diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
index c88eddf..c3d4279 100644
--- a/.github/workflows/python-app.yml
+++ b/.github/workflows/python-app.yml
@@ -28,7 +28,7 @@ jobs:
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        flake8 . --count --select=E9,F63,F7,F82 --ignore=F824 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with unittest
diff --git a/.gitignore b/.gitignore
index 9629b3f..5c7c742 100644
--- a/.gitignore
+++ b/.gitignore
@@ -140,3 +140,10 @@ cython_debug/
 .DS_Store
 .vscode/
 .idea/
+reports/exported/
+
+naturerecorder.db
+naturerecorder_dev.db
+
+clean.sh
+clean.bat
diff --git a/README.md b/README.md
index 742770b..f99567d 100644
--- a/README.md
+++ b/README.md
@@ -31,6 +31,9 @@ Nature Recorder is an application for recording wildlife sightings, maintaining
   - Conservation schemes, acting as containers for a set of rating values
   - Conservation status ratings, values for the conservation status within a scheme
   - Species conservation status ratings, status ratings for a species with effective start and end dates
+- Reports
+  - Simple reports built into the application
+  - Flexible reporting using Jupyter Notebooks, outside the application
 
 # Getting Started
 
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 45edbdc..6ce5da3 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -1,11 +1,12 @@
 FROM python:3.10-slim-bullseye AS runtime
 
-COPY naturerecorderpy-1.13.0.0 /opt/naturerecorderpy
+COPY naturerecorderpy-1.14.0.0 /opt/naturerecorderpy
+
 WORKDIR /opt/naturerecorderpy
 
 RUN apt-get update -y
 RUN pip install -r requirements.txt
-RUN pip install nature_recorder-1.13.0-py3-none-any.whl
+RUN pip install nature_recorder-1.14.0-py3-none-any.whl
 
