diff --git a/.gitignore b/.gitignore
index d0e466d..f940ab2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -45,3 +45,6 @@ sandbox/
 
 # ignore pickle files
 *.pickle
+
+# ignore yaml files
+*.yaml
diff --git a/data/V38/.gitkeep b/data/V38/.gitkeep
new file mode 100644
index 0000000..e69de29
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-  - psycopg[binary]>=3.0.7 ; extra == 'postgresql-psycopgbinary'
-  - pymysql ; extra == 'pymysql'
-  - greenlet>=1 ; extra == 'aiomysql'
-  - aiomysql>=0.2.0 ; extra == 'aiomysql'
-  - greenlet>=1 ; extra == 'aioodbc'
-  - aioodbc ; extra == 'aioodbc'
-  - greenlet>=1 ; extra == 'asyncmy'
-  - asyncmy>=0.2.3,!=0.2.4,!=0.2.6 ; extra == 'asyncmy'
-  - greenlet>=1 ; extra == 'aiosqlite'
-  - aiosqlite ; extra == 'aiosqlite'
-  - typing-extensions!=3.10.0.1 ; extra == 'aiosqlite'
-  - sqlcipher3-binary ; extra == 'sqlcipher'
-  requires_python: '>=3.7'
-- pypi: https://files.pythonhosted.org/packages/67/08/4e6c533d4c7f5e7c4cbb6fe8a2c4e813202a40f05700d4009a44ec6e236d/sqlalchemy-2.0.42-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-  name: sqlalchemy
-  version: 2.0.42
-  sha256: 9cae6c2b05326d7c2c7c0519f323f90e0fb9e8afa783c6a05bb9ee92a90d0f04
-  requires_dist:
-  - importlib-metadata ; python_full_version < '3.8'
-  - greenlet>=1 ; (python_full_version < '3.14' and platform_machine == 'AMD64') or (python_full_version < '3.14' and platform_machine == 'WIN32') or (python_full_version < '3.14' and platform_machine == 'aarch64') or (python_full_version < '3.14' and platform_machine == 'amd64') or (python_full_version < '3.14' and platform_machine == 'ppc64le') or (python_full_version < '3.14' and platform_machine == 'win32') or (python_full_version < '3.14' and platform_machine == 'x86_64')
-  - typing-extensions>=4.6.0
-  - greenlet>=1 ; extra == 'asyncio'
-  - mypy>=0.910 ; extra == 'mypy'
-  - pyodbc ; extra == 'mssql'
-  - pymssql ; extra == 'mssql-pymssql'
-  - pyodbc ; extra == 'mssql-pyodbc'
-  - mysqlclient>=1.4.0 ; extra == 'mysql'
-  - mysql-connector-python ; extra == 'mysql-connector'
-  - mariadb>=1.0.1,!=1.1.2,!=1.1.5,!=1.1.10 ; extra == 'mariadb-connector'
-  - cx-oracle>=8 ; extra == 'oracle'
-  - oracledb>=1.0.1 ; extra == 'oracle-oracledb'
-  - psycopg2>=2.7 ; extra == 'postgresql'
-  - pg8000>=1.29.1 ; extra == 'postgresql-pg8000'
-  - greenlet>=1 ; extra == 'postgresql-asyncpg'
-  - asyncpg ; extra == 'postgresql-asyncpg'
-  - psycopg2-binary ; extra == 'postgresql-psycopg2binary'
-  - psycopg2cffi ; extra == 'postgresql-psycopg2cffi'
-  - psycopg>=3.0.7 ; extra == 'postgresql-psycopg'
-  - psycopg[binary]>=3.0.7 ; extra == 'postgresql-psycopgbinary'
-  - pymysql ; extra == 'pymysql'
-  - greenlet>=1 ; extra == 'aiomysql'
-  - aiomysql>=0.2.0 ; extra == 'aiomysql'
-  - greenlet>=1 ; extra == 'aioodbc'
-  - aioodbc ; extra == 'aioodbc'
-  - greenlet>=1 ; extra == 'asyncmy'
-  - asyncmy>=0.2.3,!=0.2.4,!=0.2.6 ; extra == 'asyncmy'
-  - greenlet>=1 ; extra == 'aiosqlite'
-  - aiosqlite ; extra == 'aiosqlite'
-  - typing-extensions!=3.10.0.1 ; extra == 'aiosqlite'
-  - sqlcipher3-binary ; extra == 'sqlcipher'
-  requires_python: '>=3.7'
-- pypi: https://files.pythonhosted.org/packages/e5/a1/9d8c93434d1d983880d976400fcb7895a79576bd94dca61c3b7b90b1ed0d/sqlalchemy-2.0.42-cp313-cp313-macosx_11_0_arm64.whl
-  name: sqlalchemy
-  version: 2.0.42
-  sha256: 95d3d06a968a760ce2aa6a5889fefcbdd53ca935735e0768e1db046ec08cbf01
-  requires_dist:
-  - importlib-metadata ; python_full_version < '3.8'
-  - greenlet>=1 ; (python_full_version < '3.14' and platform_machine == 'AMD64') or (python_full_version < '3.14' and platform_machine == 'WIN32') or (python_full_version < '3.14' and platform_machine == 'aarch64') or (python_full_version < '3.14' and platform_machine == 'amd64') or (python_full_version < '3.14' and platform_machine == 'ppc64le') or (python_full_version < '3.14' and platform_machine == 'win32') or (python_full_version < '3.14' and platform_machine == 'x86_64')
-  - typing-extensions>=4.6.0
-  - greenlet>=1 ; extra == 'asyncio'
-  - mypy>=0.910 ; extra == 'mypy'
-  - pyodbc ; extra == 'mssql'
-  - pymssql ; extra == 'mssql-pymssql'
-  - pyodbc ; extra == 'mssql-pyodbc'
-  - mysqlclient>=1.4.0 ; extra == 'mysql'
-  - mysql-connector-python ; extra == 'mysql-connector'
-  - mariadb>=1.0.1,!=1.1.2,!=1.1.5,!=1.1.10 ; extra == 'mariadb-connector'
-  - cx-oracle>=8 ; extra == 'oracle'
-  - oracledb>=1.0.1 ; extra == 'oracle-oracledb'
-  - psycopg2>=2.7 ; extra == 'postgresql'
-  - pg8000>=1.29.1 ; extra == 'postgresql-pg8000'
-  - greenlet>=1 ; extra == 'postgresql-asyncpg'
-  - asyncpg ; extra == 'postgresql-asyncpg'
-  - psycopg2-binary ; extra == 'postgresql-psycopg2binary'
-  - psycopg2cffi ; extra == 'postgresql-psycopg2cffi'
-  - psycopg>=3.0.7 ; extra == 'postgresql-psycopg'
-  - psycopg[binary]>=3.0.7 ; extra == 'postgresql-psycopgbinary'
-  - pymysql ; extra == 'pymysql'
-  - greenlet>=1 ; extra == 'aiomysql'
-  - aiomysql>=0.2.0 ; extra == 'aiomysql'
-  - greenlet>=1 ; extra == 'aioodbc'
-  - aioodbc ; extra == 'aioodbc'
-  - greenlet>=1 ; extra == 'asyncmy'
-  - asyncmy>=0.2.3,!=0.2.4,!=0.2.6 ; extra == 'asyncmy'
-  - greenlet>=1 ; extra == 'aiosqlite'
-  - aiosqlite ; extra == 'aiosqlite'
-  - typing-extensions!=3.10.0.1 ; extra == 'aiosqlite'
-  - sqlcipher3-binary ; extra == 'sqlcipher'
-  requires_python: '>=3.7'
-- pypi: https://files.pythonhosted.org/packages/e9/7e/25d8c28b86730c9fb0e09156f601d7a96d1c634043bf8ba36513eb78887b/sqlalchemy-2.0.42-cp313-cp313-macosx_10_13_x86_64.whl
-  name: sqlalchemy
-  version: 2.0.42
-  sha256: 941804f55c7d507334da38133268e3f6e5b0340d584ba0f277dd884197f4ae8c
-  requires_dist:
-  - importlib-metadata ; python_full_version < '3.8'
-  - greenlet>=1 ; (python_full_version < '3.14' and platform_machine == 'AMD64') or (python_full_version < '3.14' and platform_machine == 'WIN32') or (python_full_version < '3.14' and platform_machine == 'aarch64') or (python_full_version < '3.14' and platform_machine == 'amd64') or (python_full_version < '3.14' and platform_machine == 'ppc64le') or (python_full_version < '3.14' and platform_machine == 'win32') or (python_full_version < '3.14' and platform_machine == 'x86_64')
-  - typing-extensions>=4.6.0
-  - greenlet>=1 ; extra == 'asyncio'
-  - mypy>=0.910 ; extra == 'mypy'
-  - pyodbc ; extra == 'mssql'
-  - pymssql ; extra == 'mssql-pymssql'
-  - pyodbc ; extra == 'mssql-pyodbc'
-  - mysqlclient>=1.4.0 ; extra == 'mysql'
-  - mysql-connector-python ; extra == 'mysql-connector'
-  - mariadb>=1.0.1,!=1.1.2,!=1.1.5,!=1.1.10 ; extra == 'mariadb-connector'
-  - cx-oracle>=8 ; extra == 'oracle'
-  - oracledb>=1.0.1 ; extra == 'oracle-oracledb'
-  - psycopg2>=2.7 ; extra == 'postgresql'
-  - pg8000>=1.29.1 ; extra == 'postgresql-pg8000'
-  - greenlet>=1 ; extra == 'postgresql-asyncpg'
-  - asyncpg ; extra == 'postgresql-asyncpg'
-  - psycopg2-binary ; extra == 'postgresql-psycopg2binary'
-  - psycopg2cffi ; extra == 'postgresql-psycopg2cffi'
-  - psycopg>=3.0.7 ; extra == 'postgresql-psycopg'
-  - psycopg[binary]>=3.0.7 ; extra == 'postgresql-psycopgbinary'
-  - pymysql ; extra == 'pymysql'
-  - greenlet>=1 ; extra == 'aiomysql'
-  - aiomysql>=0.2.0 ; extra == 'aiomysql'
-  - greenlet>=1 ; extra == 'aioodbc'
-  - aioodbc ; extra == 'aioodbc'
-  - greenlet>=1 ; extra == 'asyncmy'
-  - asyncmy>=0.2.3,!=0.2.4,!=0.2.6 ; extra == 'asyncmy'
-  - greenlet>=1 ; extra == 'aiosqlite'
-  - aiosqlite ; extra == 'aiosqlite'
-  - typing-extensions!=3.10.0.1 ; extra == 'aiosqlite'
-  - sqlcipher3-binary ; extra == 'sqlcipher'
-  requires_python: '>=3.7'
+- conda: https://conda.anaconda.org/conda-forge/linux-64/sqlalchemy-2.0.42-py313h07c4f96_0.conda
+  sha256: b6b4df6caabe3e5efab3279c15b9c178ad02b3c9e514778924579016f654c5fd
+  md5: 309f451c11ea341b985ec48225a8a05e
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - greenlet !=0.4.17
+  - libgcc >=14
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - typing-extensions >=4.6.0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/sqlalchemy?source=hash-mapping
+  size: 3643568
+  timestamp: 1753804754840
+- conda: https://conda.anaconda.org/conda-forge/osx-64/sqlalchemy-2.0.42-py313h585f44e_0.conda
+  sha256: 76ab9f6d279d3cba6807cc54a803c5bde10e39d55e4d8bccb3f7863b26d5d872
+  md5: b4d96fff13958c8d1624aaa14e5f8774
+  depends:
+  - __osx >=10.13
+  - greenlet !=0.4.17
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - typing-extensions >=4.6.0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/sqlalchemy?source=hash-mapping
+  size: 3621030
+  timestamp: 1753804895609
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlalchemy-2.0.42-py313hcdf3177_0.conda
+  sha256: a9d105dd0294b4270bfc5b52c45c119c352e776f34ae87cb28bdede406a9d764
+  md5: ebf23265611e366917e199da39e39acf
+  depends:
+  - __osx >=11.0
+  - greenlet !=0.4.17
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  - typing-extensions >=4.6.0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/sqlalchemy?source=hash-mapping
+  size: 3632867
+  timestamp: 1753804946446
+- conda: https://conda.anaconda.org/conda-forge/win-64/sqlalchemy-2.0.42-py313h5ea7bf4_0.conda
+  sha256: b885bc4b7d0c6ae3863b6140ea2b66febbd9d9fc85d10bbf4c0812ca09a8c5a9
+  md5: c063f5d63cb8584bbc5490d0810294cf
+  depends:
+  - greenlet !=0.4.17
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - typing-extensions >=4.6.0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/sqlalchemy?source=hash-mapping
+  size: 3604361
+  timestamp: 1753804937235
 - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda
   sha256: 570da295d421661af487f1595045760526964f41471021056e993e73089e9c41
   md5: b1b505328da7a6b246787df4b5a49fbc
@@ -16020,35 +15878,18 @@ packages:
   - pkg:pypi/ukkonen?source=hash-mapping
   size: 17210
   timestamp: 1725784604368
-- pypi: https://files.pythonhosted.org/packages/e5/4d/2e577f6db7aa0f932d19f799c18f604b2b302c65f733419b900ec07dbade/universal_pathlib-0.2.6-py3-none-any.whl
-  name: universal-pathlib
-  version: 0.2.6
-  sha256: 700dec2b58ef34b87998513de6d2ae153b22f083197dfafb8544744edabd1b18
-  requires_dist:
-  - fsspec>=2022.1.0,!=2024.3.1
-  - pytest>=8 ; extra == 'tests'
-  - pytest-sugar>=0.9.7 ; extra == 'tests'
-  - pytest-cov>=4.1.0 ; extra == 'tests'
-  - pytest-mock>=3.12.0 ; extra == 'tests'
-  - pylint>=2.17.4 ; extra == 'tests'
-  - mypy>=1.10.0 ; extra == 'tests'
-  - pytest-mypy-plugins>=3.1.2 ; extra == 'tests'
-  - packaging ; extra == 'tests'
-  - adlfs ; extra == 'dev'
-  - aiohttp ; extra == 'dev'
-  - requests ; extra == 'dev'
-  - gcsfs ; extra == 'dev'
-  - s3fs ; extra == 'dev'
-  - moto[s3,server] ; extra == 'dev'
-  - webdav4[fsspec] ; extra == 'dev'
-  - paramiko ; extra == 'dev'
-  - wsgidav ; extra == 'dev'
-  - cheroot ; extra == 'dev'
-  - pydantic ; extra == 'dev'
-  - pydantic-settings ; extra == 'dev'
-  - smbprotocol ; extra == 'dev'
-  - typing-extensions ; python_full_version < '3.11' and extra == 'dev'
-  requires_python: '>=3.8'
+- conda: https://conda.anaconda.org/conda-forge/noarch/universal_pathlib-0.2.6-pyhd8ed1ab_1.conda
+  sha256: 2f03eceeb977ca8ed82f9f81e675203eac625a97bb167c90ed56665ebf4a35ed
+  md5: 1cef652915518439df1c6062446a3d29
+  depends:
+  - fsspec >=2022.1.0,!=2024.3.1
+  - python >=3.9
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/universal-pathlib?source=hash-mapping
+  size: 45126
+  timestamp: 1744798402969
 - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda
   sha256: e0eb6c8daf892b3056f08416a96d68b0a358b7c46b99c8a50481b22631a4dfc0
   md5: e7cb0f5745e4c5035a460248334af7eb
diff --git a/pyproject.toml b/pyproject.toml
index 3d7c703..9a04081 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -67,7 +67,7 @@ allow-direct-references = true
 # Pixi configuration
 # ======================================================================================
 
