diff --git a/pyzebra/app/panel_hdf_param_study.py b/pyzebra/app/panel_hdf_param_study.py
index bfe3cd8..a163836 100644
--- a/pyzebra/app/panel_hdf_param_study.py
+++ b/pyzebra/app/panel_hdf_param_study.py
@@ -260,10 +260,10 @@ def create():
 
     def update_overview_plot():
         scan = _get_selected_scan()
-        h5_data = scan["data"]
-        n_im, n_y, n_x = h5_data.shape
-        overview_x = np.mean(h5_data, axis=1)
-        overview_y = np.mean(h5_data, axis=2)
+        counts = scan["counts"]
+        n_im, n_y, n_x = counts.shape
+        overview_x = np.mean(counts, axis=1)
+        overview_y = np.mean(counts, axis=2)
 
         # normalize for simpler colormapping
         overview_max_val = max(np.max(overview_x), np.max(overview_y))
diff --git a/pyzebra/app/panel_hdf_viewer.py b/pyzebra/app/panel_hdf_viewer.py
index f3b9c72..2add50a 100644
--- a/pyzebra/app/panel_hdf_viewer.py
+++ b/pyzebra/app/panel_hdf_viewer.py
@@ -115,7 +115,7 @@ def create():
             print("Could not read data from the file.")
             return
 
-        last_im_index = scan["data"].shape[0] - 1
+        last_im_index = scan["counts"].shape[0] - 1
 
         index_spinner.value = 0
         index_spinner.high = last_im_index
@@ -157,7 +157,7 @@ def create():
         if index is None:
             index = index_spinner.value
 
-        current_image = scan["data"][index]
+        current_image = scan["counts"][index]
         proj_v_line_source.data.update(
             x=np.arange(0, IMAGE_W) + 0.5, y=np.mean(current_image, axis=0)
         )
@@ -223,10 +223,10 @@ def create():
         )
 
     def update_overview_plot():
-        h5_data = scan["data"]
-        n_im, n_y, n_x = h5_data.shape
-        overview_x = np.mean(h5_data, axis=1)
-        overview_y = np.mean(h5_data, axis=2)
+        counts = scan["counts"]
+        n_im, n_y, n_x = counts.shape
+        overview_x = np.mean(counts, axis=1)
+        overview_y = np.mean(counts, axis=2)
 
         # normalize for simpler colormapping
         overview_max_val = max(np.max(overview_x), np.max(overview_y))
@@ -438,13 +438,13 @@ def create():
 
     def box_edit_callback(_attr, _old, new):
         if new["x"]:
-            h5_data = scan["data"]
-            x_val = np.arange(h5_data.shape[0])
+            counts = scan["counts"]
+            x_val = np.arange(counts.shape[0])
             left = int(np.floor(new["x"][0]))
             right = int(np.ceil(new["x"][0] + new["width"][0]))
             bottom = int(np.floor(new["y"][0]))
             top = int(np.ceil(new["y"][0] + new["height"][0]))
-            y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2))
+            y_val = np.sum(counts[:, bottom:top, left:right], axis=(1, 2))
         else:
             x_val = []
             y_val = []
diff --git a/pyzebra/h5.py b/pyzebra/h5.py
index 5f56ad4..deb2b07 100644
--- a/pyzebra/h5.py
+++ b/pyzebra/h5.py
@@ -68,13 +68,13 @@ def read_detector_data(filepath, cami_meta=None):
         ndarray: A 3D array of data, omega, gamma, nu.
     """
     with h5py.File(filepath, "r") as h5f:
-        data = h5f["/entry1/area_detector2/data"][:]
+        counts = h5f["/entry1/area_detector2/data"][:]
 
-        # reshape data to a correct shape (2006 issue)
-        n, cols, rows = data.shape
-        data = data.reshape(n, rows, cols)
+        # reshape images (counts) to a correct shape (2006 issue)
+        n, cols, rows = counts.shape
+        counts = counts.reshape(n, rows, cols)
 
-        scan = {"data": data}
+        scan = {"counts": counts}
         scan["original_filename"] = filepath
 
         if "/entry1/zebra_mode" in h5f:
@@ -145,7 +145,7 @@ def read_detector_data(filepath, cami_meta=None):
 
 
 def fit_event(scan, fr_from, fr_to, y_from, y_to, x_from, x_to):
-    data_roi = scan["data"][fr_from:fr_to, y_from:y_to, x_from:x_to]
+    data_roi = scan["counts"][fr_from:fr_to, y_from:y_to, x_from:x_to]
 
     model = GaussianModel()
     fr = np.arange(fr_from, fr_to)