diff --git a/.github/workflows/cleanup-caches.yml b/.github/workflows/cleanup-caches.yml
index 2b65aa371..86dd7569d 100644
--- a/.github/workflows/cleanup-caches.yml
+++ b/.github/workflows/cleanup-caches.yml
@@ -3,12 +3,19 @@ on:
   pull_request_target:
     types:
       - closed
+  push:
+    # Trigger on pushes to master or develop and for git tag pushes
+    branches:
+      - master
+      - develop
+    tags:
+      - v*
 
 jobs:
   cleanup:
     runs-on: ubuntu-latest
     steps:
-      - name: Cleanup
+      - name: Cleanup caches
         run: |
           gh extension install actions/gh-actions-cache
 
diff --git a/.github/workflows/cpu-tests.yml b/.github/workflows/cpu-tests.yml
index 347163ab7..48f0a456c 100644
--- a/.github/workflows/cpu-tests.yml
+++ b/.github/workflows/cpu-tests.yml
@@ -1,6 +1,7 @@
 name: CPU tests
 
 on:
+  workflow_dispatch:
   pull_request:
     # Trigger on pull requests to master or develop that are
     # marked as "ready for review" (non-draft PRs)
@@ -33,7 +34,7 @@ jobs:
     runs-on: ubuntu-22.04
     steps:
       - name: Check out repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Set environment variables
         run: |
@@ -45,7 +46,7 @@ jobs:
           echo "IMAGE_REPO=$IMAGE_REPO"
 
       - name: Restore cache
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         id: cache-docker
         with:
           path: ${{ env.DOCKER_CACHE_PATH }}
@@ -73,7 +74,7 @@ jobs:
             CACHE=$IMAGE_REPO/$IMAGE_NAME:latest
           fi
 
-          docker build . --file Dockerfile --tag $IMAGE_NAME:local --cache-from=$CACHE --build-arg DEVICE=cpu
+          DOCKER_BUILDKIT=0 docker build . --file Dockerfile --tag $IMAGE_NAME:local --cache-from=$CACHE --build-arg DEVICE=cpu
 
           # Show images
           docker images --filter=reference=$IMAGE_NAME --filter=reference=$IMAGE_REPO/$IMAGE_NAME
@@ -122,7 +123,7 @@ jobs:
     steps:
       - name: "Prepare environment: Restore cache"
         if: env.DOCKER_TAG != 'latest'
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         id: cache-docker
         with:
           path: ${{ env.DOCKER_CACHE_PATH }}
@@ -153,14 +154,14 @@ jobs:
           [[ $(docker inspect --format '{{json .State.Running}}' mala-cpu) == 'true' ]]
 
       - name: Check out repository (mala)
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Install mala package
         # Exec all commands inside the mala-cpu container
         shell: 'bash -c "docker exec -i mala-cpu bash < {0}"'
         run: |
-          # epxort Docker image Conda environment for a later comparison
-          conda env export -n mala-cpu > env_1.yml
+          # export Docker image Conda environment for a later comparison
+          conda env export -n mala-cpu > env_before.yml
 
           # install mala package
           pip --no-cache-dir install -e .[opt,test] --no-build-isolation
@@ -169,26 +170,46 @@ jobs:
         shell: 'bash -c "docker exec -i mala-cpu bash < {0}"'
         run: |
           # export Conda environment _with_ mala package installed in it (and extra dependencies)
-          conda env export -n mala-cpu > env_2.yml
+          conda env export -n mala-cpu > env_after.yml
 
           # if comparison fails, `install/mala_cpu_[base]_environment.yml` needs to be aligned with
           # `requirements.txt` and/or extra dependencies are missing in the Docker Conda environment
-          diff env_1.yml env_2.yml
+          diff --side-by-side --color=always env_before.yml env_after.yml
 
