diff --git a/.github/workflows/eva_tests_notebook.yml b/.github/workflows/eva_tests_notebook.yml
index 2c64cf63..bbf05d75 100644
--- a/.github/workflows/eva_tests_notebook.yml
+++ b/.github/workflows/eva_tests_notebook.yml
@@ -13,10 +13,10 @@ jobs:
     steps:
 
     # Setup Python
-    - name: Set up Python 3.9
+    - name: Set up Python 3.10
       uses: actions/setup-python@v2
       with:
-        python-version: 3.9
+        python-version: 3.10.10
 
     # Update conda
     - name: Update conda
diff --git a/requirements-github.txt b/requirements-github.txt
index f5f2894d..1a4e2af7 100644
--- a/requirements-github.txt
+++ b/requirements-github.txt
@@ -8,4 +8,4 @@ xarray==2022.6.0
 seaborn==0.12.2
 hvplot==0.8.2
 nbconvert==6.5.4
-bokeh==2.4.3
+bokeh==3.1.1
diff --git a/setup.py b/setup.py
index 1534508e..a4ad321f 100644
--- a/setup.py
+++ b/setup.py
@@ -16,7 +16,7 @@
 
 setuptools.setup(
     name='eva',
-    version='1.3.5',
+    version='1.4.0',
     author='Community owned code',
     description='Evaluation and Verification of an Analysis',
     url='https://github.com/JCSDA-internal/eva',
diff --git a/src/eva/data/cubed_sphere_restart.py b/src/eva/data/cubed_sphere_restart.py
index 5bc25603..14e4bbf7 100644
--- a/src/eva/data/cubed_sphere_restart.py
+++ b/src/eva/data/cubed_sphere_restart.py
@@ -54,63 +54,62 @@ class CubedSphereRestart(EvaBase):
 
     # ----------------------------------------------------------------------------------------------
 
-    def execute(self, data_collections, timing):
+    def execute(self, dataset_config, data_collections, timing):
 
-        for dataset in self.config.get('datasets'):
-            # Filenames to be read into this collection
-            # -----------------------------------------
-            fv3_filenames = get(dataset, self.logger, 'fv3_filenames')
-            orog_filenames = get(dataset, self.logger, 'orog_filenames')
+        # Filenames to be read into this collection
+        # -----------------------------------------
+        fv3_filenames = get(dataset_config, self.logger, 'fv3_filenames')
+        orog_filenames = get(dataset_config, self.logger, 'orog_filenames')
 
-            # File variable type
-            variable = get(dataset, self.logger, 'variable')
+        # File variable type
+        variable = get(dataset_config, self.logger, 'variable')
 
-            # File resolution
-            resolution = get(dataset, self.logger, 'resolution')
-            resolution = int(resolution.replace('C', ''))
+        # File resolution
+        resolution = get(dataset_config, self.logger, 'resolution')
+        resolution = int(resolution.replace('C', ''))
 
-            # Get missing value threshold
-            # ---------------------------
-            threshold = float(get(dataset, self.logger, 'missing_value_threshold', 1.0e30))
+        # Get missing value threshold
+        # ---------------------------
+        threshold = float(get(dataset_config, self.logger, 'missing_value_threshold', 1.0e30))
 
-            # Get the groups to be read
-            # -------------------------
-            groups = get(dataset, self.logger, 'groups')
+        # Get the groups to be read
+        # -------------------------
+        groups = get(dataset_config, self.logger, 'groups')
 
-            for group in groups:
+        for group in groups:
 
-                # Group name and variables
-                group_name = get(group, self.logger, 'name')
-                group_vars = get(group, self.logger, 'variables', 'all')
+            # Group name and variables
+            group_name = get(group, self.logger, 'name')
+            group_vars = get(group, self.logger, 'variables', 'all')
 
-                # Set the collection name
-                collection_name = dataset['name']
+            # Set the collection name
+            collection_name = dataset_config['name']
 
-                var_dict = {}
+            var_dict = {}
 
-                # Loop through group vars to create data dictionary
-                for var in group_vars:
-                    if var in ['geolon', 'geolat']:
-                        var_dict[group_name + '::' + var] = (["lon", "lat", "tile"],
-                                                             read_nc(orog_filenames, var,
-                                                                     resolution, self.logger))
+            # Loop through group vars to create data dictionary
+            for var in group_vars:
+                if var in ['geolon', 'geolat']:
+                    var_dict[group_name + '::' + var] = (["lon", "lat", "tile"],
+                                                         read_nc(orog_filenames, var,
+                                                                 resolution, self.logger))
 
-                    else:
-                        var_dict[group_name + '::' + var] = (["lon", "lat", "tile"],
-                                                             read_nc(fv3_filenames, var,
-                                                                     resolution, self.logger))
+                else:
+                    var_dict[group_name + '::' + var] = (["lon", "lat", "tile"],
+                                                         read_nc(fv3_filenames, var,
+                                                                 resolution, self.logger))
 
-                # Create dataset from data dictionary
-                ds = xr.Dataset(var_dict)
+            # Create dataset_config from data dictionary
+            ds = xr.Dataset(var_dict)
 
-                # Assert that the collection contains at least one variable
-                if not ds.keys():
-                    self.logger.abort('Collection \'' + dataset['name'] + '\', group \'' +
-                                      group_name + '\' in file ' + filename +
-                                      ' does not have any variables.')
+            # Assert that the collection contains at least one variable
+            if not ds.keys():
+                self.logger.abort('Collection \'' + dataset_config['name'] + '\', group \'' +
+                                  group_name + '\' in file ' + filename +
+                                  ' does not have any variables.')
 
-            # Add the dataset to the collections
-            data_collections.create_or_add_to_collection(collection_name, ds)
+        # Add the dataset_config to the collections
+        data_collections.create_or_add_to_collection(collection_name, ds)
 
