diff --git a/docs/about.rst b/docs/about.rst
index 713b8db99..b90b49647 100644
--- a/docs/about.rst
+++ b/docs/about.rst
@@ -43,11 +43,13 @@ or are never even translated from the original papers into useable tools.
 NiMARE operates on NIMADS-format datasets, which users will be able to compile by searching the NeuroStore database with the pyNIMADS library.
 A number of other services in the ecosystem will then use NiMARE functions to perform meta-analyses, including Neurosynth 2.0 and `NeuroVault`_.
 
+
 Other Meta-Analytic Tools
 -------------------------
 
 Outside of the shared ecosystem detailed above, there are a number of tools.
 
+
 Coordinate-based meta-analysis tools
 ````````````````````````````````````
 
@@ -60,11 +62,13 @@ The `SDM`_ Toolbox: This toolbox contains the hybrid coordinate/image-based seed
 
 `NeuRoi Toolbox`_: This toolbox contains an implementation of the `Analysis of Brain Coordinates`_ (ABC) CBMA algorithm.
 
+
 Image-based meta-analysis tools
 ```````````````````````````````
 
 `IBMA SPM extension`_: This SPM extension implements a number of image-based meta-analysis algorithms.
 
+
 Meta-analysis tools for other neuroimaging modalities
 `````````````````````````````````````````````````````
 
diff --git a/docs/conf.py b/docs/conf.py
index f755d1c8d..e56c5619c 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -174,7 +174,7 @@
     "matplotlib": ("https://matplotlib.org/", (None, "https://matplotlib.org/objects.inv")),
     "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
     "nibabel": ("https://nipy.org/nibabel/", None),
-    "nilearn": ("http://nilearn.github.io/", None),
+    "nilearn": ("http://nilearn.github.io/stable/", None),
     "pymare": ("https://pymare.readthedocs.io/en/latest/", None),
     "skimage": ("https://scikit-image.org/docs/stable/", None),
 }
diff --git a/docs/guide/annotation.rst b/docs/guide/annotation.rst
new file mode 100644
index 000000000..9a212f6ab
--- /dev/null
+++ b/docs/guide/annotation.rst
@@ -0,0 +1,15 @@
+Annotating Meta-Analytic Data
+==============================
+
+Downloading abstracts for papers
+--------------------------------
+
+Extracting features from text
+-----------------------------
+
+Annotating Datasets
+-------------------
+
+References
+----------
+.. footbibliography::
diff --git a/docs/guide/cbma.rst b/docs/guide/cbma.rst
new file mode 100644
index 000000000..fd82d9a9e
--- /dev/null
+++ b/docs/guide/cbma.rst
@@ -0,0 +1,107 @@
+Coordinate-Based Meta-Analysis
+==============================
+
+Coordinate-based meta-analysis (CBMA) is a popular tool for measuring consistency across neuroimaging studies.
+While :ref:`ibma` techniques leverage more information than CBMA, and are thus superior,
+CBMA is much more popular because the majority of fMRI papers report peaks from significant clusters in tables,
+rather than uploading unthresholded statistical maps to a database, like NeuroVault.
+
+.. note::
+    This page walks through coordinate-based meta-analysis (CBMA) in NiMARE from a practical perspective.
+    The focus is on performing basic CBMAs.
+    For a more detailed description of the classes and functions used for CBMA, see
+    :ref:`sphx_glr_auto_examples_02_meta-analyses_03_plot_kernel_transformers.py`,
+    :ref:`sphx_glr_auto_examples_02_meta-analyses_01_plot_cbma.py`, and
+    :ref:`sphx_glr_auto_examples_02_meta-analyses_05_plot_correctors.py`.
+
+Types of CBMA studies
+---------------------
+1. One large dataset, with multiple subsets.
+
+    1. Create a single, large Dataset with annotations indicating the subsets.
+    2. Perform a meta-analysis on the full Dataset.
+
+        - This omnibus analysis is typically interpreted as evaluating convergent results across the subsets.