-[tool.pixi.project]
+[tool.pixi.workspace]
 channels = ["conda-forge"]
 platforms = ["linux-64", "osx-64", "osx-arm64", "win-64"]
 
@@ -78,6 +78,7 @@ pre-commit = "*"
 pygraphviz = "*"
 pyarrow = ">=21.0.0,<22"
 pandas = ">=2.3.1,<3"
+pytask-parallel = ">=0.5.1,<0.6"
 
 [tool.pixi.pypi-dependencies]
 soep_preparation = {path = ".", editable = true}
diff --git a/src/soep_preparation/clean_variables/__init__.py b/src/soep_preparation/clean_modules/__init__.py
similarity index 100%
rename from src/soep_preparation/clean_variables/__init__.py
rename to src/soep_preparation/clean_modules/__init__.py
diff --git a/src/soep_preparation/clean_variables/biobirth.py b/src/soep_preparation/clean_modules/biobirth.py
similarity index 100%
rename from src/soep_preparation/clean_variables/biobirth.py
rename to src/soep_preparation/clean_modules/biobirth.py
diff --git a/src/soep_preparation/clean_variables/bioedu.py b/src/soep_preparation/clean_modules/bioedu.py
similarity index 100%
rename from src/soep_preparation/clean_variables/bioedu.py
rename to src/soep_preparation/clean_modules/bioedu.py
diff --git a/src/soep_preparation/clean_variables/biol.py b/src/soep_preparation/clean_modules/biol.py
similarity index 100%
rename from src/soep_preparation/clean_variables/biol.py
rename to src/soep_preparation/clean_modules/biol.py
diff --git a/src/soep_preparation/clean_variables/bioparen.py b/src/soep_preparation/clean_modules/bioparen.py
similarity index 100%
rename from src/soep_preparation/clean_variables/bioparen.py
rename to src/soep_preparation/clean_modules/bioparen.py
diff --git a/src/soep_preparation/clean_variables/design.py b/src/soep_preparation/clean_modules/design.py
similarity index 100%
rename from src/soep_preparation/clean_variables/design.py
rename to src/soep_preparation/clean_modules/design.py
diff --git a/src/soep_preparation/clean_variables/hgen.py b/src/soep_preparation/clean_modules/hgen.py
similarity index 100%
rename from src/soep_preparation/clean_variables/hgen.py
rename to src/soep_preparation/clean_modules/hgen.py
diff --git a/src/soep_preparation/clean_variables/hl.py b/src/soep_preparation/clean_modules/hl.py
similarity index 100%
rename from src/soep_preparation/clean_variables/hl.py
rename to src/soep_preparation/clean_modules/hl.py
diff --git a/src/soep_preparation/clean_variables/hpathl.py b/src/soep_preparation/clean_modules/hpathl.py
similarity index 100%
rename from src/soep_preparation/clean_variables/hpathl.py
rename to src/soep_preparation/clean_modules/hpathl.py
diff --git a/src/soep_preparation/clean_variables/hwealth.py b/src/soep_preparation/clean_modules/hwealth.py
similarity index 77%
rename from src/soep_preparation/clean_variables/hwealth.py
rename to src/soep_preparation/clean_modules/hwealth.py
index 07e591d..44f1366 100644
--- a/src/soep_preparation/clean_variables/hwealth.py
+++ b/src/soep_preparation/clean_modules/hwealth.py
@@ -60,42 +60,51 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     out["hh_net_overall_wealth_d"] = apply_smallest_float_dtype(raw_data["w011hd"])
     out["hh_net_overall_wealth_e"] = apply_smallest_float_dtype(raw_data["w011he"])
 
-    out["hh_vehicles_value_a"] = apply_smallest_float_dtype(raw_data["v010ha"])
-    out["hh_vehicles_value_b"] = apply_smallest_float_dtype(raw_data["v010hb"])
-    out["hh_vehicles_value_c"] = apply_smallest_float_dtype(raw_data["v010hc"])
-    out["hh_vehicles_value_d"] = apply_smallest_float_dtype(raw_data["v010hd"])
-    out["hh_vehicles_value_e"] = apply_smallest_float_dtype(raw_data["v010he"])
+    out["hh_vehicles_value_a"] = apply_smallest_float_dtype(
+        raw_data["v010ha"].replace({-8: pd.NA})
+    )
+    out["hh_vehicles_value_b"] = apply_smallest_float_dtype(
+        raw_data["v010hb"].replace({-8: pd.NA})
+    )
+    out["hh_vehicles_value_c"] = apply_smallest_float_dtype(
+        raw_data["v010hc"].replace({-8: pd.NA})
+    )
+    out["hh_vehicles_value_d"] = apply_smallest_float_dtype(
+        raw_data["v010hd"].replace({-8: pd.NA})
+    )
+    out["hh_vehicles_value_e"] = apply_smallest_float_dtype(
+        raw_data["v010he"].replace({-8: pd.NA})
+    )
 
     out["hh_gross_overall_wealth_including_vehicles_a"] = apply_smallest_float_dtype(
-        raw_data["n010ha"]
+        raw_data["n010ha"].replace({-8: pd.NA})
     )
     out["hh_gross_overall_wealth_including_vehicles_b"] = apply_smallest_float_dtype(
-        raw_data["n010hb"]
+        raw_data["n010hb"].replace({-8: pd.NA})
     )
     out["hh_gross_overall_wealth_including_vehicles_c"] = apply_smallest_float_dtype(
-        raw_data["n010hc"]
+        raw_data["n010hc"].replace({-8: pd.NA})
     )
     out["hh_gross_overall_wealth_including_vehicles_d"] = apply_smallest_float_dtype(
-        raw_data["n010hd"]
+        raw_data["n010hd"].replace({-8: pd.NA})
     )
     out["hh_gross_overall_wealth_including_vehicles_e"] = apply_smallest_float_dtype(
-        raw_data["n010he"]
+        raw_data["n010he"].replace({-8: pd.NA})
     )
-
     out["hh_net_overall_wealth_including_vehicles_and_student_loans_a"] = (
-        apply_smallest_float_dtype(raw_data["n011ha"])
+        apply_smallest_float_dtype(raw_data["n011ha"].replace({-8: pd.NA}))
     )
     out["hh_net_overall_wealth_including_vehicles_and_student_loans_b"] = (
-        apply_smallest_float_dtype(raw_data["n011hb"])
+        apply_smallest_float_dtype(raw_data["n011hb"].replace({-8: pd.NA}))
     )
     out["hh_net_overall_wealth_including_vehicles_and_student_loans_c"] = (
-        apply_smallest_float_dtype(raw_data["n011hc"])
+        apply_smallest_float_dtype(raw_data["n011hc"].replace({-8: pd.NA}))
     )
     out["hh_net_overall_wealth_including_vehicles_and_student_loans_d"] = (
-        apply_smallest_float_dtype(raw_data["n011hd"])
+        apply_smallest_float_dtype(raw_data["n011hd"].replace({-8: pd.NA}))
     )
     out["hh_net_overall_wealth_including_vehicles_and_student_loans_e"] = (
-        apply_smallest_float_dtype(raw_data["n011he"])
+        apply_smallest_float_dtype(raw_data["n011he"].replace({-8: pd.NA}))
     )
 
