diff --git a/docs/source/howto/_data_management.md b/docs/source/howto/_data_management.md
index bf80e02a..00a70a02 100644
--- a/docs/source/howto/_data_management.md
+++ b/docs/source/howto/_data_management.md
@@ -1,18 +1,22 @@
 # Setup APS Data Management
 
-This document describes how to setup and use the [APS Data
-Management](https://git.aps.anl.gov/DM/dm-docs/-/wikis/home) (DM) Python
+This document describes how to setup and submit a workflow job using the [APS
+Data Management](https://git.aps.anl.gov/DM/dm-docs/-/wikis/home) (DM) Python
 [API](https://git.aps.anl.gov/DM/dm-docs/-/wikis/DM/Beamline-Services/API-Reference)
 (tools) in a Bluesky session.
 
 This document provides guidance for workstations at the APS, where DM tools and
 services are available.
 
-## Background
+For more information, see the DM API reference for more information about how to
+use the DM API and tools. See the `apstools`
+[documentation](https://bcda-aps.github.io/apstools/latest/api/_utils.html#apstools.utils.aps_data_management),
+for a list of the support code available.
 
-As stated in the DM [_Getting
-Started_](https://git.aps.anl.gov/DM/dm-docs/-/wikis/DM/HowTos/Getting-Started)
-guide:
+## About APS Data Management (DM)
+
+As stated in the DM _Getting Started_
+[guide](https://git.aps.anl.gov/DM/dm-docs/-/wikis/DM/HowTos/Getting-Started):
 
 > The APS Data Management System is a system for gathering together experimental
 > data, metadata about the experiment and providing users access to the data
@@ -20,10 +24,10 @@ guide:
 
 ## DM is configured by Environment Variables
 
-The [_Getting
-Started_](https://git.aps.anl.gov/DM/dm-docs/-/wikis/DM/HowTos/Getting-Started#setting-up-the-environment)
-guide explains how to setup a pre-configured conda environment to use the DM
-tools from the command line directly.  The setup procedure uses this shell command:
+The DM _Getting Started_
+[guide](https://git.aps.anl.gov/DM/dm-docs/-/wikis/DM/HowTos/Getting-Started)
+explains how to activate a pre-configured conda environment to use the DM tools
+directly from the command line.  The setup procedure uses this shell command:
 
 ```bash
 /home/DM_INSTALL_DIR/etc/dm.setup.sh
@@ -46,13 +50,13 @@ session.
 
 The Bluesky conda environment has all the packages for both Bluesky and DM
 already installed (for APS installations).  One of those packages,
-[`apstools`](https://bcda-aps.github.io/apstools/latest/api/_utils.html#aps-data-management),
+[apstools](https://bcda-aps.github.io/apstools/latest/api/_utils.html#aps-data-management),
 provides support for using DM in a Bluesky session.
 
 <details>
 
-Function
-[`dm_source_environ()`](https://bcda-aps.github.io/apstools/latest/api/_utils.html#apstools.utils.aps_data_management.dm_source_environ)
+The `dm_source_environ()`
+[function](https://bcda-aps.github.io/apstools/latest/api/_utils.html#apstools.utils.aps_data_management.dm_source_environ)
 is used internally to install the environment variables.  It expects a global
 variable `DM_SETUP_FILE` to be defined in the module.  
 
@@ -82,3 +86,74 @@ In typical Bluesky installations at APS, this file name is defined in the
 # Use bash shell, deactivate all conda environments, source this file:
 DM_SETUP_FILE: "/home/dm/etc/dm.setup.sh"
 ```
+
+### Example at APS XPCS station 8-ID-I
+
+Show how many DM workflow jobs are processing now:
+
+```py
+In [1]: from apstools.utils import dm_setup
+   ...: 
+   ...: dm_setup("/home/dm/etc/dm.setup.sh")
+   ...: 
+Out[1]: '8idi'
+
+In [2]: from dm.proc_web_service.api.procApiFactory import ProcApiFactory
+   ...: api = ProcApiFactory.getWorkflowProcApi()
+   ...: jobs = api.listProcessingJobs()
+   ...: for j in jobs:
+   ...:     if j["status"] not in ("done", "failed"):
+   ...:         print(f"{j['id']=!r}  {j.get('submissionTimestamp')=!r}  {j['status']=!r}")
+Out[2]:  # lots of jobs, only showing a few of them
+j['id']='6754e679-cedb-482b-bb4d-b58137f84001'  j.get('submissionTimestamp')='2024/11/08 04:48:31 CST'  j['status']='pending'
+j['id']='ad7328ae-35ba-4418-a9fd-b3dcc873348f'  j.get('submissionTimestamp')='2024/11/08 04:48:34 CST'  j['status']='pending'
+...
+j['id']='72b6d1b7-b6e0-4eb8-87d5-5f52792a043b'  j.get('submissionTimestamp')='2024/11/08 08:31:22 CST'  j['status']='running'
+j['id']='19252b7d-8961-4994-8977-86929811a988'  j.get('submissionTimestamp')='2024/11/08 08:31:28 CST'  j['status']='running'
+
+```
+
+## Submit a DM workflow job from a Bluesky session
+
+Here, we demonstrate one way to start a DM workflow from a Bluesky session.
+
+To submit a workflow job from a Bluesky session, first call `dm_setup()` as described above.  Then,
+get the "DM Processing API" as follows:
+
+```py
+from apstools.utils import dm_api_proc
+
+api = dm_api_proc()
+```
+
+Choose the workflow by name:
+
+```py
+workflowOwner = api.username
+workflowName = "xpcs8-02-gladier-boost"
+```
+
+Define the workflow arguments in a Python dictionary (these arguments are
+specific to the XPCS workflow named above):
+
+```py
+argsDict = {
+    "filePath": "H001_005_test_Feb_7-01000.h5",
+    "qmap": "eiger4M_qmap_d36_s360.h5",
+    "experimentName": "zhang202402",
+    # any other keyword arguments required by the workflow come next ...
+}
+```
+
+Start the processing job:
+
+```py
+job = api.startProcessingJob(workflowOwner, workflowName, argsDict)
+```
+
+Show the processing job ID:
+
+```py
+print(f"{job['id']=!r}")
+'c322e87c-ec43-4077-b074-eeef8522889c'
+```