diff --git a/notebooks/2020-09-03-visualize-qsiprep-qc.ipynb b/notebooks/2020-09-03-visualize-qsiprep-qc.ipynb
new file mode 100644
index 0000000..0a5fc6a
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+++ b/notebooks/2020-09-03-visualize-qsiprep-qc.ipynb
@@ -0,0 +1,298 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dask.dataframe as dd\n",
+ "import altair as alt\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def scatter_brush(df, title):\n",
+ " # Brush for selection\n",
+ " brush = alt.selection(type='interval')\n",
+ "\n",
+ " # Scatter Plot\n",
+ " points = alt.Chart(df).mark_point().encode(\n",
+ " x=alt.X(\"raw_neighbor_corr:Q\", scale=alt.Scale(zero=False)),\n",
+ " y=alt.Y(\"t1_neighbor_corr:Q\", scale=alt.Scale(zero=False)),\n",
+ " color=\"mean_fd:Q\",\n",
+ " size=\"max_fd:Q\",\n",
+ " tooltip=\"subject_id:N\",\n",
+ " ).add_selection(brush).properties(title=title)\n",
+ "\n",
+ " # Base chart for data tables\n",
+ " ranked_text = alt.Chart(df).mark_text().encode(\n",
+ " y=alt.Y(\"row_number:O\",axis=None)\n",
+ " ).transform_window(\n",
+ " row_number=\"row_number()\"\n",
+ " ).transform_filter(\n",
+ " brush\n",
+ " ).transform_window(\n",
+ " rank=\"rank(row_number)\"\n",
+ " ).transform_filter(\n",
+ " alt.datum.rank<20\n",
+ " )\n",
+ "\n",
+ " # Data Tables\n",
+ " subjects = ranked_text.encode(text=\"subject_id:N\").properties(\n",
+ " title=\"Subjects\", width=125\n",
+ " )\n",
+ "\n",
+ " # Build chart\n",
+ " chart = alt.hconcat(\n",
+ " points,\n",
+ " subjects\n",
+ " ).resolve_legend(\n",
+ " color=\"independent\"\n",
+ " )\n",
+ " \n",
+ " return chart"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['raw_dimension_x', 'raw_dimension_y', 'raw_dimension_z',\n",
+ " 'raw_voxel_size_x', 'raw_voxel_size_y', 'raw_voxel_size_z', 'raw_max_b',\n",
+ " 'raw_neighbor_corr', 'raw_num_bad_slices', 'raw_num_directions',\n",
+ " 't1_dimension_x', 't1_dimension_y', 't1_dimension_z', 't1_voxel_size_x',\n",
+ " 't1_voxel_size_y', 't1_voxel_size_z', 't1_max_b', 't1_neighbor_corr',\n",
+ " 't1_num_bad_slices', 't1_num_directions', 'mean_fd', 'max_fd',\n",
+ " 'max_rotation', 'max_translation', 'max_rel_rotation',\n",
+ " 'max_rel_translation', 't1_dice_distance', 'file_name', 'subject_id',\n",
+ " 'session_id', 'task_id', 'acq_id', 'space_id', 'rec_id', 'run_id'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_camcan = dd.read_csv('s3://cam-can-mri/derivatives/qsiprep/sub-*/dwi/sub-*_desc-ImageQC_dwi.csv')\n",
+ "df_camcan = df_camcan.compute()\n",
+ "df_camcan.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.HConcatChart(...)"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scatter_brush(df=df_camcan, title=\"Cam-CAN\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['raw_dimension_x', 'raw_dimension_y', 'raw_dimension_z',\n",
+ " 'raw_voxel_size_x', 'raw_voxel_size_y', 'raw_voxel_size_z', 'raw_max_b',\n",
+ " 'raw_neighbor_corr', 'raw_num_bad_slices', 'raw_num_directions',\n",
+ " 't1_dimension_x', 't1_dimension_y', 't1_dimension_z', 't1_voxel_size_x',\n",
+ " 't1_voxel_size_y', 't1_voxel_size_z', 't1_max_b', 't1_neighbor_corr',\n",
+ " 't1_num_bad_slices', 't1_num_directions', 'mean_fd', 'max_fd',\n",
+ " 'max_rotation', 'max_translation', 'max_rel_rotation',\n",
+ " 'max_rel_translation', 't1_dice_distance', 'file_name', 'subject_id',\n",
+ " 'session_id', 'task_id', 'acq_id', 'space_id', 'rec_id', 'run_id'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_hbn = dd.read_csv('s3://hbn-derivatives/derivatives/qsiprep/sub-*/dwi/sub-*_desc-ImageQC_dwi.csv')\n",
+ "df_hbn = df_hbn.compute()\n",
+ "df_hbn.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.HConcatChart(...)"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scatter_brush(df=df_hbn, title=\"HBN\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/viz-environment.yml b/viz-environment.yml
new file mode 100644
index 0000000..077748c
--- /dev/null
+++ b/viz-environment.yml
@@ -0,0 +1,6 @@
+name: viz-qsiprep-qc
+channels:
+ - conda-forge
+dependencies:
+ - altair==4.1.0
+ - dask==2.6.0