         # Nan out unphysical values
         data_collections.nan_float_values_outside_threshold(threshold)
diff --git a/src/eva/data/data_driver.py b/src/eva/data/data_driver.py
new file mode 100644
index 00000000..bc5e0b2c
--- /dev/null
+++ b/src/eva/data/data_driver.py
@@ -0,0 +1,58 @@
+# (C) Copyright 2021-2023 NOAA/NWS/EMC
+#
+# (C) Copyright 2021-2023 United States Government as represented by the Administrator of the
+# National Aeronautics and Space Administration. All Rights Reserved.
+#
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
+
+
+# --------------------------------------------------------------------------------------------------
+
+
+from eva.utilities.config import get
+from eva.eva_base import EvaBase, EvaFactory
+
+import importlib
+import os
+
+
+# --------------------------------------------------------------------------------------------------
+
+
+class DataDriver(EvaBase):
+
+    def execute(self, data_collections, timing):
+
+        # Get list of dataset dictionaries
+        datasets = get(self.config['data'], self.logger, 'datasets')
+
+        # Loop over datasets
+        for dataset in datasets:
+
+            # Extract name for this diagnostic data type
+            try:
+                eva_data_class_name = dataset['type']
+            except Exception as e:
+                msg = '\'type\' key not found. \'diagnostic_data_config\': ' \
+                    f'{diagnostic_data_config}, error: {e}'
+                raise KeyError(msg)
+
+            # Create the data object
+            creator = EvaFactory()
+            timing.start('DataObjectConstructor')
+            eva_data_object = creator.create_eva_object(eva_data_class_name,
+                                                        'data',
+                                                        dataset,
+                                                        self.logger,
+                                                        timing)
+            timing.stop('DataObjectConstructor')
+
+            # Prepare diagnostic data
+            self.logger.info(f'Running execute for {eva_data_object.name}')
+            timing.start('DataObjectExecute')
+            eva_data_object.execute(dataset, data_collections, timing)
+            timing.stop('DataObjectExecute')
+
+# --------------------------------------------------------------------------------------------------
diff --git a/src/eva/data/eva_interactive.py b/src/eva/data/eva_interactive.py
index 82cb772c..02bc27c0 100644
--- a/src/eva/data/eva_interactive.py
+++ b/src/eva/data/eva_interactive.py
@@ -33,17 +33,17 @@ def __init__(self):
 
     def load_ioda(self, filename):
         self.filename = filename
-        eva_dict = {'datasets': [{'filenames': [filename],
-                                  'groups': [],
-                                  'missing_value_threshold': 1.0e06,
-                                  'name': self.collection}]}
+        eva_dict = {'filenames': [filename],
+                    'groups': [],
+                    'missing_value_threshold': 1.0e06,
+                    'name': self.collection}
         creator = EvaFactory()
         eva_data_object = creator.create_eva_object('IodaObsSpace',
                                                     'data',
                                                     eva_dict,
                                                     self.logger,
                                                     self.timer)
-        eva_data_object.execute(self.data_collection, self.timer)
+        eva_data_object.execute(eva_dict, self.data_collection, self.timer)
 
     def scatter(self, x, y):
         x_group, x_var = x.split('::')
diff --git a/src/eva/data/gsi_obs_space.py b/src/eva/data/gsi_obs_space.py
index dd77bb4e..c70ac91c 100644
--- a/src/eva/data/gsi_obs_space.py
+++ b/src/eva/data/gsi_obs_space.py
@@ -88,7 +88,7 @@ def subset_channels(ds, channels, logger, add_channels_variable=False):
 
 def satellite_dataset(ds):
     """
-    Builds a new dataset to reshape satellite
+    Builds a new dataset_config to reshape satellite
     data.
     """
     nchans = ds.dims['nchans']
@@ -134,7 +134,7 @@ def satellite_dataset(ds):
                 data = np.reshape(ds[var].data, (iters, nchans))
                 data_vars[var] = (('nobs', 'nchans'), data)
 
-    # create dataset
+    # create dataset_config
     new_ds = Dataset(data_vars=data_vars,
                      coords=coords,
                      attrs=ds.attrs)
@@ -149,102 +149,98 @@ class GsiObsSpace(EvaBase):
 
     # ----------------------------------------------------------------------------------------------
 
-    def execute(self, data_collections, timeing):
+    def execute(self, dataset_config, data_collections, timeing):
 
-        # Loop over the datasets
-        # ----------------------
-        for dataset in self.config.get('datasets'):
+        # Get channels for radiances
+        # --------------------------
+        channels_str_or_list = get(dataset_config, self.logger, 'channels', [])
 
-            # Get channels for radiances
-            # --------------------------
-            channels_str_or_list = get(dataset, self.logger, 'channels', [])
+        # Convert channels to list
+        channels = []
+        if channels_str_or_list is not []:
+            channels = parse_channel_list(channels_str_or_list, self.logger)
 
-            # Convert channels to list
-            channels = []
-            if channels_str_or_list is not []:
-                channels = parse_channel_list(channels_str_or_list, self.logger)
+        # Filenames to be read into this collection
+        # -----------------------------------------
+        filenames = get(dataset_config, self.logger, 'filenames')
 
