diff --git a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
index bcebd84b419ba40547abc10ae76c15cf76383cfd..a36384bf3c831bffa89aef83ec1b09cba5beaf8d 100644
--- a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
+++ b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
@@ -63,7 +63,7 @@ marie@compute$ R -e 'install.packages("ggplot2")'
 The deep learning frameworks perform extremely fast when run on accelerators such as GPU.
 Therefore, using nodes with built-in GPUs, e.g., clusters
 [`Capella`](../jobs_and_resources/hardware_overview.md#capella),
-[`Alpha`](../jobs_and_resources/hardware_overview.md#alpha_centauri) and
+[`Alpha`](../jobs_and_resources/hardware_overview.md#alpha-centauri) and
 [`Power9`](../jobs_and_resources/hardware_overview.md) is beneficial for the examples here.
 
 ### R Interface to TensorFlow