update references

Author: jo-mueller

index.md

@@ -65,9 +65,9 @@ parallelization.
 
 As a result, a number of formats have been developed more recently which provide
 the basic data structure of an HDF5 file, but do so in a more cloud-friendly way.
-In the [PyData](https://pydata.org/) community, the Zarr [[zarr]] format was developed
+In the [PyData](https://pydata.org/) community, the Zarr (@zarr) format was developed
 for easily storing collections of [NumPy](https://numpy.org/) arrays. In the
-[ImageJ](https://imagej.net/) community, N5 [[n5]] was developed to work around
+[ImageJ](https://imagej.net/) community, N5 (@n5) was developed to work around
 the limitations of HDF5 ("N5" was originally short for "Not-HDF5").
 Both of these formats permit storing individual chunks of data either locally in
 separate files or in cloud-based object stores as separate keys.
@@ -88,7 +88,7 @@ binary containers. Eventually, we hope, the moniker "next-generation" will no lo
 applicable, and this will simply be the most efficient, common, and useful representation
 of bioimaging data, whether during acquisition or sharing in the cloud.
 
-Note: The following text makes use of OME-Zarr [[ome-zarr-py]], the current prototype implementation,
+Note: The following text makes use of OME-Zarr (@ome-zarr-py), the current prototype implementation,
 for all examples.
 
 ### On-disk (or in-cloud) layout