",
+    "        \"\"\"\n",
+    "        \"\"\"\n",
+    "        with AudioStage() as stage:\n",
+    "\n",
+    "            #TODO: replace with linear axis when implemented\n",
+    "\n",
+    "            lin_ax = ax_gen(\n",
+    "                domain=(min(xs), max(xs)),\n",
+    "                range_=(-1. , +1.)\n",
+    "            )\n",
+    "\n",
+    "            pitch_ax = ax_gen(\n",
+    "                domain=(min(ys), max(ys)),\n",
+    "                range_=(110, 880)\n",
+    "            )\n",
+    "\n",
+    "            x_deltas = [xs[idx+1] - xs[idx] for idx in range(len(xs) - 1)]\n",
+    "            x_deltas.append(0.)\n",
+    "\n",
+    "            gap_ax = ax_gen(\n",
+    "                domain=(min(x_deltas), max(x_deltas)),\n",
+    "                range_=(0.25 , 1.25)\n",
+    "            ) \n",
+    "\n",
+    "            marks = []\n",
+    "\n",
+    "            for idx, (x , y) in enumerate(zip(xs, ys)):\n",
+    "                \n",
+    "                audio_src = Wave(AUDIO_PATH) if y_mode == \"static\" else SineWave(vol = 0.85, f = pitch_ax(y), secs=0.5)\n",
+    "                if x_mode == \"spatial\":\n",
+    "\n",
+    "                    marks.extend([\n",
+    "                            audio_src.add_effect(SimplePanner(dir = lin_ax(x))),\n",
+    "                            Silence(secs = gaps)\n",
+    "                    ])\n",
+    "\n",
+    "                elif x_mode == \"gaps\":\n",
+    "\n",
+    "                    marks.append(audio_src)\n",
+    "                    if x_deltas[idx] > 0.:\n",
+    "                        marks.append(\n",
+    "                            Silence(secs = gap_ax(x_deltas[idx]))\n",
+    "                            )\n",
+    "\n",
+    "            if save:\n",
+    "                stage.save(Chain(*marks), save)\n",
+    "\n",
+    "            else:\n",
+    "                stage.play(Chain(*marks))\n",
+    "\n",
+    "    def plot_four(datasets, idxs, y_mode, show_correct = None):\n",
+    "        fig, axes = plt.subplots(2, 2, figsize=(8, 8))\n",
+    "\n",
+    "        for i, ax in enumerate(axes.flat):\n",
+    "            ax.scatter(datasets[idxs[i]][\"x\"], datasets[idxs[i]][\"y\"] if y_mode == \"pitch\" else [0] * len(datasets[idxs[i]][\"x\"]))\n",
+    "            ax.set_title(f\"Plot {i + 1}\")\n",
+    "            ax.grid(True)\n",
+    "\n",
+    "            if show_correct is not None:\n",
+    "                for spine in ax.spines.values():\n",
+    "                    if idxs[i] == show_correct:\n",
+    "                            spine.set_linewidth(2)\n",
+    "                            spine.set_color(\"green\")\n",
+    "                    else:\n",
+    "                            spine.set_color(\"red\")\n",
+    "\n",
+    "\n",
+    "        plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = [\n",
+    "    {\n",
+    "        \"x\":[0, 40, 50, 55, 90],\n",
+    "        \"y\":[10, 15, 20, 15, 10]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\":[0, 20, 40, 60, 80],\n",
+    "        \"y\":[10, 15, 20, 15, 10]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\":[0, 20, 60, 120, 240, 360],\n",
+    "        \"y\":[20, 15, 20, 15, 10, 5]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 5, 15, 45, 135 , 405],\n",
+    "        \"y\": [12, 14, 19, 14, 11, 8]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 20, 40, 60, 80, 100],\n",
+    "        \"y\": [15, 20, 25, 20, 15, 10]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 60, 65, 70, 72, 74],\n",
+    "        \"y\": [10, 18, 16, 20, 15, 12]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 2, 4, 8, 10, 90],\n",
+    "        \"y\": [8, 12, 18, 22, 18, 10]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 25, 50, 75, 100, 125],\n",
+    "        \"y\": [20, 18, 22, 18, 14, 10]\n",
+    "    }\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM dsnoop:0\n",
+      "ALSA lib pcm_dmix.c:999:(snd_pcm_dmix_open) unable to open slave\n",
+      "ALSA lib pcm_oss.c:397:(_snd_pcm_oss_open) Cannot open device /dev/dsp\n",
+      "ALSA lib pcm_oss.c:397:(_snd_pcm_oss_open) Cannot open device /dev/dsp\n",
+      "ALSA lib confmisc.c:160:(snd_config_get_card) Invalid field card\n",
+      "ALSA lib pcm_usb_stream.c:482:(_snd_pcm_usb_stream_open) Invalid card 'card'\n",
+      "ALSA lib confmisc.c:160:(snd_config_get_card) Invalid field card\n",
+      "ALSA lib pcm_usb_stream.c:482:(_snd_pcm_usb_stream_open) Invalid card 'card'\n",
+      "ALSA lib pcm_dmix.c:999:(snd_pcm_dmix_open) unable to open slave\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "sonify_idxs = random.sample(range(len(datasets)), 4) \n",
+    "\n",
+    "correct_one = sonify_idxs[random.randint(0, len(sonify_idxs) - 1)]\n",
+    "x_mode = \"spatial\" # gaps , spatial\n",
+    "y_mode = \"static\" # static , pitch\n",
+    "\n",
+    "sonify(datasets[correct_one][\"x\"],\n",
+    "        datasets[correct_one][\"y\"],\n",
+    "        x_mode=x_mode,\n",
+    "        y_mode=y_mode,\n",
+    "        save=\"test_audio.wav\"\n",
+    "        )\n",
+    "\n",
+    "plot_four(datasets, sonify_idxs, y_mode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_four(datasets , sonify_idxs, y_mode, correct_one)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hrtf_vs_pitch.ipynb b/hrtf_vs_pitch.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fb551ddf9940abaddc64c55a4d3242221247e6c4
--- /dev/null
+++ b/hrtf_vs_pitch.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Elevation vs Pitch Experiment\n",
+    "\n",
+    "The goal in this experiment is to compare the effectiveness of the `elevation` channel in spatial audio and the `pitch` channel.\n",
+    "\n",
+    "## Methodology\n",
+    "\n",
+    "- A set of graphs are sonified using `TOPH` in two distinct ways. The x-axis is always represent by the `azimuth` or left / right channel in the `SpatialPanner`.\n",
+    "- The y-axis is either represented by the `pitch` or the `elevation`.\n",
+    "\n",
+    "The user's are asked to identify the chart that coresponded with the sonification they heard from a set.\n",
+    "\n",
+    "The coresponding chart can be presented as a visual chart for non-BVI individuals, and screen reader descriptions for BVI individuals.\n",
+    "\n",
+    "I chose to implement the former below;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    from toph.audio.playable import Chain,SineWave, Silence, Wave\n",
+    "    from toph.audio.spatial import SpatialPanner\n",
+    "    from toph.audio.stage import AudioStage\n",
+    "    from toph.axis.spatial import LinearSpatialAxis\n",
+    "\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    AUDIO_PATH = \"assets/ding.wav\"\n",
+    "\n",
+    "    def ax_gen(domain, range_):\n",
+    "\n",
+    "        do_scale = max(domain) - min(domain)\n",
+    "        ra_scale = max(range_) - min(range_)\n",
+    "\n",
+    "        def foo(dp):\n",
+    "\n",
+    "            dx = dp - min(domain) \n",
+    "            sdx = dx / do_scale \n",
+    "            return min(range_) + sdx * ra_scale \n",
+    "\n",
+    "        return foo\n",
+    "\n",
+    "    def sonify(xs, ys, mode, gaps=0.5, save=None):\n",
+    "        \"\"\"\n",
+    "        \"\"\"\n",
+    "        with AudioStage() as stage:\n",
+    "\n",
+    "            space_ax = LinearSpatialAxis(\n",