-            # Filenames to be read into this collection
-            # -----------------------------------------
-            filenames = get(dataset, self.logger, 'filenames')
-
-            # File variable type
-            if 'satellite' in dataset:
-                satellite = get(dataset, self.logger, 'satellite')
-                sensor = get(dataset, self.logger, 'sensor')
-            else:
-                variable = get(dataset, self.logger, 'variable')
-
-            # Get missing value threshold
-            # ---------------------------
-            threshold = float(get(dataset, self.logger, 'missing_value_threshold', 1.0e30))
-
-            # Get the groups to be read
-            # -------------------------
-            groups = get(dataset, self.logger, 'groups')
-
-            # Loop over filenames
-            # -------------------
-            for filename in filenames:
-
-                # Loop over groups
-                for group in groups:
-
-                    # Group name and variables
-                    group_name = get(group, self.logger, 'name')
-                    group_vars = get(group, self.logger, 'variables', 'all')
-
-                    # Set the collection name
-                    collection_name = dataset['name']
-
-                    ds = open_dataset(filename, mask_and_scale=False,
-                                      decode_times=False)
-
-                    # If user specifies all variables set to group list
-                    if group_vars == 'all':
-                        group_vars = list(ds.data_vars)
-
-                    # Reshape variables if satellite diag
-                    if 'nchans' in ds.dims:
-                        ds = satellite_dataset(ds)
-                        ds = subset_channels(ds, channels, self.logger)
-
-                    # Adjust variable names if uv
-                    if 'variable' in locals():
-                        if variable == 'uv':
-                            group_vars = uv(group_vars)
-
-                    # Check that all user variables are in the dataset
-                    if not all(v in list(ds.data_vars) for v in group_vars):
-                        self.logger.abort('For collection \'' + dataset['name'] + '\', group \'' +
-                                          group_name + '\' in file ' + filename +
-                                          f' . Variables {group_vars} not all present in ' +
-                                          f'the data set variables: {list(ds.keys())}')
-
-                    # Drop data variables not in user requested variables
-                    vars_to_remove = list(set(list(ds.keys())) - set(group_vars))
-                    ds = ds.drop_vars(vars_to_remove)
-
-                    # Explicitly add the channels to the collection (we do not want to include this
-                    # in the 'variables' list in the YAML to avoid transforms being applied to them)
-                    if 'nchans' in ds.dims:
-                        channels_used = ds['nchans']
-                        ds[group_name + '::channelNumber'] = channels_used
-
-                    # Rename variables with group
-                    rename_dict = {}
-                    for group_var in group_vars:
-                        rename_dict[group_var] = group_name + '::' + group_var
-                    ds = ds.rename(rename_dict)
-
-                    # Assert that the collection contains at least one variable
-                    if not ds.keys():
-                        self.logger.abort('Collection \'' + dataset['name'] + '\', group \'' +
-                                          group_name + '\' in file ' + filename +
-                                          ' does not have any variables.')
-
-                # Add the dataset to the collections
-                data_collections.create_or_add_to_collection(collection_name, ds, 'nobs')
+        # File variable type
+        if 'satellite' in dataset_config:
+            satellite = get(dataset_config, self.logger, 'satellite')
+            sensor = get(dataset_config, self.logger, 'sensor')
+        else:
+            variable = get(dataset_config, self.logger, 'variable')
+
+        # Get missing value threshold
+        # ---------------------------
+        threshold = float(get(dataset_config, self.logger, 'missing_value_threshold', 1.0e30))
+
+        # Get the groups to be read
+        # -------------------------
+        groups = get(dataset_config, self.logger, 'groups')
+
+        # Loop over filenames
+        # -------------------
+        for filename in filenames:
+
+            # Loop over groups
+            for group in groups:
+
+                # Group name and variables
+                group_name = get(group, self.logger, 'name')
+                group_vars = get(group, self.logger, 'variables', 'all')
+
+                # Set the collection name
+                collection_name = dataset_config['name']
+
+                ds = open_dataset(filename, mask_and_scale=False,
+                                  decode_times=False)
+
+                # If user specifies all variables set to group list
+                if group_vars == 'all':
+                    group_vars = list(ds.data_vars)
+
+                # Reshape variables if satellite diag
+                if 'nchans' in ds.dims:
+                    ds = satellite_dataset(ds)
+                    ds = subset_channels(ds, channels, self.logger)
+
+                # Adjust variable names if uv
+                if 'variable' in locals():
+                    if variable == 'uv':
+                        group_vars = uv(group_vars)
+
+                # Check that all user variables are in the dataset_config
+                if not all(v in list(ds.data_vars) for v in group_vars):
+                    self.logger.abort('For collection \'' + dataset_config['name']
+                                      + '\', group \'' + group_name + '\' in file ' + filename +
+                                      f' . Variables {group_vars} not all present in ' +
+                                      f'the data set variables: {list(ds.keys())}')
+
+                # Drop data variables not in user requested variables
+                vars_to_remove = list(set(list(ds.keys())) - set(group_vars))
+                ds = ds.drop_vars(vars_to_remove)
+
+                # Explicitly add the channels to the collection (we do not want to include this
+                # in the 'variables' list in the YAML to avoid transforms being applied to them)
+                if 'nchans' in ds.dims:
+                    channels_used = ds['nchans']
+                    ds[group_name + '::channelNumber'] = channels_used
+
+                # Rename variables with group
+                rename_dict = {}
+                for group_var in group_vars:
+                    rename_dict[group_var] = group_name + '::' + group_var
+                ds = ds.rename(rename_dict)
+
+                # Assert that the collection contains at least one variable
+                if not ds.keys():
+                    self.logger.abort('Collection \'' + dataset_config['name'] + '\', group \'' +
+                                      group_name + '\' in file ' + filename +
+                                      ' does not have any variables.')
+
+            # Add the dataset_config to the collections
+            data_collections.create_or_add_to_collection(collection_name, ds, 'nobs')
 
         # Nan out unphysical values
         data_collections.nan_float_values_outside_threshold(threshold)
diff --git a/src/eva/data/ioda_obs_space.py b/src/eva/data/ioda_obs_space.py
index c253dc34..e59f4cf2 100644
--- a/src/eva/data/ioda_obs_space.py
+++ b/src/eva/data/ioda_obs_space.py
@@ -48,144 +48,141 @@ class IodaObsSpace(EvaBase):
 