     out[
diff --git a/src/soep_preparation/clean_variables/kidlong.py b/src/soep_preparation/clean_modules/kidlong.py
similarity index 100%
rename from src/soep_preparation/clean_variables/kidlong.py
rename to src/soep_preparation/clean_modules/kidlong.py
diff --git a/src/soep_preparation/clean_variables/pbrutto.py b/src/soep_preparation/clean_modules/pbrutto.py
similarity index 100%
rename from src/soep_preparation/clean_variables/pbrutto.py
rename to src/soep_preparation/clean_modules/pbrutto.py
diff --git a/src/soep_preparation/clean_variables/pequiv.py b/src/soep_preparation/clean_modules/pequiv.py
similarity index 100%
rename from src/soep_preparation/clean_variables/pequiv.py
rename to src/soep_preparation/clean_modules/pequiv.py
diff --git a/src/soep_preparation/clean_variables/pgen.py b/src/soep_preparation/clean_modules/pgen.py
similarity index 100%
rename from src/soep_preparation/clean_variables/pgen.py
rename to src/soep_preparation/clean_modules/pgen.py
diff --git a/src/soep_preparation/clean_variables/pkal.py b/src/soep_preparation/clean_modules/pkal.py
similarity index 84%
rename from src/soep_preparation/clean_variables/pkal.py
rename to src/soep_preparation/clean_modules/pkal.py
index e09c16b..a61be03 100644
--- a/src/soep_preparation/clean_variables/pkal.py
+++ b/src/soep_preparation/clean_modules/pkal.py
@@ -78,6 +78,7 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         The processed pkal data.
     """
     out = pd.DataFrame()
+    tmp = pd.DataFrame()
 
     out["p_id"] = apply_smallest_int_dtype(raw_data["pid"])
     out["hh_id"] = apply_smallest_int_dtype(raw_data["hid"])
@@ -102,11 +103,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     # the second the timeframe 1998 until 2022
     # individual employment status by month
     # Month 1 - Jan
-    out["tmp_ft_employed_m_v1_1"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_1"] = object_to_str_categorical(
         series=raw_data["kal1a001_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_1"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_1"] = object_to_str_categorical(
         series=raw_data["kal1a001_v2"],
         renaming={
             "[1] Jan Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -114,8 +115,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_1"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_1"],
-        series_2=out["tmp_ft_employed_m_v2_1"],
+        series_1=tmp["tmp_ft_employed_m_v1_1"],
+        series_2=tmp["tmp_ft_employed_m_v2_1"],
         ordered=False,
     )
     out["pt_employed_m_1"] = object_to_str_categorical(
@@ -131,11 +132,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 2 - Feb
-    out["tmp_ft_employed_m_v1_2"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_2"] = object_to_str_categorical(
         series=raw_data["kal1a002_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_2"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_2"] = object_to_str_categorical(
         series=raw_data["kal1a002_v2"],
         renaming={
             "[1] Feb Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -143,8 +144,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_2"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_2"],
-        series_2=out["tmp_ft_employed_m_v2_2"],
+        series_1=tmp["tmp_ft_employed_m_v1_2"],
+        series_2=tmp["tmp_ft_employed_m_v2_2"],
         ordered=False,
     )
     out["pt_employed_m_2"] = object_to_str_categorical(
@@ -160,11 +161,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 3 - Mrz
-    out["tmp_ft_employed_m_v1_3"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_3"] = object_to_str_categorical(
         series=raw_data["kal1a003_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_3"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_3"] = object_to_str_categorical(
         series=raw_data["kal1a003_v2"],
         renaming={
             "[1] Mrz Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -172,8 +173,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_3"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_3"],
-        series_2=out["tmp_ft_employed_m_v2_3"],
+        series_1=tmp["tmp_ft_employed_m_v1_3"],
+        series_2=tmp["tmp_ft_employed_m_v2_3"],
         ordered=False,
     )
     out["pt_employed_m_3"] = object_to_str_categorical(
@@ -189,11 +190,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 4 - Apr
-    out["tmp_ft_employed_m_v1_4"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_4"] = object_to_str_categorical(
         series=raw_data["kal1a004_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_4"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_4"] = object_to_str_categorical(
         series=raw_data["kal1a004_v2"],
         renaming={
             "[1] Apr Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -201,8 +202,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_4"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_4"],
-        series_2=out["tmp_ft_employed_m_v2_4"],
+        series_1=tmp["tmp_ft_employed_m_v1_4"],
+        series_2=tmp["tmp_ft_employed_m_v2_4"],
         ordered=False,
     )
     out["pt_employed_m_4"] = object_to_str_categorical(
@@ -218,11 +219,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 5 - Mai
-    out["tmp_ft_employed_m_v1_5"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_5"] = object_to_str_categorical(
         series=raw_data["kal1a005_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_5"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_5"] = object_to_str_categorical(
         series=raw_data["kal1a005_v2"],
         renaming={
             "[1] Mai Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -230,8 +231,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_5"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_5"],
-        series_2=out["tmp_ft_employed_m_v2_5"],
+        series_1=tmp["tmp_ft_employed_m_v1_5"],
+        series_2=tmp["tmp_ft_employed_m_v2_5"],
         ordered=False,
     )
     out["pt_employed_m_5"] = object_to_str_categorical(
@@ -247,11 +248,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 6 - Jun
-    out["tmp_ft_employed_m_v1_6"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_6"] = object_to_str_categorical(
         series=raw_data["kal1a006_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_6"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_6"] = object_to_str_categorical(
         series=raw_data["kal1a006_v2"],
         renaming={
             "[1] Jun Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -259,8 +260,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_6"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_6"],
-        series_2=out["tmp_ft_employed_m_v2_6"],
+        series_1=tmp["tmp_ft_employed_m_v1_6"],
+        series_2=tmp["tmp_ft_employed_m_v2_6"],
         ordered=False,
     )
     out["pt_employed_m_6"] = object_to_str_categorical(
@@ -276,11 +277,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 7 - Jul
-    out["tmp_ft_employed_m_v1_7"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_7"] = object_to_str_categorical(
         series=raw_data["kal1a007_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_7"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_7"] = object_to_str_categorical(
         series=raw_data["kal1a007_v2"],
         renaming={
             "[1] Jul Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -288,8 +289,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_7"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_7"],
-        series_2=out["tmp_ft_employed_m_v2_7"],
+        series_1=tmp["tmp_ft_employed_m_v1_7"],
+        series_2=tmp["tmp_ft_employed_m_v2_7"],
         ordered=False,
     )
     out["pt_employed_m_7"] = object_to_str_categorical(
@@ -305,11 +306,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 8 - Aug
-    out["tmp_ft_employed_m_v1_8"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_8"] = object_to_str_categorical(
         series=raw_data["kal1a008_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_8"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_8"] = object_to_str_categorical(
         series=raw_data["kal1a008_v2"],
         renaming={
             "[1] Aug Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -317,8 +318,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_8"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_8"],
-        series_2=out["tmp_ft_employed_m_v2_8"],
+        series_1=tmp["tmp_ft_employed_m_v1_8"],
+        series_2=tmp["tmp_ft_employed_m_v2_8"],
         ordered=False,
     )
     out["pt_employed_m_8"] = object_to_str_categorical(
@@ -334,11 +335,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 9 - Sep
-    out["tmp_ft_employed_m_v1_9"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_9"] = object_to_str_categorical(
         series=raw_data["kal1a009_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_9"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_9"] = object_to_str_categorical(
         series=raw_data["kal1a009_v2"],
         renaming={
             "[1] Sep Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -346,8 +347,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_9"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_9"],
-        series_2=out["tmp_ft_employed_m_v2_9"],
+        series_1=tmp["tmp_ft_employed_m_v1_9"],
+        series_2=tmp["tmp_ft_employed_m_v2_9"],
         ordered=False,
     )
     out["pt_employed_m_9"] = object_to_str_categorical(
@@ -363,11 +364,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 10 - Okt
-    out["tmp_ft_employed_m_v1_10"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_10"] = object_to_str_categorical(
         series=raw_data["kal1a010_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_10"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_10"] = object_to_str_categorical(
         series=raw_data["kal1a010_v2"],
         renaming={
             "[1] Okt Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -375,8 +376,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_10"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_10"],
-        series_2=out["tmp_ft_employed_m_v2_10"],
+        series_1=tmp["tmp_ft_employed_m_v1_10"],
+        series_2=tmp["tmp_ft_employed_m_v2_10"],
         ordered=False,
     )
     out["pt_employed_m_10"] = object_to_str_categorical(
@@ -392,11 +393,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 11 - Nov
-    out["tmp_ft_employed_m_v1_11"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_11"] = object_to_str_categorical(
         series=raw_data["kal1a011_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_11"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_11"] = object_to_str_categorical(
         series=raw_data["kal1a011_v2"],
         renaming={
             "[1] Nov Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -404,8 +405,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_11"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_11"],
-        series_2=out["tmp_ft_employed_m_v2_11"],
+        series_1=tmp["tmp_ft_employed_m_v1_11"],
+        series_2=tmp["tmp_ft_employed_m_v2_11"],
         ordered=False,
     )
     out["pt_employed_m_11"] = object_to_str_categorical(
@@ -421,11 +422,11 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Month 12 - Dez
-    out["tmp_ft_employed_m_v1_12"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v1_12"] = object_to_str_categorical(
         series=raw_data["kal1a012_v1"],
         renaming={"[1] Ja": "Vollzeit erwerbstätig"},
     )
-    out["tmp_ft_employed_m_v2_12"] = object_to_str_categorical(
+    tmp["tmp_ft_employed_m_v2_12"] = object_to_str_categorical(
         series=raw_data["kal1a012_v2"],
         renaming={
             "[1] Dez Vollzeit erwerbst.": "Vollzeit erwerbstätig",
@@ -433,8 +434,8 @@ def clean(raw_data: pd.DataFrame) -> pd.DataFrame:
         },
     )
     out["ft_employed_m_12"] = combine_first_and_make_categorical(
-        series_1=out["tmp_ft_employed_m_v1_12"],
-        series_2=out["tmp_ft_employed_m_v2_12"],
+        series_1=tmp["tmp_ft_employed_m_v1_12"],
+        series_2=tmp["tmp_ft_employed_m_v2_12"],
         ordered=False,
     )
     out["pt_employed_m_12"] = object_to_str_categorical(
diff --git a/src/soep_preparation/clean_variables/pl.py b/src/soep_preparation/clean_modules/pl.py
similarity index 100%
rename from src/soep_preparation/clean_variables/pl.py
rename to src/soep_preparation/clean_modules/pl.py
diff --git a/src/soep_preparation/clean_variables/ppathl.py b/src/soep_preparation/clean_modules/ppathl.py
similarity index 100%
rename from src/soep_preparation/clean_variables/ppathl.py
rename to src/soep_preparation/clean_modules/ppathl.py
diff --git a/src/soep_preparation/clean_modules/task.py b/src/soep_preparation/clean_modules/task.py
new file mode 100644
index 0000000..90a893d
--- /dev/null
+++ b/src/soep_preparation/clean_modules/task.py
@@ -0,0 +1,59 @@
+"""Script to clean existing variables in SOEP data files."""
+
+from pathlib import Path
+from typing import Annotated
+
+import pandas as pd
+from pytask import task
+
+from soep_preparation.config import DATA_CATALOGS, MODULE_STRUCTURE, SRC
+from soep_preparation.utilities.error_handling import (
+    fail_if_expected_function_missing,
+    fail_if_input_has_invalid_type,
+)
+from soep_preparation.utilities.general import load_script
+
+for data_file_name in MODULE_STRUCTURE["cleaned_modules"]:
+
+    @task(id=data_file_name)
+    def task_clean_one_data_file(
+        raw_data: Annotated[pd.DataFrame, DATA_CATALOGS["raw_pandas"][data_file_name]],
+        script_path: Annotated[
+            Path,
+            SRC / "clean_modules" / f"{data_file_name}.py",
+        ],
+    ) -> Annotated[pd.DataFrame, DATA_CATALOGS["cleaned_modules"][data_file_name]]:
+        """Cleans variables of a module using the corresponding cleaning script.
+
+        Cleaning scripts contain function `clean` taking the raw pandas DataFrame
+        as input and assigning variables with adequate data types and values to
+        meaningful variable names. The cleaned DataFrame is returned.
+        The result is stored in the corresponding DataCatalog for further processing.
+
+        Parameters:
+            raw_data: The raw pandas DataFrame to be cleaned.
+            script_path: The path to the cleaning script.
+
+        Returns:
+                The cleaned data to be saved to the DataCatalog.
+
+        Raises:
+            ImportError: If there is an error loading the cleaning script.
+            TypeError: If raw_data is not a pandas.DataFrame or
+            script_path is not a pathlib.Path object.
+            AttributeError: If cleaning script does not
+            contain expected function.
+        """
+        fail_if_input_has_invalid_type(
+            input_=raw_data, expected_dtypes=["pandas.core.frame.DataFrame"]
+        )
+        fail_if_input_has_invalid_type(
+            input_=script_path, expected_dtypes=["pathlib._local.PosixPath"]
+        )
+        fail_if_expected_function_missing(
+            script_path=script_path, expected_function="clean"
+        )
+        script = load_script(script_path)
+        return script.clean(
+            raw_data=raw_data,
+        )
diff --git a/src/soep_preparation/clean_variables/task.py b/src/soep_preparation/clean_variables/task.py
deleted file mode 100644
index efcc3ed..0000000
--- a/src/soep_preparation/clean_variables/task.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""Module to clean existing variables in SOEP data files."""
-
-from pathlib import Path
-from typing import Annotated, Any
-
-import pandas as pd
-from pytask import task
-
-from soep_preparation.config import DATA_CATALOGS, SRC
-from soep_preparation.utilities.error_handling import fail_if_input_has_invalid_type
-from soep_preparation.utilities.general import load_module
-
-
-def _fail_if_cleaning_module_missing(module_path: Path) -> None:
-    module = load_module(module_path)
-
-    if not hasattr(module, "clean"):
-        msg = f"""The cleaning module {module_path}
-          does not contain expected cleaning function."""
-        raise AttributeError(
-            msg,
-        )
-
-
-for data_file_name, raw_data in DATA_CATALOGS["raw_pandas"]._entries.items():  # noqa: SLF001
-
-    @task(id=data_file_name)
-    def task_clean_one_data_file(
-        raw_data: Annotated[Path, raw_data],
-        module_path: Annotated[
-            Path,
-            SRC / "clean_variables" / f"{data_file_name}.py",
-        ],
-    ) -> Annotated[pd.DataFrame, DATA_CATALOGS["cleaned_variables"][data_file_name]]:
-        """Cleans variables of a data file using the corresponding cleaning module.
-
-        Cleaning modules contain function `clean` taking the raw pandas DataFrame
-        as input and assigning variables with adequate data types and values to
-        meaningful variable names. The cleaned DataFrame is returned.
-        The result is stored in the corresponding DataCatalog for further processing.
-
-        Parameters:
-            raw_data: The path to the data file to be cleaned.
-            module_path: The path to the cleaning module.
-
-        Returns:
-                The cleaned data to be saved to the DataCatalog.
-
-        Raises:
-            ImportError: If there is an error loading the cleaning module.
-            TypeError: If raw_data is not a pandas.DataFrame or
-            module_path is not a pathlib.Path object.
-            AttributeError: If cleaning module module does not
-            contain expected function.
-        """
-        _error_handling_task(data=raw_data, module_path=module_path)
-        module = load_module(module_path)
-        return module.clean(
-            raw_data=raw_data,
-        )
-
-
-def _error_handling_task(data: Any, module_path: Any) -> None:
-    fail_if_input_has_invalid_type(
-        input_=data, expected_dtypes=["pandas.core.frame.DataFrame"]
-    )
-    fail_if_input_has_invalid_type(
-        input_=module_path, expected_dtypes=["pathlib._local.PosixPath"]
-    )
-    _fail_if_cleaning_module_missing(module_path)
diff --git a/src/soep_preparation/combine_modules/__init__.py b/src/soep_preparation/combine_modules/__init__.py
new file mode 100644
index 0000000..2510a7d
--- /dev/null
+++ b/src/soep_preparation/combine_modules/__init__.py
@@ -0,0 +1 @@
+"""Combine variables from multiple cleaned modules of the SOEP data."""
diff --git a/src/soep_preparation/combine_modules/hpathl_design.py b/src/soep_preparation/combine_modules/hpathl_design.py
new file mode 100644
index 0000000..57057a9
--- /dev/null
+++ b/src/soep_preparation/combine_modules/hpathl_design.py
@@ -0,0 +1,28 @@
+"""Combine variables from the modules hpathl and design."""
+
+import pandas as pd
+
+from soep_preparation.utilities.data_manipulator import (
+    combine_first_and_make_categorical,
+)
+
+
+def combine(hpathl: pd.DataFrame, design: pd.DataFrame) -> pd.DataFrame:
+    """Combine variables from the cleaned hpathl and design modules.
+
+    Args:
+        hpathl: Cleaned hpathl module.
+        design: Cleaned design module.
+
+    Returns:
+        Combined hpathl and design modules.
+    """
+    out = pd.DataFrame(index=hpathl.index)
+    merged = pd.merge(hpathl, design, on=["hh_id"], how="outer")
+    out["hh_id"] = hpathl["hh_id"].copy()
+    out["hh_soep_sample"] = combine_first_and_make_categorical(
+        series_1=merged["hh_soep_sample_hpathl"],
+        series_2=merged["hh_soep_sample_design"],
+        ordered=False,
+    )
+    return out
diff --git a/src/soep_preparation/combine_modules/pequiv_hl.py b/src/soep_preparation/combine_modules/pequiv_hl.py
new file mode 100644
index 0000000..fac6e8c
--- /dev/null
+++ b/src/soep_preparation/combine_modules/pequiv_hl.py
@@ -0,0 +1,34 @@
+"""Combine variables from the modules pequiv and hl."""
+
+import pandas as pd
+
+
+def combine(pequiv: pd.DataFrame, hl: pd.DataFrame) -> pd.DataFrame:
+    """Combine variables from the cleaned pequiv and hl modules.
+
+    Args:
+        pequiv: Cleaned pequiv module.
+        hl: Cleaned hl module.
+
+    Returns:
+        Combined pequiv and hl modules.
+    """
+    out = pd.DataFrame(index=pequiv.index)
+    merged = pd.merge(pequiv, hl, on=["hh_id", "survey_year"], how="outer")
+    out[["p_id", "hh_id", "survey_year"]] = pequiv[
+        ["p_id", "hh_id", "survey_year"]
+    ].copy()
+    out["arbeitslosengeld_2_m_hh"] = merged[
+        "arbeitslosengeld_2_m_hh_pequiv"
+    ].combine_first(merged["arbeitslosengeld_2_m_hh_hl"])
+    out["kindergeld_m_hh"] = merged["kindergeld_m_hh_pequiv"].combine_first(
+        merged["kindergeld_m_hh_hl"]
+    )
+    out["kinderzuschlag_m_hh"] = merged["kinderzuschlag_m_hh_pequiv"].combine_first(
+        merged["kinderzuschlag_m_hh_hl"]
+    )
+    out["wohngeld_m_hh"] = merged["wohngeld_m_hh_pequiv"].combine_first(
+        merged["wohngeld_m_hh_hl"]
+    )
+
+    return out
diff --git a/src/soep_preparation/combine_variables/personal.py b/src/soep_preparation/combine_modules/pequiv_pl.py
similarity index 50%
rename from src/soep_preparation/combine_variables/personal.py
rename to src/soep_preparation/combine_modules/pequiv_pl.py
index 1eddbc8..86f65e4 100644
--- a/src/soep_preparation/combine_variables/personal.py
+++ b/src/soep_preparation/combine_modules/pequiv_pl.py
@@ -1,4 +1,4 @@
-"""Module to combine personal variables from multiple sources."""
+"""Combine variables from the modules pequiv and pl."""
 
