diff --git a/README.md b/README.md
index 51df1c1..64508ba 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,14 @@
-# inm-icf-utilities
+# Utilities for managing the INM-ICF DICOM data store at Research Center Jülich
 
 [![Documentation Status](https://readthedocs.org/projects/inm-icf-utilities/badge/?version=latest)](https://inm-icf-utilities.readthedocs.io/en/latest/?badge=latest)
-
 [![Build status](https://ci.appveyor.com/api/projects/status/jaife669slqyru52/branch/main?svg=true)](https://ci.appveyor.com/project/mih/inm-icf-utilities/branch/main)
+
+
+## Acknowledgements
+
+This software was developed with support from the German Federal Ministry of
+Education and Research (BMBF 01GQ1905), the US National Science Foundation (NSF
+1912266), the Helmholtz research center Jülich (RDM challenge 2022), and the
+Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant
+SFB 1451 ([431549029](https://gepris.dfg.de/gepris/projekt/431549029), INF
+project).
diff --git a/bin/catalogify_studyvisit_from_meta b/bin/catalogify_studyvisit_from_meta
index 4e07101..5a33587 100755
--- a/bin/catalogify_studyvisit_from_meta
+++ b/bin/catalogify_studyvisit_from_meta
@@ -12,7 +12,6 @@ import sys
 import tempfile
 from uuid import uuid4
 
-import datalad.api as dl
 from datalad_catalog.catalog import Catalog
 from datalad_catalog.webcatalog import WebCatalog
 
@@ -95,7 +94,7 @@ def get_catalog(study_id, catalog_path):
         # 3. set catalog home page
         ctlg.main_id = study_entry.get('dataset_id')
         ctlg.main_version = study_entry.get('dataset_version')
-        ctlg.set_main_dataset() 
+        ctlg.set_main_dataset()
     return ctlg
 
 
@@ -109,7 +108,7 @@ def generate_study_entry(study_id):
         ds_version='latest',
         ds_name=study_id,
         ds_description=desc)
-    
+
 
 def update_entry(ds_id, ds_version, ds_name, key, value, study_catalog_path):
     meta_item = {
@@ -247,7 +246,6 @@ def format_bytes(bytes, decimals=2):
     return f"{round(bytes / math.pow(k, i), dm)} {sizes[i]}"
 
 
-
 if __name__ == '__main__':
     import argparse
     p = argparse.ArgumentParser(description=__doc__)
diff --git a/bin/dataladify_studyvisit_from_meta b/bin/deposit_visit_dataset
similarity index 73%
rename from bin/dataladify_studyvisit_from_meta
rename to bin/deposit_visit_dataset
index 6249169..a70ba38 100755
--- a/bin/dataladify_studyvisit_from_meta
+++ b/bin/deposit_visit_dataset
@@ -1,20 +1,27 @@
 #!/usr/bin/env python3
 """
-
+This command reads the metadata deposit from `deposit_visit_metadata` for a
+visit in a study (given by their respective identifiers) from the data store,
+and generates a DataLad dataset from it. This DataLad dataset provides
+versioned access to the visit's DICOM data, up to single-image granularity.
+Moreover, all DICOM files are annotated with basic DICOM tags that enable
+on-demand dataset views for particular applications (e.g., DICOMs sorted
+by image series and protocol name). The DataLad dataset is deposited in
+two files in the study directory:
+
+- `{visit_id}_XDLRA--refs`
+- `{visit_id}_XDLRA--repo-export`
+
+where the former enables `datalad/git clone` operations, and the latter
+represents the actual dataset as a compressed archive.
 """
 import json
 import os
 from pathlib import Path
-import sys
 import tempfile
 
 import datalad.api as dl
 
-# this points to the top of the ICF data store.
-# internally it will be amended with the missing components
-# for study and visit deposit locations
-icfstore_baseurl = 'https://data.inm-icf.de'
-
 # which DICOM tags to extract from DICOM files and store as
 # git-annex metadata (e.g., to enable metadata-driven views
 # of visit datasets)
@@ -28,9 +35,12 @@ dicom_metadata_keys = [
 ]
 
