diff --git a/gallery/experiments/experimental_abinitio_pipeline_11618.py b/gallery/experiments/experimental_abinitio_pipeline_11618.py
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+"""
+Abinitio Pipeline - Experimental Data EMPIAR 11618
+==================================================
+
+This notebook introduces a selection of
+components corresponding to loading real Relion picked
+particle cryo-EM data and running key ASPIRE-Python
+ab initio model components as a pipeline.
+
+Specifically this pipeline uses the
+EMPIAR 11618 picked particles data, available here:
+
+https://www.ebi.ac.uk/empiar/EMPIAR-11618
+"""
+
+# %%
+# Imports
+# -------
+# Import packages that will be used throughout this experiment.
+
+import logging
+from pathlib import Path
+
+from aspire.classification import RandomClassSelector
+from aspire.denoising import LegacyClassAvgSource
+from aspire.reconstruction import MeanEstimator
+from aspire.source import OrientedSource, RelionSource
+
+logger = logging.getLogger(__name__)
+
+
+# %%
+# Parameters
+# ---------------
+#
+# Use of GPU is expected for a large configuration.
+
+# Inputs
+# User is responsible for downloading `11618` raw data from EMPIAR,
+# and assigning correct path.
+input_dir = "11618/data/particles/"
+starfile_in = f"{input_dir}/J43_particles.star"
+data_folder = f"{input_dir}"
+
+# Config
+n_imgs = None  # Set to None for all images in starfile; set smaller for tests
+img_size = 129  # Downsample the images/reconstruction to a desired resolution
+n_classes = 1000  # Number of class averages to use for reconstruction
+n_nbor = 32  # How many neighbors to stack for each class average
+
+# Outputs
+preprocessed_fn = f"11618_preprocessed_pf_ds{img_size}px_nbg_wt_inv.star"
+class_avg_fn = f"11618_rand{n_classes}_cls_avgs_m{n_nbor}_{img_size}px.star"
+volume_output_filename = f"11618_abinitio_c{n_classes}_m{n_nbor}_{img_size}px.mrc"
+
+# %%
+# Source data and Preprocessing
+# -----------------------------
+#
+# ``RelionSource`` is used to access the experimental data via a `STAR` file.
+# Begin by preprocessing to correct for CTF, then downsample to ``img_size``
+# and apply noise correction.
+
+
+# Create a source object for the experimental images
+src = RelionSource(starfile_in, max_rows=n_imgs, data_folder=data_folder)
+
+# Use phase_flip to attempt correcting for CTF.
+# Caching is used throughout for speeding up large datasets on high memory machines.
+logger.info("Perform phase flip to input images.")
+src = src.phase_flip().cache()
+
+# Downsample the images.
+logger.info(f"Set the resolution to {img_size} X {img_size}")
+src = src.downsample(img_size).cache()
+
+logger.info("Apply noise correction to images")
+# Normalize the background of the images.
+src = src.normalize_background().cache()
+
+# Estimate the noise and whiten based on the estimated noise.
+src = src.whiten().cache()
+
+# Optionally invert image contrast.
+logger.info("Invert the global density contrast if needed")
+src = src.invert_contrast().cache()
+
+# Save the preprocessed images.
+# These can be reused to experiment with later stages of the pipeline
+# without repeating the preprocessing computations.
+src.save(preprocessed_fn, save_mode="single", overwrite=True)
+
+# %%
+# Class Averaging
+# ----------------------
+#
+# Now perform classification and averaging for each class.
+# `RandomClassSelector` will randomize the class averages chosen for
+# reconstruction.  This avoids having to compute all class averages
+# and sort them by some notion of quality, which is computationally
+# intensive.  Instead this example computes a small random sample of
+# classes from the entire data set.
+
+logger.info("Begin Class Averaging")
+avgs = LegacyClassAvgSource(src, class_selector=RandomClassSelector(), n_nbor=n_nbor)
+
+# Compute and cache the random set of `n_classes` from `src`.
+avgs = avgs[:n_classes].cache()
+
+# Save the random set of class averages to disk so they can be re-used.
+avgs.save(class_avg_fn, save_mode="single", overwrite=True)
+
+
+# %%
+# Common Line Estimation
+# ----------------------
+#
+# Estimate orientation of projections and assign to source by
+# applying ``OrientedSource`` to the class averages from the prior
+# step. By default this applies the Common Line with Synchronization
+# Voting ``CLSync3N`` method.
+
+logger.info("Begin Orientation Estimation")
+oriented_src = OrientedSource(avgs)
+
+# %%
+# Volume Reconstruction
+# ----------------------
+#
+# Using the oriented source, attempt to reconstruct a volume.
+
+logger.info("Begin Volume reconstruction")
+# Set up an estimator to perform the backprojection.
+estimator = MeanEstimator(oriented_src)
+
+# Perform the estimation and save the volume.
+estimated_volume = estimator.estimate()
+estimated_volume.save(volume_output_filename, overwrite=True)
+logger.info(f"Saved Volume to {str(Path(volume_output_filename).resolve())}")