diff --git a/nwbwidgets/file.py b/nwbwidgets/file.py
index 8b036a44..d7b36c6d 100644
--- a/nwbwidgets/file.py
+++ b/nwbwidgets/file.py
@@ -56,4 +56,7 @@ def show_nwbfile(nwbfile: NWBFile, neurodata_vis_spec: dict) -> widgets.Widget:
     func_ = partial(view.nwb2widget, neurodata_vis_spec=neurodata_vis_spec)
     accordion = lazy_show_over_data(neuro_data, func_, labels=labels, style=widgets.Accordion)
 
-    return widgets.VBox(info + [accordion])
+    from .time_grid import TimeGrid
+
+    time_grid_widget = TimeGrid(nwbfile)
+    return widgets.VBox(info + [time_grid_widget] + [accordion])
diff --git a/nwbwidgets/time_grid.py b/nwbwidgets/time_grid.py
new file mode 100644
index 00000000..edad125e
--- /dev/null
+++ b/nwbwidgets/time_grid.py
@@ -0,0 +1,273 @@
+import matplotlib.cm as mplcm
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+from hdmf.common import DynamicTable, VectorData
+from pynwb import NWBFile, TimeSeries
+
+
+def get_partial_path_from_parents(obj):
+    # ndx-events do not have object.data
+    parents = []
+    while obj is not None and obj.name != "root":
+        parents.append(obj.name)
+        obj = obj.parent  # assuming the object has a 'parent' attribute
+    return parents
+
+
+def extract_timing_information_from_nwbfile(nwbfile: NWBFile, verbose: bool = False) -> dict:
+    temporal_information_dict = {}
+    for object_id, object in nwbfile.objects.items():
+        object_name = object.name
+        if hasattr(object, "data") and hasattr(object.data, "name"):
+            object_path = object.data.name
+            top_module = object_path[1:].split("/")[0]
+            if top_module == "processing":
+                top_module = object_path[1:].split("/")[1]
+
+        elif hasattr(object, "data"):
+            object_path = get_partial_path_from_parents(object)[::-1]
+            top_module = object_path[0] if object_path else ""
+        else:
+            object_path = get_partial_path_from_parents(object)[::-1]
+            top_module = object_path[0] if object_path else ""
+
+        if isinstance(object, DynamicTable) and hasattr(object, "start_time") and hasattr(object, "stop_time"):
+            if verbose:
+                print("------------------")
+                print(f"branch: dynamic table with intervals for object {object_name} with path {object_path}")
+                print(f"Top module {top_module}")
+                print("------------------")
+
+            start_time = object.start_time.data[:]
+            stop_time = object.stop_time.data[:]
+
+            intervals = [
+                {"start_time": start_time, "stop_time": stop_time}
+                for start_time, stop_time in zip(start_time, stop_time)
+            ]
+            timing_dict = dict(start_time=intervals[0]["start_time"], stop_time=intervals[-1]["stop_time"])
+            object_info = dict(
+                object_path=object_path, object_name=object_name, object_id=object_id, top_module=top_module
+            )
+            temporal_information_dict[object_name] = dict(
+                timing_dict=timing_dict, info=object_info, intervals=intervals
+            )
+
+        elif isinstance(object, DynamicTable) and hasattr(object, "start_time"):
+            if verbose:
+                print("------------------")
+                print("NOT DONE YET")
+                print(f"Branch of dynamic table with only start_time for object {object_name} with path {object_path}")
+                print(f"Top module {top_module}")
+                print("------------------")
+
+        elif isinstance(object, TimeSeries):
+            time_series = object
+            if verbose:
+                print("------------------")
+                print(f"branch: time series for object {object_name} with path {object_path}")
+                print(f"Top module {top_module}")
+                print("------------------")
+            timing_dict = {}
+            if time_series.timestamps is not None:
+                timing_dict["start_time"] = time_series.timestamps[0]
+                timing_dict["stop_time"] = time_series.timestamps[-1]
+            else:
+                timing_dict["start_time"] = time_series.starting_time
+                timing_dict["stop_time"] = time_series.starting_time + time_series.num_samples / time_series.rate
+
+            # TODO This fails when the series has timestamps with gaps. One option to find out the gaps and write the series
+            # As intervals is that the naive way np.diff generates a too large memory allocation.