+
+    3. Slice the Dataset into different subsets.
+    4. Perform a meta-analysis on each subset.
+    5. Perform a subtraction analysis between each subset and the rest of the subsets (combined in one Dataset).
+    6. Perform functional decoding comparing something and something.
+    7. Plot significant results.
+
+2. A direct comparison between two datasets.
+
+    .. note::
+        One conundrum meta-analysts often face is that papers may report a mixture of
+        within-group analyses and between-group analyses.
+        For example, if you are planning to perform a meta-analysis of the n-back task with the
+        comparison between 3-back and 1-back conditions,
+        you will likely find many papers that report both 3-back > control and 1-back > control results,
+        as well as many papers that only report 3-back > 1-back results.
+
+        In the case of the former, the standard approach is to perform a subtraction analysis.
+        In the latter case, you would perform a single univariate meta-analysis.
+        Unfortunately, you cannot combine the two sets of results.
+
+    1. Create two Dataset objects.
+    2. Perform a meta-analysis on each Dataset.
+    3. Perform a subtraction analysis comparing the two Datasets.
+    4. Perform a conjunction analysis assessing convergence between the two meta-analyses.
+    5. Run :class:`~nimare.diagnostics.FocusCounter` or :class:`~nimare.diagnostics.Jackknife` on all meta-analysis results.
+    6. Run :func:`~nilearn.reporting.get_clusters_table` on all meta-analysis results.
+    7. Plot significant results.
+
+3. Large-scale analyses on a database.
+
+    1. Download Neurosynth or NeuroQuery.
+    2. ...
+    3. Plot significant results.
+
+Selecting studies for a meta-analysis
+-------------------------------------
+
+Organizing the dataset in NiMARE
+--------------------------------
+NiMARE contains several functions for converting common formats to Dataset objects.
+
+Performing the meta-analysis
+----------------------------
+
+.. literalinclude:: ../../examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
+    :start-at: from nimare.meta.cbma import ALE
+    :end-at: knowledge_results = ale.fit(knowledge_dset)
+
+.. figure:: ../auto_examples/02_meta-analyses/images/sphx_glr_08_plot_cbma_subtraction_conjunction_001.png
+    :target: ../auto_examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.html
+    :align: center
+    :scale: 100
+
+Multiple comparisons correction
+-------------------------------
+
+.. literalinclude:: ../../examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
+    :start-at: from nimare.correct import FWECorrector
+    :end-at: knowledge_corrected_results = corr.transform(knowledge_results)
+
+.. figure:: ../auto_examples/02_meta-analyses/images/sphx_glr_08_plot_cbma_subtraction_conjunction_002.png
+    :target: ../auto_examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.html
+    :align: center
+    :scale: 100
+
+Saving the results
+------------------
+
+.. literalinclude:: ../../examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
+    :start-at: knowledge_corrected_results.save_maps(
+    :end-at: )
+
+Performing additional followup analyses
+---------------------------------------
+
+.. literalinclude:: ../../examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
+    :start-at: from nimare.diagnostics import Jackknife
+    :end-at: knowledge_jackknife_table
+
+References
+----------
+.. footbibliography::
diff --git a/docs/guide/data.rst b/docs/guide/data.rst
new file mode 100644
index 000000000..4ee5de441
--- /dev/null
+++ b/docs/guide/data.rst
@@ -0,0 +1,24 @@
+Working with Meta-Analytic Data
+===============================
+
+The Neuroimaging Meta-Analysis Data Standard (NIMADS)
+-----------------------------------------------------
+
+Downloading large databases
+---------------------------
+
+Converting datasets from BrainMap
+---------------------------------
+
+The Dataset object
+------------------
+
+:ref:`sphx_glr_auto_examples_01_datasets_01_plot_dataset_io.py`
+
+.. literalinclude:: ../../examples/01_datasets/01_plot_dataset_io.py
+    :start-after: # Now we can load and save the Dataset object
+    :end-before: os.remove("pain_dset.pkl")  # cleanup
+
+References
+----------
+.. footbibliography::
diff --git a/docs/guide/decoding.rst b/docs/guide/decoding.rst
new file mode 100644
index 000000000..76d22a528
--- /dev/null
+++ b/docs/guide/decoding.rst
@@ -0,0 +1,18 @@
+Meta-Analytic Functional Decoding
+=================================
+
+Decoding ROIs
+-------------
+
+Decoding VOIs
+-------------
+
+Decoding based on arbitrary subsets of the Dataset
+--------------------------------------------------
+
+Decoding statistical maps
+-------------------------
+
+References
+----------
+.. footbibliography::
diff --git a/docs/guide/ibma.rst b/docs/guide/ibma.rst
new file mode 100644
index 000000000..9a27cbe38
--- /dev/null
+++ b/docs/guide/ibma.rst
@@ -0,0 +1,31 @@
+Image-Based Meta-Analysis
+==============================
+
+Types of IBMA studies
+---------------------
+1. A manually-curated set of statistical maps.