 ENV NATURE_RECORDER_DATA_FOLDER=/var/opt/naturerecorderpy
 ENV NATURE_RECORDER_DB=/var/opt/naturerecorderpy/naturerecorder.db
diff --git a/reports/README.md b/reports/README.md
new file mode 100644
index 0000000..e6a7b10
--- /dev/null
+++ b/reports/README.md
@@ -0,0 +1,49 @@
+# Nature Recorder Reporting
+
+This folder contains Jupyter notebooks and supporting files for reporting on wildlife sightings stored in the Nature Recorder SQLite database. This provides more flexible reporting than the built-in reports in the application itself.
+
+The following reports are currently available:
+
+| Notebook | Report Type |
+| --- | --- |
+| heatmap.ipynb | Heatmap of number of sightings of each species in a category at a location during a specified year | 
+
+## Setting Up the Reporting Environment
+
+The reports have been written and tested using [Visual Studio Code](https://code.visualstudio.com/download) and the Jupyter extension from Microsoft using a Python virtual environment with the requirements listed in requirements.txt installed as the kernel for running the notebooks.
+
+### Set Environment Variables
+
+The following environment variable to be set *before* running code and opening the notebook:
+
+``` bash
+export NATURE_RECORDER_DB=/path/to/naturerecorder.db
+```
+
+Or, in PowerShell:
+
+```powershell
+$env: NATURE_RECORDER_DB = C:\path\to\naturerecorder.db
+```
+
+### Build the Virtual Environment
+
+To build the virtual environment, run the following command:
+
+```bash
+./make_venv.sh
+```
+
+Or, in PowerShell:
+
+```powershell
+.\make_venv.bat
+```
+
+## Running a Report in Visual Studio Code
+
+- Open the Jupyter notebook for the report of interest
+- If using Visual Studio Code, select the Python virtual environment as the kernel for running the notebook
+- Review the instructions at the top of the report and make any required changes to e.g. reporting parameters
+- Click on "Run All" to run the report and export the results
+- Exported results are written to a folder named "exported" within the reports folder
diff --git a/reports/heatmap.ipynb b/reports/heatmap.ipynb
new file mode 100644
index 0000000..d4f67cc
--- /dev/null
+++ b/reports/heatmap.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "28ca77da",
+   "metadata": {},
+   "source": [
+    "# Nature Recorder Heatmap\n",
+    "\n",
+    "This notebook generates and exports a heatmap of species sightings for a category for a given location and year. To use it, update the year, location, category and required export format in the first code cell, below, before running the notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d90edda3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Year to report on\n",
+    "year = \"\"\n",
+    "\n",
+    "# Name of the location to report on\n",
+    "location = \"\"\n",
+    "\n",
+    "# Species category to report on\n",
+    "category = \"\"\n",
+    "\n",
+    "# Export format - either PNG or PDF\n",
+    "export_format = \"PNG\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "bd8ad911",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "# Read the query file\n",
+    "query_file_path = Path(\"sql\") / \"sightings.sql\"\n",
+    "with open(query_file_path.absolute(), \"r\") as f:\n",
+    "    query = f.read().replace(\"\\n\", \" \")\n",
+    "\n",
+    "# Replace the location and year placeholders\n",
+    "query = query.replace(\"$YEAR\", year) \\\n",
+    "             .replace(\"$LOCATION\", location) \\\n",
+    "             .replace(\"$CATEGORY\", category) \\\n",
+    "             .replace(\"$SPECIES\", \"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "2533c4ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import sqlite3\n",
+    "import os\n",
+    "\n",
+    "# Connect to the database, execute the query and read the results into a dataframe\n",
+    "database_path = os.environ[\"NATURE_RECORDER_DB\"]\n",
+    "connection = sqlite3.connect(database_path)\n",
+    "df = pd.read_sql_query(query, connection, parse_dates=[\"Date\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4cf1ec61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import calendar\n",
+    "\n",
+    "# Pre-process the data to provide a heatmap data source\n",
+    "df[\"Month\"] = df[\"Date\"].dt.month\n",
+    "heatmap_data = df.groupby([\"Species\", \"Month\"]).size().unstack(fill_value=0)\n",
+    "heatmap_data.columns = [calendar.month_abbr[m] for m in heatmap_data.columns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "20afb45a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x2400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Generate the heatmap\n",
+    "plt.figure(figsize=(12, 24))\n",
+    "sns.heatmap(heatmap_data, cmap=\"YlOrRd\", annot=False)\n",
+    "plt.title(f\"Number of Sightings of {category} at {location} in {year}\")\n",
+    "plt.xlabel(\"\")\n",
+    "plt.ylabel(\"\")\n",
+    "\n",
+    "# Create the folder to hold exported reports\n",
+    "export_folder_path = Path(\"exported\")\n",
+    "export_folder_path.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Export to PNG\n",