+    "                domain=((min(xs), max(xs)), (min(ys), max(ys))),\n",
+    "                range_=((-50.0, 50.0), (-45.0, 90.0)),\n",
+    "            )\n",
+    "\n",
+    "            #TODO: replace with linear axis when implemented\n",
+    "            lin_ax = ax_gen(\n",
+    "                domain=(min(ys), max(ys)),\n",
+    "                range_=(110, 880)\n",
+    "            )\n",
+    "\n",
+    "            marks = []\n",
+    "\n",
+    "            for x , y in zip(xs, ys):\n",
+    "                \n",
+    "                if mode == \"full_spatial\":\n",
+    "\n",
+    "                    r, az, el = space_ax((x, y)) \n",
+    "\n",
+    "                    marks.extend([\n",
+    "                            Wave(AUDIO_PATH).add_effect(SpatialPanner(az, el, r)),\n",
+    "                            Silence(secs = gaps)\n",
+    "                    ])\n",
+    "\n",
+    "                elif mode == \"pitch\":\n",
+    "\n",
+    "                    # keep the y as 0 to get a good r\n",
+    "                    r, az, el = space_ax((x, 0.))\n",
+    "                    freq = lin_ax(y)\n",
+    "\n",
+    "                    print(r, az, el, freq)\n",
+    "                    marks.extend([\n",
+    "                            SineWave(vol = 0.85, f = freq, secs=0.5).add_effect(SpatialPanner(az, 0. , r)),\n",
+    "                            Silence(secs = gaps)\n",
+    "                    ])\n",
+    "\n",
+    "                elif mode == \"both\":\n",
+    "                    # keep the y as 0 to get a good r\n",
+    "                    r, az, el = space_ax((x, y))\n",
+    "                    freq = lin_ax(y)\n",
+    "\n",
+    "                    marks.extend([\n",
+    "                            SineWave(vol = 0.85, f = freq, secs=0.5).add_effect(SpatialPanner(az, el , r)),\n",
+    "                            Silence(secs = gaps)\n",
+    "                    ])\n",
+    "\n",
+    "            if save:\n",
+    "                stage.save(Chain(*marks), save)\n",
+    "\n",
+    "            else:\n",
+    "                stage.play(Chain(*marks))\n",
+    "\n",
+    "    def plot_four(datasets, idxs, show_correct = None):\n",
+    "        fig, axes = plt.subplots(2, 2, figsize=(8, 8))\n",
+    "\n",
+    "        for i, ax in enumerate(axes.flat):\n",
+    "            ax.scatter(datasets[idxs[i]][\"x\"], datasets[idxs[i]][\"y\"])\n",
+    "            ax.set_title(f\"Plot {i + 1}\")\n",
+    "            ax.grid(True)\n",
+    "\n",
+    "            if show_correct is not None:\n",
+    "                for spine in ax.spines.values():\n",
+    "                    if idxs[i] == show_correct:\n",
+    "                            spine.set_linewidth(2)\n",
+    "                            spine.set_color(\"green\")\n",
+    "                    else:\n",
+    "                            spine.set_color(\"red\")\n",
+    "\n",
+    "\n",
+    "        plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = [\n",
+    "    {\n",
+    "        \"x\":[0, 10, 30, 40, 90],\n",
+    "        \"y\":[10, 15, 20, 15, 10]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\":[0, 10, 20, 30, 40],\n",
+    "        \"y\":[10, 15, 20, 15, 10]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\":[0, 10, 20, 40, 80, 160],\n",
+    "        \"y\":[20, 15, 20, 15, 10, 5]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 5, 15, 30, 45, 60],\n",
+    "        \"y\": [12, 14, 19, 14, 11, 8]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 20, 40, 60, 80, 100],\n",
+    "        \"y\": [15, 20, 25, 20, 15, 10]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 15, 35, 55, 75, 95],\n",
+    "        \"y\": [10, 18, 16, 20, 15, 12]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 12, 25, 50, 75, 100],\n",
+    "        \"y\": [8, 12, 18, 22, 18, 10]\n",
+    "    },\n",
+    "    {\n",
+    "        \"x\": [0, 25, 50, 75, 100, 125],\n",
+    "        \"y\": [20, 18, 22, 18, 14, 10]\n",
+    "    }\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM dsnoop:0\n",
+      "ALSA lib pcm_dmix.c:999:(snd_pcm_dmix_open) unable to open slave\n",
+      "ALSA lib pcm_oss.c:397:(_snd_pcm_oss_open) Cannot open device /dev/dsp\n",
+      "ALSA lib pcm_oss.c:397:(_snd_pcm_oss_open) Cannot open device /dev/dsp\n",
+      "ALSA lib confmisc.c:160:(snd_config_get_card) Invalid field card\n",
+      "ALSA lib pcm_usb_stream.c:482:(_snd_pcm_usb_stream_open) Invalid card 'card'\n",
+      "ALSA lib confmisc.c:160:(snd_config_get_card) Invalid field card\n",
+      "ALSA lib pcm_usb_stream.c:482:(_snd_pcm_usb_stream_open) Invalid card 'card'\n",
+      "ALSA lib pcm_dmix.c:999:(snd_pcm_dmix_open) unable to open slave\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "sonify_idxs = random.sample(range(len(datasets)), 4) \n",
+    "\n",
+    "correct_one = sonify_idxs[random.randint(0, len(sonify_idxs) - 1)]\n",
+    "mode = \"both\" # full_spatial , pitch , both\n",
+    "sonify(datasets[correct_one][\"x\"],\n",
+    "        datasets[correct_one][\"y\"],\n",
+    "        mode=mode,\n",
+    "        save=\"test_audio.wav\"\n",
+    "        )\n",
+    "\n",
+    "plot_four(datasets, sonify_idxs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_four(datasets , sonify_idxs, correct_one)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hrtf_vs_pitch_sonifier.py b/hrtf_vs_pitch_sonifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..81051cced5aeb8b8d10e87ead163a910f4a9040b
--- /dev/null
+++ b/hrtf_vs_pitch_sonifier.py
@@ -0,0 +1,105 @@
+import matplotlib.pyplot as plt
+
+from toph.audio.effect import SimplePanner
+from toph.audio.playable import Chain, Silence, SineWave, Wave
+from toph.audio.stage import AudioStage
+
+AUDIO_PATH = "assets/ding.wav"
+
+
+def ax_gen(domain, range_):
+
+    do_scale = max(domain) - min(domain)
+    ra_scale = max(range_) - min(range_)
+
+    def foo(dp):
+
+        dx = dp - min(domain)
+        sdx = dx / do_scale
+        return min(range_) + sdx * ra_scale
+
+    return foo
+
+
+def sonify(xs, ys, x_mode, y_mode, gaps=0.5, save=None):
+    """ """
+    with AudioStage(frame_rate=48000) as stage:
+
+        # TODO: replace with linear axis when implemented
+
+        lin_ax = ax_gen(domain=(min(xs), max(xs)), range_=(-1.0, +1.0))
+
+        pitch_ax = ax_gen(domain=(min(ys), max(ys)), range_=(110, 880))
+
+        x_deltas = [xs[idx + 1] - xs[idx] for idx in range(len(xs) - 1)]
+        x_deltas.append(0.0)
+
+        gap_ax = ax_gen(domain=(min(x_deltas), max(x_deltas)), range_=(0.25, 1.25))
+
+        marks = []
+
+        for idx, (x, y) in enumerate(zip(xs, ys)):
+
+            audio_src = (
+                Wave(AUDIO_PATH)
+                if y_mode == "static"
+                else SineWave(vol=0.85, f=pitch_ax(y), secs=0.5)
+            )
+            if x_mode == "spatial":
+
+                marks.extend(
+                    [
+                        audio_src.add_effect(SimplePanner(dir=lin_ax(x))),
+                        Silence(secs=gaps),
+                    ]
+                )
+
+            elif x_mode == "gaps":
+
+                marks.append(audio_src)
+                if x_deltas[idx] > 0.0:
+                    marks.append(Silence(secs=gap_ax(x_deltas[idx])))
+
+        if save:
+            stage.save(Chain(*marks), save)
+
+        else:
+            stage.play(Chain(*marks))
+
+