+    1. Perform image-based meta-analysis. The choice of algorithm depends on the data available.
+    2. Plot significant results.
+    3. Characterize results in a table.
+
+Selecting studies for a meta-analysis
+-------------------------------------
+
+Organizing the dataset in NiMARE
+--------------------------------
+
+Performing the meta-analysis
+----------------------------
+
+Multiple comparisons correction
+-------------------------------
+
+Saving the results
+------------------
+
+Performing additional followup analyses
+---------------------------------------
+
+References
+----------
+.. footbibliography::
diff --git a/docs/guide/introduction.rst b/docs/guide/introduction.rst
new file mode 100644
index 000000000..71dbf449c
--- /dev/null
+++ b/docs/guide/introduction.rst
@@ -0,0 +1,23 @@
+.. include:: ../links.rst
+
+Introduction to NiMARE
+=====================================
+
+Meta-Analysis and Analyses Using Meta-Analytic Data
+---------------------------------------------------
+
+Getting Help
+------------
+
+If you have usage-related questions, please create a new topic on `NeuroStars <https://neurostars.org>`_
+with the `"nimare" tag <https://neurostars.org/tag/nimare>`_.
+The ``NiMARE`` developers follow NeuroStars, and will be able to answer your question there.
+
+If you have identified a bug in NiMARE, or would like to request a new feature or change,
+you can `open an issue <https://github.com/neurostuff/NiMARE/issues/new/choose>`_ on GitHub.
+Please look through `open issues <https://github.com/neurostuff/NiMARE/issues>`_ before opening a new one,
+to ensure that you are not duplicating an existing issue.
+
+References
+----------
+.. footbibliography::
diff --git a/docs/guide/toc.rst b/docs/guide/toc.rst
new file mode 100644
index 000000000..3ad151bfd
--- /dev/null
+++ b/docs/guide/toc.rst
@@ -0,0 +1,12 @@
+User Guide
+==========
+
+.. toctree::
+   :numbered:
+
+   introduction.rst
+   data.rst
+   cbma.rst
+   ibma.rst
+   annotation.rst
+   decoding.rst
diff --git a/docs/index.rst b/docs/index.rst
index 71caa82de..6fbd3c81d 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -122,15 +122,16 @@ Then, to cite NiMARE in your manuscript, we recommend something like the followi
 
    about
    installation
+   guide/toc
    api
    auto_examples/index
-   contributing
-   dev_guide
    cli
    outputs
    methods
    changelog
    glossary
+   contributing
+   dev_guide
 
 Indices and tables
 ------------------
diff --git a/docs/references.bib b/docs/references.bib
index c7f681f96..80a255a92 100644
--- a/docs/references.bib
+++ b/docs/references.bib
@@ -221,6 +221,18 @@ @article{langner2014meta
   doi={10.1016/j.neuroimage.2014.06.007}
 }
 
+@article{muller2018ten,
+  title={Ten simple rules for neuroimaging meta-analysis},
+  author={M{\"u}ller, Veronika I and Cieslik, Edna C and Laird, Angela R and Fox, Peter T and Radua, Joaquim and Mataix-Cols, David and Tench, Christopher R and Yarkoni, Tal and Nichols, Thomas E and Turkeltaub, Peter E and others},
+  journal={Neuroscience \& Biobehavioral Reviews},
+  volume={84},
+  pages={151--161},
+  year={2018},
+  publisher={Elsevier},
+  doi={10.1016/j.neubiorev.2017.11.012},
+  url={https://doi.org/10.1016/j.neubiorev.2017.11.012}
+}
+
 @article{newman2009distributed,
   title={Distributed algorithms for topic models.},