 import pandas as pd
 
@@ -7,36 +7,15 @@
 )
 
 
-def derive_birth_month(ppathl: pd.DataFrame, bioedu: pd.DataFrame) -> pd.DataFrame:
-    """Combine the birth_month variables from ppathl and bioedu.
+def combine(pequiv: pd.DataFrame, pl: pd.DataFrame) -> pd.DataFrame:
+    """Combine variables from the cleaned pequiv and pl modules.
 
     Args:
-        ppathl: Cleaned ppathl data.
-        bioedu: Cleaned bioedu data.
+        pequiv: Cleaned pequiv module.
+        pl: Cleaned pl module.
 
     Returns:
-        Combined birth_month variable.
-    """
-    out = pd.DataFrame()
-    merged = pd.merge(left=ppathl, right=bioedu, on="p_id", how="outer")
-    out["p_id"] = merged["p_id"].unique()
-    out["birth_month"] = combine_first_and_make_categorical(
-        series_1=merged["birth_month_ppathl"],
-        series_2=merged["birth_month_bioedu"],
-        ordered=False,
-    )
-    return out
-
-
-def derive_medical_variables(pequiv: pd.DataFrame, pl: pd.DataFrame) -> pd.DataFrame:
-    """Combine the medical variables from pequiv and pl.
-
-    Args:
-        pequiv: Cleaned pequiv data.
-        pl: Cleaned pl data.
-
-    Returns:
-        Combined medical variables.
+        Combined pequiv and pl modules.
     """
     out = pd.DataFrame(index=pequiv.index)
     merged = pd.merge(pequiv, pl, on=["p_id", "hh_id", "survey_year"], how="outer")
@@ -92,50 +71,9 @@ def derive_medical_variables(pequiv: pd.DataFrame, pl: pd.DataFrame) -> pd.DataF
         "med_subjective_status_dummy_pequiv"
     ].combine_first(merged["med_subjective_status_dummy_pl"])
     out["frailty"] = merged["frailty_pequiv"].combine_first(merged["frailty_pl"])
-    return out
 
+    out["kindesunterhalt_erhalten_m"] = merged[
+        "kindesunterhalt_erhalten_m_pequiv"
+    ].combine_first(merged["kindesunterhalt_erhalten_m_pl"])
 
