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diff --git a/code/sunlab/sunflow/data/utilities.py b/code/sunlab/sunflow/data/utilities.py
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+from sunlab.common import ShapeDataset
+from sunlab.common import MaxAbsScaler
+
+
+def process_and_load_dataset(
+    dataset_file, model_folder, magnification=10, scaler=MaxAbsScaler
+):
+    """# Load a dataset and process a models' Latent Space on the Dataset"""
+    from ..models import load_aae
+    from sunlab.common import import_full_dataset
+
+    model = load_aae(model_folder, normalization_scaler=scaler)
+    dataset = import_full_dataset(
+        dataset_file, magnification=magnification, scaler=model.scaler
+    )
+    latent = model.encoder(dataset.dataset).numpy()
+    assert len(latent.shape) == 2, "Only 1D Latent Vectors Supported"
+    for dim in range(latent.shape[1]):
+        dataset.dataframe[f"Latent-{dim}"] = latent[:, dim]
+    return dataset
+
+
+def process_and_load_datasets(
+    dataset_file_list, model_folder, magnification=10, scaler=MaxAbsScaler
+):
+    from pandas import concat
+    from ..models import load_aae
+
+    dataframes = []
+    datasets = []
+    for dataset_file in dataset_file_list:
+        dataset = process_and_load_dataset(
+            dataset_file, model_folder, magnification, scaler
+        )
+        model = load_aae(model_folder, normalization_scaler=scaler)
+        dataframe = dataset.dataframe
+        for label in ["ActinEdge", "Filopodia", "Bleb", "Lamellipodia"]:
+            if label in dataframe.columns:
+                dataframe[label.lower()] = dataframe[label]
+            if label.lower() not in dataframe.columns:
+                dataframe[label.lower()] = 0
+        latent_columns = [f"Latent-{dim}" for dim in range(model.latent_size)]
+        datasets.append(dataset)
+        dataframes.append(
+            dataframe[
+                dataset.data_columns
+                + dataset.label_columns
+                + latent_columns
+                + ["Frames", "CellNum"]
+                + ["actinedge", "filopodia", "bleb", "lamellipodia"]
+            ]
+        )
+    return datasets, concat(dataframes)