 
-def main(store_dir: str,
-         study_id: str,
-         visit_id: str):
+def main(
+    store_dir: str,
+    store_url: str,
+    study_id: str,
+    visit_id: str,
+):
     store_base_dir = Path(store_dir)
     # where to deposit the final datalad dataset
     repo_base_path = store_base_dir / study_id / f'{visit_id}_'
@@ -48,20 +58,27 @@ def main(store_dir: str,
         f'{visit_id}_metadata_dicoms.json'
 
     with tempfile.TemporaryDirectory(prefix='dataladify_visit_') as wdir:
-        runshit(
+        deposit_dataset(
             # workdir
             wdir,
             # path to deposited dataset metadata
             dataset_metadata_path.absolute(),
             # path to deposited file metadata
             file_metadata_path.absolute(),
+            # base URL of the store to complete access URLs
+            store_url,
             # path to deposit the repo at
             repo_base_path.absolute(),
         )
 
 
-def runshit(wdir, metapath_dataset, metapath_file, repobasepath):
-
+def deposit_dataset(
+    wdir: Path,
+    metapath_dataset: Path,
+    metapath_files: Path,
+    store_url: str,
+    repobasepath: Path,
+):
     # read tar metadata dict
     tar_metadata = read_json_file(metapath_dataset)
     expected_keys = ('size', 'md5', 'dspath', 'storepath')
@@ -88,7 +105,7 @@ def runshit(wdir, metapath_dataset, metapath_file, repobasepath):
     uncurl_uuid = repo.call_annex_records(['info', 'uncurl'])[0]['uuid']
     assert uncurl_uuid
     # register the URL of the tarball
-    tar_metadata['url'] = f"{icfstore_baseurl}/{tar_metadata['storepath']}"
+    tar_metadata['url'] = f"{store_url}/{tar_metadata['storepath']}"
     res = ds.addurls(
         [tar_metadata],
         '{url}',
@@ -98,9 +115,11 @@ def runshit(wdir, metapath_dataset, metapath_file, repobasepath):
     # fish out annex key of tarball.
     # we could also construct that, but let's not duplicate the setup above
     tarpath = Path(tar_metadata.get('dspath'))
-    tarkey = [r.get('annexkey') for r in res
-              if r.get('action') == 'fromkey'
-              and r.get('path', '').endswith(tarpath.name)]
+    tarkey = [
+        r.get('annexkey') for r in res
+        if r.get('action') == 'fromkey'
+        and r.get('path', '').endswith(tarpath.name)
+    ]
     assert len(tarkey) == 1
     tarkey = tarkey[0]
     assert tarkey
@@ -123,7 +142,7 @@ def runshit(wdir, metapath_dataset, metapath_file, repobasepath):
     assert archivist_uuid
 
     # load dicom metadata
-    dicoms = read_json_file(metapath_file)
+    dicoms = read_json_file(metapath_files)
     # add to dataset
     dicom_recs = ds.addurls(
         dicoms,
@@ -146,7 +165,10 @@ def runshit(wdir, metapath_dataset, metapath_file, repobasepath):
         repo.call_annex(['setpresentkey', dicomkey, archivist_uuid, '1'])
 
     repo.call_git([
-        'remote', 'add', 'icfstore',
+        'remote', 'add',
+        # the remote name is arbitrary, it will not end up in the resulting
+        # deposit
+        'store',
         # this is a little twisted:
         # the first line is an f-string, because we need to get the base URL
         # pointing to the study directory into the remote URL
@@ -163,7 +185,7 @@ def runshit(wdir, metapath_dataset, metapath_file, repobasepath):
     # to be able to actually push everything
     repo.call_annex(['whereis', '--key', dicomkeys[0]])
     ds.push(
-        to='icfstore',
+        to='store',
         # under no circumstances do we want to push annexed content.
         # and there also should be none
         data='nothing',
@@ -174,31 +196,36 @@ def read_json_file(file_path):
     """
     Load content from catalog metadata file for current node
     """
-    try:
-        with open(file_path) as f:
-            return json.load(f)
-    except OSError as err:
-        raise("OS error: {0}".format(err))
-    except:
-        raise("Unexpected error:", sys.exc_info()[0])
+    with open(file_path) as f:
+        return json.load(f)
 