-def derive_p_bezog_mutterschaftsgeld(
-    pl: pd.DataFrame, pkal: pd.DataFrame
-) -> pd.DataFrame:
-    """Merge the personal bezog_mutterschaftsgeld variable from pl and pkal.
-
-    Args:
-        pl: Cleaned pl data.
-        pkal: Cleaned pkal data.
-
-    Returns:
-        Merged bezog_mutterschaftsgeld variable.
-    """
-    out = pd.DataFrame(index=pl.index)
-    merged = pd.merge(pl, pkal, on=["hh_id", "survey_year"], how="outer")
-    out[["p_id", "hh_id", "survey_year"]] = pl[["p_id", "hh_id", "survey_year"]].copy()
-    out["bezog_mutterschaftsgeld"] = combine_first_and_make_categorical(
-        series_1=merged["bezog_mutterschaftsgeld_pl"],
-        series_2=merged["bezog_mutterschaftsgeld_pkal"],
-        ordered=False,
-    )
-    return out
-
-
-def derive_p_kindesunterhalt_erhalten(
-    pl: pd.DataFrame, pequiv: pd.DataFrame
-) -> pd.DataFrame:
-    """Merge the personal kindesunterhalt_erhalten variable from pl and pequiv.
-
-    Args:
-        pl: Cleaned pl data.
-        pequiv: Cleaned pequiv data.
-
-    Returns:
-        Merged kindesunterhalt_erhalten variable.
-    """
-    out = pd.DataFrame(index=pl.index)
-    merged = pd.merge(pl, pequiv, on=["hh_id", "survey_year"], how="outer")
-    out[["p_id", "hh_id", "survey_year"]] = pl[["p_id", "hh_id", "survey_year"]].copy()
-    out["kindesunterhalt_erhalten_m_pequiv"] = combine_first_and_make_categorical(
-        series_1=merged["kindesunterhalt_erhalten_m_pl"],
-        series_2=merged["kindesunterhalt_erhalten_m_pequiv"],
-        ordered=False,
-    )
     return out
diff --git a/src/soep_preparation/combine_modules/pl_pkal.py b/src/soep_preparation/combine_modules/pl_pkal.py
new file mode 100644
index 0000000..c538c17
--- /dev/null
+++ b/src/soep_preparation/combine_modules/pl_pkal.py
@@ -0,0 +1,28 @@
+"""Combine variables from the modules pl and pkal."""
+
+import pandas as pd
+
+from soep_preparation.utilities.data_manipulator import (
+    combine_first_and_make_categorical,
+)
+
+
+def combine(pl: pd.DataFrame, pkal: pd.DataFrame) -> pd.DataFrame:
+    """Combine variables from the cleaned pl and pkal modules.
+
+    Args:
+        pl: Cleaned pl module.
+        pkal: Cleaned pkal module.
+
+    Returns:
+        Combined pl and pkal modules.
+    """
+    out = pd.DataFrame(index=pl.index)
+    merged = pd.merge(pl, pkal, on=["hh_id", "survey_year"], how="outer")
+    out[["p_id", "hh_id", "survey_year"]] = pl[["p_id", "hh_id", "survey_year"]].copy()
+    out["bezog_mutterschaftsgeld"] = combine_first_and_make_categorical(
+        series_1=merged["bezog_mutterschaftsgeld_pl"],
+        series_2=merged["bezog_mutterschaftsgeld_pkal"],
+        ordered=False,
+    )
+    return out
diff --git a/src/soep_preparation/combine_modules/ppathl_bioedu.py b/src/soep_preparation/combine_modules/ppathl_bioedu.py
new file mode 100644
index 0000000..8dd94e3
--- /dev/null
+++ b/src/soep_preparation/combine_modules/ppathl_bioedu.py
@@ -0,0 +1,28 @@
+"""Combine variables from the modules ppathl and bioedu."""
+
+import pandas as pd
+
+from soep_preparation.utilities.data_manipulator import (
+    combine_first_and_make_categorical,
+)
+
+
+def combine(ppathl: pd.DataFrame, bioedu: pd.DataFrame) -> pd.DataFrame:
+    """Combine variables from the cleaned ppathl and bioedu modules.
+
+    Args:
+        ppathl: Cleaned ppathl module.
+        bioedu: Cleaned bioedu module.
+
+    Returns:
+        Combined ppathl and bioedu modules.
+    """
+    out = pd.DataFrame()
+    merged = pd.merge(left=ppathl, right=bioedu, on="p_id", how="outer")
+    out["p_id"] = merged["p_id"].unique()
+    out["birth_month"] = combine_first_and_make_categorical(
+        series_1=merged["birth_month_ppathl"],
+        series_2=merged["birth_month_bioedu"],
+        ordered=False,
+    )
+    return out
diff --git a/src/soep_preparation/combine_modules/task.py b/src/soep_preparation/combine_modules/task.py
new file mode 100644
index 0000000..327e204
--- /dev/null
+++ b/src/soep_preparation/combine_modules/task.py
@@ -0,0 +1,60 @@
+"""Functions to create combined variables."""
+
+from pathlib import Path
+from typing import Annotated
+
+import pandas as pd
+from pytask import task
+
+from soep_preparation.config import DATA_CATALOGS, MODULE_STRUCTURE, SRC
+from soep_preparation.utilities.error_handling import (
+    fail_if_expected_function_missing,
+    fail_if_input_has_invalid_type,
+)
+from soep_preparation.utilities.general import (
+    load_script,
+)
+
+for script_name in MODULE_STRUCTURE["combined_modules"]:
+    modules_to_combine = {
+        module: DATA_CATALOGS["cleaned_modules"][module]
+        for module in script_name.split("_")
+    }
+
+    @task(id=f"{script_name}")
+    def task_combine_modules(
+        modules_to_combine: Annotated[dict[str, pd.DataFrame], modules_to_combine],
+        script_path: Annotated[Path, SRC / "combine_modules" / f"{script_name}.py"],
+        script_name: Annotated[str, script_name],
+    ) -> Annotated[pd.DataFrame, DATA_CATALOGS["combined_modules"][script_name]]:
+        """Combine variables from multiple modules into one module.
+
+        Args:
+            modules_to_combine: A dictionary where keys are
+                module names and values are the corresponding dataframes to be combined.
+            script_path: The path to the script that contains the combine function.
+            script_name: The name of the script being executed.
+
+        Returns:
+            The combined variables from the input modules.
+        """
+        fail_if_input_has_invalid_type(input_=script_path, expected_dtypes=["path"])
+        fail_if_expected_function_missing(script_path, "combine")
+        _fail_if_too_many_or_too_few_dataframes(
+            dataframes=modules_to_combine, expected_entries=len(script_name.split("_"))
+        )
+        fail_if_input_has_invalid_type(
+            input_=modules_to_combine, expected_dtypes=["dict"]
+        )
+        script = load_script(script_path)
+        return script.combine(**modules_to_combine)
+
+
+def _fail_if_too_many_or_too_few_dataframes(
+    dataframes: dict, expected_entries: int
+) -> None:
+    if len(dataframes.keys()) != expected_entries:
+        msg = f"Expected {expected_entries} dataframes, got {len(dataframes.keys())}"
+        raise ValueError(
+            msg,
+        )
diff --git a/src/soep_preparation/combine_variables/__init__.py b/src/soep_preparation/combine_variables/__init__.py
deleted file mode 100644
index 191ba6c..0000000
--- a/src/soep_preparation/combine_variables/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Create variables out of the SOEP data."""
diff --git a/src/soep_preparation/combine_variables/household.py b/src/soep_preparation/combine_variables/household.py
deleted file mode 100644
index bb6b225..0000000
--- a/src/soep_preparation/combine_variables/household.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Module to combine household variables from multiple sources."""
-
-import pandas as pd
-
-from soep_preparation.utilities.data_manipulator import (
-    combine_first_and_make_categorical,
-)
-
-
-def derive_hh_soep_sample(hpathl: pd.DataFrame, design: pd.DataFrame) -> pd.DataFrame:
-    """Merge the hh_soep_sample variable from hpathl and design.
-
-    Args:
-        hpathl: Cleaned hpathl data.
-        design: Cleaned design data.
-
-    Returns:
-        Merged hh_soep_sample variable.
-    """
-    out = pd.DataFrame(index=hpathl.index)
-    merged = pd.merge(hpathl, design, on=["hh_id"], how="outer")
-    out["hh_id"] = hpathl["hh_id"].copy()
-    out["hh_soep_sample"] = combine_first_and_make_categorical(
-        series_1=merged["hh_soep_sample_hpathl"],
-        series_2=merged["hh_soep_sample_design"],
-        ordered=False,
-    )
-    return out
-
-
-def derive_hh_received_transfers(
-    pequiv: pd.DataFrame, hl: pd.DataFrame
-) -> pd.DataFrame:
-    """Merge the household received transfer variables from pequiv and hl.
-
-    Args:
-        pequiv: Cleaned pequiv data.
-        hl: Cleaned hl data.
-
-    Returns:
-        Merged received transfer variables.
-    """
-    out = pd.DataFrame(index=pequiv.index)
-    merged = pd.merge(pequiv, hl, on=["hh_id", "survey_year"], how="outer")
-    out[["p_id", "hh_id", "survey_year"]] = pequiv[
-        ["p_id", "hh_id", "survey_year"]
-    ].copy()
-    out["arbeitslosengeld_2_m_hh"] = combine_first_and_make_categorical(
-        series_1=merged["arbeitslosengeld_2_m_hh_pequiv"],
-        series_2=merged["arbeitslosengeld_2_m_hh_hl"],
-        ordered=False,
-    )
-    out["kindergeld_m_hh"] = combine_first_and_make_categorical(
-        series_1=merged["kindergeld_m_hh_pequiv"],
-        series_2=merged["kindergeld_m_hh_hl"],
-        ordered=False,
-    )
-    out["kinderzuschlag_m_hh"] = combine_first_and_make_categorical(
-        series_1=merged["kinderzuschlag_m_hh_pequiv"],
-        series_2=merged["kinderzuschlag_m_hh_hl"],
-        ordered=False,
-    )
-    out["wohngeld_m_hh"] = combine_first_and_make_categorical(
-        series_1=merged["wohngeld_m_hh_pequiv"],
-        series_2=merged["wohngeld_m_hh_hl"],
-        ordered=False,
-    )
-
-    return out
diff --git a/src/soep_preparation/combine_variables/task.py b/src/soep_preparation/combine_variables/task.py
deleted file mode 100644
index 44d7751..0000000
--- a/src/soep_preparation/combine_variables/task.py
+++ /dev/null
@@ -1,94 +0,0 @@
-"""Functions to create derived variables."""
-
-from typing import Annotated, Any
-
-import pandas as pd
-from pytask import PickleNode, task
-
-from soep_preparation.config import DATA_CATALOGS, SRC
-from soep_preparation.utilities.error_handling import fail_if_input_has_invalid_type
-from soep_preparation.utilities.general import (
-    get_script_names,
-    load_module,
-)
-
-
-def _fail_if_too_many_or_too_few_dataframes(
-    dataframes: dict, expected_entries: int
-) -> None:
-    if len(dataframes.keys()) != expected_entries:
-        msg = f"Expected {expected_entries} dataframes, got {len(dataframes.keys())}"
-        raise ValueError(
-            msg,
-        )
-
-
-def _get_relevant_data_files_mapping(
-    function_: Any,
-) -> dict[str, PickleNode]:
-    # get the relevant data file names from the function annotations
-    relevant_data_file_names = [
-        data_file_name
-        for data_file_name in function_.__annotations__
-        if data_file_name in DATA_CATALOGS["cleaned_variables"]._entries  # noqa: SLF001
-    ]
-    # create a mapping of data file names to DataFrames
-    # using the data catalog
-    return {
-        data_name: DATA_CATALOGS["cleaned_variables"][data_name]
-        for data_name in relevant_data_file_names
-    }
-
-
-def _get_variable_names_in_module(module: Any) -> list[str]:
-    """Get the variable names in the module.
-
-    Args:
-        module: The module to get the variable names from.
-
-    Returns:
-        The variable names in the module.
-    """
-    return [
-        variable_name.split("derive_")[-1]
-        for variable_name in module.__dict__
-        if variable_name.startswith("derive_")
-    ]
-
-
-script_names = get_script_names(SRC / "combine_variables")
-for script_name in script_names:
-    module = load_module(SRC / "combine_variables" / f"{script_name}.py")
-    variable_names = _get_variable_names_in_module(module)
-    for variable_name in variable_names:
-        function_ = getattr(module, f"derive_{variable_name}")
-        data_files = _get_relevant_data_files_mapping(function_=function_)
-
-        @task(id=variable_name)
-        def task_create_merged_variables(
-            data_files: Annotated[dict[str, pd.DataFrame], data_files],
-            function_: Annotated[Any, function_],
-        ) -> Annotated[
-            pd.DataFrame, DATA_CATALOGS["combined_variables"][variable_name]
-        ]:
-            """Merge variables for the meta dataset.
-
-            Args:
-                data_files: A mapping of data file names to DataFrames.
-                function_: Function to create derived variables.
-
-            Returns:
-                Derived variables.
-
-            Raises:
-                TypeError: If input data files or function is not of expected type.
-                ValueError: If number of dataframes is not as expected.
-            """
-            _error_handling_derived_variables(data=data_files, function_=function_)
-            return function_(**data_files)
-
-
-def _error_handling_derived_variables(data: Any, function_: Any) -> None:
-    fail_if_input_has_invalid_type(input_=data, expected_dtypes=["dict"])
-    _fail_if_too_many_or_too_few_dataframes(dataframes=data, expected_entries=2)
-    fail_if_input_has_invalid_type(input_=function_, expected_dtypes=["function"])
diff --git a/src/soep_preparation/config.py b/src/soep_preparation/config.py
index 18d9ff9..e7c38a9 100644
--- a/src/soep_preparation/config.py
+++ b/src/soep_preparation/config.py
@@ -5,6 +5,11 @@
 import pandas as pd
 from pytask import DataCatalog
 