-      - name: Download test data repository
-        shell: 'bash -c "docker exec -i mala-cpu bash < {0}"'
+      - name: Download test data repository from RODARE
+        shell: 'bash -c "docker exec -i mala-cpu python < {0}"'
         run: |
-          # Download test data repository from RODARE. If the version changes
-          # this URL has to be adapted (the number after /record/ and the
-          # version have to be incremented)
-          wget "https://rodare.hzdr.de/record/2999/files/mala-project/test-data-1.8.0.zip"
-
-          # Once downloaded, we have to unzip the file. The name of the root
-          # folder in the zip file has to be updated for data repository
-          # updates as well - the string at the end is the hash of the data
-          # repository commit.
-          unzip -q test-data-1.8.0.zip
-          mv mala-project-test-data-d5694c7  mala_data
+          import requests, shutil, zipfile
+
+          # This DOI represents all versions, and will always resolve to the latest one
+          DOI = "https://doi.org/10.14278/rodare.2900"
+
+          # Resolve DOI and get record ID and the associated API URL
+          response = requests.get(DOI)
+          *_, record_id = response.url.split("/")
+          api_url = f"https://rodare.hzdr.de/api/records/{record_id}"
+
+          # Download record from API and get the first file
+          response = requests.get(api_url)
+          record = response.json()
+          size = record["files"][0]["size"]
+          download_link = record["files"][0]["links"]["self"]
+
+          print(size, "bytes", "--", download_link)
+
+          # TODO: implement some sort of auto retry for failed HTTP requests
+          response = requests.get(download_link)
+
+          # Saving downloaded content to a file
+          with open("test-data.zip", mode="wb") as file:
+            file.write(response.content)
+
+          # Get top level directory name
+          dir_name = zipfile.ZipFile("test-data.zip").namelist()[0]
+          shutil.unpack_archive("test-data.zip", ".")
+
+          print(f"Rename {dir_name} to mala_data")
+          shutil.move(dir_name, "mala_data")
 
       - name: Test mala
         shell: 'bash -c "docker exec -i mala-cpu bash < {0}"'
@@ -209,11 +230,11 @@ jobs:
       || startsWith(github.ref, 'refs/tags/')
     steps:
       - name: Check out repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: "Prepare environment: Restore cache"
         if: env.DOCKER_TAG != 'latest'
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         id: cache-docker
         with:
           path: ${{ env.DOCKER_CACHE_PATH }}
diff --git a/.github/workflows/mirror-to-casus.yml b/.github/workflows/mirror-to-casus.yml
index a231093bc..a862d6cac 100644
--- a/.github/workflows/mirror-to-casus.yml
+++ b/.github/workflows/mirror-to-casus.yml
@@ -1,4 +1,4 @@
-name: mirror
+name: Mirror to CASUS
 
 on: [push, delete]
 
@@ -6,13 +6,14 @@ jobs:
   mirror-to-CASUS:
     runs-on: ubuntu-22.04
     steps:
-    - uses: actions/checkout@v3
-      with:
-        fetch-depth: 0
-    - name: mirror-repository
-      uses: spyoungtech/mirror-action@v0.6.0
-      with:
-        REMOTE: 'ssh://git@github.com/casus/mala.git'
-        GIT_SSH_PRIVATE_KEY: ${{ secrets.GIT_SSH_KEY }}
-        GIT_SSH_NO_VERIFY_HOST: "true"
-        DEBUG: "true"
+      - name: Check out repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: mirror-repository
+        uses: spyoungtech/mirror-action@v0.6.0
+        with:
+          REMOTE: 'ssh://git@github.com/casus/mala.git'
+          GIT_SSH_PRIVATE_KEY: ${{ secrets.GIT_SSH_KEY }}
+          GIT_SSH_NO_VERIFY_HOST: "true"
+          DEBUG: "true"
diff --git a/Dockerfile b/Dockerfile
index 724ed44e5..3167d4ed7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -21,7 +21,6 @@ RUN conda env create -f mala_${DEVICE}_environment.yml && rm -rf /opt/conda/pkgs
 RUN /opt/conda/envs/mala-${DEVICE}/bin/pip install --no-input --no-cache-dir \
     pytest \
     oapackage==2.6.8 \
-    openpmd-api==0.15.1 \
     pqkmeans
 
 RUN echo "source activate mala-${DEVICE}" > ~/.bashrc
diff --git a/examples/advanced/ex01_checkpoint_training.py b/examples/advanced/ex01_checkpoint_training.py
index eb8dda0a5..ca1c1bc21 100644
--- a/examples/advanced/ex01_checkpoint_training.py
+++ b/examples/advanced/ex01_checkpoint_training.py
@@ -3,9 +3,7 @@
 import mala
 from mala import printout
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
 Shows how a training run can be paused and
diff --git a/examples/advanced/ex02_shuffle_data.py b/examples/advanced/ex02_shuffle_data.py
index 467da7922..db75d5154 100644
--- a/examples/advanced/ex02_shuffle_data.py
+++ b/examples/advanced/ex02_shuffle_data.py
@@ -2,14 +2,12 @@
 
 import mala
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
 Shows how data can be shuffled amongst multiple
-snapshots, which is very useful in the lazy loading case, where this cannot be 
-easily done in memory. 
+snapshots, which is very useful in the lazy loading case, where this cannot be
+easily done in memory.
 """
 
 
diff --git a/examples/advanced/ex03_tensor_board.py b/examples/advanced/ex03_tensor_board.py
index 00728a560..b15239495 100644
--- a/examples/advanced/ex03_tensor_board.py
+++ b/examples/advanced/ex03_tensor_board.py
@@ -3,13 +3,10 @@
 import mala
 from mala import printout
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
-
+from mala.datahandling.data_repo import data_path
 