+            # Probably, a bisection algorithm can be used to find the gaps.
+
+            object_info = dict(
+                object_path=object_path, object_name=object_name, object_id=object_id, top_module=top_module
+            )
+            temporal_information_dict[object_name] = dict(timing_dict=timing_dict, info=object_info)
+
+    return temporal_information_dict
+
+
+def generate_epoch_guides_trace(temporal_data_dict):
+    if "epochs" not in temporal_data_dict:
+        return []
+
+    epoch_dict = temporal_data_dict["epochs"]
+    intervals = epoch_dict["intervals"]
+    x_epoch = []
+    y_epoch = []
+    for i, epoch in enumerate(intervals, start=1):
+        start_time = epoch["start_time"]
+        stop_time = epoch["stop_time"]
+        x_epoch.extend([start_time, start_time, None])
+        x_epoch.extend([stop_time, stop_time, None])
+        y_epoch.extend([0.9, len(temporal_data_dict) + 1, None])
+        y_epoch.extend([0.9, len(temporal_data_dict) + 1, None])
+
+    trace = go.Scatter(
+        x=x_epoch,
+        y=y_epoch,
+        mode="lines",
+        line=dict(color="#808080", width=1.5, dash="dash"),
+        name="epochs_guides",
+        visible=False,
+    )
+
+    return trace
+
+
+def generate_epoch_annotations(temporal_data_dict) -> list:
+    if "epochs" not in temporal_data_dict:
+        return []
+
+    epoch_dict = temporal_data_dict["epochs"]
+    intervals = epoch_dict["intervals"]
+
+    annotation_dict_list = []
+    for i, epoch in enumerate(intervals, start=1):
+        start_time = epoch["start_time"]
+        stop_time = epoch["stop_time"]
+
+        # Add a text box for each epoch
+        annotation_x = start_time + (stop_time - start_time) / 2
+        annotation_y = len(temporal_data_dict) + len(temporal_data_dict) * 0.05
+        annotation_dict = dict(
+            x=annotation_x,
+            y=annotation_y,
+            text=f"Epoch {i}",
+            showarrow=False,
+            font=dict(size=8, color="#000000"),
+            align="center",
+            borderwidth=2,
+            borderpad=4,
+            opacity=0.6,
+        )
+        annotation_dict_list.append(annotation_dict)
+    return annotation_dict_list
+
+
+def generate_total_duration_traces(temporal_data_dict, styling_dict):
+    traces_dict = dict()
+    for i, object_name in enumerate(styling_dict, start=1):
+        times_info_dict = temporal_data_dict[object_name]
+        info_dict = times_info_dict["info"]
+        timing_dict = times_info_dict["timing_dict"]
+        start_time = timing_dict["start_time"]
+        stop_time = timing_dict["stop_time"]
+        top_module = info_dict["top_module"]
+        color = styling_dict[object_name]["color"]
+
+        trace = go.Scatter(
+            x=[start_time, stop_time],
+            y=[i, i],
+            mode="lines",
+            line=dict(color=color, width=6),
+            name=f"{top_module} - {object_name}",
+            legendgroup=top_module,  # Group by top_module
+            hovertemplate=f"{object_name}<br>start: {start_time}<br>end: {stop_time}<extra></extra>",
+        )
+        traces_dict[object_name] = trace
+
+    return traces_dict
+
+
+def generate_time_grid_widget(temporal_data_dict: dict) -> go.FigureWidget:
+    fig = go.FigureWidget()
+
+    # Key modules for temporal organization ad trials and epochs
+    key_modules = ["epochs", "trials"]
+    sorted_keys = [key for key in temporal_data_dict.keys() if key not in key_modules]