   author={Newman, David and Asuncion, Arthur and Smyth, Padhraic and Welling, Max},
@@ -292,6 +304,19 @@ @article{Salo2022
   journal = {NeuroLibre}
 }
 
+@article{samartsidis2017coordinate,
+  title={The coordinate-based meta-analysis of neuroimaging data},
+  author={Samartsidis, Pantelis and Montagna, Silvia and Nichols, Thomas E and Johnson, Timothy D},
+  journal={Statistical science: a review journal of the Institute of Mathematical Statistics},
+  volume={32},
+  number={4},
+  pages={580},
+  year={2017},
+  publisher={NIH Public Access},
+  doi={10.1214/17-STS624},
+  url={https://doi.org/10.1214/17-STS624}
+}
+
 @article{shaffer1995multiple,
   title={Multiple hypothesis testing},
   author={Shaffer, Juliet Popper},
diff --git a/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py b/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
index 483f7652e..e1581d17c 100644
--- a/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
+++ b/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
@@ -19,10 +19,6 @@
 5. Compare the two samples with a subtraction analysis.
 6. Compare the two within-sample meta-analyses with a conjunction analysis.
 """
-import os
-
-import matplotlib.pyplot as plt
-from nilearn.plotting import plot_stat_map
 
 ###############################################################################
 # Load Sleuth text files into Datasets
@@ -33,33 +29,78 @@
 # (e.g., correctly naming an object after hearing its auditory description)
 # while a second group of studies asked children to decide if two (or more)
 # words were semantically related to one another or not.
+import os
+
 from nimare.io import convert_sleuth_to_dataset
 from nimare.utils import get_resource_path
 
-knowledge_file = os.path.join(get_resource_path(), "semantic_knowledge_children.txt")
-related_file = os.path.join(get_resource_path(), "semantic_relatedness_children.txt")
+knowledge_file = os.path.join(get_resource_path(), "Enge2021_knowledge.txt")
+related_file = os.path.join(get_resource_path(), "Enge2021_relatedness.txt")
+# Some papers reported MNI coordinates
+objects_file_mni = os.path.join(get_resource_path(), "Enge2021_objects_mni.txt")
+# Other papers reported Talairach coordinates
+objects_file_talairach = os.path.join(get_resource_path(), "Enge2021_objects_talairach.txt")
 
 knowledge_dset = convert_sleuth_to_dataset(knowledge_file)
 related_dset = convert_sleuth_to_dataset(related_file)
+objects_dset = convert_sleuth_to_dataset(
+    [
+        objects_file_mni,
+        objects_file_talairach,  # NiMARE will automatically convert the Talairach foci to MNI
+    ]
+)
 
 ###############################################################################
-# Individual group ALEs
+# View the contents of one of the Sleuth files
+with open(knowledge_file, "r") as fo:
+    sleuth_file_contents = fo.readlines()
+
+sleuth_file_contents = sleuth_file_contents[:20]
+print("".join(sleuth_file_contents))
+
+###############################################################################
+# Meta-analysis of semantic knowledge experiments
 # -----------------------------------------------------------------------------
-# Computing separate ALE analyses for each group is not strictly necessary for
-# performing the subtraction analysis but will help the experimenter to appreciate the
-# similarities and differences between the groups.