     # ----------------------------------------------------------------------------------------------
 
-    def execute(self, data_collections, timing):
-
-        # Loop over the datasets
-        # ----------------------
-        for dataset in self.config.get('datasets'):
-
-            # Get channels for radiances
-            # --------------------------
-            channels_str_or_list = get(dataset, self.logger, 'channels', [])
-
-            # Convert channels to list
-            channels = []
-            if channels_str_or_list is not []:
-                channels = parse_channel_list(channels_str_or_list, self.logger)
-
-            # Filenames to be read into this collection
-            # -----------------------------------------
-            filenames = get(dataset, self.logger, 'filenames')
-
-            # Get missing value threshold
-            # ---------------------------
-            threshold = float(get(dataset, self.logger, 'missing_value_threshold', 1.0e30))
-
-            # Get the groups to be read
-            # -------------------------
-            groups = get(dataset, self.logger, 'groups')
-
-            # Loop over filenames
-            # -------------------
-            total_loc = 0
-            for filename in filenames:
-                # Assert that file exists
-                if not os.path.exists(filename):
-                    logger.abort(f'In IodaObsSpace file \'{filename}\' does not exist')
-
-                # Get file header
-                ds_header = open_dataset(filename)
-
-                # Fix location in case ioda did not set it
-                locations_this_file = range(total_loc, total_loc + ds_header['Location'].size)
-                ds_header = ds_header.assign_coords({"Location": locations_this_file})
-                total_loc = total_loc + ds_header['Location'].size
-
-                if 'Cluster' in ds_header.keys():
-                    clusters_this_file = range(0, ds_header['Cluster'].size)
-                    ds_header = ds_header.assign_coords({"Cluster": clusters_this_file})
-
-                # Read header part of the file to get coordinates
-                ds_groups = Dataset()
-
-                # Save sensor_channels for later
-                add_channels = False
-                if 'Channel' in ds_header.keys():
-                    sensor_channels = ds_header['Channel']
-                    add_channels = True
-
-                # Merge in the header and close
-                ds_groups = ds_groups.merge(ds_header)
-                ds_header.close()
-
-                # Set the channels based on user selection and add channels variable
-                ds_groups = subset_channels(ds_groups, channels, True)
-
-                # If groups is empty, read in file to retrieve group list
-                groups_present = True
-                if not groups:
-                    groups_present = False
-                    nc_ds = nc.Dataset(filename)
-                    groups = list(nc_ds.groups.keys())
-                    nc_ds.close()
-
-                # Loop over groups
-                for group in groups:
-
-                    # Group name and variables
-                    if groups_present:
-                        group_name = get(group, self.logger, 'name')
-                        group_vars = get(group, self.logger, 'variables', 'all')
-                    else:
-                        group_name = group
-                        group_vars = 'all'
-
-                    # Set the collection name
-                    collection_name = dataset['name']
-
-                    # Read the group
-                    timing.start(f'IodaObsSpace: open_dataset {os.path.basename(filename)}')
-                    ds = open_dataset(filename, group=group_name, mask_and_scale=False,
-                                      decode_times=False)
-                    timing.stop(f'IodaObsSpace: open_dataset {os.path.basename(filename)}')
-
-                    # If user specifies all variables set to group list
-                    if group_vars == 'all':
-                        group_vars = list(ds.data_vars)
-
-                    # Check that all user variables are in the dataset
-                    if not all(v in list(ds.data_vars) for v in group_vars):
-                        self.logger.abort('For collection \'' + dataset['name'] + '\', group \'' +
-                                          group_name + '\' in file ' + filename +
-                                          f' . Variables {group_vars} not all present in ' +
-                                          f'the data set variables: {list(ds.keys())}')
-
-                    # Drop data variables not in user requested variables
-                    vars_to_remove = list(set(list(ds.keys())) - set(group_vars))
-                    ds = ds.drop_vars(vars_to_remove)
-
-                    # Rename variables with group
-                    rename_dict = {}
-                    for group_var in group_vars:
-                        rename_dict[group_var] = group_name + '::' + group_var
-                    ds = ds.rename(rename_dict)
-
-                    # Reset channel numbers from header and copy channel numbers
-                    # into MetaData for easier use
-                    if add_channels:
-                        ds['Channel'] = sensor_channels
-                        # Explicitly add the channels to the collection (we do not want to
-                        # include this in the 'variables' list in the YAML to avoid transforms
-                        # being applied to them)
-                        ds['MetaData::channelNumber'] = sensor_channels
-
-                    # Set channels
-                    ds = subset_channels(ds, channels)
-
-                    # Assert that the collection contains at least one variable
-                    if not ds.keys():
-                        self.logger.abort('Collection \'' + dataset['name'] + '\', group \'' +
-                                          group_name + '\' in file ' + filename +
-                                          ' does not have any variables.')
-
-                    # Merge with other groups
-                    ds_groups = ds_groups.merge(ds)
-
-                    # Close dataset
-                    ds.close()
-
-                # Add the dataset to the collections
-                data_collections.create_or_add_to_collection(collection_name, ds_groups, 'Location')
+    def execute(self, dataset_config, data_collections, timing):
+
+        # Get channels for radiances
+        # --------------------------
+        channels_str_or_list = get(dataset_config, self.logger, 'channels', [])
+
+        # Convert channels to list
+        channels = []
+        if channels_str_or_list is not []:
+            channels = parse_channel_list(channels_str_or_list, self.logger)
+
+        # Filenames to be read into this collection
+        # -----------------------------------------
+        filenames = get(dataset_config, self.logger, 'filenames')
+
+        # Get missing value threshold
+        # ---------------------------
+        threshold = float(get(dataset_config, self.logger, 'missing_value_threshold', 1.0e30))
+
+        # Get the groups to be read
+        # -------------------------
+        groups = get(dataset_config, self.logger, 'groups')
+
+        # Loop over filenames
+        # -------------------
+        total_loc = 0
+        for filename in filenames:
+            # Assert that file exists
+            if not os.path.exists(filename):
+                logger.abort(f'In IodaObsSpace file \'{filename}\' does not exist')
+
+            # Get file header
+            ds_header = open_dataset(filename)
+
+            # Fix location in case ioda did not set it
+            locations_this_file = range(total_loc, total_loc + ds_header['Location'].size)
+            ds_header = ds_header.assign_coords({"Location": locations_this_file})
+            total_loc = total_loc + ds_header['Location'].size
+
+            if 'Cluster' in ds_header.keys():
+                clusters_this_file = range(0, ds_header['Cluster'].size)
+                ds_header = ds_header.assign_coords({"Cluster": clusters_this_file})
+
+            # Read header part of the file to get coordinates
+            ds_groups = Dataset()
+
+            # Save sensor_channels for later
+            add_channels = False
+            if 'Channel' in ds_header.keys():
+                sensor_channels = ds_header['Channel']
+                add_channels = True
+
+            # Merge in the header and close
+            ds_groups = ds_groups.merge(ds_header)
+            ds_header.close()
+
+            # Set the channels based on user selection and add channels variable
+            ds_groups = subset_channels(ds_groups, channels, True)
+
+            # If groups is empty, read in file to retrieve group list
+            groups_present = True
+            if not groups:
+                groups_present = False
+                nc_ds = nc.Dataset(filename)
+                groups = list(nc_ds.groups.keys())
+                nc_ds.close()
+
+            # Loop over groups
+            for group in groups:
+
+                # Group name and variables
+                if groups_present:
+                    group_name = get(group, self.logger, 'name')
+                    group_vars = get(group, self.logger, 'variables', 'all')
+                else:
+                    group_name = group
+                    group_vars = 'all'
+
+                # Set the collection name
+                collection_name = dataset_config['name']
+
+                # Read the group
+                timing.start(f'IodaObsSpace: open_dataset {os.path.basename(filename)}')
+                ds = open_dataset(filename, group=group_name, mask_and_scale=False,
+                                  decode_times=False)
+                timing.stop(f'IodaObsSpace: open_dataset {os.path.basename(filename)}')
+
+                # If user specifies all variables set to group list
+                if group_vars == 'all':
+                    group_vars = list(ds.data_vars)
+
+                # Check that all user variables are in the dataset_config
+                if not all(v in list(ds.data_vars) for v in group_vars):
+                    self.logger.abort('For collection \'' + dataset_config['name'] +
+                                      '\', group \'' +
+                                      group_name + '\' in file ' + filename +
+                                      f' . Variables {group_vars} not all present in ' +
+                                      f'the data set variables: {list(ds.keys())}')
+
+                # Drop data variables not in user requested variables
+                vars_to_remove = list(set(list(ds.keys())) - set(group_vars))
+                ds = ds.drop_vars(vars_to_remove)
+
+                # Rename variables with group
+                rename_dict = {}
+                for group_var in group_vars:
+                    rename_dict[group_var] = group_name + '::' + group_var
+                ds = ds.rename(rename_dict)
+
+                # Reset channel numbers from header and copy channel numbers
+                # into MetaData for easier use
+                if add_channels:
+                    ds['Channel'] = sensor_channels
+                    # Explicitly add the channels to the collection (we do not want to
+                    # include this in the 'variables' list in the YAML to avoid transforms
+                    # being applied to them)
+                    ds['MetaData::channelNumber'] = sensor_channels
+
+                # Set channels
+                ds = subset_channels(ds, channels)
+
+                # Assert that the collection contains at least one variable
+                if not ds.keys():
+                    self.logger.abort('Collection \'' + dataset_config['name'] + '\', group \'' +
+                                      group_name + '\' in file ' + filename +
+                                      ' does not have any variables.')
+
+                # Merge with other groups
+                ds_groups = ds_groups.merge(ds)
+
+                # Close dataset_config
+                ds.close()
+
+            # Add the dataset_config to the collections
+            data_collections.create_or_add_to_collection(collection_name, ds_groups, 'Location')
 