+def plot_four(datasets, idxs, y_mode, show_correct=None):
+    fig, axes = plt.subplots(2, 2, figsize=(8, 8))
+
+    for i, ax in enumerate(axes.flat):
+        ax.scatter(
+            datasets[idxs[i]]["x"],
+            (
+                datasets[idxs[i]]["y"]
+                if y_mode == "pitch"
+                else [0] * len(datasets[idxs[i]]["x"])
+            ),
+        )
+        ax.set_title(f"Plot {i + 1}")
+        ax.grid(True)
+
+        if show_correct is not None:
+            for spine in ax.spines.values():
+                if idxs[i] == show_correct:
+                    spine.set_linewidth(2)
+                    spine.set_color("green")
+                else:
+                    spine.set_color("red")
+
+    plt.tight_layout()
+
+
+datasets = [
+    {"x": [0, 10, 30, 40, 90], "y": [10, 15, 20, 15, 10]},
+    {"x": [0, 10, 20, 30, 40], "y": [10, 15, 20, 15, 10]},
+    {"x": [0, 10, 20, 40, 80, 160], "y": [20, 15, 20, 15, 10, 5]},
+    {"x": [0, 5, 15, 30, 45, 60], "y": [12, 14, 19, 14, 11, 8]},
+    {"x": [0, 20, 40, 60, 80, 100], "y": [15, 20, 25, 20, 15, 10]},
+    {"x": [0, 15, 35, 55, 75, 95], "y": [10, 18, 16, 20, 15, 12]},
+    {"x": [0, 12, 25, 50, 75, 100], "y": [8, 12, 18, 22, 18, 10]},
+    {"x": [0, 25, 50, 75, 100, 125], "y": [20, 18, 22, 18, 14, 10]},
+]
diff --git a/packages.txt b/packages.txt
new file mode 100644
index 0000000000000000000000000000000000000000..78ef5d5e5fa2b75731d23ddd232fbd2d661fa7e4
--- /dev/null
+++ b/packages.txt
@@ -0,0 +1,2 @@
+libportaudio2
+portaudio19-dev
diff --git a/pages/__init__.py b/pages/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/pages/__pycache__/__init__.cpython-310.pyc b/pages/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8c8165dc84d40cb50c9bf52f06b96c57e4c2d70f
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diff --git a/pages/__pycache__/common.cpython-310.pyc b/pages/__pycache__/common.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6935a052db4b49a608805f128c8e29ee3c49fe38
Binary files /dev/null and b/pages/__pycache__/common.cpython-310.pyc differ
diff --git a/pages/__pycache__/db.cpython-310.pyc b/pages/__pycache__/db.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..aed4870592b3e432d24402b32a539774f4f2413e
Binary files /dev/null and b/pages/__pycache__/db.cpython-310.pyc differ
diff --git a/pages/__pycache__/sonification_utils.cpython-310.pyc b/pages/__pycache__/sonification_utils.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d2b3877579768de72877b713abb0f2da34514267
Binary files /dev/null and b/pages/__pycache__/sonification_utils.cpython-310.pyc differ
diff --git a/pages/common.py b/pages/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2bbc797940b23945e7ab764a8977c433ab68822
--- /dev/null
+++ b/pages/common.py
@@ -0,0 +1,36 @@
+import uuid
+
+import streamlit as st
+from streamlit_extras.switch_page_button import switch_page
+
+
+# hide sidebar
+def hide_sidebar():
+    st.set_page_config(initial_sidebar_state="collapsed")
+
+    st.markdown(
+        """
+    <style>
+        [data-testid="stSidebarCollapsedControl"] {
+            display: none
+        }
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
+
+
+# Define a function to move to the next page
+pages = {2: "headphones", 3: "hrtf_selection", 4: "questions"}
+
+
+def next_page():
+    st.session_state.page += 1
+    switch_page(pages.get(st.session_state.page, "final"))
+
+
+def get_new_ref():
+    return str(uuid.uuid4()).upper().split("-")[0]
+
+
+AUDIO_PATH = "assets/snare3.wav"
diff --git a/pages/contribute.py b/pages/contribute.py
new file mode 100644
index 0000000000000000000000000000000000000000..7531ab441d79ace586573a54c87c74956c9f41ba
--- /dev/null
+++ b/pages/contribute.py
@@ -0,0 +1,16 @@
+from pages.common import *
+
+hide_sidebar()
+# Page 1 - Collect Input
+st.write("### Page 2: Additional Information")
+st.write("Name:", st.session_state.user_data.get("name"))
+st.write("Age:", st.session_state.user_data.get("age"))
+
+occupation = st.text_input("Enter your occupation:")
+
+if st.button("Submit Page 2"):
+    # Save additional response and move to next page
+    st.session_state.user_data["occupation"] = occupation
+    next_page()
+
+# Page 3 - Final Page
diff --git a/pages/db.py b/pages/db.py
new file mode 100644
index 0000000000000000000000000000000000000000..48e951bb2c38c1216fba5d038e9da48d331904be
--- /dev/null
+++ b/pages/db.py
@@ -0,0 +1,25 @@
+import json
+
+import gspread
+from oauth2client.service_account import ServiceAccountCredentials
+
+# Define the scope and authenticate with the service account
+scope = [
+    "https://spreadsheets.google.com/feeds",
+    "https://www.googleapis.com/auth/drive",
+]
+json_key = json.loads(os.getenv("GKEY"),strict=False)
+credentials = ServiceAccountCredentials.from_json_keyfile_dict(json_key, scope)
+client = gspread.authorize(credentials)
+
+# Open the Google Sheet by name
+spreadsheet = client.open("Team 7 Data")
+worksheet = spreadsheet.sheet1  # Access the first sheet
+
+
+# Function to insert the JSON string into the first column
+def submit_json_to_sheets(json_data):
+    # Convert JSON data to string format
+    json_string = json.dumps(json_data)
+    # Append the JSON string as a new row with one cell in the first column
+    worksheet.append_row([json_string])
diff --git a/pages/final.py b/pages/final.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b8ffdaa87545eb315f78900deba6365d12d6d89
--- /dev/null
+++ b/pages/final.py
@@ -0,0 +1,6 @@
+from pages.common import *
+
+hide_sidebar()
+
+st.write("# Thank you for completing our study!")
+st.write("Your data has been submitted!")
diff --git a/pages/headphones.py b/pages/headphones.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb4957c14e35e52b227033542e02137daa7c71d9
--- /dev/null
+++ b/pages/headphones.py
@@ -0,0 +1,35 @@
+from pages.common import *
+
+hide_sidebar()
+
+st.write(
+    """- Thank you for joining our study. We just need to ask a few preliminary questions
+         to find the best **settings** for you!"""
+)
+
+st.write(
+    f"""- If you run into any issues or have any questions for the research team,
+         your reference number is **{st.session_state.user_data['ref_number']}**"""
+)
+
+st.write("# Headphones")
+
+st.write("For this part of study, we are mainly focusing on `in-ear` headpones")
+st.write("However, if you only have `over-the-ear` ones we still accept submissions.")
+
+st.write("**For reference:**")
+
+st.image("assets/images/headphone_types.png", width=400)
+
+
+headphones = st.selectbox("Headphone Type:", ("In-ear", "Over-the-ear"))
+
+age = st.number_input("Age:", min_value=9, max_value=99)
+sex = st.selectbox("Sex:", ("Male", "Female"))
+
+if st.button("Continue", type="primary"):
+    # Save responses in session state and move to next page
+    st.session_state.user_data["headphone_type"] = headphones
+    st.session_state.user_data["sex"] = sex
+    st.session_state.user_data["age"] = age
+    next_page()
diff --git a/pages/hrtf_selection.py b/pages/hrtf_selection.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e8de8790823f4708111420fdd2903f12d84b221
--- /dev/null
+++ b/pages/hrtf_selection.py
@@ -0,0 +1,95 @@
+import os
+
+from pages.common import *
+
+hide_sidebar()
+
+
+st.write(
+    """This is a study about using **Directional Audio** in data encoding. To simulate directional audio using headphones, we must find the appropriate **settings** for you."""