-from nimare.correct import FWECorrector
 from nimare.meta.cbma import ALE
 
 ale = ALE(null_method="approximate")
 knowledge_results = ale.fit(knowledge_dset)
-related_results = ale.fit(related_dset)
+
+###############################################################################
+# Plot the uncorrected statistical map
+# `````````````````````````````````````````````````````````````````````````````
+from nilearn.plotting import plot_stat_map
+
+plot_stat_map(
+    knowledge_results.get_map("z"),
+    cut_coords=4,
+    display_mode="z",
+    title="Semantic knowledge",
+    threshold=2.326,  # cluster-level p < .01, one-tailed
+    cmap="RdBu_r",
+    vmax=4,
+)
+
+###############################################################################
+# This z-statistic map is not corrected for multiple comparisons.
+# In order to account for the many voxel-wise tests that are performed in
+# parallel, we must apply some type of multiple comparisons correction.
+# To that end, we will use an :class:`~nimare.correct.FWECorrector` with the
+# Monte Carlo method.
+#
+# Multiple comparisons correction with a Monte Carlo procedure
+# -----------------------------------------------------------------------------
+# We will use the cluster-level corrected map, using a cluster-defining
+# threshold of p < 0.001 and 100 iterations.
+# In the actual paper, :footcite:`enge2021meta` used 10000 iterations instead.
+from nimare.correct import FWECorrector
 
 corr = FWECorrector(method="montecarlo", voxel_thresh=0.001, n_iters=100, n_cores=2)
 knowledge_corrected_results = corr.transform(knowledge_results)
-related_corrected_results = corr.transform(related_results)
 
-fig, axes = plt.subplots(figsize=(12, 10), nrows=2)
+###############################################################################
+# Plot the corrected statistical map
+# `````````````````````````````````````````````````````````````````````````````
 knowledge_img = knowledge_corrected_results.get_map(
     "z_desc-size_level-cluster_corr-FWE_method-montecarlo"
 )
@@ -71,10 +112,45 @@
     threshold=2.326,  # cluster-level p < .01, one-tailed
     cmap="RdBu_r",
     vmax=4,
-    axes=axes[0],
-    figure=fig,
 )
 
+###############################################################################
+# Save the results to disk
+# `````````````````````````````````````````````````````````````````````````````
+knowledge_corrected_results.save_maps(
+    output_dir=".",
+    prefix="Enge2021_knowledge",
+)
+
+###############################################################################
+# Characterize the relative contributions of experiments in the results
+# `````````````````````````````````````````````````````````````````````````````
+# NiMARE contains two methods for this: :class:`~nimare.diagnostics.Jackknife`
+# and :class:`~nimare.diagnostics.FocusCounter`.
+# We will show both below, but for the sake of speed we will only apply one to
+# each subgroup meta-analysis.
+from nimare.diagnostics import Jackknife
+
+jackknife = Jackknife(
+    target_image="z_desc-size_level-cluster_corr-FWE_method-montecarlo",
+    voxel_thresh=None,
+)
+knowledge_jackknife_table, _ = jackknife.transform(knowledge_corrected_results)
+knowledge_jackknife_table
+
+###############################################################################
+# Meta-analysis of semantic relatedness experiments
+# -----------------------------------------------------------------------------
+ale = ALE(null_method="approximate")
+related_results = ale.fit(related_dset)
+
+# Perform Monte Carlo-based multiple comparisons correction
+corr = FWECorrector(method="montecarlo", voxel_thresh=0.001, n_iters=100, n_cores=2)
+related_corrected_results = corr.transform(related_results)
+
+###############################################################################
+# Plot the resulting statistical map
+# `````````````````````````````````````````````````````````````````````````````
 related_img = related_corrected_results.get_map(
     "z_desc-size_level-cluster_corr-FWE_method-montecarlo"
 )
@@ -86,59 +162,91 @@
     threshold=2.326,  # cluster-level p < .01, one-tailed
     cmap="RdBu_r",
     vmax=4,
-    axes=axes[1],
-    figure=fig,
 )
-fig.show()
 
 ###############################################################################
-# Characterize the relative contributions of experiments in the ALE results
-# -----------------------------------------------------------------------------
-# NiMARE contains two methods for this: :class:`~nimare.diagnostics.Jackknife`
-# and :class:`~nimare.diagnostics.FocusCounter`.
