--- a
+++ b/src/move/tasks/encode_data.py
@@ -0,0 +1,75 @@
+__all__ = ["encode_data"]
+
+from pathlib import Path
+
+import numpy as np
+
+from move.conf.schema import DataConfig
+from move.core.logging import get_logger
+from move.data import io, preprocessing
+from move.visualization.dataset_distributions import plot_value_distributions
+
+
+def encode_data(config: DataConfig):
+    """Encodes categorical and continuous datasets specified in configuration.
+    Categorical data is one-hot encoded, whereas continuous data is z-score
+    normalized.
+
+    Args:
+        config: data configuration
+    """
+    logger = get_logger(__name__)
+    logger.info("Beginning task: encode data")
+
+    raw_data_path = Path(config.raw_data_path)
+    raw_data_path.mkdir(exist_ok=True)
+    interim_data_path = Path(config.interim_data_path)
+    interim_data_path.mkdir(exist_ok=True, parents=True)
+    output_path = Path(config.results_path) / "encoded_datasets"
+    output_path.mkdir(exist_ok=True, parents=True)
+
+    sample_names = io.read_names(raw_data_path / f"{config.sample_names}.txt")
+
+    mappings = {}
+    for dataset_name in config.categorical_names:
+        # ! the string representation seems to be the variable value (here a string)
+        logger.info(f"Encoding '{dataset_name}'")
+        filepath = raw_data_path / f"{dataset_name}.tsv"
+        names, values = io.read_tsv(filepath, sample_names)
+        values, mapping = preprocessing.one_hot_encode(values)
+        mappings[dataset_name] = mapping
+        io.dump_names(interim_data_path / f"{dataset_name}.txt", names)
+        np.save(interim_data_path / f"{dataset_name}.npy", values)
+    if mappings:
+        io.dump_mappings(interim_data_path / "mappings.json", mappings)
+
+    for input_config in config.continuous_inputs:
+        scale = not hasattr(input_config, "scale") or input_config.scale
+        action_name = "Encoding" if scale else "Reading"
+        dataset_name = input_config.name
+        logger.info(f"{action_name} '{dataset_name}'")
+        filepath = raw_data_path / f"{dataset_name}.tsv"
+        names, values = io.read_tsv(filepath, sample_names)
+
+        # Plotting the value distribution for all continuous datasets
+        # before preprocessing:
+        fig = plot_value_distributions(values)
+        fig_path = str(
+            output_path / f"Value_distribution_{dataset_name}_unprocessed.png"
+        )
+        fig.savefig(fig_path)
+
+        if scale:
+            logger.debug(
+                f"Scaling dataset: {dataset_name}, log2 transform: {input_config.log2}"
+            )
+            values, mask_1d = preprocessing.scale(values, input_config.log2)
+            names = names[mask_1d]
+            logger.debug(f"Columns with zero variance: {np.sum(~mask_1d)}")
+            # Plotting the value distribution for all continuous datasets:
+            fig = plot_value_distributions(values)
+            fig_path = str(output_path / f"Value_distribution_{dataset_name}.png")
+            fig.savefig(fig_path)
+
+        io.dump_names(interim_data_path / f"{dataset_name}.txt", names)
+        np.save(interim_data_path / f"{dataset_name}.npy", values)