Added few more repos

_data/repositories.yml

@@ -25,6 +25,10 @@ github_repos:
   - mikgroup/Caterpillar-Traps
   - mikgroup/DiSpectMRI
   - mikgroup/phase_cycling
+  - mikgroup/MEL_MRI
+  - mikgroup/DCSNet
+  - mikgroup/auto-espirit
+  - mikgroup/data_crimes
 
 
   # - alshedivat/al-folio

_pages/repositories.md

@@ -2,41 +2,12 @@
 layout: page
 permalink: /sofware/
 title: software
-description: # Edit the `_data/repositories.yml` and change the `github_users` and `github_repos` lists to include your own GitHub profile and repositories.
+description: 
 nav: true
 nav_order: 4
 ---
 
 
-
-{% if site.data.repositories.github_users %}
-
-## GitHub users
-
-<div class="repositories d-flex flex-wrap flex-md-row flex-column justify-content-between align-items-center">
-  {% for user in site.data.repositories.github_users %}
-    {% include repository/repo_user.liquid username=user %}
-  {% endfor %}
-</div>
-
-<!-- ---
-
-{% if site.repo_trophies.enabled %}
-{% for user in site.data.repositories.github_users %}
-{% if site.data.repositories.github_users.size > 1 %}
-
-  <h4>{{ user }}</h4>
-  {% endif %}
-  <div class="repositories d-flex flex-wrap flex-md-row flex-column justify-content-between align-items-center">
-  {% include repository/repo_trophies.liquid username=user %}
-  </div>
-
----
-
-{% endfor %}
-{% endif %} -->
-{% endif %}
-
 ## BART
 
 <div class="repositories d-flex flex-wrap flex-md-row flex-column justify-content-between align-items-center">
@@ -55,15 +26,15 @@ The Berkeley Advanced Reconstruction Toolbox (BART) is a free and open-source im
   {% endfor %}
 </div>
 
-This package is built to operate directly on numpy arrays on CPU and cupy arrays on GPU. Its main features include: a unified CPU/GPU interface to signal processing functions, Linear operator classes, Proximal operator classes (Prox), Iterative algorithm classes (Alg), and Application classes (App) that wrap Alg, Linop, and Prox to form a final deliverable for each application.
+SigPy is a package for signal processing, with emphasis on iterative methods. This package is built to operate directly on numpy arrays on CPU and cupy arrays on GPU. Its main features include: a unified CPU/GPU interface to signal processing functions, Linear operator classes, Proximal operator classes (Prox), Iterative algorithm classes (Alg), and Application classes (App) that wrap Alg, Linop, and Prox to form a final deliverable for each application.
 
 SigPy also provides a submodule sigpy.mri for MRI iterative reconstruction methods. It includes Apps for common MRI reconstruction methods such as SENSE reconstruction, l1-wavelet reconstruction, total-variation reconstruction, and JSENSE reconstruction. This module also includes convenient simulation and sampling functions, including poisson-disc sampling function, and Shepp-Logan phantom generation function.
 
 ## Data
 
 ### [mridata.org](http://mridata.org)
 
-This website provides open datasets to researchers who desire to contribute to a community of reproducible research, where they can test and validate their algorithms against known undersampled acquisitions. These datasets were acquired through a collaboration between Michael Lustig at UC Berkeley and Dr. Shreyas Vasanawala at Stanford's Lucille Packard Children's Hospital. The undersampled datasets are of two varieties: variable-density undersampling and uniform-density undersampling. At present, all of the datasets are of knee images. In addition to undersampled datasets, we also provide separate cases of fully sampled knees, for researchers who wish to experiment with their own undersampling patterns
+This website provides open datasets to researchers who desire to contribute to a community of reproducible research, where they can test and validate their algorithms against known undersampled acquisitions. These datasets were acquired through a collaboration with Dr. Shreyas Vasanawala at Stanford's Lucille Packard Children's Hospital. The undersampled datasets are of two varieties: variable-density undersampling and uniform-density undersampling. At present, all of the datasets are of knee images. In addition to undersampled datasets, we also provide separate cases of fully sampled knees, for researchers who wish to experiment with their own undersampling patterns
 
 ## ESPIRiT
 
@@ -76,9 +47,9 @@ This website provides open datasets to researchers who desire to contribute to a
 
 <!-- ## Compressed Sensing -->
 
-{% if site.data.repositories.github_repos %}
+## Other Repositories
 
-## Github Repositories
+{% if site.data.repositories.github_repos %}
 
 <div class="repositories d-flex flex-wrap flex-md-row flex-column justify-content-between align-items-center">
   {% for repo in site.data.repositories.github_repos %}