-# We will show both below, but for the sake of speed we will only apply one to
-# each subgroup meta-analysis.
-from nimare.diagnostics import FocusCounter
-
-counter = FocusCounter(
+# Characterize the relative contributions of experiments in the results
+# `````````````````````````````````````````````````````````````````````````````
+jackknife = Jackknife(
     target_image="z_desc-size_level-cluster_corr-FWE_method-montecarlo",
     voxel_thresh=None,
 )
-knowledge_count_table, _ = counter.transform(knowledge_corrected_results)
-knowledge_count_table.head(10)
+related_jackknife_table, _ = jackknife.transform(related_corrected_results)
+related_jackknife_table
 
 ###############################################################################
-from nimare.diagnostics import Jackknife
+# Meta-analysis of semantic object experiments
+# -----------------------------------------------------------------------------
+ale = ALE(null_method="approximate")
+objects_results = ale.fit(objects_dset)
+
+# Perform Monte Carlo-based multiple comparisons correction
+corr = FWECorrector(method="montecarlo", voxel_thresh=0.001, n_iters=100, n_cores=2)
+objects_corrected_results = corr.transform(objects_results)
+
+###############################################################################
+# Plot the resulting statistical map
+# `````````````````````````````````````````````````````````````````````````````
+objects_img = objects_corrected_results.get_map(
+    "z_desc-size_level-cluster_corr-FWE_method-montecarlo"
+)
+plot_stat_map(
+    objects_img,
+    cut_coords=4,
+    display_mode="z",
+    title="Semantic objects",
+    threshold=2.326,  # cluster-level p < .01, one-tailed
+    cmap="RdBu_r",
+    vmax=4,
+)
 
+###############################################################################
+# Characterize the relative contributions of experiments in the results
+# `````````````````````````````````````````````````````````````````````````````
 jackknife = Jackknife(
     target_image="z_desc-size_level-cluster_corr-FWE_method-montecarlo",
     voxel_thresh=None,
 )
-related_jackknife_table, _ = jackknife.transform(related_corrected_results)
-related_jackknife_table.head(10)
+objects_jackknife_table, _ = jackknife.transform(objects_corrected_results)
+objects_jackknife_table
 
 ###############################################################################
-# Subtraction analysis
+# Compare semantic knowledge to the other conditions with subtraction analysis
 # -----------------------------------------------------------------------------
-# Typically, one would use at least 10000 iterations for a subtraction analysis.
-# However, we have reduced this to 100 iterations for this example.
+# The semantic knowledge experiments can be compared to the experiments from
+# the other two conditions by first merging the other two sets of experiments
+# into a single Dataset, and then performing a subtraction analysis between the
+# semantic knowledge Dataset and the new semantic relatedness/objects Dataset.
+#
+# In :footcite:t:`enge2021meta`, additional subtraction analyses were performed
+# for semantic relatedness vs. (knowledge + objects) and semantic objects vs.
+# (knowledge + relatedness).
+# However, for the sake of executing this example online, we only perform the
+# first of these analyses.
+#
+# .. important::
+#   Typically, one would use at least 10000 iterations for a subtraction analysis.
