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alan-turing-institute · Oct 11, 2023 · 2d18f21 · 2d18f21
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diff --git a/...s/2021-02-26-US-election/2021-02-26-Modelling-Mail-In-Votes-In-the-2020-US-Election.ipynb b/...s/2021-02-26-US-election/2021-02-26-Modelling-Mail-In-Votes-In-the-2020-US-Election.ipynb
@@ -4,25 +4,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modelling Mail-In Votes In the 2020 US Election\n",
-    "\n",
-    "> Using hierarchical Bayesian modeling to predict outcomes and associated uncertainties in the 2020 US election.\n",
-    "\n",
-    "\n",
-    "- toc: false\n",
-    "- categories: [US Election, Linear regression, Bayesian modeling]\n",
-    "- author: Eric Daub\n",
-    "- image: images/story_previews/2021-02-26-US-Election.png"
+    "---\n",
+    "abstract: Using hierarchical Bayesian modeling to predict outcomes and associated uncertainties in the 2020 US election.\n",
+    "author:\n",
+    "- name: Eric Daub\n",
+    "  url: https://www.turing.ac.uk/people/researchers/eric-daub\n",
+    "  affiliation: \n",
+    "  - id: ati\n",
+    "    name: The Alan Turing Institute\n",
+    "    city: London\n",
+    "    url: www.turing.ac.uk\n",
+    "date: '2021-02-26'\n",
+    "keywords:\n",
+    "- us election\n",
+    "- linear regression\n",
+    "- bayesian modeling\n",
+    "license: CC BY\n",
+    "reviewers:\n",
+    "- name: Ryan Chan\n",
+    "- name: David Llewellyn-Jones\n",
+    "title: Modelling Mail-In Votes In the 2020 US Election\n",
+    "---"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Author\n",
-    "\n",
-    "* **Eric Daub**, Alan Turing Institute, GitHub: [@edaub](https://github.com/edaub).\n",
-    "\n",
     "## Other Contributors\n",
     "\n",
     "* **Camila Rangel Smith**, Alan Turing Institute,\n",