 
 if __name__ == '__main__':
     import argparse
-    p = argparse.ArgumentParser(description=__doc__)
+    p = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    p.add_argument(
+        '--id', nargs=2, metavar=('STUDY-ID', 'VISIT-ID'), required=True,
+        help="study and visit identifiers, used to "
+        "locate the visit archive in the storage organization. "
+    )
     p.add_argument(
         "-o", "--store-dir", metavar='PATH', default=os.getcwd(),
-        help="Root directory of the ICF data store. "
+        help="root directory of the data store. "
         "Visit data will be read from it, and the DataLad dataset will be "
         "deposited into it."
     )
     p.add_argument(
-        '--id', nargs=2, metavar=('STUDY-ID', 'VISIT-ID'), required=True,
-        help="The study and visit identifiers, used to "
-        "locate the visit archive in the storage organization. "
+        '--store-url', metavar='URL', default='https://data.inm-icf.de',
+        help="base URL of the DICOM data store. This URL is used to "
+        "register TAR archive download URLs in the generated DataLad "
+        "dataset."
     )
     args = p.parse_args()
     main(store_dir=args.store_dir,
+         store_url=args.store_url,
          study_id=args.id[0],
          visit_id=args.id[1],
     )
diff --git a/bin/getmeta_studyvisit b/bin/deposit_visit_metadata
similarity index 76%
rename from bin/getmeta_studyvisit
rename to bin/deposit_visit_metadata
index 80b8e96..6371b26 100755
--- a/bin/getmeta_studyvisit
+++ b/bin/deposit_visit_metadata
@@ -1,6 +1,25 @@
 #!/usr/bin/env python3
 """
+This command locates the DICOM tarball for a particular visit in a study (given
+by their respective identifiers) in the data store, and extracts a minimal set
+of metadata tags for each DICOM image, and the TAR archive as a whole. These
+metadata are then deposited in two files, in JSON format, in the study
+directory:
 
+- `{visit_id}_metadata_tarball.json`
+
+  JSON object with basic properties of the archive, such as 'size', and
+  'md5'.
+
+- `{visit_id}_metadata_dicoms.json`
+
+  JSON array with essential properties for each DICOM image file, such as
+  'path' (relative path inside the TAR archive), 'md5' (MD5 checksum of
+  the DICOM file), 'size' (in bytes), and select standard DICOM tags,
+  such as "SeriesDescription", "SeriesNumber", "Modality",
+  "MRAcquisitionType", "ProtocolName", "PulseSequenceName". The latter
+  enable a rough, technical characterization of the images in the TAR
+  archive.
 """
 import logging
 import os
@@ -17,11 +36,6 @@ from datalad.utils import md5sum
 
 lgr = logging.getLogger('inm-icf-utilities')
 
-# this points to the top of the ICF data store.
-# internally it will be amended with the missing components
-# for study and visit deposit locations
-icfstore_baseurl = 'https://data.inm-icf.de'
-
 # which DICOM tags to extract from DICOM files and store as
 # git-annex metadata (e.g., to enable metadata-driven views
 # of visit datasets)
@@ -58,7 +72,7 @@ def main(store_dir: str,
     if not tar_path.exists():
         raise ValueError(f'no tarball at {tar_path}')
 
-    runshit(
+    describe_tarball(
         # source visit tarball
         tar_path.resolve(),
         # source visit tarball URL, relative to store
@@ -70,7 +84,7 @@ def main(store_dir: str,
     )
 
 
-def runshit(tarpath, tarurl, metapath_dataset, metapath_file):
+def describe_tarball(tarpath, tarurl, metapath_dataset, metapath_file):
     # construct and dump dataset metadata
     tar_meta = {
         'size': tarpath.stat().st_size,
@@ -115,10 +129,13 @@ def runshit(tarpath, tarurl, metapath_dataset, metapath_file):
 
