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+---
+
+# Tutorials
+
+The easiest way to get familiar with ehrapy is to follow along with our tutorials.
+Many are also designed to work seamlessly in Binder, a free cloud computing platform.
+
+:::{note}
+For questions about the usage of ehrapy use the [zulip forum](https://scverse.zulipchat.com/#narrow/channel/465075-ehrapy).
+:::
+
+## Quick start
+
+```{eval-rst}
+.. nbgallery::
+
+   notebooks/ehrapy_introduction
+   notebooks/mimic_2_introduction
+   notebooks/mimic_2_fate
+   notebooks/mimic_2_survival_analysis
+   notebooks/mimic_2_effect_estimation
+   notebooks/mimic_2_causal_inference
+   notebooks/medcat
+   notebooks/ml_usecases
+   notebooks/ontology_mapping
+   notebooks/fhir
+   notebooks/cohort_tracking
+   notebooks/bias
+   notebooks/out_of_core
+   notebooks/patient_trajectory
+
+```
+
+### Glossary
+
+```{eval-rst}
+.. tab-set::
+
+    .. tab-item:: AnnData
+
+        `AnnData <https://github.com/theislab/anndata>`_ is short for Annotated Data and is the primary datastructure that ehrapy uses.
+        It is based on the principle of a single Numpy matrix X embraced by two Pandas DataFrames.
+        All rows are called observations (in our case patients/patient visits or similar) and the columns
+        are known as variables (any feature such as e.g. age, B12 level or similar).
+        For a more in depth introduction please read the `AnnData paper <https://doi.org/10.1101/2021.12.16.473007>`_.
+
+
+    .. tab-item:: scanpy
+
+        The implementation of ehrapy is based on `scanpy <https://github.com/theislab/scanpy>`_, a framework to analyze single-cell sequencing data.
+        ehrapy reuses the implemented algorithms in scanpy and wraps them for simple access.
+        For a more in depth introduction please read the `Scanpy paper <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1382-0>`_.
+```
+
+[zulip forum]: https://scverse.zulipchat.com/#narrow/channel/465075-ehrapy