diff --git a/base/constants.py b/base/constants.py
index ec717b89..95ed7a30 100644
--- a/base/constants.py
+++ b/base/constants.py
@@ -39,6 +39,7 @@ class PRICES:
 
 GOOGLE_CLOUD_BUCKET = 'elegansvariation.org'
 GOOGLE_CLOUD_PROJECT_ID = 'andersen-lab'
+GOOGLE_CLOUD_LOCATION = 'us-central1'
 
 # WI Strain Info Dataset
 GOOGLE_SHEETS = {"orders": "1BCnmdJNRjQR3Bx8fMjD_IlTzmh3o7yj8ZQXTkk6tTXM",
diff --git a/base/models.py b/base/models.py
index 9ec61bb3..07935a6d 100644
--- a/base/models.py
+++ b/base/models.py
@@ -383,6 +383,33 @@ def save(self, *args, **kwargs):
       super(markdown_ds, self).save(*args, **kwargs)
 
 
+
+class h2calc_ds(datastore_model):
+    """
+        The Heritability Calculation Task Model - for creating and retrieving
+        data and status information about a heritability calculation task 
+        executed in Google Cloud Run
+    """
+    kind = 'h2calc'
+    kind = '{}{}'.format(config['DS_PREFIX'], kind)
+
+    
+    def __init__(self, *args, **kwargs):
+      super(h2calc_ds, self).__init__(*args, **kwargs)
+
+    def query_by_username(self, username, keys_only=False):
+      filters = [('username', '=', username)]
+      results = query_item(self.kind, filters=filters, keys_only=keys_only)
+      return results
+
+    def save(self, *args, **kwargs):
+      now = arrow.utcnow().datetime
+      self.modified_on = now
+      if not self._exists:
+        self.created_on = now
+      super(h2calc_ds, self).save(*args, **kwargs)
+
+
 class data_report_ds(datastore_model):
   """
       The Data Report model - for creating and retrieving
diff --git a/base/queue.yaml b/base/queue.yaml
new file mode 100644
index 00000000..bfa0d41c
--- /dev/null
+++ b/base/queue.yaml
@@ -0,0 +1,6 @@
+queue:
+- name: heritability-calc
+  rate: 1/s
+  retry_parameters:
+    task_retry_limit: 2
+    task_age_limit: 1d
diff --git a/base/templates/data_v2.html b/base/templates/data_v2.html
index 950abdc2..d286941a 100644
--- a/base/templates/data_v2.html
+++ b/base/templates/data_v2.html
@@ -1,19 +1,24 @@
-{% extends "_layouts/default.html" %} {% block custom_head %}
+{% extends "_layouts/default.html" %} 
+
+{% block custom_head %}
+
 <style>
 #checkout.affix {
-    position: fixed;
-    top: 20px;
-    width: 136px;
+  position: fixed;
+  top: 20px;
+  width: 136px;
 }
 body, html{
-    width: 100%;
-    height: 100%;
+  width: 100%;
+  height: 100%;
 }
 </style>
+
 {% endblock %}
 
 
 {% block content %}
+
 <div class="row">
     <div class="col-md-12">
         <ul class="nav nav-pills">
@@ -256,9 +261,10 @@ <h2>Datasets</h2>
 </div>{# /Download Tab #}
 
 
-
 {% endblock %}
+
 {% block script %}
+
 <script>
 $(document).ready(function() {
 
