diff --git a/examples/40_paper/2015_neurips_feurer_example.py b/examples/40_paper/2015_neurips_feurer_example.py
index 106d120df..6f0de618e 100644
--- a/examples/40_paper/2015_neurips_feurer_example.py
+++ b/examples/40_paper/2015_neurips_feurer_example.py
@@ -10,9 +10,80 @@
 ~~~~~~~~~~~
 
 | Efficient and Robust Automated Machine Learning
-| Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank Hutter
+| Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank Hutter  # noqa F401
 | In *Advances in Neural Information Processing Systems 28*, 2015
 | Available at http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf
-
-This is currently a placeholder.
 """
+
+import pandas as pd
+
+import openml
+
+####################################################################################################
+# List of dataset IDs given in the supplementary material of Feurer et al.:
+# https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning-supplemental.zip
+dataset_ids = [
+    3, 6, 12, 14, 16, 18, 21, 22, 23, 24, 26, 28, 30, 31, 32, 36, 38, 44, 46,
+    57, 60, 179, 180, 181, 182, 184, 185, 273, 293, 300, 351, 354, 357, 389,
+    390, 391, 392, 393, 395, 396, 398, 399, 401, 554, 679, 715, 718, 720, 722,
+    723, 727, 728, 734, 735, 737, 740, 741, 743, 751, 752, 761, 772, 797, 799,
+    803, 806, 807, 813, 816, 819, 821, 822, 823, 833, 837, 843, 845, 846, 847,
+    849, 866, 871, 881, 897, 901, 903, 904, 910, 912, 913, 914, 917, 923, 930,
+    934, 953, 958, 959, 962, 966, 971, 976, 977, 978, 979, 980, 991, 993, 995,
+    1000, 1002, 1018, 1019, 1020, 1021, 1036, 1040, 1041, 1049, 1050, 1053,
+    1056, 1067, 1068, 1069, 1111, 1112, 1114, 1116, 1119, 1120, 1128, 1130,
+    1134, 1138, 1139, 1142, 1146, 1161, 1166,
+]
+
+####################################################################################################
+# The dataset IDs could be used directly to load the dataset and split the data into a training
+# and a test set. However, to be reproducible, we will first obtain the respective tasks from
+# OpenML, which define both the target feature and the train/test split.
+#
+# .. note::
+#    It is discouraged to work directly on datasets and only provide dataset IDs in a paper as
+#    this does not allow reproducibility (unclear splitting). Please do not use datasets but the
+#    respective tasks as basis for a paper and publish task IDS. This example is only given to
+#    showcase the use OpenML-Python for a published paper and as a warning on how not to do it.
+#    Please check the `OpenML documentation of tasks <https://docs.openml.org/#tasks>`_ if you
+#    want to learn more about them.
+
+####################################################################################################
+# This lists both active and inactive tasks (because of ``status='all'``). Unfortunately,
+# this is necessary as some of the datasets contain issues found after the publication and became
+# deactivated, which also deactivated the tasks on them. More information on active or inactive
+# datasets can be found in the `online docs <https://docs.openml.org/#dataset-status>`_.
+tasks = openml.tasks.list_tasks(
+    task_type_id=openml.tasks.TaskTypeEnum.SUPERVISED_CLASSIFICATION,
+    status='all',
+    output_format='dataframe',
+)
+
+# Query only those with holdout as the resampling startegy.
+tasks = tasks.query('estimation_procedure == "33% Holdout set"')
+
+task_ids = []
+for did in dataset_ids:
+    tasks_ = list(tasks.query("did == {}".format(did)).tid)
+    if len(tasks_) >= 1:  # if there are multiple task, take the one with lowest ID (oldest).
+        task_id = min(tasks_)
+    else:
+        raise ValueError(did)
+
+    # Optional - Check that the task has the same target attribute as the
+    # dataset default target attribute
+    # (disabled for this example as it needs to run fast to be rendered online)
+    # task = openml.tasks.get_task(task_id)
+    # dataset = task.get_dataset()
+    # if task.target_name != dataset.default_target_attribute:
+    #     raise ValueError(
+    #         (task.target_name, dataset.default_target_attribute)
+    #     )
+
+    task_ids.append(task_id)
+
+assert len(task_ids) == 140
+task_ids.sort()
+
+# These are the tasks to work with:
+print(task_ids)
diff --git a/examples/40_paper/2018_neurips_fusi_example.py b/examples/40_paper/2018_neurips_fusi_example.py
deleted file mode 100644
index 656f617fa..000000000
--- a/examples/40_paper/2018_neurips_fusi_example.py
+++ /dev/null
@@ -1,17 +0,0 @@
-"""
-Fusi et al. (2018)
-==================
-
-A tutorial on how to get the datasets used in the paper introducing *Probabilistic Matrix
-Factorization for Automated Machine Learning* by Fusi et al..
-
-Publication
-~~~~~~~~~~~
-
-| Probabilistic Matrix Factorization for Automated Machine Learning
-| Nicolo Fusi and Rishit Sheth and Melih Elibol
-| In *Advances in Neural Information Processing Systems 31*, 2018
-| Available at http://papers.nips.cc/paper/7595-probabilistic-matrix-factorization-for-automated-machine-learning.pdf
-
-This is currently a placeholder.
-"""