 """
-Shows how a NN training by MALA can be visualized using 
+Shows how a NN training by MALA can be visualized using
 tensorboard. The training is a basic MALA network training.
 """
 
diff --git a/examples/advanced/ex04_acsd.py b/examples/advanced/ex04_acsd.py
index 5390ae210..53b4b82bd 100644
--- a/examples/advanced/ex04_acsd.py
+++ b/examples/advanced/ex04_acsd.py
@@ -1,13 +1,11 @@
 import os
 
 import mala
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
 Shows how MALA can be used to optimize descriptor
-parameters based on the ACSD analysis (see hyperparameter paper in the 
+parameters based on the ACSD analysis (see hyperparameter paper in the
 documentation for mathematical details).
 """
 
diff --git a/examples/advanced/ex05_checkpoint_hyperparameter_optimization.py b/examples/advanced/ex05_checkpoint_hyperparameter_optimization.py
index c7f741d70..cef7c8f4f 100644
--- a/examples/advanced/ex05_checkpoint_hyperparameter_optimization.py
+++ b/examples/advanced/ex05_checkpoint_hyperparameter_optimization.py
@@ -2,16 +2,14 @@
 
 import mala
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
-Shows how a hyperparameter optimization run can 
+Shows how a hyperparameter optimization run can
 be paused and resumed. Delete all ex04_*.pkl and ex04_*.pth prior to execution.
-Afterwards, execute this script twice to see how MALA progresses from a 
+Afterwards, execute this script twice to see how MALA progresses from a
 checkpoint. As the number of trials cannot be divided by the number
-of epochs after which a checkpoint is created without residual, this will 
+of epochs after which a checkpoint is created without residual, this will
 lead to MALA performing the missing trials again.
 """
 
diff --git a/examples/advanced/ex06_distributed_hyperparameter_optimization.py b/examples/advanced/ex06_distributed_hyperparameter_optimization.py
index 2a67acb3c..b34f9bb8b 100644
--- a/examples/advanced/ex06_distributed_hyperparameter_optimization.py
+++ b/examples/advanced/ex06_distributed_hyperparameter_optimization.py
@@ -2,14 +2,12 @@
 
 import mala
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
-ex09_distributed_hyperopt.py: Shows how a hyperparameter 
+ex09_distributed_hyperopt.py: Shows how a hyperparameter
 optimization can be sped up using a RDB storage. Ideally this should be done
-using a database server system, such as PostgreSQL or MySQL. 
+using a database server system, such as PostgreSQL or MySQL.
 For this easy example, sqlite will be used. It is highly advisory not to
 to use this for actual, at-scale calculations!
 
diff --git a/examples/advanced/ex07_advanced_hyperparameter_optimization.py b/examples/advanced/ex07_advanced_hyperparameter_optimization.py
index 629d47962..8165ef01e 100644
--- a/examples/advanced/ex07_advanced_hyperparameter_optimization.py
+++ b/examples/advanced/ex07_advanced_hyperparameter_optimization.py
@@ -3,12 +3,10 @@
 import mala
 from mala import printout
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
-Shows how recent developments in hyperparameter optimization techniques can be 
+Shows how recent developments in hyperparameter optimization techniques can be
 used (OAT / training-free NAS).
 
 REQUIRES OAPACKAGE.
diff --git a/examples/advanced/ex08_visualize_observables.py b/examples/advanced/ex08_visualize_observables.py
index 3b8bbed3d..be344b878 100644
--- a/examples/advanced/ex08_visualize_observables.py
+++ b/examples/advanced/ex08_visualize_observables.py
@@ -2,18 +2,15 @@
 
 import mala
 
-from mala.datahandling.data_repo import data_repo_path
+from mala.datahandling.data_repo import data_path
 
-atoms_path = os.path.join(
-    os.path.join(data_repo_path, "Be2"), "Be_snapshot1.out"
-)
-ldos_path = os.path.join(
-    os.path.join(data_repo_path, "Be2"), "Be_snapshot1.out.npy"
-)
 """
-Shows how MALA can be used to visualize observables of interest. 
+Shows how MALA can be used to visualize observables of interest.
 """
 
