diff --git a/docs/source/conf.py b/docs/source/conf.py
index 20a20b5..687db8e 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,42 +1,24 @@
-#
 # Configuration file for the Sphinx documentation builder.
 #
-# This file does only contain a selection of the most common options. For a
-# full list see the documentation:
-# http://www.sphinx-doc.org/en/master/config
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
-
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
 
 # -- Project information -----------------------------------------------------
-
-project = 'P3 Analysis Library'
-copyright = ' Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others'
-author = 'Intel Corporation'
-
-# The short X.Y version
-version = ''
-# The full version, including alpha/beta/rc tags
-release = ''
-
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = "P3 Analysis Library"
+copyright = (
+    " Intel Corporation. Intel, the Intel logo, and other Intel marks are "
+    + "trademarks of Intel Corporation or its subsidiaries. Other names and "
+    + "brands may be claimed as the property of others."
+)
+author = "Intel Corporation"
+version = ""
+release = ""
 
 # -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.napoleon',
@@ -45,167 +27,25 @@
     'sphinx.ext.intersphinx',
 ]
 
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-#
-# source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
-
-# The master toctree document.
-master_doc = 'index'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = "en"
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
+templates_path = ["_templates"]
 exclude_patterns = []
 
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = None
-
-
 # -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'furo'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#
-html_theme_options = {}
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Custom sidebar templates, must be a dictionary that maps document names
-# to template names.
-#
-# The default sidebars (for documents that don't match any pattern) are
-# defined by theme itself.  Builtin themes are using these templates by
-# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
-# 'searchbox.html']``.
-#
-# html_sidebars = {}
-
+html_theme = "furo"
+html_static_path = ["_static"]
 html_title = "P3 Analysis Library"
 
-
-# -- Options for HTMLHelp output ---------------------------------------------
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'p3-analysis-library-doc'
-
-
-# -- Options for LaTeX output ------------------------------------------------
-
-latex_elements = {
-    # The paper size ('letterpaper' or 'a4paper').
-    #
-    # 'papersize': 'letterpaper',
-
-    # The font size ('10pt', '11pt' or '12pt').
-    #
-    # 'pointsize': '10pt',
-
-    # Additional stuff for the LaTeX preamble.
-    #
-    # 'preamble': '',
-
-    # Latex figure (float) alignment
-    #
-    # 'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (
-        master_doc, 'p3-analysis-library.tex',
-        'P3 Analysis Library Documentation',
-        'Intel Corporation', 'manual',
-    ),
-]
-
-
-# -- Options for manual page output ------------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
-    (
-        master_doc, 'p3-analysis-library', 'P3 Analysis Library Documentation',
-        [author], 1,
-    ),
-]
-
-
-# -- Options for Texinfo output ----------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-    (
-        master_doc, 'p3-analysis-library', 'P3 Analysis Library Documentation',
-        author, 'p3-analysis-library',
-        'A library simplifying the collection and interpretation of P3 data.',
-        'Miscellaneous',
-    ),
-]
-
-
-# -- Options for Epub output -------------------------------------------------
-
-# Bibliographic Dublin Core info.
-epub_title = project
-
-# The unique identifier of the text. This can be a ISBN number
-# or the project homepage.
-#
-# epub_identifier = ''
-
-# A unique identification for the text.
-#
-# epub_uid = ''
-
-# A list of files that should not be packed into the epub file.
-epub_exclude_files = ['search.html']
-
-
-# -- Extension configuration -------------------------------------------------
-
-from sphinx_gallery.scrapers import matplotlib_scraper
-
-
-class tight_scraper:
-
-    def __repr__(self):
-        return self.__class__.__name__
-
-    def __call__(self, *args, **kwargs):
-        return matplotlib_scraper(*args, bbox_inches="tight", **kwargs)
-
 sphinx_gallery_conf = {
      'examples_dirs': ['../../examples','../../case-studies'],
      'gallery_dirs': ['examples','case-studies'],
-     'image_scrapers': (tight_scraper(),),
+     'run_stale_examples': True,
+     'filename_pattern': '/',
+     'backreferences_dir'  : 'gen_modules/backreferences',
 }
 
