diff --git a/src/depiction_targeted_preproc/app_interface/dispatch_individual_resources.py b/src/depiction_targeted_preproc/app_interface/dispatch_individual_resources.py
deleted file mode 100644
index b8e55ca..0000000
--- a/src/depiction_targeted_preproc/app_interface/dispatch_individual_resources.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, Self
-
-import yaml
-from loguru import logger
-from pydantic import BaseModel, ConfigDict, model_validator
-
-from bfabric import Bfabric
-from bfabric.entities import Resource, Dataset
-from bfabric.experimental.app_interface.workunit.definition import WorkunitDefinition
-
-
-class ConfigResourceFlow(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-    filter_suffix: str | None = None
-
-
-class ConfigDatasetFlow(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-    resource_column: str = "Imzml"
-    param_columns: list[tuple[str, str]] = [("PanelDataset", "mass_list_id")]
-
-
-class ConfigDispatchIndividualResources(BaseModel):
-    resource_flow: ConfigResourceFlow | None
-    dataset_flow: ConfigDatasetFlow | None
-
-    @model_validator(mode="after")
-    def check_at_least_one_flow(self) -> Self:
-        if self.resource_flow is None and self.dataset_flow is None:
-            raise ValueError("either resource_flow or dataset_flow must be provided")
-        return self
-
-
-def config_msi_imzml() -> ConfigDispatchIndividualResources:
-    return ConfigDispatchIndividualResources(
-        resource_flow=ConfigResourceFlow(filter_suffix=".imzML"),
-        dataset_flow=ConfigDatasetFlow(resource_column="Imzml", param_columns=[("PanelDataset", "mass_list_id")]),
-    )
-
-
-class DispatchIndividualResources:
-    """Dispatches jobs on individual resources specified in the workunit."""
-
-    def __init__(self, client: Bfabric, config: ConfigDispatchIndividualResources, out_dir: Path) -> None:
-        self._client = client
-        self._config = config
-        self._out_dir = out_dir
-
-    def dispatch_job(self, resource: Resource, params: dict[str, Any]) -> Path:
-        raise NotImplementedError
-
-    def dispatch_workunit(self, definition: WorkunitDefinition) -> None:
-        params = definition.execution.raw_parameters
-        if definition.execution.resources:
-            paths = self._dispatch_jobs_resource_flow(definition, params)
-        elif definition.execution.dataset:
-            paths = self._dispatch_jobs_dataset_flow(definition, params)
-        else:
-            raise ValueError("either dataset or resources must be provided")
-        self._write_workunit_definition(definition=definition)
-        self._write_chunks(chunks=paths)
-
-    def _write_workunit_definition(self, definition: WorkunitDefinition) -> None:
-        self._out_dir.mkdir(exist_ok=True, parents=True)
-        with (self._out_dir / "workunit_definition.yml").open("w") as f:
-            yaml.safe_dump(definition.model_dump(mode="json"), f)
-
-    def _write_chunks(self, chunks: list[Path]) -> None:
-        self._out_dir.mkdir(exist_ok=True, parents=True)
-        with (self._out_dir / "chunks.yml").open("w") as f:
-            data = {"chunks": [str(chunk) for chunk in chunks]}
-            yaml.safe_dump(data, f)
-
-    def _dispatch_jobs_resource_flow(self, definition: WorkunitDefinition, params: dict[str, Any]) -> list[Path]:
-        if self._config.resource_flow is None:
-            raise ValueError("resource_flow is not configured")
-        resources = Resource.find_all(ids=definition.execution.resources, client=self._client)
-        paths = []
-        for resource in sorted(resources.values()):
-            if self._config.resource_flow.filter_suffix is not None and not resource["name"].endswith(
-                self._config.resource_flow.filter_suffix
-            ):
-                logger.info(f"Skipping resource {resource['name']!r} as it does not match the extension filter.")
-                continue
-            paths.append(self.dispatch_job(resource=resource, params=params))
-        return paths
-
-    def _dispatch_jobs_dataset_flow(self, definition: WorkunitDefinition, params: dict[str, Any]) -> list[Path]:
-        if self._config.dataset_flow is None:
-            raise ValueError("dataset_flow is not configured")
-        dataset = Dataset.find(id=definition.execution.dataset, client=self._client)
-        dataset_df = dataset.to_polars()
-        resources = Resource.find_all(
-            ids=dataset_df[self._config.dataset_flow.resource_column].unique().to_list(), client=self._client
-        )
-        paths = []
-        for row in dataset_df.iter_rows(named=True):
-            resource_id = int(row[self._config.dataset_flow.resource_column])
-            row_params = {name: row[dataset_name] for dataset_name, name in self._config.dataset_flow.param_columns}
-            paths.append(self.dispatch_job(resource=resources[resource_id], params=params | row_params))
-        return paths