+    "if export_format.casefold() == \"png\":\n",
+    "    export_file_path = export_folder_path / f\"{year}-{category}-Heatmap.png\"\n",
+    "    plt.savefig(export_file_path.absolute(), format=\"png\", dpi=300, bbox_inches=\"tight\")\n",
+    "\n",
+    "# Export to PDF\n",
+    "if export_format.casefold() == \"pdf\":\n",
+    "    export_file_path = Path(\"exported\") / f\"{year}-{category}-Heatmap.pdf\"\n",
+    "    plt.savefig(export_file_path.absolute(), format=\"pdf\", bbox_inches=\"tight\")\n",
+    "\n",
+    "# And show the plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/reports/make_venv.bat b/reports/make_venv.bat
new file mode 100644
index 0000000..0d55132
--- /dev/null
+++ b/reports/make_venv.bat
@@ -0,0 +1,19 @@
+@ECHO OFF
+
+REM Deactivate and remove the old virtual environment, if present
+ECHO Removing existing Virtual Environment, if present ...
+deactivate > nul 2>&1
+RMDIR /S /Q venv
+
+REM Create a new environment and activate it
+ECHO Creating new Virtual Environment ...
+python -m venv venv
+CALL venv\Scripts\activate.bat
+
+REM Make sure pip is up to date
+python -m pip install --upgrade pip
+
+REM Install the requirements
+python -m pip install -r requirements.txt
+
+ECHO ON
diff --git a/reports/make_venv.sh b/reports/make_venv.sh
new file mode 100755
index 0000000..36efaba
--- /dev/null
+++ b/reports/make_venv.sh
@@ -0,0 +1,17 @@
+#!/bin/bash -f
+
+# Deactivate and remove the old virtual environment, if present
+echo "Removing existing Virtual Environment, if present ..."
+deactivate 2> /dev/null || true
+rm -fr venv
+
+# Create a new environment and activate it
+echo "Creating new Virtual Environment ..."
+python -m venv venv
+. venv/bin/activate
+
+# Make sure pip is up to date
+pip install --upgrade pip
+
+# Install the requirements
+pip install -r requirements.txt
diff --git a/reports/requirements.txt b/reports/requirements.txt
new file mode 100644
index 0000000..6d6825a
--- /dev/null
+++ b/reports/requirements.txt
@@ -0,0 +1,109 @@
+anyio==4.9.0
+appnope==0.1.4
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==3.0.0
+async-lru==2.0.5
+attrs==25.3.0
+babel==2.17.0
+beautifulsoup4==4.13.4
+bleach==6.2.0
+certifi==2025.1.31
+cffi==1.17.1
+charset-normalizer==3.4.1
+comm==0.2.2
+contourpy==1.3.2
+cycler==0.12.1
+debugpy==1.8.14
+decorator==5.2.1
+defusedxml==0.7.1
+executing==2.2.0
+fastjsonschema==2.21.1
+fonttools==4.57.0
+fqdn==1.5.1
+h11==0.14.0
+httpcore==1.0.8
+httpx==0.28.1
+idna==3.10
+ipykernel==6.29.5
+ipython==9.1.0
+ipython_pygments_lexers==1.1.1
+ipywidgets==8.1.6
+isoduration==20.11.0
+jedi==0.19.2
+Jinja2==3.1.6
+json5==0.12.0
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+jupyter==1.1.1
+jupyter-console==6.6.3
+jupyter-events==0.12.0
+jupyter-lsp==2.2.5
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+jupyter_server==2.15.0
+jupyter_server_terminals==0.5.3
+jupyterlab==4.4.0
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.3
+jupyterlab_widgets==3.0.14
+kiwisolver==1.4.8
+MarkupSafe==3.0.2
+matplotlib==3.10.1
+matplotlib-inline==0.1.7
+mistune==3.1.3
+nbclient==0.10.2
+nbconvert==7.16.6
+nbformat==5.10.4
+nest-asyncio==1.6.0
+notebook==7.4.0
+notebook_shim==0.2.4
+numpy==2.2.4
+overrides==7.7.0
+packaging==24.2
+pandas==2.2.3
+pandocfilters==1.5.1
+parso==0.8.4
+pexpect==4.9.0
+pillow==11.2.1
+platformdirs==4.3.7
+prometheus_client==0.21.1
+prompt_toolkit==3.0.51
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pycparser==2.22
+Pygments==2.19.1
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+python-json-logger==3.3.0
+pytz==2025.2
+PyYAML==6.0.2
+pyzmq==26.4.0
+referencing==0.36.2
+requests==2.32.3
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.24.0
+seaborn==0.13.2
+Send2Trash==1.8.3
+six==1.17.0
+sniffio==1.3.1
+soupsieve==2.6
+stack-data==0.6.3
+terminado==0.18.1
+tinycss2==1.4.0
+tornado==6.4.2
+traitlets==5.14.3
+types-python-dateutil==2.9.0.20241206
+typing_extensions==4.13.2
+tzdata==2025.2
+uri-template==1.3.0
+urllib3==2.4.0
+wcwidth==0.2.13
+webcolors==24.11.1
+webencodings==0.5.1
+websocket-client==1.8.0
+widgetsnbextension==4.0.14
diff --git a/reports/sql/sightings.sql b/reports/sql/sightings.sql
new file mode 100644
index 0000000..7f22c56
--- /dev/null
+++ b/reports/sql/sightings.sql
@@ -0,0 +1,9 @@
+SELECT l.Name AS 'Location', sp.Name AS 'Species', sp.Name AS 'Category', DATE( s.Date ) AS 'Date'
+FROM SIGHTINGS s
+INNER JOIN SPECIES sp ON sp.Id = s.SpeciesId
+INNER JOIN CATEGORIES c ON c.Id = sp.CategoryId
+INNER JOIN LOCATIONS l ON l.Id = s.LocationId
+WHERE l.Name = '$LOCATION'
+AND s.Date LIKE '$YEAR-%'
+AND sp.Name LIKE '%$SPECIES%'
+AND c.Name = "$CATEGORY";
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 453d617..b98d254 100644
--- a/setup.py
+++ b/setup.py
@@ -31,7 +31,7 @@ def find_package_files(directory, remove_root):
 
 setuptools.setup(
     name="nature_recorder",
-    version="1.13.0",
+    version="1.14.0",
     description="Wildlife sightings database",
     packages=setuptools.find_packages("src"),
     include_package_data=True,