diff --git a/declearn/quickrun/_split_data.py b/declearn/quickrun/_split_data.py
index f748bf1c9910af0cf0019ecb5e9cace030c13766..a45e5c64a0d555d5c5f7b41fa9da087b62667ed4 100644
--- a/declearn/quickrun/_split_data.py
+++ b/declearn/quickrun/_split_data.py
@@ -173,51 +173,14 @@ def _split_biased(
     return split
 
 
-def export_shard_to_csv(
-    path: str,
-    inputs: np.ndarray,
-    target: np.ndarray,
-) -> None:
-    """Export an MNIST shard to a csv file."""
-    specs = {"dtype": inputs.dtype.char, "shape": list(inputs[0].shape)}
-    with open(path, "w", encoding="utf-8") as file:
-        file.write(f"{json.dumps(specs)},target")
-        for inp, tgt in zip(inputs, target):
-            file.write(f"\n{inp.tobytes().hex()},{int(tgt)}")
-
-
-def load_mnist_from_csv(
-    path: str,
-) -> Tuple[np.ndarray, np.ndarray]:
-    """Reload an MNIST shard from a csv file."""
-    # Prepare data containers.
-    inputs = []  # type: List[np.ndarray]
-    target = []  # type: List[int]
-    # Parse the csv file.
-    with open(path, "r", encoding="utf-8") as file:
-        # Parse input features' specs from the csv header.
-        specs = json.loads(file.readline().rsplit(",", 1)[0])
-        dtype = specs["dtype"]
-        shape = specs["shape"]
-        # Iteratively deserialize features and labels from rows.
-        for row in file:
-            inp, tgt = row.strip("\n").rsplit(",", 1)
-            dat = bytes.fromhex(inp)
-            inputs.append(np.frombuffer(dat, dtype=dtype).reshape(shape))
-            target.append(int(tgt))
-    # Assemble the data into numpy arrays and return.
-    return np.array(inputs), np.array(target)
-
-
 def split_data(
-    folder: str = DEFAULT_FOLDER,  # CHECK if good practice
+    folder: str = DEFAULT_FOLDER,
     n_shards: int = 5,
     data: Optional[str] = None,
     target: Optional[Union[str, int]] = None,
     scheme: Literal["iid", "labels", "biased"] = "iid",
     perc_train: float = 0.8,
     seed: Optional[int] = None,
-    use_csv: bool = False,
 ) -> None:
     """Download and randomly split the MNIST dataset into shards.
     #TODO
@@ -267,10 +230,6 @@ def split_data(
         np.save(os.path.join(folder, f"client_{i}/{name}.npy"), data)
 
     for i, (inp, tgt) in enumerate(split):
-        if use_csv:  # TODO
-            path = os.path.join(folder, f"shard_{i}.csv")
-            export_shard_to_csv(path, inp, tgt)
-            return
         if not perc_train:
             np_save(inp, i, "train_data")
             np_save(tgt, i, "train_target")
@@ -356,13 +315,6 @@ def parse_args(args: Optional[List[str]] = None) -> argparse.Namespace:
         type=int,
         help="RNG seed to use (default: 20221109).",
     )
-    parser.add_argument(
-        "--csv",
-        default=False,
-        dest="use_csv",
-        type=bool,
-        help="Export data as csv files (for use with Fed-BioMed).",
-    )
     return parser.parse_args(args)
 
 
@@ -377,7 +329,6 @@ def main(args: Optional[List[str]] = None) -> None:
             target=cmdargs.target,
             scheme=scheme,
             seed=cmdargs.seed,
-            use_csv=cmdargs.use_csv,
         )