         # Nan out unphysical values
         data_collections.nan_float_values_outside_threshold(threshold)
diff --git a/src/eva/data/jedi_log.py b/src/eva/data/jedi_log.py
index 46604e6a..a8fa25fe 100644
--- a/src/eva/data/jedi_log.py
+++ b/src/eva/data/jedi_log.py
@@ -40,13 +40,13 @@ class JediLog(EvaBase):
 
     # ----------------------------------------------------------------------------------------------
 
-    def execute(self, data_collections, timing):
+    def execute(self, dataset_config, data_collections, timing):
 
         # Get name of the log file to parse
-        jedi_log_to_parse = self.config.get('jedi_log_to_parse')
+        jedi_log_to_parse = dataset_config.get('jedi_log_to_parse')
 
         # Collection name to use
-        collection_name = self.config.get('collection_name')
+        collection_name = dataset_config.get('collection_name')
 
         # Read log file into a string
         with open(jedi_log_to_parse) as jedi_log_to_parse_open:
@@ -86,7 +86,7 @@ def execute(self, data_collections, timing):
             self.log_chunks.append('\n'.join(chunk))
 
         # Get list of things to parse from the dictionary
-        data_to_parse = self.config.get('data_to_parse')
+        data_to_parse = dataset_config.get('data_to_parse')
 
         # Loop and add to dataset
         for metric in data_to_parse:
diff --git a/src/eva/data/lat_lon.py b/src/eva/data/lat_lon.py
index 1474da15..f14aa6fb 100644
--- a/src/eva/data/lat_lon.py
+++ b/src/eva/data/lat_lon.py
@@ -10,51 +10,47 @@ class LatLon(EvaBase):
 
     # ----------------------------------------------------------------------------------------------
 
-    def execute(self, data_collections):
-
-        # Loop over the datasets
-        # ----------------------
-        for dataset in self.config.get('datasets'):
-
-            # Filename to be read into this collection
-            filename = get(dataset, self.logger, 'filename')
-            # get list of variables
-            variables = get(dataset, self.logger, 'variables')
-            # Set the collection name
-            collection_name = dataset['name']
-            # get 'group' name
-            group = get(dataset, self.logger, 'group')
-
-            if group not in valid_groups:
-                self.logger.abort('For collection \'' + dataset['name'] + '\'' +
-                                  f' group \'{group}\' is not a valid group type for LatLon.' +
-                                  f' The valid types are {valid_groups}')
-
-            # open the input netCDF file
-            ds = xr.open_dataset(filename)
-
-            # Drop data variables not in user requested variables
-            vars_to_remove = list(set(list(ds.keys())) - set(variables))
-            ds = ds.drop_vars(vars_to_remove)
-
-            # for lat and lon, need to do a meshgrid so that each point has a lat and a lon
-
-            # rename variables in dataset
-            rename_dict = {}
-            for v in variables:
-                rename_dict[v] = f'{group}::{v}'
-            ds = ds.rename(rename_dict)
-
-            # Assert that the collection contains at least one variable
-            if not ds.keys():
-                self.logger.abort('Collection \'' + dataset['name'] + '\', group \'' +
-                                  group + '\' in file ' + filename +
-                                  ' does not have any variables.')
-
-            # add the dataset to the collections
-            data_collections.create_or_add_to_collection(collection_name, ds)
-
-            ds.close()
+    def execute(self, dataset_config, data_collections):
+
+        # Filename to be read into this collection
+        filename = get(dataset_config, self.logger, 'filename')
+        # get list of variables
+        variables = get(dataset_config, self.logger, 'variables')
+        # Set the collection name
+        collection_name = dataset_config['name']
+        # get 'group' name
+        group = get(dataset_config, self.logger, 'group')
+
+        if group not in valid_groups:
+            self.logger.abort('For collection \'' + dataset_config['name'] + '\'' +
+                              f' group \'{group}\' is not a valid group type for LatLon.' +
+                              f' The valid types are {valid_groups}')
+
+        # open the input netCDF file
+        ds = xr.open_dataset(filename)
+
+        # Drop data variables not in user requested variables
+        vars_to_remove = list(set(list(ds.keys())) - set(variables))
+        ds = ds.drop_vars(vars_to_remove)
+
+        # for lat and lon, need to do a meshgrid so that each point has a lat and a lon
+
+        # rename variables in dataset_config
+        rename_dict = {}
+        for v in variables:
+            rename_dict[v] = f'{group}::{v}'
+        ds = ds.rename(rename_dict)
+
+        # Assert that the collection contains at least one variable
+        if not ds.keys():
+            self.logger.abort('Collection \'' + dataset_config['name'] + '\', group \'' +
+                              group + '\' in file ' + filename +
+                              ' does not have any variables.')
+
+        # add the dataset_config to the collections
+        data_collections.create_or_add_to_collection(collection_name, ds)
+
+        ds.close()
 
         # Display the contents of the collections for helping the user with making plots
         data_collections.display_collections()
diff --git a/src/eva/data/mon_data_space.py b/src/eva/data/mon_data_space.py
index bb69f983..a293f1df 100644
--- a/src/eva/data/mon_data_space.py
+++ b/src/eva/data/mon_data_space.py
@@ -28,129 +28,125 @@ class MonDataSpace(EvaBase):
 
     # ----------------------------------------------------------------------------------------------
 
-    def execute(self, data_collections, timing):
-
-        # Loop over the datasets
-        # ----------------------
-        for dataset in self.config.get('datasets'):
-
-            # Set the collection name
-            # -----------------------
-            collection_name = get(dataset, self.logger, 'name')
-
-            # Get control file and parse
-            # --------------------------
-            control_file = get(dataset, self.logger, 'control_file')
-            coords, dims, attribs, nvars, vars, channo, scanpo = self.get_ctl_dict(control_file[0])
-            ndims_used, dims_arr = self.get_ndims_used(dims)
-
-            # Get the groups to be read
-            # -------------------------
-            groups = get(dataset, self.logger, 'groups')
-
-            # Trim coordinates
-            # ----------------
-            coord_dict = {
-                0: ['channels', 'Channel'],
-                1: ['regions', 'Region'],
-                2: ['levels', 'Level']
-            }
-            drop_coord = [False, False, False]
-            requested_coord = [None, None, None]
+    def execute(self, dataset_config, data_collections, timing):
 
