added a template notebook for future use.

dscop_template.ipynb

@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5f172f89-d947-45b4-9c3a-257423a59a3a",
+   "metadata": {},
+   "source": [
+    "# Data Science Community - template notebook\n",
+    "* Author: <INSERT AUTHOR>\n",
+    "* Affiliation: UK Met Office\n",
+    "* History: 1.0\n",
+    "* Last update: 2026-03-16\n",
+    "* © British Crown Copyright 2017-2026, Met Office. Please see LICENSE.md for license details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "843b846d-3f24-4ecf-ac72-f0a217f67796",
+   "metadata": {},
+   "source": [
+    "## Overview of the broad topic covered\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8614094-ea4a-4454-a81b-de0823492a3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb2b3141-9f50-49e5-89fb-721756d6a638",
+   "metadata": {},
+   "source": [
+    "### Prerequisites \n",
+    "what background information is needed to go through the notebook\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31b6d60a-4730-4cf6-8795-a816099198ec",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9be46b8d-4b9e-4ca4-86a4-d23ba5335b04",
+   "metadata": {},
+   "source": [
+    "### Learning outcomes from completing the notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4a3a84a-aa9f-4918-98bf-1640656de24a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0881c4ee-e229-4512-927a-b4cf6dbd684b",
+   "metadata": {},
+   "source": [
+    "## Tutorial \n",
+    "a balance of explanation and activity\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a527e240-d44c-4686-8822-f60d0e037e65",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1275378-6157-4922-b276-dee7f135b2b9",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "for students to try that do not have solutions but maybe have an answer or benchmark to facilitate understanding\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a9b6a7b-2574-4ff3-8346-a63cd55df6d2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a26c34-1b92-4638-90d3-dbc2c111fc8a",
+   "metadata": {},
+   "source": [
+    "### Next steps or potential follow on material\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c24c5bba-24c6-4a7c-896f-fb26dfbff80e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "085d1e85-fcbd-43a2-beb8-1dc58bcd1440",
+   "metadata": {},
+   "source": [
+    "###  Examples of Use\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aca1e6a3-606f-41c9-ab4e-ac1d441b1c90",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8732b7f2-d72b-4ae3-8255-f8526e17619b",
+   "metadata": {},
+   "source": [
+    "### Data statement\n",
+    "###     References\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c42a387f-e6b9-4fe9-b2d1-dbf7de0d73c3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ml-0.1.0 Python (Conda)",
+   "language": "python",
+   "name": "conda-env-ml-0.1.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}