 intersphinx_mapping = {
     "matplotlib": ('https://matplotlib.org/stable/', None),
+    "pandas": ('https://pandas.pydata.org/pandas-docs/stable/', None),
 }
diff --git a/examples/metrics/README.rst b/examples/metrics/README.rst
new file mode 100644
index 0000000..7a9ebb8
--- /dev/null
+++ b/examples/metrics/README.rst
@@ -0,0 +1,6 @@
+Metrics
+-------
+
+Generating metrics from raw P3 data is a pre-requisite for generating specific
+plots. Understanding how to manipulate and prepare raw P3 data enables the
+exploration of more complex "what if" scenarios and more detailed analyses.
diff --git a/examples/metrics/application_efficiency.py b/examples/metrics/application_efficiency.py
new file mode 100755
index 0000000..deae903
--- /dev/null
+++ b/examples/metrics/application_efficiency.py
@@ -0,0 +1,330 @@
+#!/usr/bin/env python3
+# Copyright (c) 2024 Intel Corporation
+# SPDX-License-Identifier: 0BSD
+"""
+Working with Application Efficiency
+===================================
+
+Understanding relative performance.
+
+One goal of P3 analysis is to understand how well a given application is able
+to adapt to and make effective use of the capabilities of different
+platforms. Comparisons of *raw* performance (e.g., time to solution) across
+platforms can't help us, because raw performance doesn't reflect how fast an
+application **should** run.
+
+To address this, the P3 Analysis Library works with normalized *performance
+efficiency* data. With normalized data, an application's performance can be
+represented as a number in the range :math:`[0, 1]`, where :math:`1` means
+an application is achieving the best possible performance.
+
+There are multiple ways we can normalize the data to measure relative
+efficiency, and for this tutorial we will consider *application efficiency*,
+defined below:
+
+**Application Efficiency**
+  The performance of an *application*, measured relative to the best known
+  performance previously demonstrated for solving the same *problem* on the
+  same *platform*.
+
+Working with application efficiency is simple because it does not rely on
+performance models or theoretical hardware limits. Although it can't tell us
+whether an application is performing as well as theoretically possible, it
+shows how an application compares to the state-of-the-art, which is often good
+enough.
+
+Calculating Application Efficiency
+----------------------------------
+
+Let's begin with a very simple example, with a single application, using the
+data below:
+
+.. list-table::
+    :widths: 20 20 20 20 20
+    :header-rows: 1
+
+    * - problem
+      - application
+      - platform
+      - fom
+      - date
+
+    * - Test
+      - MyApp
+      - A
+      - 25.0
+      - 2023
+    * - Test
+      - MyApp
+      - B
+      - 12.5
+      - 2023
+    * - Test
+      - MyApp
+      - C
+      - 25.0
+      - 2023
+    * - Test
+      - MyApp
+      - D
+      - NaN
+      - 2023
+    * - Test
+      - MyApp
+      - E
+      - 5.0
+      - 2023
+
+.. tip::
+    A NaN or 0.0 performance result is interpreted by the P3 Analysis Library
+    to mean that an application run was in some way invalid. We can use this
+    to explicitly represent cases where applications did not compile on
+    specific platforms, did not run to completion, or ran but produced
+    numerical results that failed some sort of verification.
+
+"""
+
+# %%
+# After loading this data into a :py:class:`pandas.DataFrame` (`df`), we can
+# use the :py:func:`p3.metrics.application_efficiency` function to calculate a
+# table of application efficiencies.
+
+#sphinx_gallery_start_ignore
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+import p3
+
+myapp_data = {
+    "problem": ["Test"] * 5,
+    "application": ["MyApp"] * 5,
+    "platform": ["A", "B", "C", "D", "E"],
+    "fom": [25.0, 12.5, 25.0, np.nan, 5.0],
+    "date": [2023] * 5,
+}
+
+df = pd.DataFrame(myapp_data)
+#sphinx_gallery_end_ignore
+
+effs = p3.metrics.application_efficiency(df)
+print(effs)
+
+# %%
+# These initial results may be a little surprising, because they're all
+# either 1.0 or 0.0. What happened? Since our dataset contains only one result
+# for MyApp on each platform, each non-zero result is the "best known" result
+# for that platform! The only exception is Platform D, which is assigned an
+# efficiency of 0.0 to reflect that it either did not compile, or did not run
+# successfully.
+#
+# .. tip::
+#     Calculating meaningful application efficiency results requires a minimum
+#     of two results *per platform*.
+#
+# Digging Deeper: Adding More Data
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Let's see what happens if we add some more data, from a different application
+# running on the same platforms:
+#
+# .. list-table::
+#     :widths: 20 20 20 20 20
+#     :header-rows: 1
+#
+#     * - problem
+#       - application
+#       - platform
+#       - fom
+#       - date
+#
+#     * - Test
+#       - YourApp
+#       - A
+#       - 25.0
+#       - 2023
+#     * - Test
+#       - YourApp
+#       - B
+#       - 10.0
+#       - 2023
+#     * - Test
+#       - YourApp
+#       - C
+#       - 12.5
+#       - 2023
+#     * - Test
+#       - YourApp
+#       - D
+#       - 6.0
+#       - 2023
+#     * - Test
+#       - YourApp
+#       - E
+#       - 1.0
+#       - 2023
+
+# %%
+# After updating our DataFrame, we can re-run the same function as before to
+# recompute the application efficiencies.
+
+#sphinx_gallery_start_ignore
+yourapp_data = {
+    "problem": ["Test"] * 5,
