diff --git a/cal_tools/cal_tools/agipdlib.py b/cal_tools/cal_tools/agipdlib.py
index 7a83a61d959060bbd21d224462aa513a3582ed9c..11d4438584b2db40bea2970558a1066269cc6e2c 100644
--- a/cal_tools/cal_tools/agipdlib.py
+++ b/cal_tools/cal_tools/agipdlib.py
@@ -235,24 +235,46 @@ class AgipdCorrections:
         data_path = f'{agipd_base}/image'
         data_dict = self.shared_dict[i_proc]
 
-        with h5py.File(file_name, 'r') as infile:
-            with h5py.File(ofile_name, 'w') as outfile:
+        image_fields = [
+            'trainId', 'pulseId', 'cellId', 'data', 'gain', 'mask', 'blShift',
+        ]
+        compress_fields = ['gain', 'mask']
 
-                n_img = data_dict['nImg'][0]
-                if n_img == 0:
-                    return
-                trains = data_dict['trainId'][:n_img]
+        n_img = data_dict['nImg'][0]
+        if n_img == 0:
+            return
+        trains = data_dict['trainId'][:n_img]
+
+        with h5py.File(ofile_name, 'w') as outfile:
+            # Copy any other data from the input file.
+            # This includes indexes, so it's important that the corrected data
+            # we write is aligned with the raw data.
+            with h5py.File(file_name, 'r') as infile:
                 self.copy_and_sanitize_non_cal_data(infile, outfile,
                                                     agipd_base,
                                                     idx_base, trains)
 
-                outfile[data_path]['data'] = data_dict['data'][:n_img]
-                outfile[data_path]['gain'] = data_dict['gain'][:n_img]
-                outfile[data_path]['blShift'] = data_dict['blShift'][:n_img]
-                outfile[data_path]['mask'] = data_dict['mask'][:n_img]
-                outfile[data_path]['cellId'] = data_dict['cellId'][:n_img]
-                outfile[data_path]['pulseId'] = data_dict['pulseId'][:n_img]
-                outfile[data_path]['trainId'] = data_dict['trainId'][:n_img]
+            # All corrected data goes in a /INSTRUMENT/.../image group
+            image_grp = outfile[data_path]
+
+            # Set up all the datasets before filling them. This puts the
+            # metadata about the datasets together at the start of the file,
+            # so it's efficient to examine the file structure.
+            for field in image_fields:
+                arr = data_dict[field][:n_img]
+                kw = {'fletcher32': True}
+                if field in compress_fields:
+                    kw.update(compression='gzip', compression_opts=1, shuffle=True)
+                if arr.ndim > 1:
+                    kw['chunks'] = (1,) + arr.shape[1:]  # 1 chunk = 1 image
+
+                image_grp.create_dataset(
+                    field, shape=arr.shape, dtype=arr.dtype, **kw
+                )
+
+            # Write the corrected data
+            for field in image_fields:
+                image_grp[field][:] = data_dict[field][:n_img]
 
     def cm_correction(self, i_proc, asic):
         """
@@ -1000,14 +1022,14 @@ class AgipdCorrections:
         for i in range(n_cores_files):
             self.shared_dict.append({})
             self.shared_dict[i]['cellId'] = sharedmem.empty(shape[0],
-                                                            dtype='i4')
+                                                            dtype='u2')
             self.shared_dict[i]['pulseId'] = sharedmem.empty(shape[0],
-                                                             dtype='i4')
+                                                             dtype='u8')
             self.shared_dict[i]['trainId'] = sharedmem.empty(shape[0],
-                                                             dtype='i4')
+                                                             dtype='u8')
             self.shared_dict[i]['moduleIdx'] = sharedmem.empty(1, dtype='i4')
             self.shared_dict[i]['nImg'] = sharedmem.empty(1, dtype='i4')
-            self.shared_dict[i]['mask'] = sharedmem.empty(shape, dtype='i4')
+            self.shared_dict[i]['mask'] = sharedmem.empty(shape, dtype='u4')
             self.shared_dict[i]['data'] = sharedmem.empty(shape, dtype='f4')
             self.shared_dict[i]['rawgain'] = sharedmem.empty(shape, dtype='u2')
             self.shared_dict[i]['gain'] = sharedmem.empty(shape, dtype='u1')