+#   However, we have reduced this to 100 iterations for this example.
 from nimare.meta.cbma import ALESubtraction
 
+# First, we combine the relatedness and objects Datasets
+related_and_objects_dset = related_dset.merge(objects_dset)
+
 sub = ALESubtraction(n_iters=100, n_cores=1)
-res_sub = sub.fit(knowledge_dset, related_dset)
+res_sub = sub.fit(knowledge_dset, related_and_objects_dset)
 img_sub = res_sub.get_map("z_desc-group1MinusGroup2")
 
 plot_stat_map(
     img_sub,
     cut_coords=4,
     display_mode="z",
-    title="Subtraction",
+    title="Knowledge > Other",
     cmap="RdBu_r",
     vmax=4,
 )
 
 ###############################################################################
-# Conjunction analysis
+# Evaluate convergence across datasets with a conjunction analysis
 # -----------------------------------------------------------------------------
 # To determine the overlap of the meta-analytic results, a conjunction image
 # can be computed by (a) identifying voxels that were statistically significant
@@ -149,8 +257,12 @@
 # :func:`nilearn.image.math_img`.
 from nilearn.image import math_img
 
-formula = "np.where(img1 * img2 > 0, np.minimum(img1, img2), 0)"
-img_conj = math_img(formula, img1=knowledge_img, img2=related_img)
+img_conj = math_img(
+    "np.where((img1 * img2 * img3) > 0, np.minimum(img1, np.minimum(img2, img3)), 0)",
+    img1=knowledge_img,
+    img2=related_img,
+    img3=objects_img,
+)
 
 plot_stat_map(
     img_conj,
diff --git a/nimare/resources/semantic_knowledge_children.txt b/nimare/resources/Enge2021_knowledge.txt
similarity index 100%
rename from nimare/resources/semantic_knowledge_children.txt
rename to nimare/resources/Enge2021_knowledge.txt
diff --git a/nimare/resources/Enge2021_objects_mni.txt b/nimare/resources/Enge2021_objects_mni.txt
new file mode 100644
index 000000000..51e09ac26
--- /dev/null
+++ b/nimare/resources/Enge2021_objects_mni.txt
@@ -0,0 +1,85 @@
+// Reference=MNI
+// dekker2014pw: 9-10 year olds
+// Subjects=10
+-28 -82 14
+50  -60 -8
+24  -68 56
+-48 -64 -12
+-38 26  18
+-26 -52 -14
+-44 6   28
+18  -94 8
+46  -76 -2
+-20 -88 10
+
+// dekker2014pw: 7-8 year olds
+// Subjects=11
+-36 -52 52
+30  -78 16
+-48 -64 -8
+52  -60 -18
+-50 6   26
+-24 -12 50
+18  -94 6
+
+// liebig2017nd
+// Subjects=41
+26  -96 -4
+38  -92 -6
+-27 -98 -8
+-33 -90 -8
+-42 -52 -18
+-51 -38 6
+-30 -62 42
+-39 6   30
+-51 30  18
+-51 22  28
+-4  20  52
+33  22  -4
+36  32  -6
+9   -82 -26
+-8  -81 -27
+
+// monsalvo2012cn
+// Subjects=23
+30  -45 -9
+30  -63 -6
+33  -81 9
+-27 -48 -9
+-27 -90 9
+-24 -84 -9
+39  -51 -15
+42  -81 -9
+-39 -60 -15
+-39 -84 -3
+
+// okamoto2017ad
+// Subjects=12
+-50 -68 0
+54  -74 0
+-48 -24 36
+32  -48 58
+42  52  -18
+4   -56 28
+42  20  -32
+-22 -8  -14
+18  -6  -12
+40  -72 38
+
+// passarotti2003td
+// Subjects=12
+38  -40 -17
+-40 -40 -17