+        # Set the collection name
+        # -----------------------
+        collection_name = get(dataset_config, self.logger, 'name')
+
+        # Get control file and parse
+        # --------------------------
+        control_file = get(dataset_config, self.logger, 'control_file')
+        coords, dims, attribs, nvars, vars, channo, scanpo = self.get_ctl_dict(control_file[0])
+        ndims_used, dims_arr = self.get_ndims_used(dims)
+
+        # Get the groups to be read
+        # -------------------------
+        groups = get(dataset_config, self.logger, 'groups')
+
+        # Trim coordinates
+        # ----------------
+        coord_dict = {
+            0: ['channels', 'Channel'],
+            1: ['regions', 'Region'],
+            2: ['levels', 'Level']
+        }
+        drop_coord = [False, False, False]
+        requested_coord = [None, None, None]
+
+        for x in range(len(coord_dict)):
+            str_or_list = get(dataset_config, self.logger, coord_dict[x][0], abort_on_failure=False)
+            if str_or_list is not None:
+                requested_coord[x] = parse_channel_list(str(str_or_list), self.logger)
+                drop_coord[x] = True
+
+        # Set coordinate ranges
+        # ---------------------
+        x_range, y_range, z_range = self.get_dim_ranges(coords, dims, channo)
+
+        # Filenames to be read into this collection
+        # -----------------------------------------
+        filenames = get(dataset_config, self.logger, 'filenames')
+        ds_list = []
+
+        # Get missing value threshold
+        # ---------------------------
+        threshold = float(get(dataset_config, self.logger, 'missing_value_threshold', 1.0e30))
+
+        for filename in filenames:
+
+            # read data file
+            darr, cycle_tm = self.read_ieee(filename, coords, dims, ndims_used,
+                                            dims_arr, nvars, vars)
+
+            # add cycle as a variable to data array
+            cyc_darr = self.var_to_np_array(dims, ndims_used, dims_arr, cycle_tm)
+
+            # create dataset from file contents
+            timestep_ds = None
+
+            timestep_ds = self.load_dset(vars, nvars, coords, darr, dims, ndims_used,
+                                         dims_arr, x_range, y_range, z_range, cyc_darr)
+
+            if attribs['sat']:
+                timestep_ds.attrs['satellite'] = attribs['sat']
+            if attribs['sensor']:
+                timestep_ds.attrs['sensor'] = attribs['sensor']
+
+            # add cycle_tm dim for concat
+            timestep_ds['Time'] = cycle_tm.strftime("%Y%m%d%H")
+
+            # Add this dataset to the list of ds_list
+            ds_list.append(timestep_ds)
+
+        # Concatenate datasets from ds_list into a single dataset
+        ds = concat(ds_list, dim='Time')
+
+        # Group name and variables
+        # ------------------------
+        for group in groups:
+            group_name = get(group, self.logger, 'name')
+            group_vars = get(group, self.logger, 'variables', 'all')
+
+            # Drop coordinates not in requested list
+            # --------------------------------------
             for x in range(len(coord_dict)):
-                str_or_list = get(dataset, self.logger, coord_dict[x][0], abort_on_failure=False)
-                if str_or_list is not None:
-                    requested_coord[x] = parse_channel_list(str(str_or_list), self.logger)
-                    drop_coord[x] = True
-
-            # Set coordinate ranges
-            # ---------------------
-            x_range, y_range, z_range = self.get_dim_ranges(coords, dims, channo)
-
-            # Filenames to be read into this collection
-            # -----------------------------------------
-            filenames = get(dataset, self.logger, 'filenames')
-            ds_list = []
-
-            # Get missing value threshold
-            # ---------------------------
-            threshold = float(get(dataset, self.logger, 'missing_value_threshold', 1.0e30))
-
-            for filename in filenames:
-
-                # read data file
-                darr, cycle_tm = self.read_ieee(filename, coords, dims, ndims_used,
-                                                dims_arr, nvars, vars)
-
-                # add cycle as a variable to data array
-                cyc_darr = self.var_to_np_array(dims, ndims_used, dims_arr, cycle_tm)
-
-                # create dataset from file contents
-                timestep_ds = None
-
-                timestep_ds = self.load_dset(vars, nvars, coords, darr, dims, ndims_used,
-                                             dims_arr, x_range, y_range, z_range, cyc_darr)
-
-                if attribs['sat']:
-                    timestep_ds.attrs['satellite'] = attribs['sat']
-                if attribs['sensor']:
-                    timestep_ds.attrs['sensor'] = attribs['sensor']
-
-                # add cycle_tm dim for concat
-                timestep_ds['Time'] = cycle_tm.strftime("%Y%m%d%H")
-
-                # Add this dataset to the list of ds_list
-                ds_list.append(timestep_ds)
-
-            # Concatenate datasets from ds_list into a single dataset
-            ds = concat(ds_list, dim='Time')
-
-            # Group name and variables
-            # ------------------------
-            for group in groups:
-                group_name = get(group, self.logger, 'name')
-                group_vars = get(group, self.logger, 'variables', 'all')
-
-                # Drop coordinates not in requested list
-                # --------------------------------------
-                for x in range(len(coord_dict)):
-                    if drop_coord[x]:
-                        ds = self.subset_coordinate(ds, coord_dict[x][1], requested_coord[x])
-
-                # If user specifies all variables set to group list
-                # -------------------------------------------------
-                if group_vars == 'all':
-                    group_vars = list(ds.data_vars)
-
-                # Drop data variables not in user requested variables
-                # ---------------------------------------------------
-                vars_to_remove = list(set(list(ds.keys())) - set(group_vars))
-                ds = ds.drop_vars(vars_to_remove)
-
-                # Conditionally add channel as a variable using single dimension
-                if 'channel' in group_vars:
-                    ds['channel'] = (['Channel'], channo)
-
-                # Conditionally add scan position as a variable using single dimension
-                if 'scan' in group_vars:
-                    ds['scan'] = (['scan'], scanpo)
-
-                # Rename variables with group
-                rename_dict = {}
-                for group_var in group_vars:
-                    rename_dict[group_var] = group_name + '::' + group_var
-
-                ds = ds.rename(rename_dict)
-
-                # Assert that the collection contains at least one variable
-                if not ds.keys():
-                    self.logger.abort('Collection \'' + dataset['name'] + '\', group \'' +
-                                      group_name + '\' in file ' + filename +
-                                      ' does not have any variables.')
-
-            # Add the dataset to the collections
-            data_collections.create_or_add_to_collection(collection_name, ds, 'cycle')
+                if drop_coord[x]:
+                    ds = self.subset_coordinate(ds, coord_dict[x][1], requested_coord[x])
+
+            # If user specifies all variables set to group list
+            # -------------------------------------------------
+            if group_vars == 'all':
+                group_vars = list(ds.data_vars)
+
+            # Drop data variables not in user requested variables
+            # ---------------------------------------------------
+            vars_to_remove = list(set(list(ds.keys())) - set(group_vars))
+            ds = ds.drop_vars(vars_to_remove)
+
+            # Conditionally add channel as a variable using single dimension
+            if 'channel' in group_vars:
+                ds['channel'] = (['Channel'], channo)
+
+            # Conditionally add scan position as a variable using single dimension
+            if 'scan' in group_vars:
+                ds['scan'] = (['scan'], scanpo)
+
+            # Rename variables with group
+            rename_dict = {}
+            for group_var in group_vars:
+                rename_dict[group_var] = group_name + '::' + group_var
+
+            ds = ds.rename(rename_dict)
+
+            # Assert that the collection contains at least one variable
+            if not ds.keys():
+                self.logger.abort('Collection \'' + dataset_config['name'] + '\', group \'' +
+                                  group_name + '\' in file ' + filename +
+                                  ' does not have any variables.')
+
+        # Add the dataset to the collections
+        data_collections.create_or_add_to_collection(collection_name, ds, 'cycle')
 
