diff --git a/notebooks/find_candidate_seeds.ipynb b/notebooks/find_candidate_seeds.ipynb
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+++ b/notebooks/find_candidate_seeds.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# See associated issue\n",
+    "# https://github.com/jeromekelleher/sc2ts-paper/issues/268\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Download files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "wget --quiet https://raw.githubusercontent.com/cov-lineages/pango-designation/16205e716c6a68ff1c3d0f26f0c77478682368ac/lineages.csv\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "curl -s -X 'GET' \\\n",
+    "  'https://www.ebi.ac.uk/ena/portal/api/filereport?result=read_run&accession=PRJEB37886&fields=sample_accession%2Csample_alias&limit=0&format=tsv&download=true' \\\n",
+    "  -H 'accept: */*' > filereport_read_run_PRJEB37886_tsv.txt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "wget --quiet --content-disposition https://figshare.com/ndownloader/files/49694808\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Parse files\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# lineage, sample name\n",
+    "pango = pd.read_csv(\"lineages.csv\", sep=\",\")\n",
+    "pango[\"sample_name\"] = [s.split(\"/\")[1] for s in pango[\"taxon\"]]\n",
+    "pango.head(1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run accession, sample name\n",
+    "ena = pd.read_csv(\"filereport_read_run_PRJEB37886_tsv.txt\", sep=\"\\t\")\n",
+    "ena[\"sample_name\"] = [s.split(\"/\")[1] for s in ena[\"sample_alias\"]]\n",
+    "ena.head(1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run (strain)\n",
+    "viridian = pd.read_csv(\"run_metadata.v05.tsv.gz\", sep=\"\\t\")\n",
+    "viridian = viridian[viridian[\"Date_tree\"] != \"none\"]\n",
+    "viridian[\"parsed_datetime\"] = pd.to_datetime(\n",
+    "    viridian[\"Date_tree\"],\n",
+    "    format='%Y-%m-%d',\n",
+    "    errors='coerce',\n",
+    ")\n",
+    "viridian = viridian[viridian[\"parsed_datetime\"].notna()]\n",
+    "viridian.head(1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Search among the COG-UK samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Chosen by trial-and-error\n",
+    "threshold_dates_dict = {\n",
+    "    \"B.1.617.1\": \"2021-04-01\",\n",
+    "    \"B.1.617.2\": \"2021-04-01\",\n",
+    "    \"BA.1\": \"2021-12-01\",\n",
+    "    \"BA.2\": \"2022-01-08\",\n",
+    "    \"BA.4\": \"2022-04-01\",\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for focal_pango, threshold_date in threshold_dates_dict.items():\n",
+    "    out_file = \"\".join([\n",
+    "        \"candidate_seeds\", \"_\", focal_pango, \"_\", \\\n",
+    "        \"pre\", \"-\", threshold_date, \\\n",
+    "    ]) + \".txt\"\n",
+    "\n",
+    "    designated_samples = pango[pango[\"lineage\"] == focal_pango][\"sample_name\"]\n",
+    "    coguk_runs = ena[ena[\"sample_name\"].isin(designated_samples)][\"run_accession\"]\n",
+    "    viridian_samples = viridian[viridian[\"Run\"].isin(coguk_runs)]\n",
+    "\n",
+    "    viridian_samples[\n",
+    "        (viridian_samples[\"parsed_datetime\"] < pd.to_datetime(threshold_date)) & \\\n",
+    "        (viridian_samples[\"parsed_datetime\"] != pd.to_datetime(\"2020-12-31\"))\n",
+    "    ][[\"Run\", \"Date_tree\"]].to_csv(out_file, index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Search among the South Africa samples for Omicron seeds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Chosen by trial-and-error\n",
+    "threshold_dates_dict = {\n",
+    "    \"BA.1\": \"2021-10-01\",\n",
+    "    \"BA.2\": \"2021-12-01\",\n",
+    "    \"BA.4\": \"2022-01-01\",\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for focal_pango, threshold_date in threshold_dates_dict.items():\n",
+    "    out_file = \"\".join([\n",
+    "        \"candidate_seeds\", \"_\", focal_pango, \"_\", \\\n",
+    "        \"SouthAfrica\", \"_\", \\\n",
+    "        \"pre\", \"-\", threshold_date,\n",
+    "    ]) + \".txt\"\n",
+    "\n",
+    "    viridian[\n",
+    "        (viridian[\"Viridian_pangolin\"] == focal_pango) & \\\n",
+    "        (viridian[\"Country\"] == \"South Africa\") & \\\n",
+    "        (viridian[\"parsed_datetime\"] < pd.to_datetime(threshold_date))\n",
+    "    ][[\"Run\", \"Date_tree\"]].to_csv(out_file, index=False)\n"
+   ]
+  },
+  {
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}