+)
+st.write(
+    """Listen to different settings by clicking the buttons. You should hear a sound coming from `behind` -> `above` -> `front and below`"""
+)
+
+st.write("""Pick the option that sounds the best to you""")
+
+sex = st.session_state.user_data["sex"]
+CIPIC_SUB_DIR = os.path.join("cipic_hrtf_database", sex.lower())
+
+
+import base64
+from io import BytesIO
+
+from scipy.io import wavfile
+
+import toph.audio.spatial
+from toph.audio.playable import Chain, Silence, SineWave, Wave
+from toph.audio.spatial import SpatialPanner
+from toph.audio.stage import AudioStage
+from toph.axis.spatial import LinearSpatialAxis
+
+
+def play_test_sequence(cipic_path):
+
+    with AudioStage() as stage:
+
+        toph.audio.spatial.CIPIC_BASE_PATH = cipic_path
+
+        seq = Chain(
+            Wave(AUDIO_PATH).add_effect(SpatialPanner(0.0, 180.0, 2.0)),
+            Silence(0.3),
+            Wave(AUDIO_PATH).add_effect(SpatialPanner(0.0, 90.0, 2.0)),
+            Silence(0.3),
+            Wave(AUDIO_PATH).add_effect(SpatialPanner(0.0, -44.0, 2.0)),
+            Silence(0.3),
+        )
+
+        arr = stage.get_ndarray(seq)
+        v_file = BytesIO()
+        wavfile.write(
+            v_file,
+            rate=stage.frame_rate,
+            # todo: handle formats other than int16, make it better
+            data=arr,
+        )
+        v_file.seek(0)
+        b64 = base64.b64encode(v_file.read()).decode()
+
+        md = f"""
+            <audio style='display: none' controls autoplay="true">
+            <source src="data:audio/wav;base64,{b64}" type="audio/wav">
+            Audio
+            </audio>
+            """
+
+        st.session_state.inc_int += 1
+        print(st.session_state.inc_int)
+
+        if st.session_state.inc_int % 2:
+            st.write(".")
+
+        st.markdown(
+            md,
+            unsafe_allow_html=True,
+        )
+
+        if st.session_state.inc_int % 2 == 0:
+            st.write(".")
+
+
+hrtfs = os.listdir(CIPIC_SUB_DIR)
+cols = st.columns(len(hrtfs))
+
+for idx, hrtf in enumerate(hrtfs):
+
+    hrtf_id = hrtf.split(".")[0]
+    btn = cols[idx].button(hrtf_id)
+
+    if btn:
+        play_test_sequence(os.path.join(CIPIC_SUB_DIR, hrtf))
+        st.session_state.user_data["hrtf_id"] = hrtf_id
+        st.write(f"Current selection: {hrtf_id}")
+
+if st.button("Continue", type="primary"):
+    next_page()
diff --git a/pages/questions.py b/pages/questions.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8505622c482c908b8292d2864aaa6270a4ff52b
--- /dev/null
+++ b/pages/questions.py
@@ -0,0 +1,134 @@
+from pages.common import *
+
+hide_sidebar()
+
+import os
+
+import matplotlib.pyplot as plt
+from pages.sonification_utils import (SONIFICATION_FUNCTIONS, get_audio_comp,
+                                      plot_four)
+
+last_example = getattr(st.session_state, "last_example", {})
+cur_q = st.session_state.question_idx
+cur_e = st.session_state.example_idx
+cur_u = st.session_state.user_pref_idx
+
+total = st.session_state.num_questions
+
+
+if cur_q + cur_e + cur_u < len(st.session_state.questions):
+    cur_data = st.session_state.questions[cur_q + cur_e + cur_u]
+else:  # questions are over submit and redirect to final screen
+    from pages.db import submit_json_to_sheets
+
+    submit_json_to_sheets(st.session_state.user_data)
+    next_page()
+
+prog = min((cur_q + 1) / total, 1.0)
+st.progress(prog, text=f"{cur_q}/{total}")
+
+
+print(st.session_state.user_data)
+
+if cur_data["ttype"] == "Example":
+
+    st.session_state.last_example = cur_data
+    st.write("# Example")
+    st.write(cur_data["explanation"])
+    # render the plot
+    plot_data = st.session_state.plots[cur_data["plot"]]
+
+    son_fn = SONIFICATION_FUNCTIONS[cur_data["sonify"]]
+
+    son_arr = son_fn(plot_data["x"], plot_data["y"], **cur_data["args"])
+
+    aud_comp = get_audio_comp(son_arr, 44100)
+
+    st.markdown(
+        aud_comp,
+        unsafe_allow_html=True,
+    )
+
+    # plot the example
+    fig, ax = plt.subplots()
+    ax.scatter(plot_data["x"], plot_data["y"], s=100)
+    ax.set_xlim([-0.2, 9.2])
+    ax.set_ylim([-0.05, 1.05])
+
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+    st.pyplot(fig)
+
+    btn_text = "Skip"
+
+    if st.button("Start", type="primary"):
+        st.session_state.example_idx += 1
+        switch_page("questions")
+
+elif cur_data["ttype"] == "user_preference":
+    st.write("# Preference Question")
+    st.write(cur_data["explanation"])
+
+    cols = st.columns(len(cur_data["options"]))
+
+    for idx, col in enumerate(cols):
+
+        if col.button(cur_data["options"][idx], type="primary"):
+            st.session_state.user_data[f"UserPref_{idx}"] = cur_data["options"][idx]
+            st.session_state.user_pref_idx += 1
+            switch_page("questions")
+
+
+else:
+    st.write("# Question")
+
+    fig = plot_four(
+        st.session_state.plots, cur_data["plots"], xlim=[-0.5, 9.5], ylim=[-0.05, 1.05]
+    )
+
+    st.pyplot(fig)
+
+    plot_data = st.session_state.plots[cur_data["answer"]]
+    # sonify the answer
+    son_fn = SONIFICATION_FUNCTIONS[last_example["sonify"]]
+    son_arr = son_fn(plot_data["x"], plot_data["y"], **last_example["args"])
+    aud_comp = get_audio_comp(son_arr, 44100, visible=False, autoplay=True)
+
+    play_count_idx = f"Q{cur_q}_PlayCount"
+
+    if play_count_idx not in st.session_state.user_data:
+        st.session_state.user_data[play_count_idx] = 0
+
+    if play_btn := st.button(
+        f"Play Audio (Only Once)", disabled=st.session_state.user_data[play_count_idx]
+    ):
+
+        st.session_state.inc_int += 1
+
+        if st.session_state.inc_int % 2:
+            st.write(".")
+
+        st.markdown(
+            aud_comp,
+            unsafe_allow_html=True,
+        )
+
+        if st.session_state.inc_int % 2 == 0:
+            st.write(".")