+    sorted_keys.sort(key=lambda x: temporal_data_dict[x]["info"]["top_module"])
+    unique_top_modules = list(set([temporal_data_dict[key]["info"]["top_module"] for key in sorted_keys]))
+
+    # if "epochs" in temporal_data_dict:
+    #     sorted_keys = ["epochs"] + sorted_keys
+    # if "trials" in temporal_data_dict:
+    #     sorted_keys = sorted_keys + ["trials"]
+
+    # Generate a color map for the top modules
+    num_top_modules = len(unique_top_modules)
+    cm = plt.get_cmap("Accent")  # Use 'gist_rainbow' colormap
+    cNorm = colors.Normalize(vmin=0, vmax=num_top_modules - 1)
+    scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)
+    color_map = {module: colors.rgb2hex(scalarMap.to_rgba(i)) for i, module in enumerate(unique_top_modules)}
+
+    styling_dict_epochs = dict(epochs=dict(color="#d3d3d3"))
+    styling_dict_rest = {
+        key: dict(color=color_map[temporal_data_dict[key]["info"]["top_module"]]) for key in sorted_keys
+    }
+    styling_dict_trials = dict(trials=dict(color="#d3d3d3"))
+
+    styling_dict = dict()
+    if "trials" in temporal_data_dict:
+        styling_dict.update(styling_dict_trials)
+
+    styling_dict.update(styling_dict_rest)
+
+    if "epochs" in temporal_data_dict:
+        styling_dict.update(styling_dict_epochs)
+
+    traces_dict = generate_total_duration_traces(temporal_data_dict, styling_dict)
+    for trace in traces_dict.values():
+        fig.add_trace(trace)
+
+    epoch_guide_trace = generate_epoch_guides_trace(temporal_data_dict)
+    if epoch_guide_trace:
+        fig.add_trace(epoch_guide_trace)
+
+    annotation_dict_list = generate_epoch_annotations(temporal_data_dict)
+    # Button for toggling epoch guides
+    epoch_guide_button = dict(
+        type="buttons",
+        direction="down",
+        showactive=True,
+        buttons=[
+            dict(
+                label="Toggle Epoch Guides",
+                method="update",
+                args=[{"visible": [True] * len(traces_dict) + [False]}, {"annotations": []}],
+                args2=[{"visible": [True] * len(traces_dict) + [True]}, {"annotations": annotation_dict_list}],
+            ),
+        ],
+        x=0.10,  # Position from left (0 to 1)
+        y=-0.15,  # Position from bottom (0 to 1)
+    )
+    if epoch_guide_trace:
+        updatemenus = [epoch_guide_button]
+    else:
+        updatemenus = None
+    ticktext = list(styling_dict.keys())
+    tickvals = list(range(1, len(temporal_data_dict) + 1))
+    fig.update_layout(
+        xaxis_title="Time (s)",
+        yaxis=dict(
+            tickvals=tickvals,
+            ticktext=ticktext,
+            range=[0.5, len(ticktext) * 1.25],
+            ticks="",
+            showline=False,
+            showticklabels=True,
+            showgrid=True,
+            gridwidth=0.1,
+        ),
+        updatemenus=updatemenus,
+    )
+
+    return fig
+
+
+from ipywidgets import Layout, fixed, widgets
+
+
+class TimeGrid(widgets.VBox):
+    def __init__(self, nwbfile: NWBFile):
+        super().__init__()
+
+        self.nwbfile = nwbfile
+        # This is the calculation part
+        self.temporal_data_dict = extract_timing_information_from_nwbfile(nwbfile=self.nwbfile)
+
+        # This is the figure widget
+        self.figure_widget = generate_time_grid_widget(temporal_data_dict=self.temporal_data_dict)
+        self.children = [self.figure_widget]