+58  -40 -9
+-58 40  -9
+50  -53 -17
+-52 -56 -17
+-58 -7  5
+-40 -20 -4
+-1  -25 24
+29  -35 42
+-24 -33 51
+-4  39  53
+-39 3   55
+5   19  -19
+60  21  20
diff --git a/nimare/resources/Enge2021_objects_talairach.txt b/nimare/resources/Enge2021_objects_talairach.txt
new file mode 100644
index 000000000..01a77b37a
--- /dev/null
+++ b/nimare/resources/Enge2021_objects_talairach.txt
@@ -0,0 +1,179 @@
+// Reference=Talairach
+// aylward2005ba
+// Subjects=11
+43  -44 -10
+-40 -66 -9
+39  -59 -8
+36  -68 9
+-49 -20 3
+7   13  46
+
+// corbett2008af
+// Subjects=15
+36  -56 19
+19  -8  -5
+-30 -78 -20
+23  -86 -20
+
+// kersey2016do
+// Subjects=29
+20  -70 -33
+-20 49  26
+-6  60  17
+-21 -21 58
+17  55  30
+6   61  16
+60  2   7
+53  31  5
+2   58  33
+-46 -8  -22
+-52 -11 -27
+-28 -28 -18
+-31 -15 -19
+31  -14 -25
+-20 -88 23
+16  -85 13
+-47 -69 -4
+32  -49 -6
+-43 -41 60
+23  -48 55
+-55 -23 28
+34  -4  1
+-43 -20 12
+-39 6   -27
+-40 -22 9
+-46 -2  -13
+-46 10  -24
+61  -11 12
+59  11  -4
+65  -14 -3
+
+// libertus2008dc
+// Subjects=15
+41  -53 -21
+15  -101    -7
+23  -98 -14
+-26 -86 -14
+-19 -90 -14
+-19 -98 -7
+4   41  -7
+4   19  -7
+-4  45  0
+-45 -53 -28
+-41 -49 -21
+8   -56 21
+
+
+// scherf2007vc: Adolscents
+// Subjects=10
+39  -47 -22
+45  -74 1
+60  -49 9
+55  -4  -16
+10  -59 28
+-39 -70 18
+-47 17  -8
+39  25  -9
+5   54  44
+-6  -4  57
+-5  46  -12
+42  -9  50
+21  -2  54
+26  -66 46
+-11 -67 50
+28  -79 8
+-27 -85 19
+45  -55 -8
+-45 -60 -5
+18  -87 -6
+-13 -89 -5
+26  -40 -4
+-25 -48 -1
+-22 -73 53
+32  -52 51
+-14 -95 12
+-54 -34 33
+40  -60 -7
+-41 -57 -3
+11  -81 -3
+22  -73 -18
+-27 -73 -14
+33  -88 2
+-34 -5  32
+
+// scherf2007vc: Children
+// Subjects=10
+42  -68 -19
+-15 56  5
+14  -80 38
+-14 -76 51
+6   -41 25
+29  -75 31
+-22 -79 13
+21  -85 -5
+-22 -85 -3
+13  -86 -6
+-19 -94 -5
+19  -30 -18
+-28 -53 -6
+29  -53 49
+-21 -59 43
+-23 -82 37
+31  -70 20
+18  -94 -5
+-23 -86 28
+-9  -97 -15
+41  -72 -12
+-40 -60 -17
+22  -39 -14
+-21 -79 -24
+
+// scherf2010ll (46)
+// Subjects=10
+40  -41 -23
+-39 -51 -19
+50  -61 9
+55  -50 12
+49  -58 12
+-63 -30 -7
+-41 21  -31
+-39 -60 38
+-1  57  28
+-6  42  -4
+8   43  53
+21  20  49
+5   53  46
+0   35  53
+31  58  9
+-16 28  55
+-18 35  43
+-2  63  8
+33  3   34
+-45 6   51
+-48 28  -5
+-34 -73 -41
+26  -42 -8
+-24 -45 -8
+16  -84 -7
+-5  -87 -3
+30  -76 21
+-22 -82 18
+20  -67 58
+-9  -68 56
+62  -15 25
+58  6   25
+-31 13  12
+-39 31  15
+41  -61 -11
+-41 -66 -7
+23  -62 -12
+-24 -63 -12
+7   -81 0
+31  -80 7
+-22 -85 7
+34  -48 55
+-30 -54 53
+-55 -30 37
+59  -18 27
+-45 -31 21
+-44 30  17
diff --git a/nimare/resources/semantic_relatedness_children.txt b/nimare/resources/Enge2021_relatedness.txt
similarity index 100%
rename from nimare/resources/semantic_relatedness_children.txt
rename to nimare/resources/Enge2021_relatedness.txt