@@ -299,5 +305,6 @@ <h2>Datasets</h2>
 
 
 </script>
+
 {% endblock %}
 
diff --git a/base/templates/tools/h2_result_list.html b/base/templates/tools/h2_result_list.html
new file mode 100644
index 00000000..dfe9b55f
--- /dev/null
+++ b/base/templates/tools/h2_result_list.html
@@ -0,0 +1,50 @@
+{% extends "_layouts/default.html" %}
+
+{% block content %}
+
+<div class="row">
+  <div class="col-md-10">
+  </div> <!-- /col-md-10 -->
+  <div class="col-md-2">
+    <a id='create' value="create" type='submit' href="{{ url_for('heritability.heritability_create') }}" class="btn btn-primary btn-block">
+      <span class="glyphicon glyphicon-plus" aria-hidden="true"></span> 
+      New Calculation
+    </a>
+  </div> <!-- /col-md-2 -->
+</div> <!-- /row -->
+
+<div class="row">
+  <div class="col-md-12">
+    <table>
+      <thead>
+        <tr class="header">
+          <th><strong> Label </strong></th>
+          <th><strong> Data Hash </strong></th>
+          <th><strong> Status </strong></th>
+          <th><strong> Created: </strong></th>
+          <th><strong> </strong></th>
+        </tr>
+      </thead>
+      <tbody>
+        {% for item in items %}
+          <tr>
+            {% if item %}
+              <td> {{ item.label }} </td>
+              <td> {{ item.data_hash }} </td>
+              <td> {{ item.status }} </td>
+              <td> {{ item.created_on }} </td>
+              <td>
+                <a href="{{ url_for('heritability.heritability_result', id=item.key.name) }}"> 
+                  View
+                </a>
+              </td>
+            {% endif %}
+          </tr>
+        {% endfor %}
+      </tbody>
+    </table>
+  </div> <!-- /col-md-12 -->
+</div> <!-- /row -->
+
+
+{% endblock %}
diff --git a/base/templates/tools/heritability_calculator.html b/base/templates/tools/heritability_calculator.html
index 12ad4bcf..7dde73a1 100644
--- a/base/templates/tools/heritability_calculator.html
+++ b/base/templates/tools/heritability_calculator.html
@@ -2,6 +2,7 @@
 
 {% block custom_head %}
 	<script src="//cdnjs.cloudflare.com/ajax/libs/underscore.js/1.5.2/underscore-min.js"></script>
+  <link href = "https://code.jquery.com/ui/1.10.4/themes/ui-lightness/jquery-ui.css"
 {% endblock %}
 
 
@@ -9,28 +10,27 @@
 
 {% block content %}
 
+<div class="spinner-border"></div>
+
+
 <div class="row">
   <div class="col-md-12">
-
   <p>
 	This tool will calculate the broad-sense heritability for your trait of interest using a set of <i>C. elegans</i> wild
 	isolates. The broad-sense heritability is the amount of trait variance that comes from genetic differences in the
 	assayed group of strains. Generally, it is the ratio of genetic variance to total (genetic plus environmental)
     variance.
   </p>
-  
   <p>
 	To obtain the best estimate of heritability, please measure a set of at least five wild strains in three
 	independent assays. These assays should use different nematode growths, synchronizations, bacterial food
 	preparations, and any other experimental condition. You should measure trait variance across as many
 	different experimental conditions (in one block) as you would typically encounter in a large experiment.
   </p>
-  
   <p>
 	Please organize your data in a long format, where each row is an independent observation of one strain in one
 	  trait. The columns of the data set should be:
   </p>
-  
   <ol>
     <li><strong>AssayNumber</strong> - a numeric indicator of independent assays.</li>
     <li><strong>Strain</strong> - one of the CeNDR isotype reference strain names.</li>
@@ -40,38 +40,94 @@
     <li><strong>Value</strong> - the measured output of the trait (<i>e.g.</i> 297 for BroodSize).</li>
   </ol>
 