 if __name__ == '__main__':
     import argparse
-    p = argparse.ArgumentParser(description=__doc__)
+    p = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
     p.add_argument(
         "-o", "--store-dir", metavar='PATH', default=os.getcwd(),
-        help="Root directory of the ICF data store. "
+        help="Root directory of the data store. "
         "Visit data will be read from it, and extracted metadata will be "
         "deposited into it."
     )
diff --git a/docs/source/admin.rst b/docs/source/admin.rst
new file mode 100644
index 0000000..ad2605d
--- /dev/null
+++ b/docs/source/admin.rst
@@ -0,0 +1,152 @@
+Administrator docs
+==================
+
+The INM-ICF Utilities `Github repository`_ provides a set of
+executable Python scripts which automate generation of deposits in the
+ICF archive. To simplify deployment, these scripts and all their
+dependencies are packaged as a `Singularity`_ v3 container
+(`download`_).
+
+.. _github repository: https://github.com/psychoinformatics-de/inm-icf-utilities
+.. _singularity: https://docs.sylabs.io/guides/main/user-guide/
+.. _download: https://ci.appveyor.com/api/projects/mih/inm-icf-utilities/artifacts/icf.sif
+
+Archive generation
+------------------
+
+Containerized execution
+^^^^^^^^^^^^^^^^^^^^^^^
+
+With the Singilarity image, ``icf.sif``, all scripts are made directly
+available, either through ``singularity run``:
+
+.. code-block:: console
+
+   $ singularity run <singularity options> icf.sif <script name> <script options>
+
+or by making the image file executable.
+
+The singularity image can also be installed as if it was a system
+package. For this, fill in the placeholders in the following script,
+and save it as ``icf-utils``:
+
+.. code-block:: sh
+
+   #!/bin/sh
+   set -e -u
+   singularity run -B <absolute-path-to-data> <absolute-path-to-icf.sif-file> "$@" > icf-utils
+
+The ``-B`` defines a bind path, making it accessible from within the
+container.
+
+Afterwards, install it under ``/usr/bin`` to make all functionality
+available under an ``icf-utils`` command.
+
+.. code-block::
+
+   $ sudo install -t /usr/bin icf-utils
+
+Archival workflow
+^^^^^^^^^^^^^^^^^
+
+The main part of visit archival is the creation a TAR file.
+
+The DataLad dataset can be generated and placed alongside the tarballs
+without affecting them. Placement in the study folder guarantees the
+same access permissions (authenticated https). The datasets are
+generated based on file metadata -- the TAR archive remains the only
+data source -- so storage overhead is minimal.
+
+Four scripts, executed in the given order, capture the archival
+process.
+
+Script listing
+^^^^^^^^^^^^^^
+
+``make_studyvisit_archive``
+"""""""""""""""""""""""""""
+
+This utility generates a TAR archive from a directory containing DICOM files.
+
+The input directory can have any number of files, with any organization or
+naming. However, the DICOM files are assumed to come from a single "visit"
+(i.e., the time between a person or sample entering and then leaving a
+scanner). The input directory's content is copied into a TAR archive verbatim,
+with no changes to filenames or organization.
+
+In order to generate reproducible TAR archives, the file order, recorded
+permissions and ownership, and modification times are standardized. All files
+in the TAR archive are declared to be owned by root/root (uid/gid: 0/0) with
+0644 permissions. The modification time of any DICOM file is determined
+by its contained DICOM `StudyDate/StudyTime` timestamps. The modification time
+for any non-DICOM file is set to the latest timestamp across all DICOM files.
+
+.. code-block:: console
+
+   $ icf-utils make_studyvisit_archive --help
+   usage: make_studyvisit_archive [-h] [-o PATH] --id STUDY-ID VISIT-ID <input-dir>
+
+``deposit_visit_metadata``
+""""""""""""""""""""""""""
+
+This command locates the DICOM tarball for a particular visit in a
+study (given by their respective identifiers) in the data store, and
+extracts a minimal set of metadata tags for each DICOM image, and the
+TAR archive as a whole. These metadata are then deposited in two
+files, in JSON format, in the study directory:
+
+- ``{visit_id}_metadata_tarball.json``
+
+  JSON object with basic properties of the archive, such as 'size', and
+  'md5'.
+
+- ``{visit_id}_metadata_dicoms.json``
+
+  JSON array with essential properties for each DICOM image file, such as
+  'path' (relative path inside the TAR archive), 'md5' (MD5 checksum of
+  the DICOM file), 'size' (in bytes), and select standard DICOM tags,
+  such as "SeriesDescription", "SeriesNumber", "Modality",