+atoms_path = os.path.join(data_path, "Be_snapshot1.out")
+ldos_path = os.path.join(data_path, "Be_snapshot1.out.npy")
+
 ####################
 # 1. READ ELECTRONIC STRUCTURE DATA
 # This data may be read as part of an ML-DFT model inference.
diff --git a/examples/basic/ex01_train_network.py b/examples/basic/ex01_train_network.py
index a5d14d890..95eb2d51b 100644
--- a/examples/basic/ex01_train_network.py
+++ b/examples/basic/ex01_train_network.py
@@ -2,9 +2,7 @@
 
 import mala
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
 This example shows how a neural network can be trained on material
@@ -12,7 +10,6 @@
 from *.npy files.
 """
 
-
 ####################
 # 1. PARAMETERS
 # The first step of each MALA workflow is to define a parameters object and
@@ -93,5 +90,5 @@
 test_trainer.train_network()
 additional_calculation_data = os.path.join(data_path, "Be_snapshot0.out")
 test_trainer.save_run(
-    "be_model", additional_calculation_data=additional_calculation_data
+    "Be_model", additional_calculation_data=additional_calculation_data
 )
diff --git a/examples/basic/ex02_test_network.py b/examples/basic/ex02_test_network.py
index 6ef81f880..2e4b8953c 100644
--- a/examples/basic/ex02_test_network.py
+++ b/examples/basic/ex02_test_network.py
@@ -3,17 +3,16 @@
 import mala
 from mala import printout
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
 This example shows how a trained network can be tested
 with additional test snapshots. Either execute ex01 before executing this one
 or download the appropriate model from the provided test data repo.
 """
-assert os.path.exists("be_model.zip"), "Be model missing, run ex01 first."
 
+model_name = "Be_model"
+model_path = "./" if os.path.exists("Be_model.zip") else data_path
 
 ####################
 # 1. LOADING A NETWORK
@@ -27,7 +26,9 @@
 # (output_format="list") or as an averaged value (output_format="mae")
 ####################
 
-parameters, network, data_handler, tester = mala.Tester.load_run("be_model")
+parameters, network, data_handler, tester = mala.Tester.load_run(
+    run_name=model_name, path=model_path
+)
 tester.observables_to_test = ["band_energy", "number_of_electrons"]
 tester.output_format = "list"
 parameters.data.use_lazy_loading = True
diff --git a/examples/basic/ex03_preprocess_data.py b/examples/basic/ex03_preprocess_data.py
index 72ec9490a..b0a104885 100644
--- a/examples/basic/ex03_preprocess_data.py
+++ b/examples/basic/ex03_preprocess_data.py
@@ -2,13 +2,11 @@
 
 import mala
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
 Shows how this framework can be used to preprocess
-data. Preprocessing here means converting raw DFT calculation output into 
+data. Preprocessing here means converting raw DFT calculation output into
 numpy arrays of the correct size. For the input data, this means descriptor
 calculation.
 
diff --git a/examples/basic/ex04_hyperparameter_optimization.py b/examples/basic/ex04_hyperparameter_optimization.py
index 77985f033..4c68179c2 100644
--- a/examples/basic/ex04_hyperparameter_optimization.py
+++ b/examples/basic/ex04_hyperparameter_optimization.py
@@ -2,14 +2,12 @@
 
 import mala
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 """
 Shows how a hyperparameter optimization can be done using this framework. There
 are multiple hyperparameter optimizers available in this framework. This example
-focusses on the most universal one - optuna.  
+focusses on the most universal one - optuna.
 """
 
 
diff --git a/examples/basic/ex05_run_predictions.py b/examples/basic/ex05_run_predictions.py
index 40e4303ee..84676b21d 100644
--- a/examples/basic/ex05_run_predictions.py
+++ b/examples/basic/ex05_run_predictions.py
@@ -5,19 +5,19 @@
 import mala
 from mala import printout
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
-
-assert os.path.exists("be_model.zip"), "Be model missing, run ex01 first."
+from mala.datahandling.data_repo import data_path
 
 """
-Show how a prediction can be made using MALA, based on only a
-trained network and atomic configurations.
+Show how a prediction can be made using MALA, based on only a trained network and atomic
+configurations. Either execute ex01 before executing this one or download the
+appropriate model from the provided test data repo.
 