+from soep_preparation.utilities.general import (
+    get_combine_module_names,
+    get_data_file_names,
+)
+
 pd.set_option("mode.copy_on_write", True)  # noqa: FBT003
 pd.set_option("future.infer_string", True)  # noqa: FBT003
 pd.set_option("future.no_silent_downcasting", True)  # noqa: FBT003
@@ -12,6 +17,7 @@
 
 SRC = Path(__file__).parent.resolve()
 ROOT = SRC.parent.parent.resolve()
+BLD = ROOT.joinpath("bld").resolve()
 DATA_ROOT = ROOT.joinpath("data").resolve()
 TEST_DIR = ROOT.joinpath("tests").resolve()
 
@@ -22,17 +28,30 @@
 else:
     SURVEY_YEARS = [*range(1984, 2020 + 1)]
 
+
+MODULE_STRUCTURE = {
+    "cleaned_modules": get_data_file_names(
+        directory=SRC / "clean_modules",
+        data_root=DATA_ROOT,
+        soep_version=SOEP_VERSION,
+    ),
+    "combined_modules": get_combine_module_names(directory=SRC / "combine_modules"),
+}
+
+
 DATA_CATALOGS = {
     "raw_pandas": DataCatalog(name="raw_pandas"),
-    "cleaned_variables": DataCatalog(name="cleaned_variables"),
-    "combined_variables": DataCatalog(name="combined_variables"),
+    "cleaned_modules": DataCatalog(name="cleaned_modules"),
+    "combined_modules": DataCatalog(name="combined_modules"),
     "metadata": DataCatalog(name="metadata"),
     "merged": DataCatalog(name="merged"),
 }
 
 __all__ = [
+    "BLD",
     "DATA_CATALOGS",
     "DATA_ROOT",
+    "MODULE_STRUCTURE",
     "ROOT",
     "SOEP_VERSION",
     "SRC",
diff --git a/src/soep_preparation/convert_stata_to_pandas/task.py b/src/soep_preparation/convert_stata_to_pandas/task.py
index 0fbc1fd..4339838 100644
--- a/src/soep_preparation/convert_stata_to_pandas/task.py
+++ b/src/soep_preparation/convert_stata_to_pandas/task.py
@@ -10,12 +10,12 @@
 from soep_preparation.config import (
     DATA_CATALOGS,
     DATA_ROOT,
+    MODULE_STRUCTURE,
     SOEP_VERSION,
     SRC,
 )
 from soep_preparation.utilities.error_handling import fail_if_input_has_invalid_type
 from soep_preparation.utilities.general import (
-    get_data_file_names,
     get_relevant_column_names,
 )
 
@@ -35,13 +35,7 @@ def _iteratively_read_one_data_file(
     return pd.concat(processed_chunks)
 
 
-data_file_names = get_data_file_names(
-    directory=SRC / "clean_variables",
-    data_root=DATA_ROOT,
-    soep_version=SOEP_VERSION,
-)
-
-for data_file_name in data_file_names:
+for data_file_name in MODULE_STRUCTURE["cleaned_modules"]:
 
     @task(id=data_file_name)
     def task_read_one_data_file(
@@ -50,7 +44,7 @@ def task_read_one_data_file(
         ],
         cleaning_script: Annotated[
             Path,
-            SRC / "clean_variables" / f"{data_file_name}.py",
+            SRC / "clean_modules" / f"{data_file_name}.py",
         ],
     ) -> Annotated[pd.DataFrame, DATA_CATALOGS["raw_pandas"][data_file_name]]:
         """Saves the raw data file to the data catalog.
@@ -78,7 +72,7 @@ def task_read_one_data_file(
             )
 
 
-if not data_file_names:
+if not MODULE_STRUCTURE["cleaned_modules"]:
 
     @task
     def _raise_no_data_files_found() -> None:
diff --git a/src/soep_preparation/create_metadata/__init__.py b/src/soep_preparation/create_metadata/__init__.py
index f9fdbe4..991a87d 100644
--- a/src/soep_preparation/create_metadata/__init__.py
+++ b/src/soep_preparation/create_metadata/__init__.py
@@ -1 +1 @@
-"""Modules for creating metadata information for data."""
+"""Scripts for creating metadata information for data."""
diff --git a/src/soep_preparation/create_metadata/task.py b/src/soep_preparation/create_metadata/task.py
index 47bc330..98d2788 100644
--- a/src/soep_preparation/create_metadata/task.py
+++ b/src/soep_preparation/create_metadata/task.py
@@ -1,128 +1,178 @@
 """Tasks to create metadata."""
 
+import shutil
+from pathlib import Path
 from typing import Annotated, Any
 
 import pandas as pd
-from pytask import task
+import yaml
+from pytask import Product, task
 
-from soep_preparation.config import DATA_CATALOGS
+from soep_preparation.config import BLD, DATA_CATALOGS, MODULE_STRUCTURE, SRC
 from soep_preparation.utilities.error_handling import (
+    fail_if_empty,
     fail_if_input_has_invalid_type,
 )
 
+POTENTIAL_INDEX_VARIABLES = ["p_id", "hh_id", "hh_id_original", "survey_year"]
 
-def _get_index_variables(
-    dataset: pd.DataFrame,
-    potential_index_variables: list[str],
+
+def _get_index_variables_metadata(
+    module: pd.DataFrame,
 ) -> dict:
+    """Get metadata for index variables in the module.
+
+    Args:
+        module: The data containing the index variables.
+
+    Returns:
+        Dtype information for each index variable.
+    """
     return {
         col: dtype_
-        for col, dtype_ in dataset.dtypes.items()
-        if col in potential_index_variables
+        for col, dtype_ in module.dtypes.items()
+        if col in POTENTIAL_INDEX_VARIABLES
     }
 
 
-def _get_variable_dtypes(
-    dataset: pd.DataFrame,
-    potential_index_variables: list[str],
-) -> dict:
+def _serialize_category_dtype(variable_dtype: pd.CategoricalDtype) -> dict:
+    """Serialize a pandas CategoricalDtype to a dictionary.
+
+    Args:
+        variable_dtype: The CategoricalDtype to serialize.
+
+    Returns:
+        A dictionary representation of the CategoricalDtype.
+    """
     return {
-        col: dtype_.name
-        for col, dtype_ in dataset.dtypes.items()
-        if col not in potential_index_variables
+        "categories": variable_dtype.categories.tolist(),
+        "categories_dtype": variable_dtype.categories.dtype.name,
+        "ordered": variable_dtype.ordered,
     }
 
 
-def _create_metadata_mapping(metadata: dict) -> dict[str, str]:
-    """Create a mapping of column names to data file names.
+def _get_variable_metadata(
+    module: pd.DataFrame,
+) -> dict:
+    """Get metadata for variables in the module.
 
     Args:
-        metadata: A dictionary containing metadata entries.
+        module: The data containing the variables.
 
     Returns:
-        A mapping of variable names to data file names.
+        Metadata for each variable, including dtype and survey year availability.
+    """
+    columns = module.columns.tolist()
+    survey_year_in_columns = "survey_year" in columns
+    variables = [col for col in columns if col not in POTENTIAL_INDEX_VARIABLES]
+
+    metadata = {}
+    # for each variable/column in data
+    for variable in variables:
+        # determine dtype of variable
+        variable_dtype = module[variable].dtype
+        if variable_dtype.name == "category":
+            # categorical dtypes are serialized
+            serialized_variable_dtype = _serialize_category_dtype(variable_dtype)
+        else:
+            serialized_variable_dtype = variable_dtype.name
+
+        # determine survey year availability of variable
+        variable_survey_years = None
+        if survey_year_in_columns:
+            variable_survey_years = sorted(
+                set(module[["survey_year", variable]].dropna()["survey_year"])
+            )
+
+        metadata[variable] = {
+            "dtype": serialized_variable_dtype,
+            "survey_years": variable_survey_years,
+        }
+
+    return metadata
+
+
+def _create_variable_to_metadata_mapping(
+    map_module_to_metadata: dict,
+) -> dict[str, dict]:
+    """Create a mapping of variable to metadata.
+
+    Args:
+        map_module_to_metadata: Map of module to metadata information.
+
+    Returns:
+        A mapping of variable to metadata.
     """
     mapping = {}
-    for data_name, data in metadata._entries.items():  # noqa: SLF001
-        if (
-            data_name not in DATA_CATALOGS["combined_variables"]._entries  # noqa: SLF001
-        ) and (
-            data_name not in DATA_CATALOGS["cleaned_variables"]._entries  # noqa: SLF001
-        ):
-            # Skip if data_name is neither among combined variables nor among data files
-            continue
-        variable_names = data.load()["variable_dtypes"].keys()
-        for variable_name in variable_names:
-            mapping[variable_name] = data_name
+    for module_name, metadata in map_module_to_metadata.items():
+        for variable_name, variable_metadata in metadata["variable_metadata"].items():
+            mapping[variable_name] = {"module": module_name} | variable_metadata
     return mapping
 
 
-single_data_files = dict(
-    DATA_CATALOGS["cleaned_variables"]._entries.items()  # noqa: SLF001
-)
-combined_variables = dict(
-    DATA_CATALOGS["combined_variables"]._entries.items()  # noqa: SLF001
-)
+for level, module_names in MODULE_STRUCTURE.items():
+    for module_name in module_names:
 
+        @task(id=module_name)
+        def task_create_metadata(
+            module: Annotated[pd.DataFrame, DATA_CATALOGS[level][module_name]],
+        ) -> Annotated[dict, DATA_CATALOGS["metadata"][module_name]]:
+            """Create metadata for a single module.
 