-  <div id='set-example' class='btn btn-primary btn-sm pull-right'>Use example data</div>
-  <p>NA values will not be used in broad-sense heritability calculations.</p>
-  
+<p>NA values will not be used in broad-sense heritability calculations.</p>
+
+  </div>{# /col-md-12 #}
+</div>{# /row #}
+
+
+{% if hide_form == True %}
+
+<div class="row">
+  <div class="col-md-3"></div> {# /col-md-3 #}
+  <div class="col-md-3">
+    <a id='create' value="create" type='submit' href="{{ url_for('heritability.heritability_create') }}" class="btn btn-primary btn-block">
+      <span class="glyphicon glyphicon-plus" aria-hidden="true"></span> 
+      New Calculation
+    </a>
+  </div> {# /col-md-3 #}
+  <div class="col-md-3">
+    <a id='create' value="create" type='submit' href="{{ url_for('heritability.heritability_result_list') }}" class="btn btn-primary btn-block">
+      <span class="glyphicon glyphicon-list" aria-hidden="true"></span> 
+      My Reports
+    </a>
+  </div> {# /col-md-3 #}
+  <div class="col-md-3"></div> {# /col-md-3 #}
 
-  </div>{# /col-md-8 #}
 </div>{# /row #}
 
+{% else %}
+
 <div class="row">
-	<div class="col-md-12">
+
+  <div class='col-md-3'>
+  </div>{#/ col-md-3 #}
+
+  <div class='col-md-3'></div> {# /col-md-3 #}
+
+  <div class='col-md-3'>
+    <a id='set-example' value="set-example" type='submit' class="btn btn-primary btn-block">
+      <span class="glyphicon glyphicon-import" aria-hidden="true"></span> 
+      Load sample data
+    </a>
+  </div> {# /col-md-3 #}
+
+  <div class="col-md-3">
+    <a id='hcalc' value="hcalc" type='submit' class="btn btn-primary btn-block">
+      <span class="glyphicon glyphicon-flash" aria-hidden="true"></span> 
+      Calculate Heritability
+    </a>
+  </div> {# /col-md-3 #}
   
-	<div class="row">
-		<div class="col-md-12">
-		
-			<div id="errorDisp"></div>
-			<div id="entry"></div>
-
-			<small class="text-muted">Prepare your data according to the column headers (described above). Data should be pasted in the table below.</small>
-
-			<div id="trait_summary" class="text-info"></div>
-			<div id="trait_name_error" class="text-danger"></div>
-			<div id="duplicate_error" class="text-danger"></div>
-			<div id="strain_count_error" class="text-danger"></div>		
-			
-		</div>{# /col-md-12 #}
-	</div>{# /row #}
-
-	<div class='col-md-3 pull-right'>
-		<button id="hcalc" name="hcalc" type="submit" style="margin-top: 15px;" class="btn btn-primary btn-block" disabled>Calculate Heritability</button>
-	</div>{#/ col-md-12 #}
+</div>{# /row #}
+
+<br>
+<form>
+  <div class="form-group">
+    <label for="calcLabel">Label:</label>
+    <input type="text" class="form-control" id="calcLabel" placeholder="Enter a description for this data">
+  </div>
+  <div id="calc_label_error" class="text-danger"></div>
+</form>
+
+<div class="row">
+  <div class="col-md-12">
+    <div id="errorDisp"></div>
+    <div id="trait_summary" class="text-info"></div>
+    <div id="trait_name_error" class="text-danger"></div>
+    <div id="duplicate_error" class="text-danger"></div>
+    <div id="strain_count_error" class="text-danger"></div>	
+    <div id="strain_name_error" class="text-danger"></div>	
+    <small class="text-muted">Prepare your data according to the column headers (described below). Data should be pasted in the table below.</small>
+    <div id="entry"></div>
+  </div>{# /col-md-12 #}
+</div>{# /row #}
+
+	
 
 	</div>{# /col-md-12 #}
 </div>{# /row #}
+
+
+<form id='form-submit' method='post' enctype='multipart/form-data' hidden>
+{{ form.csrf_token }}
+<input hidden name="jwt_csrf_token" value="{{ jwt_csrf_token }}">
+<input hidden name="label" value="">
+</form>
+
+{% endif %}
+
+
 {% endblock %}
 