 REQUIRES LAMMPS (and potentially the total energy module).
 """
 
+model_name = "Be_model"
+model_path = "./" if os.path.exists("Be_model.zip") else data_path
+
 
 ####################
 # 1. LOADING A NETWORK
@@ -25,7 +25,7 @@
 # Tester class interface. Afterwards, set the necessary parameters.
 ####################
 parameters, network, data_handler, predictor = mala.Predictor.load_run(
-    "be_model"
+    run_name=model_name, path=model_path
 )
 
 
diff --git a/examples/basic/ex06_ase_calculator.py b/examples/basic/ex06_ase_calculator.py
index f4ab2d337..7ba0eee1b 100644
--- a/examples/basic/ex06_ase_calculator.py
+++ b/examples/basic/ex06_ase_calculator.py
@@ -1,21 +1,21 @@
 import os
 
-import mala
 from ase.io import read
+import mala
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
-
-assert os.path.exists("be_model.zip"), "Be model missing, run ex01 first."
+from mala.datahandling.data_repo import data_path
 
 """
-Shows how MALA can be used as an ASE calculator. 
-Currently, calculation of forces is not supported. 
+Shows how MALA can be used as an ASE calculator.
+Currently, calculation of forces is not supported. Either execute ex01 before executing
+this one or download the appropriate model from the provided test data repo.
 
 REQUIRES LAMMPS AND QUANTUM ESPRESSO (TOTAL ENERGY MODULE).
 """
 
+model_name = "Be_model"
+model_path = "./" if os.path.exists("Be_model.zip") else data_path
+
 
 ####################
 # 1. LOADING A NETWORK
@@ -23,7 +23,7 @@
 # Further make sure to set the path to the pseudopotential used during
 # data generation-
 ####################
-calculator = mala.MALA.load_model("be_model")
+calculator = mala.MALA.load_model(run_name=model_name, path=model_path)
 calculator.mala_parameters.targets.pseudopotential_path = data_path
 
 ####################
diff --git a/install/mala_cpu_base_environment.yml b/install/mala_cpu_base_environment.yml
index 626008b16..f8309f5b9 100644
--- a/install/mala_cpu_base_environment.yml
+++ b/install/mala_cpu_base_environment.yml
@@ -14,3 +14,5 @@ dependencies:
   - mpmath
   - tensorboard
   - scikit-spatial
+  - pip:
+      - openpmd-api
diff --git a/install/mala_cpu_environment.yml b/install/mala_cpu_environment.yml
index 97fb82bd8..eaf4b88bc 100644
--- a/install/mala_cpu_environment.yml
+++ b/install/mala_cpu_environment.yml
@@ -63,8 +63,8 @@ dependencies:
   - libdeflate=1.14
   - libffi=3.4.2
   - libgcc-ng=12.2.0
-  - libgfortran-ng=12.2.0
-  - libgfortran5=12.2.0
+  - libgfortran-ng=12.3.0
+  - libgfortran5=12.3.0
   - libgrpc=1.51.1
   - libhwloc=2.8.0
   - libiconv=1.17
@@ -95,7 +95,7 @@ dependencies:
   - numpy=1.24.0
   - oauthlib=3.2.2
   - openjpeg=2.5.0
-  - openssl=3.0.7
+  - openssl=3.3.1
   - optuna=3.0.5
   - packaging=22.0
   - pandas=1.5.2
@@ -113,7 +113,7 @@ dependencies:
   - pyparsing=3.0.9
   - pyperclip=1.8.2
   - pysocks=1.7.1
-  - python=3.8.15
+  - python=3.8.16
   - python-dateutil=2.8.2
   - python_abi=3.8
   - pytorch=1.13.0
@@ -153,4 +153,6 @@ dependencies:
   - yarl=1.8.1
   - zipp=3.11.0
   - zlib=1.2.13
-  - zstd=1.5.2
+  - zstd=1.5.5
+  - pip:
+      - openpmd-api==0.15.2
diff --git a/mala/common/physical_data.py b/mala/common/physical_data.py
index e756e96d1..7ec85623d 100644
--- a/mala/common/physical_data.py
+++ b/mala/common/physical_data.py
@@ -642,6 +642,11 @@ def write_to_openpmd_iteration(
 