-for name, data in (single_data_files | combined_variables).items():
-
-    @task(id=name)
-    def task_create_metadata(
-        data: Annotated[pd.DataFrame, data],
-    ) -> Annotated[dict, DATA_CATALOGS["metadata"][name]]:
-        """Create metadata for DataFrame.
-
-        Args:
-            data: The data to create metadata for.
-
-        Returns:
-            Metadata information for DataFrame.
-
-        Raises:
-            TypeError: If input data is not of expected type.
-        """
-        fail_if_input_has_invalid_type(
-            input_=data, expected_dtypes=["pandas.core.frame.DataFrame"]
-        )
-        potential_index_variables = ["p_id", "hh_id", "hh_id_original", "survey_year"]
-        index_variables = _get_index_variables(
-            dataset=data, potential_index_variables=potential_index_variables
-        )
-        variable_dtypes = _get_variable_dtypes(
-            dataset=data, potential_index_variables=potential_index_variables
-        )
-        return {
-            "index_variables": index_variables,
-            "variable_dtypes": variable_dtypes,
-        }
+            Args:
+                module: The data module to create metadata for.
 
+            Returns:
+                Metadata information for index and variables contained in the module.
 
-@task(after="task_create_metadata")
-def task_create_metadata_mapping(
-    single_metadata_mapping: Annotated[dict, DATA_CATALOGS["metadata"]],
-) -> Annotated[dict[str, dict], DATA_CATALOGS["metadata"]["merged"]]:
-    """Create a mapping of variable names to data file names.
+            Raises:
+                TypeError: If input data is not of expected type.
+            """
+            fail_if_input_has_invalid_type(
+                input_=module, expected_dtypes=["pandas.core.frame.DataFrame"]
+            )
+            index_variables_metadata = _get_index_variables_metadata(module)
+            variable_metadata = _get_variable_metadata(module)
+            return {
+                "index_variables": index_variables_metadata,
+                "variable_metadata": variable_metadata,
+            }
 
-    Args:
-        single_metadata_mapping: A dictionary containing single metadata entries.
 
-    Returns:
-        A mapping of variable names to data file names.
+def task_create_variable_to_metadata_mapping(
+    modules_metadata: Annotated[
+        dict[str, pd.DataFrame], DATA_CATALOGS["metadata"]._entries
+    ],
+    path: Annotated[Path, Product] = BLD / "variable_to_metadata_mapping.yaml",
+) -> None:
+    """Create a mapping of variables to metadata and store as YAML file.
+
+    Args:
+        modules_metadata: Map of module to metadata information.
+        path: The path to the YAML file to write.
 
     Raises:
         TypeError: If input data or data name is not of expected type.
     """
-    _error_handling_mapping_task(single_metadata_mapping)
-    return _create_metadata_mapping(single_metadata_mapping)
-
-
-def _error_handling_mapping_task(mapping: Any) -> None:
-    fail_if_input_has_invalid_type(
-        input_=mapping, expected_dtypes=["_pytask.data_catalog.DataCatalog"]
-    )
-    for data_name, data in mapping._entries.items():  # noqa: SLF001
-        fail_if_input_has_invalid_type(input_=data_name, expected_dtypes=["str"])
-        fail_if_input_has_invalid_type(
-            input_=data, expected_dtypes=["_pytask.nodes.PickleNode"]
-        )
+    _error_handling_mapping_task(modules_metadata)
+    mapping = _create_variable_to_metadata_mapping(modules_metadata)
+    with Path.open(path, "w", encoding="utf-8") as file:
+        yaml.dump(mapping, file, encoding="utf-8", allow_unicode=True)
+
+
+def task_copy_yaml_mapping_to_src(
+    in_path: Annotated[Path, BLD / "variable_to_metadata_mapping.yaml"],
+    out_path: Annotated[Path, Product] = SRC
+    / "create_metadata"
+    / "variable_to_metadata_mapping.yaml",
+) -> None:
+    """Copy the YAML mapping file from BLD to SRC."""
+    shutil.copy(in_path, out_path)
+
+
+def _error_handling_mapping_task(modules_metadata: Any) -> None:
+    fail_if_input_has_invalid_type(input_=modules_metadata, expected_dtypes=["dict"])
+    fail_if_empty(input_=modules_metadata, name="modules_metadata")
+    for metadata_name, metadata in modules_metadata.items():
+        fail_if_input_has_invalid_type(input_=metadata_name, expected_dtypes=["str"])
+        fail_if_input_has_invalid_type(input_=metadata, expected_dtypes=["dict"])
diff --git a/src/soep_preparation/dataset_merging/helper.py b/src/soep_preparation/dataset_merging/helper.py
index 0cbd02f..149d8ae 100644
--- a/src/soep_preparation/dataset_merging/helper.py
+++ b/src/soep_preparation/dataset_merging/helper.py
@@ -3,9 +3,11 @@
 from difflib import get_close_matches
 
 import pandas as pd
+from pytask import PNode, PProvisionalNode
 
 from soep_preparation.config import DATA_CATALOGS, SURVEY_YEARS
 from soep_preparation.utilities.error_handling import (
+    fail_if_empty,
     fail_if_input_has_invalid_type,
 )
 
@@ -14,9 +16,7 @@ def create_dataset_from_variables(
     variables: list[str],
     min_and_max_survey_years: tuple[int, int] | None = None,
     survey_years: list[int] | None = None,
-    variable_to_data_file_mapping: dict[str, list[str]] = DATA_CATALOGS["metadata"][
-        "merged"
-    ],
+    map_variable_to_module: (dict[str, list[str]] | PNode | PProvisionalNode) = {},  # noqa : B006
     merging_behavior: str = "outer",  # make only outer
 ) -> pd.DataFrame:
     """Create a dataset by merging different specified variables.
@@ -29,8 +29,8 @@ def create_dataset_from_variables(
         min_and_max_survey_years: Range of survey years.
         survey_years: Survey years to be included in the dataset.
         Either `survey_years` or `min_and_max_survey_years` must be provided.
-        variable_to_data_file_mapping: A mapping of variable names to dataset names.
-        Defaults to `DATA_CATALOGS["metadata"]["merged"]`.
+        map_variable_to_module: A mapping of variable names to dataset names.
+        Defaults to `DATA_CATALOGS["metadata"]["mapping"]`.
         merging_behavior: The merging behavior to be used.
         Any out of "left", "right", "outer", or "inner".
         Defaults to "outer".
@@ -55,9 +55,9 @@ def create_dataset_from_variables(
         Otherwise, `min_and_max_survey_years=(2024,2025)`
         and `survey_years=[2024, 2025]`
         both return a merged dataset with information from the two survey years.
-        `variable_to_data_file_mapping` is created automatically by the pipeline,
+        `map_variable_to_module` is created automatically by the pipeline,
         it can be accessed and provided to the function at
-        `DATA_CATALOGS["metadata"]["merged"]`.
+        `DATA_CATALOGS["metadata"]["mapping"]`.
         Specify `merging_behavior` to control the creation of the dataset
         from the different variables.
         The default value is "outer" and sufficient for most cases.
@@ -66,7 +66,7 @@ def create_dataset_from_variables(
         For an example see `task_example.py`.
     """
     _error_handling(
-        variable_to_data_file_mapping,
+        map_variable_to_module,
         variables,
         min_and_max_survey_years,
         survey_years,
@@ -79,7 +79,7 @@ def create_dataset_from_variables(
         variables,
     )
     dataset_merging_information = _get_sorted_dataset_merging_information(
-        variable_to_data_file_mapping,
+        map_variable_to_module,
         variables,
         survey_years,
     )
@@ -91,14 +91,15 @@ def create_dataset_from_variables(
 
 
 def _error_handling(
-    variable_to_data_file_mapping: dict[str, list[str]],
+    map_variable_to_module: dict[str, list[str]],
     variables: list[str],
     min_and_max_survey_years: tuple[int, int] | None,
     survey_years: list[int] | None,
     merging_behavior: str,
 ) -> None:
     fail_if_input_has_invalid_type(
-        input_=variable_to_data_file_mapping, expected_dtypes=["dict"]
+        input_=map_variable_to_module,
+        expected_dtypes=["dict", "PNode", "PProvisionalNode"],
     )
     fail_if_input_has_invalid_type(input_=variables, expected_dtypes=["list"])
     fail_if_input_has_invalid_type(
@@ -108,8 +109,8 @@ def _error_handling(
         input_=survey_years, expected_dtypes=("list", "None")
     )
     fail_if_input_has_invalid_type(input_=merging_behavior, expected_dtypes=["str"])
-    _fail_if_empty(variable_to_data_file_mapping)
-    _fail_if_empty(variables)
+    fail_if_empty(input_=map_variable_to_module, name="map_variable_to_module")
+    fail_if_empty(variables, name="variables")
     if survey_years is not None:
         _fail_if_survey_years_not_valid(
             survey_years=survey_years, valid_survey_years=SURVEY_YEARS
@@ -120,25 +121,25 @@ def _error_handling(
         )
         _fail_if_min_larger_max(min_and_max_survey_years)
     _fail_if_invalid_variable(
-        variables=variables, variable_to_data_file_mapping=variable_to_data_file_mapping
+        variables=variables, map_variable_to_module=map_variable_to_module
     )
     _fail_if_invalid_merging_behavior(merging_behavior)
 
 
 def _fail_if_invalid_variable(
     variables: list[str],
-    variable_to_data_file_mapping: dict[str, list[str]],
+    map_variable_to_module: dict[str, list[str]],
 ) -> None:
     for variable in variables:
-        if variable not in variable_to_data_file_mapping:
+        if variable not in map_variable_to_module:
             closest_matches = get_close_matches(
                 variable,
-                variable_to_data_file_mapping.keys(),
+                map_variable_to_module.keys(),
                 n=3,
                 cutoff=0.6,
             )
             matches = {
-                match: variable_to_data_file_mapping[match] for match in closest_matches
+                match: map_variable_to_module[match] for match in closest_matches
             }
             msg = f"""variable {variable} not found in any data file.
             The closest matches with the corresponding data files are:
@@ -146,12 +147,6 @@ def _fail_if_invalid_variable(
             raise ValueError(msg)
 