+
+    # answer stuff
+    if st.session_state.user_data[play_count_idx]:
+
+        cols = st.columns(4)
+        answer_idx = f"Q{cur_q}_Answer"
+        correct_idx = f"Q{cur_q}_Correct"
+        for idx, col in enumerate(cols):
+            if col.button(f"{idx+1}", type="primary"):
+                st.session_state.question_idx += 1
+                cplot = cur_data["plots"][idx]
+                st.session_state.user_data[answer_idx] = cplot
+                st.session_state.user_data[correct_idx] = cplot == cur_data["answer"]
+                switch_page("questions")
+
+    st.session_state.user_data[play_count_idx] += 1
diff --git a/pages/sonification_utils.py b/pages/sonification_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..46cba9721d6ee9533dc8ab36a3a20a1adaf4db0a
--- /dev/null
+++ b/pages/sonification_utils.py
@@ -0,0 +1,174 @@
+import base64
+from io import BytesIO
+
+import matplotlib.pyplot as plt
+from pages.common import AUDIO_PATH
+from scipy.io import wavfile
+
+from toph.audio.effect import SimplePanner
+from toph.audio.playable import Chain, Silence, SineWave, Wave
+from toph.audio.spatial import SpatialPanner
+from toph.audio.stage import AudioStage
+from toph.axis.spatial import LinearSpatialAxis
+
+
+def ax_gen(domain, range_):
+
+    do_scale = max(domain) - min(domain)
+    ra_scale = max(range_) - min(range_)
+
+    def foo(dp):
+
+        dx = dp - min(domain)
+        sdx = dx / do_scale
+        return min(range_) + sdx * ra_scale
+
+    return foo
+
+
+def sonify_y_ax(xs, ys, mode, gaps=0.5):
+    """ """
+    with AudioStage() as stage:
+
+        space_ax = LinearSpatialAxis(
+            domain=((min(xs), max(xs)), (min(ys), max(ys))),
+            range_=((-50.0, 50.0), (-45.0, 90.0)),
+        )
+
+        # TODO: replace with linear axis when implemented
+        lin_ax = ax_gen(domain=(min(ys), max(ys)), range_=(110, 880))
+
+        simple_lr_ax = ax_gen(domain=(min(xs), max(xs)), range_=(-0.98, +0.99))
+
+        marks = []
+
+        for x, y in zip(xs, ys):
+
+            if mode == "full_spatial":
+
+                r, az, el = space_ax((x, y))
+
+                marks.extend(
+                    [
+                        Wave(AUDIO_PATH).add_effect(SpatialPanner(az, el, r)),
+                        Silence(secs=gaps),
+                    ]
+                )
+
+            elif mode == "pitch":
+
+                # keep the y as 0 to get a good r
+                az = simple_lr_ax(x)
+                freq = lin_ax(y)
+
+                print(az, freq)
+                marks.extend(
+                    [
+                        SineWave(vol=0.25, f=freq, secs=0.5).add_effect(
+                            SimplePanner(dir=az)
+                        ),
+                        Silence(secs=gaps),
+                    ]
+                )
+
+            elif mode == "both":
+                # keep the y as 0 to get a good r
+                r, az, el = space_ax((x, y))
+                freq = lin_ax(y)
+
+                marks.extend(
+                    [
+                        SineWave(vol=0.85, f=freq, secs=0.5).add_effect(
+                            SpatialPanner(az, el, r)
+                        ),
+                        Silence(secs=gaps),
+                    ]
+                )
+
+        return stage.get_ndarray(Chain(*marks))
+
+
+def sonify_x_ax(xs, ys, x_mode, y_mode, gaps=0.5):
+    """ """
+    with AudioStage(frame_rate=48000) as stage:
+
+        # TODO: replace with linear axis when implemented
+
+        lin_ax = ax_gen(domain=(min(xs), max(xs)), range_=(-1.0, +1.0))
+
+        pitch_ax = ax_gen(domain=(min(ys), max(ys)), range_=(110, 880))
+
+        x_deltas = [xs[idx + 1] - xs[idx] for idx in range(len(xs) - 1)]
+        x_deltas.append(0.0)
+
+        gap_ax = ax_gen(domain=(min(x_deltas), max(x_deltas)), range_=(0.25, 1.25))
+
+        marks = []
+
+        for idx, (x, y) in enumerate(zip(xs, ys)):
+
+            audio_src = (
+                Wave(AUDIO_PATH)
+                if y_mode == "static"
+                else SineWave(vol=0.85, f=pitch_ax(y), secs=0.5)
+            )
+            if x_mode == "spatial":
+
+                marks.extend(
+                    [
+                        audio_src.add_effect(SimplePanner(dir=lin_ax(x))),
+                        Silence(secs=gaps),
+                    ]
+                )
+
+            elif x_mode == "gaps":
+
+                marks.append(audio_src)
+                if x_deltas[idx] > 0.0:
+                    marks.append(Silence(secs=gap_ax(x_deltas[idx])))
+
+        return stage.get_ndarray(Chain(*marks))
+
+
+def get_audio_comp(arr, frame_rate, visible=True, autoplay=False) -> str:
+    """Takes an audio array
+    returns an audio component
+    """
+    v_file = BytesIO()
+    wavfile.write(
+        v_file,
+        rate=frame_rate,
+        # todo: handle formats other than int16, make it better
+        data=arr,
+    )
+    v_file.seek(0)
+    b64 = base64.b64encode(v_file.read()).decode()
+
+    md = f"""<audio {"style='display: none'" if not visible else ''} controls {'autoplay="true"' if autoplay else ''}>
+        """
+    src = f"""<source src="data:audio/wav;base64,{b64}" type="audio/wav">"""
+
+    return md + src + "</audio>"
+
+
+SONIFICATION_FUNCTIONS = {"sonify_y_ax": sonify_y_ax, "sonify_x_ax": sonify_x_ax}
+
+
+def plot_four(datasets, idxs, xlim=None, ylim=None, s=75):
+    fig, axes = plt.subplots(2, 2, figsize=(8, 8))
+
+    for i, ax in enumerate(axes.flat):
+        ax.scatter(datasets[idxs[i]]["x"], datasets[idxs[i]]["y"], s=100)
+        ax.set_title(f"Option {i + 1}")
+        ax.grid(True)
+
+        if xlim:
+            ax.set_xlim(xlim)
+        if ylim:
+            ax.set_ylim(ylim)
+
+        ax.set_xticks([])
+        ax.set_yticks([])
+
+    plt.tight_layout()
+    return fig
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..72642f80a8d9348ec0ae5bcf8f37d4890c47d9a2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+git+https://github.com/CristiFati/pyaudio
+streamlit
+gspread
+oauth2client
+git+https://github.com/UlascanErsoy/Team-7---Multimodal-Accessibility@main#subdirectory=toph
+streamlit_extras
diff --git a/run_experiment.py b/run_experiment.py
new file mode 100644
index 0000000000000000000000000000000000000000..521be4b9d97e97d31b0a7b405ccc0a4ceb7c29fa
--- /dev/null
+++ b/run_experiment.py
@@ -0,0 +1,48 @@
+from hrtf_vs_pitch_sonifier import *
+
+normal_color = 'black'
+hover_color = 'blue'
+
+# Store original colors to reset them after hover
+fig, axes = plt.subplots(2, 2, figsize=(8, 8))
+
+def is_inside_axes(event, ax):
+    return ax.get_position().contains(event.x, event.y)
+
+# Event handler for mouse movement
+def on_hover(event):
+    for ax in axes:
+        # Check if the cursor is inside the current subplot
+        if is_inside_axes(event, ax):
+            # Change color if inside
+            for spine in ax.spines.values():
+                spine.set_color(hover_color)
+        else:
+            # Reset to normal color if outside
+            for spine in ax.spines.values():
+                spine.set_color(normal_color)
+    
+    fig.canvas.draw_idle()  # Update the figure
+
+def run_test(datasets, idxs, y_mode, show_correct = None):
+    fig.canvas.mpl_connect('motion_notify_event', on_hover)
+
+    for i, ax in enumerate(axes.flat):
+        ax.scatter(datasets[idxs[i]]["x"], datasets[idxs[i]]["y"] if y_mode == "pitch" else [0] * len(datasets[idxs[i]]["x"]))
+        ax.set_title(f"Plot {i + 1}")
+        ax.grid(True)
+
+        if show_correct is not None:
+            for spine in ax.spines.values():
+                if idxs[i] == show_correct:
+                        spine.set_linewidth(2)
+                        spine.set_color("green")
+                else:
+                        spine.set_color("red")
+
+    plt.tight_layout()
+    plt.show()
+
+y_mode = "static"
+
+run_test(datasets, [0, 2, 3, 5], y_mode)
diff --git a/scatter_data/0.json b/scatter_data/0.json
new file mode 100644
index 0000000000000000000000000000000000000000..97ea53454623fce3a6c7891270b226c4d12ba8b7
--- /dev/null
+++ b/scatter_data/0.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
+  ],
+  "y": [
+    1.0,
+    0.47174803327853765,
+    0.2222194727796593,
+    0.1043505268463764,