+  "MRAcquisitionType", "ProtocolName", "PulseSequenceName". The latter
+  enable a rough, technical characterization of the images in the TAR
+  archive.
+
+.. code-block:: console
+
+  $ icf-utils getmeta_studyvisit -h
+  usage: getmeta_studyvisit [-h] [-o PATH] --id STUDY-ID VISIT-ID
+
+``deposit_visit_dataset``
+"""""""""""""""""""""""""
+
+This command reads the metadata deposit from
+``deposit_visit_metadata`` for a visit in a study (given by their
+respective identifiers) from the data store, and generates a DataLad
+dataset from it. This DataLad dataset provides versioned access to the
+visit's DICOM data, up to single-image granularity.  Moreover, all
+DICOM files are annotated with basic DICOM tags that enable on-demand
+dataset views for particular applications (e.g., DICOMs sorted by
+image series and protocol name). The DataLad dataset is deposited in
+two files in the study directory:
+
+- ``{visit_id}_XDLRA--refs``
+- ``{visit_id}_XDLRA--repo-export``
+
+where the former enables `datalad/git clone` operations, and the latter
+represents the actual dataset as a compressed archive.
+
+.. code-block:: console
+
+   $ icf-utils dataladify_studyvisit_from_meta -h
+   usage: dataladify_studyvisit_from_meta [-h] [-o PATH] --id STUDY-ID VISIT-ID
+
+``catalogify_studyvisit_from_meta``
+"""""""""""""""""""""""""""""""""""
+
+This command creates or updates a DataLad catalog -- a user-facing
+html rendering of dataset contents. It is placed in the ``catalog``
+folder in the study directory.
+
+.. code-block:: console
+
+  $ icf-utils dataladify_studyvisit_from_meta --help
+  usage: dataladify_studyvisit_from_meta [-h] [-o PATH] --id STUDY-ID VISIT-ID
diff --git a/docs/source/developer.rst b/docs/source/developer.rst
index b04eaf4..db6f751 100644
--- a/docs/source/developer.rst
+++ b/docs/source/developer.rst
@@ -1,2 +1,59 @@
 Developer docs
 ==============
+
+Contributions in the form of bug reports or code are warmly welcomed.
+You can find the source code as well as an issue tracker on `GitHub`_.
+
+Development environment
+^^^^^^^^^^^^^^^^^^^^^^^
+
+Clone the source repository::
+
+    git clone git@github.com:psychoinformatics-de/inm-icf-utilities.git
+    cd inm-icf-utilities
+
+Install development software requirements in a virtual environment::
+
+    # create and enter a new virtual environment (optional)
+    $ virtualenv --python=python3 ~/env/icf
+    $ . ~/env/icf/bin/activate
+    pip install -r requirements-devel.txt
+    pip install -r docs/requirements.txt
+
+Running tests
+^^^^^^^^^^^^^
+
+This package uses pytest for integration testing, and a CI test suite runs in
+the source repository on GitHub automatically.
+As the tooling are meant to run on specific systems only, executing tests locally
+requires additional setup steps that likely make it infeasible unless your system
+are the ICF servers.
+
+The requirements are local DICOM data (not distributed alongside this software)
+that matches the layout, naming scheme, and permission set that the ICF uses.
+In addition, it requires the icf-utils Singularity image (see :ref:`singularity`),
+which needs to be installed as ``icf-utils`` in ``/usr/bin``.
+Code that achieves this setup can be found in `.appveyor.yml <https://github.com/psychoinformatics-de/inm-icf-utilities/blob/main/.appveyor.yml>`_.
+
+If this is in place, set the environment variable ``INM_ICF_TEST_STUDIES``
+to point to the directory with DICOM data, and execute integrations tests with::
+
+   pytest -s -v .
+
+.. _singularity:
+
+Bundled utilities
+^^^^^^^^^^^^^^^^^
+
+The INM-ICF utilities are distributed as a bundle in the form of a
+Singularity software container. This container is updated regularly
+and can be downloaded from `ci.appveyor.com/api/projects/mih/inm-icf-utilities/artifacts/icf.sif <https://ci.appveyor.com/api/projects/mih/inm-icf-utilities/artifacts/icf.sif>`_.
+When testing changes to the INM-ICF-utilities, the Singularity image needs to be
+rebuilt with the changes included.
+Its recipe can be found under ``singularity/icf.def``.
+The image can be rebuilt automatically using the Appveyor-based CI test suite.
+If only software dependencies change, an update is **not** triggered automatically
+but requires that the `build cache is wiped <https://www.appveyor.com/docs/build-cache/#cleaning-up-cache>`_.
+
+
+.. _GitHub: https://github.com/psychoinformatics-de/inm-icf-utilities
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 86c308d..b40546f 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -16,12 +16,15 @@ individuals.
    :caption: Contents:
 