 
-def _fail_if_empty(input_: dict | list) -> None:
-    if len(input_) == 0:
-        msg = f"Expected {input_} to be non-empty."
-        raise ValueError(msg)
-
-
 def _fail_if_survey_years_not_valid(
     survey_years: list[int] | tuple[int],
     valid_survey_years: list[int],
@@ -178,13 +173,13 @@ def _fail_if_invalid_merging_behavior(merging_behavior: str) -> None:
 
 
 def _get_data_file_name_to_variables_mapping(
-    variable_to_data_file_mapping: dict[str, str],
+    map_variable_to_module: dict[str, str],
     variables: list[str],
 ) -> dict[str, list[str]]:
     data_file_name_to_variables_mapping = {}
     for variable in variables:
-        if variable in variable_to_data_file_mapping:
-            data_file_name = variable_to_data_file_mapping[variable]
+        if variable in map_variable_to_module:
+            data_file_name = map_variable_to_module[variable]
             if data_file_name not in data_file_name_to_variables_mapping:
                 data_file_name_to_variables_mapping[data_file_name] = []
             data_file_name_to_variables_mapping[data_file_name].append(variable)
@@ -219,21 +214,21 @@ def _fix_user_input(
 
 
 def _get_sorted_dataset_merging_information(
-    variable_to_data_file_mapping: dict[str, dict],
+    map_variable_to_module: dict[str, dict],
     variables: list,
     survey_years: list[int],
 ) -> dict[str, dict]:
     data_mapping = _get_data_file_name_to_variables_mapping(
-        variable_to_data_file_mapping,
+        map_variable_to_module,
         variables,
     )
 
     dataset_merging_information = {}
     for data_name, data_variables in data_mapping.items():
         raw_data = (
-            DATA_CATALOGS["cleaned_variables"][data_name].load()
-            if data_name in DATA_CATALOGS["cleaned_variables"]._entries  # noqa: SLF001
-            else DATA_CATALOGS["combined_variables"][data_name].load()
+            DATA_CATALOGS["cleaned_modules"][data_name].load()
+            if data_name in DATA_CATALOGS["cleaned_modules"]._entries  # noqa: SLF001
+            else DATA_CATALOGS["combined_modules"][data_name].load()
         )
         index_variables = sorted(
             DATA_CATALOGS["metadata"][data_name].load()["index_variables"].keys(),
diff --git a/src/soep_preparation/dataset_merging/task_example.py b/src/soep_preparation/dataset_merging/task_example.py
index daa1222..70b105c 100644
--- a/src/soep_preparation/dataset_merging/task_example.py
+++ b/src/soep_preparation/dataset_merging/task_example.py
@@ -1,11 +1,13 @@
 """Example task to create merge variables to a dataset."""
 
+from pathlib import Path
 from typing import Annotated, Any
 
 import pandas as pd
 import pytask
+import yaml
 
-from soep_preparation.config import DATA_CATALOGS, SURVEY_YEARS
+from soep_preparation.config import DATA_CATALOGS, SRC, SURVEY_YEARS
 from soep_preparation.dataset_merging.helper import create_dataset_from_variables
 from soep_preparation.utilities.error_handling import fail_if_input_has_invalid_type
 
@@ -22,15 +24,17 @@
 ]
 
 
-@pytask.mark.try_last
+@pytask.mark.skip()
 def task_merge_variables(
-    variable_to_data_file_mapping: Annotated[dict, DATA_CATALOGS["metadata"]["merged"]],
+    map_path: Annotated[
+        Path, SRC / "create_metadata" / "variable_to_metadata_mapping.yaml"
+    ],
     variables: Annotated[list[str], VARIABLES],
 ) -> Annotated[pd.DataFrame, DATA_CATALOGS["merged"]["example_merged_dataset"]]:
     """Example task merging based on variable names to create dataset.
 
     Args:
-        variable_to_data_file_mapping: A mapping of variable names to dataset names.
+        map_path: Path to the variable to module mapping.
         variables: A list of variable names to be used for merging.
 
     Returns:
@@ -39,14 +43,16 @@ def task_merge_variables(
     Raises:
         TypeError: If input mapping or variables is not of expected type.
     """
-    _error_handling_task(mapping=variable_to_data_file_mapping, variables=variables)
+    _error_handling_task(map_path=map_path, variables=variables)
+    with Path.open(map_path, "r", encoding="utf-8") as file:
+        map_variable_to_module = yaml.safe_load(file)
     return create_dataset_from_variables(
         variables=variables,
         min_and_max_survey_years=(min(SURVEY_YEARS), max(SURVEY_YEARS)),
-        variable_to_data_file_mapping=variable_to_data_file_mapping,
+        map_variable_to_data_file=map_variable_to_module,
     )
 
 
-def _error_handling_task(mapping: Any, variables: Any) -> None:  # noqa: ANN401
-    fail_if_input_has_invalid_type(input_=mapping, expected_dtypes=["dict"])
+def _error_handling_task(map_path: Any, variables: Any) -> None:  # noqa: ANN401
+    fail_if_input_has_invalid_type(input_=map_path, expected_dtypes=["pathlib.Path"])
     fail_if_input_has_invalid_type(input_=variables, expected_dtypes=["list"])
diff --git a/src/soep_preparation/utilities/error_handling.py b/src/soep_preparation/utilities/error_handling.py
index fb9e9f9..f271906 100644
--- a/src/soep_preparation/utilities/error_handling.py
+++ b/src/soep_preparation/utilities/error_handling.py
@@ -1,10 +1,13 @@
 """Error handling utilities for data validation."""
 
 from collections.abc import Iterable
+from pathlib import Path
 from typing import Any
 
 import pandas as pd
 
+from soep_preparation.utilities.general import load_script
+
 
 def _fail_if_series_wrong_dtype(series: pd.Series, expected_dtype: str) -> None:
     if expected_dtype not in series.dtype.name and expected_dtype != "Any":
@@ -12,6 +15,43 @@ def _fail_if_series_wrong_dtype(series: pd.Series, expected_dtype: str) -> None:
         raise TypeError(msg)
 
 
+def fail_if_empty(input_: dict | list, name: str) -> None:
+    """Fail if the input is empty.
+
+    Args:
+        input_: The input to check.
+        name: The name of the input (for error messages).
+
+    Raises:
+        ValueError: If input is empty.
+    """
+    if len(input_) == 0:
+        msg = f"Expected {name} to be non-empty."
+        raise ValueError(msg)
+
+
+def fail_if_expected_function_missing(
+    script_path: Path, expected_function: str
+) -> None:
+    """Fail if expected function is missing in script.
+
+    Args:
+        script_path: The path to the script to check.
+        expected_function: The expected function name.
+
+    Raises:
+        AttributeError: If expected function is missing in script.
+    """
+    script = load_script(script_path)
+
+    if not hasattr(script, expected_function):
+        msg = f"""The cleaning script at {script_path}
+          does not contain the expected function {expected_function}."""
+        raise AttributeError(
+            msg,
+        )
+
+
 def fail_if_input_has_invalid_type(input_: Any, expected_dtypes: Iterable[str]) -> None:  # noqa: ANN401
     """Fail if input is not of any of expected types.
 
diff --git a/src/soep_preparation/utilities/general.py b/src/soep_preparation/utilities/general.py
index 427673c..d8060b6 100644
--- a/src/soep_preparation/utilities/general.py
+++ b/src/soep_preparation/utilities/general.py
@@ -5,6 +5,7 @@
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
 from types import ModuleType
+from typing import Any
 
 
 def _fail_if_raw_data_file_missing(
@@ -12,11 +13,30 @@ def _fail_if_raw_data_file_missing(
 ) -> None:
     raw_data_file_path = data_root / f"{soep_version}" / f"{script_name}.dta"
     if not raw_data_file_path.exists():
-        msg = f"""Raw data file {raw_data_file_path} not found for SOEP {soep_version}.
-        Ensure the file is present in the data directory for the corresponding wave."""
+        msg = (
+            f"Raw data file {raw_data_file_path} not found for SOEP {soep_version}.\n"
+            f"Ensure the file is present in the data directory\n"
+            f" corresponding to the specified wave."
+        )
         raise FileNotFoundError(msg)
 
 
+def get_variable_names_in_script(script: Any) -> list[str]:  # noqa: ANN401
+    """Get the variable names in the script.
+
+    Args:
+        script: The script to get the variable names from.
+
+    Returns:
+        The variable names in the script.
+    """
+    return [
+        variable_name.split("derive_")[-1]
+        for variable_name in script.__dict__
+        if variable_name.startswith("derive_")
+    ]
+
+
 def get_script_names(directory: Path) -> list[str]:
     """Get the names of all scripts in the given directory.
 
@@ -53,6 +73,29 @@ def get_data_file_names(
     return script_names
 
 
+def get_combine_module_names(directory: Path) -> list[str]:
+    """Get names of all combine scripts in the directory with valid raw modules.
+
+    Args:
+        directory: The directory containing combine scripts.
+
+    Returns:
+        A list of combine module names.
+
+    """
+    combine_module_names = []
+    script_names = get_script_names(directory)
+    for combine_module in script_names:
+        for raw_module in combine_module.split("_"):
+            if raw_module not in get_script_names(
+                directory=directory.parent / "clean_modules"
+            ):
+                break
+        else:
+            combine_module_names.append(combine_module)
+    return combine_module_names
+
+
 def get_relevant_column_names(script_path: Path) -> list[str]:
     """Get relevant column names from the cleaning script.
 
@@ -62,12 +105,12 @@ def get_relevant_column_names(script_path: Path) -> list[str]:
     Returns:
         A list of relevant column names.
     """
-    module = load_module(script_path)
+    script = load_script(script_path)
     # Remove the docstring, if existent.
     function_source = re.sub(
         r'""".*?"""|\'\'\'.*?\'\'\'',
         "",
-        inspect.getsource(module.clean),
+        inspect.getsource(script.clean),
         flags=re.DOTALL,
     )
     # Find all occurrences of raw["column_name"] or ['column_name'].
@@ -76,17 +119,17 @@ def get_relevant_column_names(script_path: Path) -> list[str]:
     return list(dict.fromkeys(matches))
 
 
-def load_module(script_path: Path) -> ModuleType:
-    """Load module from path.
+def load_script(script_path: Path) -> ModuleType:
+    """Load script from path.
 
     Args:
         script_path: The path to the script.
 
     Returns:
-        The loaded module.
+        The loaded script.
     """
-    module_name = script_path.stem
-    spec = spec_from_file_location(name=module_name, location=script_path)
-    module = module_from_spec(spec)
-    spec.loader.exec_module(module)
-    return module
+    script_name = script_path.stem
+    spec = spec_from_file_location(name=script_name, location=script_path)
+    script = module_from_spec(spec)
+    spec.loader.exec_module(script)
+    return script
diff --git a/tests/dataset_merging/test_panel_dataset_creation.py b/tests/dataset_merging/test_panel_dataset_creation.py
index 6571435..ec2a640 100644
--- a/tests/dataset_merging/test_panel_dataset_creation.py
+++ b/tests/dataset_merging/test_panel_dataset_creation.py
@@ -84,7 +84,7 @@ def test_get_data_file_name_to_variables_mapping_assert_type():
         },
     )
     input_ = {
-        "variable_to_data_file_mapping": {
+        "map_variable_to_data_file": {
             "column1": "dataset1",
             "column2": "dataset2",
             "column3": "dataset1",
@@ -101,7 +101,7 @@ def test_get_data_file_name_to_variables_mapping_assert_mapping():
         "dataset2": ["column2"],
     }
     input_ = {
-        "variable_to_data_file_mapping": {
+        "map_variable_to_data_file": {
             "column1": "dataset1",
             "column2": "dataset2",
             "column3": "dataset1",
@@ -115,7 +115,7 @@ def test_get_data_file_name_to_variables_mapping_assert_mapping():
 def test_get_data_file_name_to_variables_mapping_assert_datasets():
     expected = ["dataset1", "dataset2"]
     input_ = {
-        "variable_to_data_file_mapping": {
+        "map_variable_to_data_file": {
             "column1": "dataset1",
             "column2": "dataset2",
             "column3": "dataset1",