         # Nan out unphysical values
         data_collections.nan_float_values_outside_threshold(threshold)
diff --git a/src/eva/eva_base.py b/src/eva/eva_base.py
index 99d82ebe..92756ff4 100644
--- a/src/eva/eva_base.py
+++ b/src/eva/eva_base.py
@@ -145,33 +145,25 @@ def eva(eva_config, eva_logger=None):
             msg = "diagnostic config must contain 'data' and 'graphics'"
             raise KeyError(msg)
 
-        # Extract name for this diagnostic data type
-        try:
-            eva_data_class_name = diagnostic_config['data']['type']
-        except Exception as e:
-            msg = '\'type\' key not found. \'diagnostic_data_config\': ' \
-                  f'{diagnostic_data_config}, error: {e}'
-            raise KeyError(msg)
-
         # Create the data collections
         # ---------------------------
         data_collections = DataCollections()
 
         # Create the data object
         creator = EvaFactory()
-        timing.start('DataObjectConstructor')
-        eva_data_object = creator.create_eva_object(eva_data_class_name,
+        timing.start('DataConstructor')
+        eva_data_object = creator.create_eva_object('DataDriver',
                                                     'data',
-                                                    diagnostic_config['data'],
+                                                    diagnostic_config,
                                                     eva_logger,
                                                     timing)
-        timing.stop('DataObjectConstructor')
+        timing.stop('DataConstructor')
 
         # Prepare diagnostic data
         logger.info(f'Running execute for {eva_data_object.name}')
-        timing.start('DataObjectExecute')
+        timing.start('DataExecute')
         eva_data_object.execute(data_collections, timing)
-        timing.stop('DataObjectExecute')
+        timing.stop('DataExecute')
 
         # Create the transforms
         if 'transforms' in diagnostic_config:
diff --git a/src/eva/tests/config/testCubedSphereRestart.yaml b/src/eva/tests/config/testCubedSphereRestart.yaml
index e8db39fb..4ecf782d 100644
--- a/src/eva/tests/config/testCubedSphereRestart.yaml
+++ b/src/eva/tests/config/testCubedSphereRestart.yaml
@@ -3,9 +3,9 @@ diagnostics:
   # Data read
   # ---------
 - data:
-    type: CubedSphereRestart
     datasets:
       - name: experiment
+        type: CubedSphereRestart
         variable: T
         resolution: C48
         fv3_filenames:
diff --git a/src/eva/tests/config/testGsiObsSpaceAmsuaMetop-A.yaml b/src/eva/tests/config/testGsiObsSpaceAmsuaMetop-A.yaml
index 241a9d46..2e3f6eab 100644
--- a/src/eva/tests/config/testGsiObsSpaceAmsuaMetop-A.yaml
+++ b/src/eva/tests/config/testGsiObsSpaceAmsuaMetop-A.yaml
@@ -3,9 +3,9 @@ diagnostics:
   # Data read
   # ---------
 - data:
-    type: GsiObsSpace
     datasets:
       - name: experiment
+        type: GsiObsSpace
         satellite: metop-a
         sensor: amsua
         filenames:
diff --git a/src/eva/tests/config/testGsiObsSpaceConvT.yaml b/src/eva/tests/config/testGsiObsSpaceConvT.yaml
index 9c0c047a..ae13bae8 100644
--- a/src/eva/tests/config/testGsiObsSpaceConvT.yaml
+++ b/src/eva/tests/config/testGsiObsSpaceConvT.yaml
@@ -3,9 +3,9 @@ diagnostics:
   # Data read
   # ---------
 - data:
-    type: GsiObsSpace
     datasets:
       - name: experiment
+        type: GsiObsSpace
         variable: t
         filenames:
           - ${data_input_path}/gsi_obs_space.conv_t_ges.2020092000.nc4
diff --git a/src/eva/tests/config/testIodaObsSpaceAircraft.yaml b/src/eva/tests/config/testIodaObsSpaceAircraft.yaml
index ca3ef703..28f6299b 100644
--- a/src/eva/tests/config/testIodaObsSpaceAircraft.yaml
+++ b/src/eva/tests/config/testIodaObsSpaceAircraft.yaml
@@ -3,9 +3,9 @@ diagnostics:
   # Data read
   # ---------
 - data:
-    type: IodaObsSpace
     datasets:
       - name: experiment
+        type: IodaObsSpace
         filenames:
           - ${data_input_path}/ioda_obs_space.aircraft.hofx.2020-12-14T210000Z.nc4
         groups:
diff --git a/src/eva/tests/config/testIodaObsSpaceAmsuaN19.yaml b/src/eva/tests/config/testIodaObsSpaceAmsuaN19.yaml
index a724f4b9..757662e5 100644
--- a/src/eva/tests/config/testIodaObsSpaceAmsuaN19.yaml
+++ b/src/eva/tests/config/testIodaObsSpaceAmsuaN19.yaml
@@ -3,9 +3,9 @@ diagnostics:
   # Data read
   # ---------
 - data:
-    type: IodaObsSpace
     datasets:
       - name: experiment
+        type: IodaObsSpace
         filenames:
           - ${data_input_path}/ioda_obs_space.amsua_n19.hofx.2020-12-14T210000Z.nc4
         channels: &channels 3,8
diff --git a/src/eva/tests/config/testIodaObsSpaceIASI_Metop-A.yaml b/src/eva/tests/config/testIodaObsSpaceIASI_Metop-A.yaml
index a45b3d93..672abe0b 100644
--- a/src/eva/tests/config/testIodaObsSpaceIASI_Metop-A.yaml
+++ b/src/eva/tests/config/testIodaObsSpaceIASI_Metop-A.yaml
@@ -1,8 +1,8 @@
 diagnostics:
 - data:
-    type: IodaObsSpace
     datasets:
       - name: experiment
+        type: IodaObsSpace
         filenames:
           - ${data_input_path}/ioda_obs_space.iasi_metop-a.hofx.2021-08-01T000000Z.nc4
         channels: [16, 29, 32, 35, 38, 41, 44, 47, 49, 50, 51, 53, 55, 56, 57, 59, 61, 62, 63, 66, 68,
diff --git a/src/eva/tests/config/testJediLog.yaml b/src/eva/tests/config/testJediLog.yaml
index d1358f4d..7d47d381 100644
--- a/src/eva/tests/config/testJediLog.yaml
+++ b/src/eva/tests/config/testJediLog.yaml
@@ -3,11 +3,12 @@ diagnostics:
   # Data read
   # ---------
 - data:
-    type: JediLog
-    collection_name: jedi_log_test
-    jedi_log_to_parse: ${data_input_path}/jedi_log.var_rpcg.txt
-    data_to_parse:
-      convergence: true
+    datasets:
+      - type: JediLog
+        collection_name: jedi_log_test
+        jedi_log_to_parse: ${data_input_path}/jedi_log.var_rpcg.txt
+        data_to_parse:
+          convergence: true
 
