Add example of setting Units.resolution in the ecephys tutorial

CHANGELOG.md

@@ -5,6 +5,7 @@
 ### Documentation and tutorial enhancements
 - Added documentation for `ExternalImage` to the images tutorial. @h-mayorquin [#2159](https://github.com/NeurodataWithoutBorders/pynwb/pull/2159)
 - Fixed broken and redirecting links in documentation. @bendichter [#2165](https://github.com/NeurodataWithoutBorders/pynwb/pull/2165)
+- Added example of setting `Units.resolution` in the ecephys tutorial. @h-mayorquin [#2174](https://github.com/NeurodataWithoutBorders/pynwb/pull/2174)
 
 ### Added
 - Added support for HERD (HDMF External Resources Data Structure) as the `external_resources` field on `NWBFile`, enabling users to associate external resource annotations (e.g., ontology term mappings) with their NWB files. `link_resources` and `get_external_resources` are inherited from `HERDManager` in hdmf. @mavaylon1, @rly [#2111](https://github.com/NeurodataWithoutBorders/pynwb/pull/2111)

docs/gallery/domain/ecephys.py

@@ -343,6 +343,22 @@
     spike_times = np.where(np.random.rand((res * duration)) < (firing_rate / res))[0] / res
     nwbfile.add_unit(spike_times=spike_times, quality="good")
 
+####################
+# The ``resolution`` field on the :py:class:`~pynwb.misc.Units` table documents the precision of spike timing
+# data, typically ``1 / sampling_rate`` of the acquisition system (i.e., the smallest measurable difference
+# between two spike times, in seconds). Setting it helps downstream users judge whether fine-timescale
+# analyses are appropriate for the dataset.
+#
+# When creating the :py:class:`~pynwb.misc.Units` table directly, you can pass it to the constructor:
+#
+# .. code-block:: python
+#
+#     units = Units(name="units", resolution=1 / 30000)  # 30 kHz recording system
+#
+# Since the tutorial uses :py:meth:`.NWBFile.add_unit`, we set it after the fact:
+
+nwbfile.units.resolution = 1 / res  # resolution in seconds (1 / sampling rate)
+
 #######################
 # The :py:class:`~pynwb.misc.Units` table can also be converted to a pandas :py:class:`~pandas.DataFrame`.
 #