+    0.04867317918065452,
+    0.02237306239803448,
+    0.00994976689674215,
+    0.0040814176271137355,
+    0.0013094057123391094,
+    0.0
+  ],
+  "description": "exp decrease (scale = 0.75)"
+}
\ No newline at end of file
diff --git a/scatter_data/1.json b/scatter_data/1.json
new file mode 100644
index 0000000000000000000000000000000000000000..4864e65f54c718aefcf72da232885b5a54d1d566
--- /dev/null
+++ b/scatter_data/1.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
+  ],
+  "y": [
+    1.0,
+    0.7527397438033054,
+    0.5601732626548834,
+    0.41020233634318737,
+    0.2934048614936946,
+    0.20244289662014694,
+    0.13160164714691436,
+    0.07643042658340408,
+    0.033463036805537104,
+    0.0
+  ],
+  "description": "exp decrease (scale = 0.25)"
+}
\ No newline at end of file
diff --git a/scatter_data/10.json b/scatter_data/10.json
new file mode 100644
index 0000000000000000000000000000000000000000..da335efd7454f84e3f792ba3b91ba9d3aea08cba
--- /dev/null
+++ b/scatter_data/10.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
+  ],
+  "y": [
+    0.0,
+    0.5282519667214622,
+    0.7777805272203405,
+    0.8956494731536235,
+    0.9513268208193454,
+    0.9776269376019654,
+    0.9900502331032578,
+    0.9959185823728862,
+    0.9986905942876608,
+    1.0
+  ],
+  "description": "inv exp increase (scale = 0.75)"
+}
\ No newline at end of file
diff --git a/scatter_data/11.json b/scatter_data/11.json
new file mode 100644
index 0000000000000000000000000000000000000000..0512cc3eb161cb8cb2a21a6558ec8568513b080b
--- /dev/null
+++ b/scatter_data/11.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
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+  "y": [
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+  "description": "inv exp increase (scale = 0.25)"
+}
\ No newline at end of file
diff --git a/scatter_data/12.json b/scatter_data/12.json
new file mode 100644
index 0000000000000000000000000000000000000000..206ff6ed39a2b15809efd649739bf43e0cf4a5e9
--- /dev/null
+++ b/scatter_data/12.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
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+    0.4102023363431873,
+    0.5601732626548833,
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+  "description": "exp increase (scale = 0.25)"
+}
\ No newline at end of file
diff --git a/scatter_data/13.json b/scatter_data/13.json
new file mode 100644
index 0000000000000000000000000000000000000000..840d190e12a6ea96d2a4d0b38f19a656e7ff79b4
--- /dev/null
+++ b/scatter_data/13.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
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+  "y": [
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+}
\ No newline at end of file
diff --git a/scatter_data/14.json b/scatter_data/14.json
new file mode 100644
index 0000000000000000000000000000000000000000..8cf50e3d971e7be27f1f7b5f73fdc1ff99b88c92
--- /dev/null
+++ b/scatter_data/14.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
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+  "y": [
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+    0.2,
+    0.2,
+    0.4,
+    0.4,
+    0.6,
+    0.6,
+    0.8,
+    0.8,
+    1.0
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+  "description": "Step increase (stepsize = 2)"
+}
\ No newline at end of file
diff --git a/scatter_data/15.json b/scatter_data/15.json
new file mode 100644
index 0000000000000000000000000000000000000000..27bb35faeb899db16e36c3960bcc69cf5f93857e
--- /dev/null
+++ b/scatter_data/15.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
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+  "y": [
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+    0.3333333333333333,
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+    0.6666666666666666,
+    0.6666666666666666,
+    0.6666666666666666,
+    1.0,
+    1.0,
+    1.0
+  ],
+  "description": "Step increase (stepsize = 3)"
+}
\ No newline at end of file
diff --git a/scatter_data/16.json b/scatter_data/16.json
new file mode 100644
index 0000000000000000000000000000000000000000..22a544cc4fd0ae48b3609fba2d13e75e0f791990
--- /dev/null
+++ b/scatter_data/16.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
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+  "y": [
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+    0.6666666666666666,
+    0.6666666666666666,
+    0.6666666666666666,
+    0.33333333333333337,
+    0.33333333333333337,
+    0.33333333333333337,
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+    0.0,
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+  "description": "Step decrease (stepsize = 3)"
+}
\ No newline at end of file
diff --git a/scatter_data/17.json b/scatter_data/17.json
new file mode 100644
index 0000000000000000000000000000000000000000..fc4ab770483bd6314e0f8975e17d642b05f31291
--- /dev/null
+++ b/scatter_data/17.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+    3,
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+  "y": [
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+    0.4,
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+    0.19999999999999996,
+    0.19999999999999996,
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+  "description": "Step decrease (stepsize = 2)"
+}
\ No newline at end of file
diff --git a/scatter_data/2.json b/scatter_data/2.json
new file mode 100644
index 0000000000000000000000000000000000000000..89be2fea51edabf99fdecd59e2fcbf41da24878a
--- /dev/null
+++ b/scatter_data/2.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
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+    9
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+  "y": [
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+    0.888888888888889,
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+    0.6666666666666666,
+    0.5555555555555556,
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+    0.33333333333333326,
+    0.22222222222222215,
+    0.11111111111111108,
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+  "description": "linear decrease"
+}
\ No newline at end of file
diff --git a/scatter_data/21.json b/scatter_data/21.json
new file mode 100644
index 0000000000000000000000000000000000000000..79fc1a41c7d9da60a1e083431c696fcade8f0489
--- /dev/null
+++ b/scatter_data/21.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+    1.8484749760000954,
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+  "y": [
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+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5
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+}
\ No newline at end of file
diff --git a/scatter_data/22.json b/scatter_data/22.json
new file mode 100644
index 0000000000000000000000000000000000000000..db6d2d052b6318a127f0566c71ca38cbcd035401
--- /dev/null
+++ b/scatter_data/22.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+  "y": [
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+    0.5,
+    0.5,
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+  "description": "Uniform Spread"
+}
\ No newline at end of file
diff --git a/scatter_data/23.json b/scatter_data/23.json
new file mode 100644
index 0000000000000000000000000000000000000000..d7d3cc711d3ec7dc1a10e57f7e4cbf56fa4d8a68
--- /dev/null
+++ b/scatter_data/23.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+    0.5,
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+    0.5
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+  "description": "Clustered Right"
+}
\ No newline at end of file
diff --git a/scatter_data/24.json b/scatter_data/24.json
new file mode 100644
index 0000000000000000000000000000000000000000..757e962c58fbc7ad8797baaa42d3b6bded6935dd
--- /dev/null
+++ b/scatter_data/24.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+  "y": [
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+    0.5,
+    0.5,
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+  "description": "Dense Center"
+}
\ No newline at end of file
diff --git a/scatter_data/25.json b/scatter_data/25.json
new file mode 100644
index 0000000000000000000000000000000000000000..54e9af3e6134b4d053163acb5bef83227aab5af2
--- /dev/null
+++ b/scatter_data/25.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+  "y": [
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+  "description": "Left Dense, Right Sparse"