         # Third loop: Extra flushes to harmonize ranks
         for _ in range(extra_flushes):
+            # This following line is a workaround for issue
+            # https://github.com/openPMD/openPMD-api/issues/1616
+            # Fixed in openPMD-api 0.16 by
+            # https://github.com/openPMD/openPMD-api/pull/1619
+            iteration.dt = iteration.dt
             iteration.series_flush()
 
         iteration.close(flush=True)
diff --git a/mala/datahandling/data_repo.py b/mala/datahandling/data_repo.py
index 178872b60..203885c12 100644
--- a/mala/datahandling/data_repo.py
+++ b/mala/datahandling/data_repo.py
@@ -14,9 +14,11 @@
 name = "MALA_DATA_REPO"
 if name in os.environ:
     data_repo_path = os.environ[name]
+    data_path = os.path.join(data_repo_path, "Be2")
 else:
     parallel_warn(
         f"Environment variable {name} not set. You won't be able "
         "to run all examples and tests."
     )
     data_repo_path = None
+    data_path = None
diff --git a/requirements.txt b/requirements.txt
index b8c1d7b64..b784a6c69 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,5 +8,5 @@ optuna
 scipy
 pandas
 tensorboard
-openpmd-api>=0.15
+openpmd-api
 scikit-spatial
diff --git a/test/all_lazy_loading_test.py b/test/all_lazy_loading_test.py
index f5cc74006..065cbb86e 100644
--- a/test/all_lazy_loading_test.py
+++ b/test/all_lazy_loading_test.py
@@ -7,9 +7,7 @@
 import torch
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # This test compares the data scaling using the regular scaling procedure and
 # the lazy-loading one (incremental fitting).
diff --git a/test/basic_gpu_test.py b/test/basic_gpu_test.py
index 943862b3d..dcd588ad1 100644
--- a/test/basic_gpu_test.py
+++ b/test/basic_gpu_test.py
@@ -6,9 +6,9 @@
 which MALA relies on). Two things are tested:
 
 1. Whether or not your system has GPU support.
-2. Whether or not the GPU does what it is supposed to. For this, 
+2. Whether or not the GPU does what it is supposed to. For this,
 a training is performed. It is measured whether or not the utilization
-of the GPU results in a speed up. 
+of the GPU results in a speed up.
 """
 import os
 import time
@@ -19,9 +19,7 @@
 import pytest
 import torch
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 test_checkpoint_name = "test"
 
diff --git a/test/checkpoint_hyperopt_test.py b/test/checkpoint_hyperopt_test.py
index f3435e7ab..28889c2df 100644
--- a/test/checkpoint_hyperopt_test.py
+++ b/test/checkpoint_hyperopt_test.py
@@ -4,9 +4,7 @@
 from mala import printout
 import numpy as np
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 checkpoint_name = "test_ho"
 
diff --git a/test/checkpoint_training_test.py b/test/checkpoint_training_test.py
index bf7f62090..4c56ed8eb 100644
--- a/test/checkpoint_training_test.py
+++ b/test/checkpoint_training_test.py
@@ -4,9 +4,8 @@
 from mala import printout
 import numpy as np
 
-from mala.datahandling.data_repo import data_repo_path
+from mala.datahandling.data_repo import data_path
 
-data_path = os.path.join(data_repo_path, "Be2")
 test_checkpoint_name = "test"
 
 # Define the accuracy used in the tests.
diff --git a/test/complete_interfaces_test.py b/test/complete_interfaces_test.py
index 127ba8f82..d793da77f 100644
--- a/test/complete_interfaces_test.py
+++ b/test/complete_interfaces_test.py
@@ -8,9 +8,7 @@
 import pytest
 
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 
 # This test checks whether MALA interfaces to other codes, mainly the ASE
diff --git a/test/descriptor_test.py b/test/descriptor_test.py
index 4a208f832..74cae40f5 100644
--- a/test/descriptor_test.py
+++ b/test/descriptor_test.py
@@ -6,9 +6,7 @@
 import numpy as np
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Accuracy of test.
 accuracy_descriptors = 5e-8
diff --git a/test/examples_test.py b/test/examples_test.py
index 5d74ec164..b5aa9143a 100644
--- a/test/examples_test.py
+++ b/test/examples_test.py
@@ -1,61 +1,126 @@
 """Test whether the examples are still working."""
 
 import importlib
+import os
 import runpy
 
 import pytest
 
-
 @pytest.mark.examples
 class TestExamples:
-    def test_basic_ex01(self):
-        runpy.run_path("../examples/basic/ex01_train_network.py")
+    dir_path = os.path.dirname(__file__)
+
+    def test_basic_ex01(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/basic/ex01_train_network.py"
+        )
 
-    def test_basic_ex02(self):
-        runpy.run_path("../examples/basic/ex02_test_network.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_basic_ex02(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/basic/ex02_test_network.py"
+        )
 
