diff --git a/docs/gallery/advanced_io/zarr_io.py b/docs/gallery/advanced_io/zarr_io.py
deleted file mode 100644
index c6524b64d..000000000
--- a/docs/gallery/advanced_io/zarr_io.py
+++ /dev/null
@@ -1,98 +0,0 @@
-"""
-Zarr IO
-=======
-
-Zarr is an alternative backend option for NWB files. It is a Python package that
-provides an implementation of chunked, compressed, N-dimensional arrays. Zarr is a good
-option for large datasets because, like HDF5, it is designed to store data on disk and
-only load the data into memory when needed. Zarr is also a good option for parallel
-computing because it supports concurrent reads and writes.
-
-Note that the Zarr native storage formats are optimized for storage in cloud storage
-(e.g., S3). For very large files, Zarr will create many files which can lead to
-issues for traditional file systems (that are not cloud object stores) due to
-limitations on the number of files per directory (this affects local disk,
-GDrive, Dropbox etc.).
-
-Zarr read and write is provided by the :hdmf-zarr:`hdmf-zarr package<>`. First, create an
-an NWBFile using PyNWB.
-"""
-
-# sphinx_gallery_thumbnail_path = 'figures/gallery_thumbnail_plot_nwbzarrio.png'
-
-
-from datetime import datetime
-from dateutil.tz import tzlocal
-
-import numpy as np
-from pynwb import NWBFile, TimeSeries
-
-# Create the NWBFile. Substitute your NWBFile generation here.
-nwbfile = NWBFile(
-    session_description="my first synthetic recording",
-    identifier="EXAMPLE_ID",
-    session_start_time=datetime.now(tzlocal()),
-    session_id="LONELYMTN",
-)
-
-#######################################################################################
-# Dataset Configuration
-# ---------------------
-# Like HDF5, Zarr provides options to chunk and compress datasets. To leverage these
-# features, replace all :py:class:`~hdmf.backends.hdf5.h5_utils.H5DataIO` with the analogous
-# :py:class:`~hdmf_zarr.utils.ZarrDataIO`, which takes compressors specified by the
-# `numcodecs` library. For example, to create a :py:class:`.TimeSeries`
-# with a Zarr backend, use the following:
-
-from numcodecs import Blosc
-from hdmf_zarr import ZarrDataIO
-
-data_with_zarr_data_io = ZarrDataIO(
-    data=np.random.randn(100, 100),
-    chunks=(10, 10),
-    fillvalue=0,
-    compressor=Blosc(cname='zstd', clevel=3, shuffle=Blosc.SHUFFLE)
-)
-
-#######################################################################################
-# Now add it to the `NWBFile`.
-
-nwbfile.add_acquisition(
-    TimeSeries(
-        name="synthetic_timeseries",
-        data=data_with_zarr_data_io,
-        unit="m",
-        rate=10e3,
-    )
-)
-
-#######################################################################################
-# Writing to Zarr
-# ---------------
-# To write NWB files to Zarr, replace the :py:class:`~pynwb.NWBHDF5IO` with
-# :py:class:`hdmf_zarr.nwb.NWBZarrIO` for read/write
-
-from hdmf_zarr.nwb import NWBZarrIO
-import os
-
-path = "zarr_tutorial.nwb.zarr"
-absolute_path = os.path.abspath(path)
-with NWBZarrIO(path=path, mode="w") as io:
-    io.write(nwbfile)
-
-#######################################################################################
-# The main reason for using the absolute_path here is for testing purposes to ensure
-# links and references work as expected. Otherwise, using the relative path here instead
-# is fine.
-#
-# Reading from Zarr
-# -----------------
-# To read NWB files from Zarr, replace the :py:class:`~pynwb.NWBHDF5IO` with the analogous
-# :py:class:`hdmf_zarr.nwb.NWBZarrIO`.
-
-with NWBZarrIO(path=absolute_path, mode="r") as io:
-    read_nwbfile = io.read()
-
-#######################################################################################
-# .. note::
-#    For more information, see the :hdmf-zarr:`hdmf-zarr documentation<>`.