+}
\ No newline at end of file
diff --git a/scatter_data/26.json b/scatter_data/26.json
new file mode 100644
index 0000000000000000000000000000000000000000..bf1dca0987b574642d0ff57a98d67ed6cacd7a76
--- /dev/null
+++ b/scatter_data/26.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+  "y": [
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+  "description": "Right Dense, Left Sparse"
+}
\ No newline at end of file
diff --git a/scatter_data/27.json b/scatter_data/27.json
new file mode 100644
index 0000000000000000000000000000000000000000..185d0abdb5c0dc12b99bbf05eedf6a8e0d90bfa3
--- /dev/null
+++ b/scatter_data/27.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+  "y": [
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+  "description": "Two Clusters (Left & Right)"
+}
\ No newline at end of file
diff --git a/scatter_data/28.json b/scatter_data/28.json
new file mode 100644
index 0000000000000000000000000000000000000000..fe9d9cf5435a38993bbfa92d34511f2126218720
--- /dev/null
+++ b/scatter_data/28.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+}
\ No newline at end of file
diff --git a/scatter_data/29.json b/scatter_data/29.json
new file mode 100644
index 0000000000000000000000000000000000000000..b8693d9b34cec43a545f1b4663af24c889c544b6
--- /dev/null
+++ b/scatter_data/29.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+}
\ No newline at end of file
diff --git a/scatter_data/3.json b/scatter_data/3.json
new file mode 100644
index 0000000000000000000000000000000000000000..fdfd47e5c4bb1abaab901b2d5145543953804579
--- /dev/null
+++ b/scatter_data/3.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+    1,
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+    3,
+    4,
+    5,
+    6,
+    7,
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+    9
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+  "y": [
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+  "description": "linear increase"
+}
\ No newline at end of file
diff --git a/scatter_data/30.json b/scatter_data/30.json
new file mode 100644
index 0000000000000000000000000000000000000000..d5af69c10486ef026094da8299451c843c6f6ef3
--- /dev/null
+++ b/scatter_data/30.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+  "y": [
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+    0.5,
+    0.5,
+    0.5,
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+    0.5,
+    0.5
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+  "description": "Dense in Corners"
+}
\ No newline at end of file
diff --git a/scatter_data/31.json b/scatter_data/31.json
new file mode 100644
index 0000000000000000000000000000000000000000..c91edc10560c9bb6c8582d0c9f8c1152a3d7a4d0
--- /dev/null
+++ b/scatter_data/31.json
@@ -0,0 +1,21 @@
+{
+  "x": [
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+  "description": "left dense 1"
+}
\ No newline at end of file
diff --git a/scatter_data/32.json b/scatter_data/32.json
new file mode 100644
index 0000000000000000000000000000000000000000..7781f138b44ebcf7c7cfea7d8a34f63b34688ec8
--- /dev/null
+++ b/scatter_data/32.json
@@ -0,0 +1,21 @@
+{
+  "x": [
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+}
\ No newline at end of file
diff --git a/scatter_data/33.json b/scatter_data/33.json
new file mode 100644
index 0000000000000000000000000000000000000000..021623bd256ed11da65c98dedf1b917cfb413481
--- /dev/null
+++ b/scatter_data/33.json
@@ -0,0 +1,25 @@
+{
+  "x": [
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+  "y": [
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+  "description": "clusters dense 1"
+}
\ No newline at end of file
diff --git a/scatter_data/34.json b/scatter_data/34.json
new file mode 100644
index 0000000000000000000000000000000000000000..54e9af3e6134b4d053163acb5bef83227aab5af2
--- /dev/null
+++ b/scatter_data/34.json
@@ -0,0 +1,27 @@
+{
+  "x": [
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+  "y": [
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+  "description": "Left Dense, Right Sparse"
+}
\ No newline at end of file
diff --git a/scatter_data/35.json b/scatter_data/35.json
new file mode 100644
index 0000000000000000000000000000000000000000..021623bd256ed11da65c98dedf1b917cfb413481
--- /dev/null
+++ b/scatter_data/35.json
@@ -0,0 +1,25 @@
+{
+  "x": [
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+  "description": "clusters dense 1"
+}
\ No newline at end of file
diff --git a/scatter_data/36.json b/scatter_data/36.json
new file mode 100644
index 0000000000000000000000000000000000000000..31837f5a534b23a2ddde01679bd09ee900ec79a6
--- /dev/null
+++ b/scatter_data/36.json
@@ -0,0 +1,27 @@
+{
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+    0.5,
+    0.5,
+    0.5
+  ],
+  "description": "square wave in the middle (inverted)"
+}
\ No newline at end of file
diff --git a/scatter_data/37.json b/scatter_data/37.json
new file mode 100644
index 0000000000000000000000000000000000000000..78acac54acdf9691e6cc596dd82ee40e7b99a2a4
--- /dev/null
+++ b/scatter_data/37.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0.0,
+    1.0,
+    2.0,
+    3.0,
+    4.0,
+    5.0,
+    6.0,
+    7.0,
+    8.0,
+    9.0
+  ],
+  "y": [
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5
+  ],
+  "description": "Step decrease (size = 2)"
+}
\ No newline at end of file
diff --git a/scatter_data/38.json b/scatter_data/38.json
new file mode 100644
index 0000000000000000000000000000000000000000..d1f3bab153e826372f9afeeb28840623e82b7eec
--- /dev/null
+++ b/scatter_data/38.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0.0,
+    1.0,
+    2.0,
+    3.0,
+    4.0,
+    5.0,
+    6.0,
+    7.0,
+    8.0,
+    9.0
+  ],
+  "y": [
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5,
+    0.5
+  ],
+  "description": "upper Triangle shifted-right once"
+}
\ No newline at end of file
diff --git a/scatter_data/4.json b/scatter_data/4.json
new file mode 100644
index 0000000000000000000000000000000000000000..3a9e986b141d7f727f26b56ae94f056e784d53dc
--- /dev/null
+++ b/scatter_data/4.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
+  ],
+  "y": [
+    1.0,
+    1.0,
+    1.0,
+    1.0,
+    1.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0
+  ],
+  "description": "linear increase"
+}
\ No newline at end of file
diff --git a/scatter_data/5.json b/scatter_data/5.json
new file mode 100644
index 0000000000000000000000000000000000000000..3cfe14b37a3098f37ab83ac3eb3fdaa0edf299b1
--- /dev/null
+++ b/scatter_data/5.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
+  ],
+  "y": [
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    1.0,
+    1.0,
+    1.0,
+    1.0,
+    1.0
+  ],
+  "description": "Step up middle"
+}
\ No newline at end of file
diff --git a/scatter_data/6.json b/scatter_data/6.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd1aaae02b54020f0faf4fac6bffeee609da895f
--- /dev/null
+++ b/scatter_data/6.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
+  ],
+  "y": [
+    1.0,
+    1.0,
+    1.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    1.0,
+    1.0,
+    1.0
+  ],
+  "description": "Square wave (inv)"
+}
\ No newline at end of file
diff --git a/scatter_data/7.json b/scatter_data/7.json
new file mode 100644
index 0000000000000000000000000000000000000000..06e4fb74a49672d8e78b72abd4e53fe98be6eda8
--- /dev/null
+++ b/scatter_data/7.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
+  ],
+  "y": [
+    0.0,
+    0.0,
+    0.0,
+    1.0,
+    1.0,
+    1.0,
+    1.0,
+    0.0,
+    0.0,
+    0.0
+  ],
+  "description": "Square wave"
+}
\ No newline at end of file
diff --git a/scatter_data/8.json b/scatter_data/8.json
new file mode 100644
index 0000000000000000000000000000000000000000..3a779218dc6db4052a354cf6fb40d2b490a42b63
--- /dev/null
+++ b/scatter_data/8.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
+  ],
+  "y": [
+    0.0,
+    0.25,
+    0.5,
+    0.75,
+    1.0,
+    0.75,
+    0.5,
+    0.25,
+    0.0,
+    0.0
+  ],
+  "description": "Triangle wave"
+}
\ No newline at end of file
diff --git a/scatter_data/9.json b/scatter_data/9.json
new file mode 100644
index 0000000000000000000000000000000000000000..691ffd614d8eddafe07eaeb7d569485ddf0d976e
--- /dev/null
+++ b/scatter_data/9.json
@@ -0,0 +1,27 @@
+{
