diff --git a/nwbinspector/checks/time_series.py b/nwbinspector/checks/time_series.py
index 3e99cb71..0d44f38a 100644
--- a/nwbinspector/checks/time_series.py
+++ b/nwbinspector/checks/time_series.py
@@ -92,3 +92,35 @@ def check_resolution(time_series: TimeSeries):
         return InspectorMessage(
             message=f"'resolution' should use -1.0 or NaN for unknown instead of {time_series.resolution}."
         )
+
+
+@register_check(importance=Importance.BEST_PRACTICE_SUGGESTION, neurodata_type=TimeSeries)
+def check_rows_not_nan(time_series: TimeSeries, nelems=200):
+    """Check that each row of a TimeSeries has at least one non-NaN piece of data."""
+    n_dims = len(time_series.data.shape)
+    if n_dims > 2:
+        yield
+
+    spanning_by = np.ceil(time_series.shape[0] / nelems).astype(int) if nelems else None
+    if n_dims == 1:
+        if nelems is not None and not all(np.isnan(time_series.data[:nelems])):
+            yield
+        if all(np.isnan(time_series[slice(0, None, spanning_by)]).flatten()):
+            yield InspectorMessage(
+                message=(
+                    "This TimeSeries appears to contain NaN data at each frame. "
+                    "Consider removing this object from the file."
+                )
+            )
+    elif n_dims == 2:
+        for col in range(time_series.data.shape[1]):
+            if nelems is not None and not all(np.isnan(time_series.data[:nelems, col]).flatten()):
+                continue
+
+            if all(np.isnan(time_series[slice(0, None, spanning_by), col]).flatten()):
+                yield InspectorMessage(
+                    message=(
+                        f"Column index {col} of this TimeSeries appears to contain NaN data at each frame. "
+                        "Consider removing this column from the TimeSeries."
+                    )
+                )
diff --git a/tests/unit_tests/test_time_series.py b/tests/unit_tests/test_time_series.py
index 3428d566..6caf3263 100644
--- a/tests/unit_tests/test_time_series.py
+++ b/tests/unit_tests/test_time_series.py
@@ -4,6 +4,7 @@
 import numpy as np
 import pynwb
 import pytest
+from hdmf.testing import TestCase
 
 from nwbinspector import (
     InspectorMessage,
@@ -14,6 +15,7 @@
     check_timestamps_ascending,
     check_missing_unit,
     check_resolution,
+    check_rows_not_nan,
 )
 from nwbinspector.utils import get_package_version, robust_s3_read
 
@@ -214,3 +216,75 @@ def test_check_resolution_fail():
         object_name="test",
         location="/",
     )
+
+
+def test_check_rows_not_nan_1d_pass():
+    data = np.zeros(shape=400)
+    data[0] = np.nan
+    time_series = pynwb.TimeSeries(name="test_time_series", unit="", data=data, rate=1.0)
+    assert check_rows_not_nan(time_series) is None
+
+
+def test_check_rows_not_nan_1d_fail():
+    data = np.zeros(shape=400)
+    data[:] = np.nan
+    time_series = pynwb.TimeSeries(name="test", unit="test", data=data, rate=1.0)
+    assert check_rows_not_nan(time_series) == [
+        InspectorMessage(
+            message=(
+                "This TimeSeries appears to contain NaN data at each frame. "
+                "Consider removing this object from the file."
+            ),
+            importance=Importance.BEST_PRACTICE_SUGGESTION,
+            check_function_name="check_rows_not_nan",
+            object_type="TimeSeries",
+            object_name="test",
+            location="/",
+        )
+    ]
+
+
+def test_check_rows_not_nan_2d_pass():
+    data = np.zeros(shape=(2, 3))
+    data[0, 0] = np.nan
+    time_series = pynwb.TimeSeries(name="test_time_series", unit="", data=data, rate=1.0)
+    assert check_rows_not_nan(time_series) is None
+
+
+def test_check_rows_not_nan_2d_fail():
+    data = np.zeros(shape=(2, 5))
+    data[:, [1, 4]] = np.nan
+    time_series = pynwb.TimeSeries(name="test", unit="test", data=data, rate=1.0)
+    assert check_rows_not_nan(time_series) == [
+        InspectorMessage(
+            message=(
+                "Column index 1 of this TimeSeries appears to contain NaN data at each frame. "
+                "Consider removing this column from the TimeSeries."
+            ),
+            importance=Importance.BEST_PRACTICE_SUGGESTION,
+            check_function_name="check_rows_not_nan",
+            object_type="TimeSeries",
+            object_name="test",
+            location="/",
+            file_path=None,
+        ),
+        InspectorMessage(
+            message=(
+                "Column index 4 of this TimeSeries appears to contain NaN data at each frame. "
+                "Consider removing this column from the TimeSeries."
+            ),
+            importance=Importance.BEST_PRACTICE_SUGGESTION,
+            check_function_name="check_rows_not_nan",
+            object_type="TimeSeries",
+            object_name="test",
+            location="/",
+            file_path=None,
+        ),
+    ]
+
+
+def test_check_rows_not_nan_higher_dim_skip():
+    data = np.empty(shape=(2, 3, 4))
+    data[:, 0, 0] = np.nan
+    time_series = pynwb.TimeSeries(name="test", unit="test", data=data, rate=1.0)
+    assert check_rows_not_nan(time_series) is None