diff --git a/project-templates/python/alternative-data-universe-braincompanyfilinglanguagemetricsuniverseall/research.ipynb b/project-templates/python/alternative-data-universe-braincompanyfilinglanguagemetricsuniverseall/research.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a1b2c3d4",
+   "metadata": {},
+   "source": [
+    "![QuantConnect Logo](https://cdn.quantconnect.com/web/i/icon.png)\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5f6a7b8",
+   "metadata": {},
+   "source": [
+    "## Brain Company Filing Language Metrics Research\n",
+    "\n",
+    "This notebook studies whether SEC filing sentiment helps explain future returns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9d0e1f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qb = QuantBook()\n",
+    "# Anchor the research clock to the start of 2026 for a reproducible history window.\n",
+    "qb.set_start_date(2026, 1, 1)\n",
+    "# Daily bars will have an end_time that matches the following midnight.\n",
+    "qb.settings.daily_precise_end_time = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b4c5d6",
+   "metadata": {},
+   "source": [
+    "### Build a Filing Sentiment Universe\n",
+    "\n",
+    "Select US Equities with positive sentiment in both the report and MD&A sections of their latest SEC filing, then inspect the returned universe history."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7f8a9b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def select_assets(data: List[BrainCompanyFilingLanguageMetricsUniverseAll]) -> List[Symbol]:\n",
+    "    # Keep names with positive sentiment in both the report and MD&A sections.\n",
+    "    return [d.symbol for d in data\n",
+    "            if d.report_sentiment and d.report_sentiment.sentiment and d.report_sentiment.sentiment > 0\n",
+    "            and d.management_discussion_analyasis_of_financial_condition_and_results_of_operations\n",
+    "            and d.management_discussion_analyasis_of_financial_condition_and_results_of_operations.sentiment\n",
+    "            and d.management_discussion_analyasis_of_financial_condition_and_results_of_operations.sentiment > 0]\n",
+    "\n",
+    "# Add the Brain Company Filing Language Metrics universe.\n",
+    "universe = qb.add_universe(BrainCompanyFilingLanguageMetricsUniverseAll, select_assets)\n",
+    "# Request universe history of the last 90 days.\n",
+    "universe_history = qb.universe_history(universe, qb.time - timedelta(90), qb.time - timedelta(1), flatten=True).rename_axis(['time', 'symbol']).drop(columns=['time'])\n",
+    "# Print the returned shape and columns.\n",
+    "print(f\"Shape: {universe_history.shape}\")\n",
+    "print(f\"Columns: {list(universe_history.columns)}\")\n",
+    "universe_history.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1d2e3f4",
+   "metadata": {},
+   "source": [
+    "### Universe Diagnostics\n",
+    "\n",
+    "Check how many assets pass the filter each day and summarize the factors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5b6c7d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract the numeric sentiment score from the nested report-sentiment column.\n",
+    "universe_history['reportsentiment'] = universe_history['reportsentiment'].map(lambda x: x.sentiment if x is not None else None)\n",
+    "# Count selected assets by day.\n",
+    "universe_size = universe_history.reset_index().groupby(['time', 'symbol']).size().groupby('time').size()\n",
+    "print(f\"Universe days: {len(universe_size)}\")\n",
+    "# Store the selected symbol list.\n",
+    "unique_assets = list(universe_history.index.levels[1].unique())\n",
+    "print(f\"Mean universe size per day: {universe_size.mean():.1f}\")\n",
+    "print('')\n",
+    "print(universe_history.reportsentiment.describe().map('{:0.3f}'.format))\n",
+    "universe_size.plot(title='Daily Universe Size', ylabel='Universe Size');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9f0a1b2",
+   "metadata": {},
+   "source": [
+    "### Daily Universe Prices\n",
+    "\n",
+    "Fetch daily price history for every symbol that appears in the universe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3d4e5f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract unique assets\n",
+    "symbols = list(universe_history.index.get_level_values(1).unique())\n",
+    "# Fetch daily historical price metrics using the earliest timestamp available in the index.\n",
+    "history = qb.history(symbols, universe_history.index[0][0] - timedelta(1), qb.time, Resolution.DAILY)\n",
+    "history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7b8c9d0",
+   "metadata": {},
+   "source": [
+    "### Align Filing Sentiment And Returns\n",
+    "\n",
+    "Build a joined table of report sentiment and future returns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d56c7cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Align the factor with the return from the next open to the following open.\n",
+    "dataset = (\n",
+    "    universe_history.reset_index().groupby(['time', 'symbol']).agg(reportsentiment=('reportsentiment', 'mean'))\n",
+    "    .join(history.open.unstack('symbol').sort_index().pct_change(2, fill_method=None).shift(-2).stack().rename('futurereturn'), how='inner')\n",
+    ")\n",
+    "\n",
+    "dataset.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Foundation-Py-Default",
+   "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": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}