+  "x": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9
+  ],
+  "y": [
+    1.0,
+    0.75,
+    0.5,
+    0.25,
+    0.0,
+    0.25,
+    0.5,
+    0.75,
+    1.0,
+    1.0
+  ],
+  "description": "Triangle wave (inv)"
+}
\ No newline at end of file
diff --git a/scatter_designer.ipynb b/scatter_designer.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5922248b36746c883ea926627a290277717bbd4e
--- /dev/null
+++ b/scatter_designer.ipynb
@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_PATH = \"scatter_data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import json\n",
+    "import os\n",
+    "\n",
+    "def plot_four(x , y):\n",
+    "\n",
+    "    plt.scatter(x, y)\n",
+    "    plt.grid(True)\n",
+    "\n",
+    "\n",
+    "    plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(0, 10, 1) \n",
+    "y = np.array([\n",
+    "    0.0,\n",
+    "    0.0,\n",
+    "    0.0,\n",
+    "    1.0,\n",
+    "    1.0,\n",
+    "    1.0,\n",
+    "    1.0,\n",
+    "    0.0,\n",
+    "    0.0,\n",
+    "    0.0\n",
+    "])\n",
+    "\n",
+    "#y = np.exp(1 - x * 0.75)\n",
+    "#y = y - y.min()\n",
+    "#y /= y.max()\n",
+    "\n",
+    "y = y[::-1]\n",
+    "description = \"square wave in the middle\"\n",
+    "\n",
+    "plot_four(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19\n"
+     ]
+    }
+   ],
+   "source": [
+    "NUM = max([int(name.split(\".\")[0]) for name in os.listdir(DATA_PATH)]) + 1\n",
+    "\n",
+    "dd = {\n",
+    "    \"x\":x.tolist(),\n",
+    "    \"y\":y.tolist(),\n",
+    "    \"description\":description\n",
+    "}\n",
+    "#open(os.path.join(DATA_PATH, f\"{NUM}.json\"), \"w\").write(json.dumps(dd, indent=2))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/short.json b/short.json
new file mode 100644
index 0000000000000000000000000000000000000000..6d930e431bb674d0bec288faecf462b2b92bc4fc
--- /dev/null
+++ b/short.json
@@ -0,0 +1,52 @@
+[
+  {
+    "ttype": "Example",
+    "plot":19,
+    "title":"Directional (Spatial) Audio",
+    "explanation":"In this section you are going to hear an audio file where the vertical coordinates in the graph correspond to the direction of the audio. You will be asked to match the audio with a chart.",
+    "sonify":"sonify_y_ax",
+    "args": {
+      "mode": "full_spatial"
+    }
+  },
+  {
+    "description": "Test if they can distinguish vastly different shapes",
+    "ttype": "question",
+    "plots": [
+      14,
+      0,
+      3,
+      5
+    ],
+    "answer": 5
+  },
+  {
+    "ttype": "Example",
+    "plot":19,
+    "title":"Frequency Based Audio Representation",
+    "explanation":"In this section, the audio will be encoded using `Frequency` rather than `Directionality`.",
+    "sonify":"sonify_y_ax",
+    "args": {
+      "mode": "pitch"
+    }
+  },
+  {
+    "description": "Test if they can distinguish vastly different shapes",
+    "ttype": "question",
+    "plots": [
+      19,
+      0,
+      5,
+      14 
+    ],
+    "answer": 5
+  },
+  {
+    "ttype":"user_preference",
+    "explanation":"Between `Directional` and `Frequency` based audio, which one did you prefer?",
+    "options":[
+      "Directional",
+      "Frequency Based"
+    ]
+  }
+]
\ No newline at end of file
diff --git a/spatial_questions.json b/spatial_questions.json
new file mode 100644
index 0000000000000000000000000000000000000000..0bb942d40028bb324bb4be7797e773372fbc2023
--- /dev/null
+++ b/spatial_questions.json
@@ -0,0 +1,237 @@
+[
+  {
+    "ttype": "Example",
+    "plot":4,
+    "title":"Directional (Spatial) Audio",
+    "explanation":"In this section you are going to hear an audio file where the vertical coordinates in the graph correspond to the direction of the audio. You will be asked to match the audio with a chart.",
+    "sonify":"sonify_y_ax",
+    "args": {
+      "mode": "full_spatial"
+    }
+  },
+  {
+    "description": "Test if they can distinguish vastly different shapes",
+    "ttype": "question",
+    "plots": [
+      14,
+      0,
+      3,
+      5
+    ],
+    "answer": 3
+  },
+  {
+    "description": "Can they distinguish the shape of a dip",
+    "ttype": "question",
+    "plots": [
+      9,
+      6,
+      0,
+      8 
+    ],
+    "answer": 9 
+  },
+  {
+    "description": "Can they distinguish the shape of a monotonous increase",
+    "ttype": "question",
+    "plots": [
+      10,
+      12,
+      5,
+      16
+    ],
+    "answer": 12 
+  },
+  {
+    "description": "Test if they can distinguish the shape of a monotonous decrease",
+    "ttype": "question",
+    "plots": [
+      2,
+      17,
+      1,
+      4 
+    ],
+    "answer": 1
+  },
+  {
+    "description": "Test if they can distinguish the shape of a step",
+    "ttype": "question",
+    "plots": [
+      6,
+      5,
+      14,
+      2
+    ],
+    "answer": 5
+  },
+  {
+    "ttype": "Example",
+    "plot":19,
+    "title":"Frequency Based Audio Representation",
+    "explanation":"In this section, the audio will be encoded using `Frequency` rather than `Directionality`.",
+    "sonify":"sonify_y_ax",
+    "args": {
+      "mode": "pitch"
+    }
+  },
+  {
+    "description": "Test if they can distinguish vastly different shapes",
+    "ttype": "question",
+    "plots": [
+      7,
+      14,
+      0,
+      9
+    ],
+    "answer": 7 
+  },
+  {
+    "description": "Can they distinguish the shape of a dip",
+    "ttype": "question",
+    "plots": [
+      4,
+      9,
+      6,
+      8 
+    ],
+    "answer":  6 
+  },
+  {
+    "description": "Can they distinguish the shape of a monotonous increase",
+    "ttype": "question",
+    "plots": [
+      10,
+      12,
+      15,
+      16
+    ],
+    "answer": 15 
+  },
+  {
+    "description": "Test if they can distinguish the shape of a monotonous decrease",
+    "ttype": "question",
+    "plots": [
+      2,
+      17,
+      1,
+      4 
+    ],
+    "answer": 17
+  },
+  {
+    "description": "Test if they can distinguish the shape of a step",
+    "ttype": "question",
+    "plots": [
+      2,
+      6,
+      14,
+      5
+    ],
+    "answer": 5
+  },
+ {
+    "ttype":"user_preference",
+    "explanation":"Between `Directional` and `Frequency` based audio, which one did you prefer?",
+    "options":[
+      "Directional",
+      "Frequency Based"
+    ]
+  },
+
+  {
+    "ttype": "Example",
+    "plot":24,
+    "title":"Density Based Directional Audio",
+    "explanation":"In this section, you are going to listen to audio that encodes the horizontal axis using directional audio.",
+    "sonify":"sonify_x_ax",
+    "args": {
+      "x_mode": "spatial",
+      "y_mode": "static"
+    }
+  },
+  {
+    "description": "Test if they can distinguish different densities",
+    "ttype": "question",
+    "plots": [
+      25,
+      29,
+      24,
+      26
+    ],
+    "answer": 26
+  },
+  {
+    "description": "Test if they can distinguish different densities",
+    "ttype": "question",
+    "plots": [
+      25,
+      22,
+      31,
+      24
+    ],
+    "answer": 31
+  },
+  {
+    "description": "Test if they can distinguish different densities",
+    "ttype": "question",
+    "plots": [
+      32,
+      26,
+      27,
+      33
+    ],
+    "answer": 33
+  },
+  {
+    "ttype": "Example",
+    "plot":25,
+    "title":"Silencing Based Density",
+    "explanation":"In this section the horizontal axis will be encoded using Gaps / Silences between the tones.",
+    "sonify":"sonify_x_ax",
+    "args": {
+      "x_mode": "gaps",
+      "y_mode": "static"
+    }
+  },
+  {
+    "description": "Test if they can distinguish different densities",
+    "ttype": "question",
+    "plots": [
+      25,
+      24,
+      29,
+      26
+    ],
+    "answer": 24
+  },
+  {
+    "description": "Test if they can distinguish different densities",
+    "ttype": "question",
+    "plots": [
+      24,
+      25,
+      31,
+      22
+    ],
+    "answer": 22 
+  },
+  {
+    "description": "Test if they can distinguish different densities",
+    "ttype": "question",
+    "plots": [
+      26,
+      27,
+      32,
+      33
+    ],
+    "answer": 32
+  },
+  {
+    "ttype":"user_preference",
+    "explanation":"Between `Directional` and `Gap / Silence` based audio encoding, which one did you prefer?",
+    "options":[
+      "Directional",
+      "Silence"
+    ]
+  }
+]
\ No newline at end of file