   graphics:
       - figure:
@@ -56,11 +57,12 @@ diagnostics:
               label: 'Normalized norm reduction'
 
 - data:
-    type: JediLog
-    collection_name: jedi_log_test
-    jedi_log_to_parse: ${data_input_path}/jedi_log.var_dripcg_ctest.txt
-    data_to_parse:
-      convergence: true
+    datasets:
+      - type: JediLog
+        collection_name: jedi_log_test
+        jedi_log_to_parse: ${data_input_path}/jedi_log.var_dripcg_ctest.txt
+        data_to_parse:
+          convergence: true
 
   # Make plots
   graphics:
diff --git a/src/eva/tests/config/testMonDataSpaceHirs4Metop-A.yaml b/src/eva/tests/config/testMonDataSpaceHirs4Metop-A.yaml
index 2fc8b8b6..68d2d71b 100644
--- a/src/eva/tests/config/testMonDataSpaceHirs4Metop-A.yaml
+++ b/src/eva/tests/config/testMonDataSpaceHirs4Metop-A.yaml
@@ -3,9 +3,9 @@ diagnostics:
   # Data read
   # ---------
 - data:
-    type: MonDataSpace
     datasets:
       - name: experiment
+        type: MonDataSpace
         satellite: metop-a
         sensor: hirs4
         control_file:
diff --git a/src/eva/tests/config/testMonSummary.yaml b/src/eva/tests/config/testMonSummary.yaml
index 9848ce6e..debd2e19 100644
--- a/src/eva/tests/config/testMonSummary.yaml
+++ b/src/eva/tests/config/testMonSummary.yaml
@@ -10,9 +10,9 @@ diagnostics:
   # Data read
   # ---------
 - data:
-    type: MonDataSpace
     datasets:
       - name: experiment
+        type: MonDataSpace
         satellite: metop-a
         sensor: hirs4
         control_file:
diff --git a/src/eva/tests/config/testTwoDatasetsOnePlot.yaml b/src/eva/tests/config/testTwoDatasetsOnePlot.yaml
new file mode 100644
index 00000000..d05111f2
--- /dev/null
+++ b/src/eva/tests/config/testTwoDatasetsOnePlot.yaml
@@ -0,0 +1,87 @@
+diagnostics:
+
+  # Data read
+  # ---------
+- data:
+    datasets:
+      - name: observations
+        type: IodaObsSpace
+        filenames:
+          - ${data_input_path}/ioda_obs_space.aircraft.hofx.2020-12-14T210000Z.nc4
+        groups:
+          - name: ObsValue
+            variables: [airTemperature]
+          - name: MetaData
+      - name: forecast
+        type: CubedSphereRestart
+        variable: T
+        resolution: C48
+        fv3_filenames:
+          - ${data_input_path}/20210323.150000.sfc_data.tile1.nc
+          - ${data_input_path}/20210323.150000.sfc_data.tile2.nc
+          - ${data_input_path}/20210323.150000.sfc_data.tile3.nc
+          - ${data_input_path}/20210323.150000.sfc_data.tile4.nc
+          - ${data_input_path}/20210323.150000.sfc_data.tile5.nc
+          - ${data_input_path}/20210323.150000.sfc_data.tile6.nc
+        orog_filenames:
+          - ${data_input_path}/C48_oro_data.tile1.nc
+          - ${data_input_path}/C48_oro_data.tile2.nc
+          - ${data_input_path}/C48_oro_data.tile3.nc
+          - ${data_input_path}/C48_oro_data.tile4.nc
+          - ${data_input_path}/C48_oro_data.tile5.nc
+          - ${data_input_path}/C48_oro_data.tile6.nc
+        groups:
+          - name: FV3Restart
+            variables: &variables [geolon,
+                                   geolat,
+                                   t2m]
+
+  graphics:
+    # Map plots
+    # ---------
+
+    # Observations
+    - batch figure:
+        variables: [t2m]
+      dynamic options:
+        - type: vminvmaxcmap
+          data variable: forecast::FV3Restart::t2m
+      figure:
+        figure size: [20,10]
+        layout: [1,1]
+        title: 'Aircraft T Observations | FV3 2m Temperature'
+        output name: map_plots/multi/fv3_t2m_aircraft_t.png
+      plots:
+        - mapping:
+            projection: plcarr
+            domain: global
+          add_map_features: ['coastline']
+          add_colorbar:
+            label: Temperature
+          add_grid:
+          layers:
+          - type: MapGridded
+            longitude:
+              variable: forecast::FV3Restart::geolon
+            latitude:
+              variable: forecast::FV3Restart::geolat
+            data:
+              variable: forecast::FV3Restart::t2m
+            label: 2m T
+            colorbar: true
+            cmap: jet
+            vmin: 250
+            vmax: 320
+          - type: MapScatter
+            longitude:
+              variable: observations::MetaData::longitude
+            latitude:
+              variable: observations::MetaData::latitude
+            data:
+              variable: observations::ObsValue::airTemperature
+            markersize: 2
+            label: Temperature
+            colorbar: true
+            cmap: jet
+            vmin: 250
+            vmax: 320