add links to people

Author: alimanfoo

_posts/2019-05-02-zarr-2.3-release.md

@@ -64,8 +64,9 @@ tutorial](https://zarr.readthedocs.io/en/stable/tutorial.html#distributed-cloud-
 The attraction of cloud storage is that total I/O bandwidth scales
 linearly with the size of a computing cluster, so there are no
 technical limits to the size of the data or computation you can scale
-up to. Here's a slide from a recent presentation by Ryan Abernathey
-showing how I/O scales when using Zarr over Google Cloud Storage:
+up to. Here's a slide from a recent presentation by [Ryan
+Abernathey](https://github.com/rabernat) showing how I/O scales when
+using Zarr over Google Cloud Storage:
 
 <script async class="speakerdeck-embed" data-slide="22" data-id="1621118c5987411fb55fdcf503cb331d" data-ratio="1.77777777777778" src="//speakerdeck.com/assets/embed.js"></script>
 
@@ -122,14 +123,14 @@ entry](https://github.com/pangeo-data/pangeo-datastore/blob/aa3f12bcc3be9584c1a9
 ## Compatibility with N5
 
 Around the same time that development on Zarr was getting started, a
-separate team led by [Stephan
-Saafeld](https://www.janelia.org/lab/saalfeld-lab) at the Janelia
-research campus was experiencing similar challenges storing and
-computing with large amounts of neural imaging data, and developed a
-software library called [N5](https://github.com/saalfeldlab/n5). N5 is
-implemented in Java but is very similar to Zarr in the approach it
-takes to storing both metadata and data chunks, and to decoupling the
-storage backend to enable efficient use of cloud storage.
+separate team led by [Stephan Saafeld](https://github.com/axtimwalde)
+at the Janelia research campus was experiencing similar challenges
+storing and computing with large amounts of neural imaging data, and
+developed a software library called
+[N5](https://github.com/saalfeldlab/n5). N5 is implemented in Java but
+is very similar to Zarr in the approach it takes to storing both
+metadata and data chunks, and to decoupling the storage backend to
+enable efficient use of cloud storage.
 
 There is a lot of commonality between Zarr and N5 and we are working
 jointly to bring the two approaches together. As a first experimental
@@ -175,9 +176,10 @@ handling different Python versions.
 
 There is a growing community of interest around new approaches to
 storage of array-like data, particularly in the cloud. For example,
-Theo McCaie from the UK Met Office Informatics Lab recently wrote a
-series of blog posts about the challenges involved in [storing 200TB
-of "high momentum" weather model data every
+[Theo McCaie](https://github.com/tam203) from the UK Met Office
+Informatics Lab recently wrote a series of blog posts about the
+challenges involved in [storing 200TB of "high momentum" weather model
+data every
 day](https://medium.com/informatics-lab/creating-a-data-format-for-high-momentum-datasets-a394fa48b671). This
 is an exciting space to be working in and we'd like to do what we can
 to build connections and share knowledge and ideas between