-    def test_basic_ex03(self):
-        runpy.run_path("../examples/basic/ex03_preprocess_data.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_basic_ex03(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/basic/ex03_preprocess_data.py"
+        )
 
-    def test_basic_ex04(self):
-        runpy.run_path("../examples/basic/ex04_hyperparameter_optimization.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_basic_ex04(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/basic/ex04_hyperparameter_optimization.py"
+        )
 
-    def test_basic_ex05(self):
-        runpy.run_path("../examples/basic/ex05_run_predictions.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_basic_ex05(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/basic/ex05_run_predictions.py"
+        )
 
-    def test_basic_ex06(self):
-        runpy.run_path("../examples/basic/ex06_ase_calculator.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_basic_ex06(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/basic/ex06_ase_calculator.py"
+        )
 
-    def test_advanced_ex01(self):
-        runpy.run_path("../examples/advanced/ex01_checkpoint_training.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_advanced_ex01(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/advanced/ex01_checkpoint_training.py"
+        )
 
-    def test_advanced_ex02(self):
-        runpy.run_path("../examples/advanced/ex02_shuffle_data.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_advanced_ex02(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/advanced/ex02_shuffle_data.py"
+        )
 
-    def test_advanced_ex03(self):
-        runpy.run_path("../examples/advanced/ex03_tensor_board.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_advanced_ex03(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/advanced/ex03_tensor_board.py"
+        )
 
-    def test_advanced_ex04(self):
-        runpy.run_path("../examples/advanced/ex04_acsd.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_advanced_ex04(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/advanced/ex04_acsd.py"
+        )
 
-    def test_advanced_ex05(self):
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_advanced_ex05(self, tmp_path):
+        os.chdir(tmp_path)
         runpy.run_path(
-            "../examples/advanced/ex05_checkpoint_hyperparameter_optimization.py"
+            self.dir_path +
+            "/../examples/advanced/ex05_checkpoint_hyperparameter_optimization.py"
         )
 
-    def test_advanced_ex06(self):
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_advanced_ex06(self, tmp_path):
+        os.chdir(tmp_path)
         runpy.run_path(
-            "../examples/advanced/ex06_distributed_hyperparameter_optimization.py"
+            self.dir_path +
+            "/../examples/advanced/ex06_distributed_hyperparameter_optimization.py"
         )
 
     @pytest.mark.skipif(
         importlib.util.find_spec("oapackage") is None,
         reason="No OAT found on this machine, skipping this " "test.",
     )
-    def test_advanced_ex07(self):
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_advanced_ex07(self, tmp_path):
+        os.chdir(tmp_path)
         runpy.run_path(
-            "../examples/advanced/ex07_advanced_hyperparameter_optimization.py"
+            self.dir_path +
+            "/../examples/advanced/ex07_advanced_hyperparameter_optimization.py"
         )
 
-    def test_advanced_ex08(self):
-        runpy.run_path("../examples/advanced/ex08_visualize_observables.py")
+    @pytest.mark.order(after="test_basic_ex01")
+    def test_advanced_ex08(self, tmp_path):
+        os.chdir(tmp_path)
+        runpy.run_path(
+            self.dir_path +
+            "/../examples/advanced/ex08_visualize_observables.py"
+        )
diff --git a/test/hyperopt_test.py b/test/hyperopt_test.py
index b2d93f872..bb003082a 100644
--- a/test/hyperopt_test.py
+++ b/test/hyperopt_test.py
@@ -7,9 +7,7 @@
 import mala
 import numpy as np
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Control how much the loss should be better after hyperopt compared to
 # before. This value is fairly high, but we're training on absolutely
diff --git a/test/inference_test.py b/test/inference_test.py
index 4e874570b..84e0e9cca 100644
--- a/test/inference_test.py
+++ b/test/inference_test.py
@@ -3,10 +3,8 @@
 import numpy as np
 from mala import Tester, Runner
 
-from mala.datahandling.data_repo import data_repo_path
+from mala.datahandling.data_repo import data_path
 