    user/index
-   personnel
+   admin
    developer
 
-Indices and tables
-==================
 
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
+.. rubric:: Acknowledgements
+
+This software was developed with support from the German Federal
+Ministry of Education and Research (BMBF 01GQ1905), the US National
+Science Foundation (NSF 1912266), the Helmholtz research center Jülich
+(RDM challenge 2022), and the Deutsche Forschungsgemeinschaft (DFG,
+German Research Foundation) under grant SFB 1451 (`431549029
+<https://gepris.dfg.de/gepris/projekt/431549029>`_, INF project).
diff --git a/docs/source/personnel.rst b/docs/source/personnel.rst
deleted file mode 100644
index f37a38c..0000000
--- a/docs/source/personnel.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-ICF Personnel guide
-===================
diff --git a/singularity/icf.def b/singularity/icf.def
index 6d045c6..fb1a9d6 100644
--- a/singularity/icf.def
+++ b/singularity/icf.def
@@ -13,7 +13,7 @@ From: debian:bookworm-slim
     # install all non-datalad deps from Debian proper
     apt-get update -qq
     apt-get -y install eatmydata
-    eatmydata apt-get -y install --no-install-recommends git python3-pip python3-dicom python3-tqdm
+    eatmydata apt-get -y install --no-install-recommends git python3-pip python3-dicom python3-tqdm less
     # fresh git-annex via the datalad-installer
     python3 -m pip install --break-system-packages datalad-installer
     datalad-installer -E /tmp/dlinstaller_env.sh --sudo ok git-annex -m snapshot
diff --git a/tests/test_datalad_workflows/test_pipeline.py b/tests/test_datalad_workflows/test_pipeline.py
index d96b4d9..9397ead 100644
--- a/tests/test_datalad_workflows/test_pipeline.py
+++ b/tests/test_datalad_workflows/test_pipeline.py
@@ -28,6 +28,7 @@ def run_script(name: str,
                working_directory: str | Path,
                study_id: str,
                visit_id: str,
+               *args
                ):
 
     script_path = Path(*(Path(__file__).parts[:-3] + ('bin',))) / name
@@ -37,28 +38,31 @@ def run_script(name: str,
             str(script_path),
             '--id',
             study_id,
-            visit_id
+            visit_id,
+            *args
         ]
     )
 
 
 def process_visits(studies_dir: Path,
                    studies: list[str],
-                   visits: list[str]
+                   visits: list[str],
+                   baseurl: str,
                    ):
     for study in studies:
         for visit in visits:
             # run metadata generation script
             run_script(
-                'getmeta_studyvisit',
+                'deposit_visit_metadata',
                 studies_dir,
                 study, visit
             )
             # run dataladification script
             run_script(
-                'dataladify_studyvisit_from_meta',
+                'deposit_visit_dataset',
                 studies_dir,
-                study, visit
+                study, visit,
+                '--store-url', baseurl,
             )
             # run catalogification script
             run_script(
@@ -140,6 +144,7 @@ def test_pipeline(tmp_path: Path,
         Path(test_studies_dir),
         test_study_names,
         existing_visits,
+        data_webserver,
     )
 
     # 1. Test metadata generation
@@ -166,11 +171,8 @@ def test_pipeline(tmp_path: Path,
                 dataaccess_credential,
                 credman,
             )
-            # TODO reenable once the server setup is actually compatible
-            # TODO swap the order of gets, or actually drop the tar get
-            # completely. Pulling individual files will do all that internally
-            # Try to get the tar file and the DICOMs
-            #dataset.get(f'icf/{visit}_dicom.tar')
+            # pull all individual DICOM files, this will internally
+            # access/download the archive at the store
             dataset.get(f'{study}_{visit}')
 
     # 3. Test catalog generation