+    "application": ["YourApp"] * 5,
+    "platform": ["A", "B", "C", "D", "E"],
+    "fom": [25.0, 10.0, 12.5, 6.0, 1.0],
+    "date": [2023] * 5,
+}
+
+df = pd.concat([df, pd.DataFrame(yourapp_data)], ignore_index=True)
+#sphinx_gallery_end_ignore
+
+effs = p3.metrics.application_efficiency(df)
+print(effs)
+
+# %%
+# YourApp is now the fastest (best known) application on every platform,
+# and so it assigned an application efficiency of 1.0 everywhere. The
+# application efficiency values for MyApp are all between 0.0 and 1.0,
+# reflecting how close it gets to the state-of-the-art performance on each
+# platform.
+#
+# .. important::
+#     Adding new data changed the application efficiencies for MyApp *and*
+#     YourApp. Application efficiency values can become stale over time,
+#     and accurate P3 analysis requires us to track "best known" results
+#     carefully.
+#
+# Plotting Application Efficiency
+# -------------------------------
+#
+# The P3 Analysis Library does not contain any dedicated functionality for
+# plotting application efficiency values. However, it is straightforward to use
+# :py:mod:`matplotlib` and/or the plotting functionality of :py:mod:`pandas` to
+# produce useful visualizations.
+#
+# For example, plotting a bar chart of application efficiences for one
+# application can help us to summarize that application's performance more
+# effectively than a table:
+
+filtered = effs[effs["application"]=="MyApp"]
+filtered.plot(kind="bar", x="platform", y="app eff", xlabel="Platform", ylabel="Application Efficiency", legend=False)
+plt.savefig("application_efficiency_bars_2023.png")
+
+# %%
+# We can now clearly see that MyApp can adapt to and make very effective use of
+# Platforms A and B, is within 2x of state-of-the-art performance on Platform
+# C, but performs poorly on Platforms D and E. The key takeaway from this
+# analysis is that a developer wishing to improve the "performance portability"
+# of MyApp should focus on improving support for Platforms D and E.
+#
+# Working with Historical Data
+# ----------------------------
+#
+# The performance of an application can change over time, as developers add new
+# features and optimize for new platforms, or due to changes in the software
+# stack (e.g., new compiler or driver versions).
+#
+# Let's see how this could affect the application efficiency of MyApp, by adding
+# some new data points collected at a later point in time:
+#
+# .. list-table::
+#     :widths: 20 20 20 20 20
+#     :header-rows: 1
+#
+#     * - problem
+#       - application
+#       - platform
+#       - fom
+#       - date
+#
+#     * - Test
+#       - MyApp
+#       - A
+#       - 30.0
+#       - 2024
+#     * - Test
+#       - MyApp
+#       - B
+#       - 15.0
+#       - 2024
+#     * - Test
+#       - MyApp
+#       - C
+#       - 25.0
+#       - 2024
+#     * - Test
+#       - MyApp
+#       - D
+#       - 3.0
+#       - 2024
+#     * - Test
+#       - MyApp
+#       - E
+#       - 2.5
+#       - 2024
+
+# %%
+# We can compute application efficiency as before:
+
+#sphinx_gallery_start_ignore
+new_myapp_data = {
+    "problem": ["Test"] * 5,
+    "application": ["MyApp"] * 5,
+    "platform": ["A", "B", "C", "D", "E"],
+    "fom": [30.0, 15.0, 25.0, 3.0, 2.5],
+    "date": [2024] * 5,
+}
+
+df = pd.concat([df, pd.DataFrame(new_myapp_data)], ignore_index=True)
+#sphinx_gallery_end_ignore
+
+effs = p3.metrics.application_efficiency(df)
+print(effs)
+
+# %%
+# These latest results suggest that the developers of MyApp acted upon
+# earlier results and improved support for Platforms D and E. But in doing so,
+# a small performance regression was introduced in Platforms A and B.
+#
+# .. note::
+#     Such trade-offs are very common, especially when developers wish to
+#     maintain a single source code that targets multiple platforms.
+#     Different platforms may respond differently to the same code changes,
+#     owing to architectural differences (e.g., cache size, available
+#     parallelism) or differences in the software stack (e.g., compilers
+#     performing different optimizations).
+#     For some real-life examples, see the papers
+#     `here <https://doi.org/10.1016/j.jpdc.2012.07.005>`__
+#     and
+#     `here <https://doi.org/10.48550/arXiv.2407.11488>`__.
+#
+# Computing the correct application efficiency values for MyApp and YourApp
+# requires that our dataset contains all of our historical performance results.
+# Since what we're really interested in understanding is the *latest*
+# application efficiency, we should take care to filter our data appropriately
+# before producing any plots.
+
+filtered = effs[(effs["application"]=="MyApp") & (effs["date"]==2024)]
+filtered.plot(kind="bar", x="platform", y="app eff", xlabel="Platform", ylabel="Application Efficiency", legend=False)
+plt.savefig("application_efficiency_bars_2024.png")
+
+# %%
+# Further Analysis
+# ----------------
+#
+# Computing application efficiency is often simply the first step of a
+# more detailed P3 analysis.
+#
+# The examples below show how we can use the visualization capabilities
+# of the P3 Analysis Library to compare the efficiency of different
+# applications running across the same platform set, or to gain insight
+# into how an application's efficiency relates to the code it uses on each
+# platform.
+#
+# .. minigallery::
+#     :add-heading: Examples
+#
+#     ../../examples/cascade/plot_simple_cascade.py
+#     ../../examples/navchart/plot_simple_navchart.py