-data_path = os.path.join(data_repo_path, "Be2")
-param_path = os.path.join(data_repo_path, "workflow_test/")
 accuracy_strict = 1e-16
 accuracy_coarse = 5e-7
 accuracy_very_coarse = 3
@@ -18,7 +16,7 @@ class TestInference:
     def test_unit_conversion(self):
         """Test that RAM inexpensive unit conversion works."""
         parameters, network, data_handler = Runner.load_run(
-            "workflow_test", load_runner=False, path=param_path
+            "Be_model", load_runner=False, path=data_path
         )
         parameters.data.use_lazy_loading = False
         parameters.running.mini_batch_size = 50
@@ -99,7 +97,7 @@ def test_inference_lazy_loading(self):
     def __run(use_lazy_loading=False, batchsize=46):
         # First we load Parameters and network.
         parameters, network, data_handler, tester = Tester.load_run(
-            "workflow_test", path=param_path
+            "Be_model", path=data_path
         )
         parameters.data.use_lazy_loading = use_lazy_loading
         parameters.running.mini_batch_size = batchsize
diff --git a/test/integration_test.py b/test/integration_test.py
index b27abb872..e4e22ea95 100644
--- a/test/integration_test.py
+++ b/test/integration_test.py
@@ -6,7 +6,7 @@
 import scipy as sp
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
+from mala.datahandling.data_repo import data_path
 
 # In order to test the integration capabilities of MALA we need a
 # QuantumEspresso
@@ -18,7 +18,6 @@
 # Scripts to reproduce the data files used in this test script can be found
 # in the data repo.
 
-data_path = os.path.join(data_repo_path, "Be2")
 path_to_out = os.path.join(data_path, "Be_snapshot0.out")
 path_to_ldos_npy = os.path.join(data_path, "Be_snapshot0.out.npy")
 path_to_dos_npy = os.path.join(data_path, "Be_snapshot0.dos.npy")
diff --git a/test/parallel_run_test.py b/test/parallel_run_test.py
index 89b0cbad8..6ca5c8c8d 100644
--- a/test/parallel_run_test.py
+++ b/test/parallel_run_test.py
@@ -6,9 +6,7 @@
 from ase.io import read
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Control the various accuracies..
 accuracy_snaps = 1e-4
diff --git a/test/scaling_test.py b/test/scaling_test.py
index d43648430..b7925cd9f 100644
--- a/test/scaling_test.py
+++ b/test/scaling_test.py
@@ -4,9 +4,7 @@
 import numpy as np
 import torch
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # This test checks that all scaling options are working and are not messing
 # up the data.
diff --git a/test/shuffling_test.py b/test/shuffling_test.py
index 202e40c9d..e637c7d2b 100644
--- a/test/shuffling_test.py
+++ b/test/shuffling_test.py
@@ -3,9 +3,7 @@
 import mala
 import numpy as np
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Accuracy for the shuffling test.
 accuracy = np.finfo(float).eps
diff --git a/test/tensor_memory_test.py b/test/tensor_memory_test.py
index 4a70d9719..b3cb25672 100644
--- a/test/tensor_memory_test.py
+++ b/test/tensor_memory_test.py
@@ -5,9 +5,7 @@
 from torch.utils.data import TensorDataset
 from torch.utils.data import DataLoader
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Define the accuracy used in the tests.
 accuracy = 1e-5
diff --git a/test/workflow_test.py b/test/workflow_test.py
index a652546fd..fa7dee018 100644
--- a/test/workflow_test.py
+++ b/test/workflow_test.py
@@ -5,9 +5,8 @@
 import numpy as np
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
+from mala.datahandling.data_repo import data_path
 
-data_path = os.path.join(data_repo_path, "Be2")
 # Control how much the loss should be better after training compared to
 # before. This value is fairly high, but we're training on absolutely
 # minimal amounts of data.
@@ -382,7 +381,7 @@ def test_training_with_postprocessing_data_repo(self):
         """
         # Load parameters, network and data scalers.
         parameters, network, data_handler, tester = mala.Tester.load_run(
-            "workflow_test", path=os.path.join(data_repo_path, "workflow_test")
+            "Be_model", path=data_path
         )
 
         parameters.targets.target_type = "LDOS"
@@ -431,7 +430,7 @@ def test_predictions(self):
         ####################
 
         parameters, network, data_handler, tester = mala.Tester.load_run(
-            "workflow_test", path=os.path.join(data_repo_path, "workflow_test")
+            "Be_model", path=data_path
         )
         parameters.targets.target_type = "LDOS"
         parameters.targets.ldos_gridsize = 11
@@ -518,7 +517,7 @@ def test_total_energy_predictions(self):
         ####################
 
         parameters, network, data_handler, predictor = mala.Predictor.load_run(
-            "workflow_test", path=os.path.join(data_repo_path, "workflow_test")
+            "Be_model", path=data_path
         )
         parameters.targets.target_type = "LDOS"
         parameters.targets.ldos_gridsize = 11