 {% block script %}
@@ -82,28 +138,62 @@
     ];
 var noNAs = 0;
 function dataValidator(instance, td, row, col, prop, value, cellProperties) {
-	Handsontable.renderers.TextRenderer.apply(this, arguments);
-    if (row === 0) {
-        cellProperties.readOnly = true;
+  Handsontable.renderers.TextRenderer.apply(this, arguments);
+  if (row === 0) {
+    cellProperties.readOnly = true;
 	}
-    if (row === 0) {
-        td.style.fontWeight = 'bold';
-        td.style.backgroundColor = '#EAEAEA';
-    }
+  if (row === 0) {
+    td.style.fontWeight = 'bold';
+    td.style.backgroundColor = '#EAEAEA';
+  }
 	
-    if (['NA',].indexOf(String(value).trim()) >= 0) {
-        td.style.background = '#FC6666';
-        td.style.fontWeight = 'bold';
-    }
+  if (['NA',].indexOf(String(value).trim()) >= 0) {
+    td.style.background = '#FC6666';
+    td.style.fontWeight = 'bold';
+  }
 
 	if (duplicate_rows.indexOf(row) > -1) {
-		td.style.background = '#0CEF13';
+    td.style.background = 'RED';
 	}
+
+  if (trait_names.includes(value)) {
+    td.style.background = 'RED';
+  }
+
+  if (unknown_strains.includes(value)) {
+    td.style.background = 'RED';
+  }
 	
 }
 
-var isValid = false
+{% autoescape off %}
+var strain_list = {{ strain_list }};
+{% endautoescape %}
+
+var isValid = false;
 var duplicate_rows = [];
+var trait_names = [];
+var unknown_strains = [];
+
+
+var trim_data = function(data) {
+  for (let i = 0; i < data.length; i++) {
+    for (let j = 0; j < data[i].length; j++) {
+      if (data[i][j]) {
+        data[i][j] = data[i][j].replace("\r\n", "").replace("\r", "").replace("\n", "");
+        data[i][j] = data[i][j].trim()
+      }
+    }
+    var stringData = JSON.stringify(data[i])
+    if(JSON.stringify(['','','','','']) == stringData || "[null,null,null,null,null]" == stringData) {
+      data.splice(i, 1);
+      i--;
+    }
+  }
+  return data;
+}
+
+
 var validate_data = function(data){
 	// Performs error checks:
 	// 1) Duplicates
@@ -111,36 +201,67 @@
 	// 3) Trait Name
 	pass = true
 
+  data = trim_data(data);
 	// Check for duplicates
-	dup_search = _.map(data, (item, idx) => item.toString());
-	dup_values =  _.chain(dup_search).groupBy().filter(x => x.length > 1 && x[0] !== ",,,,").flatten().uniq().value()
-	duplicate_rows = _.map(dup_search, (item, idx) => _.indexOf(dup_values, item) > -1 ? idx : null).filter(x => x !== null)
+  occurences = []
+  duplicate_rows = []
+  for (let i = 0; i < data.length; i++) {
+    item = JSON.stringify(data[i]);
+    if (occurences[item]) {
+      duplicate_rows.push(i)
+    }
+    occurences[item] = true;
+  };
 
 	// if dups are present alert user
 	$("#duplicate_error").text("")
 	if (duplicate_rows.length > 0) {
-		$("#duplicate_error").text("Please check the data because duplicate rows are present. The duplicate rows are shown in green.")
+		$("#duplicate_error").text("Please check the data because duplicate rows are present. The duplicate rows are shown in red.")
 		pass = false
 	}
 
-	// Count number of strains
-	n_strains = _.uniq(_.pluck(data, 1).filter((x, idx) => idx > 0 && x !== null)).length
+  // Strain names
+	var strain_names = _.uniq(_.pluck(data, 1).filter((x, idx) => idx > 0 && x !== null && x.length > 0));
+  unknown_strains = _.difference(strain_names, strain_list);
+  $("#strain_name_error").text("");
+	if (unknown_strains.length > 0) {
+		$("#strain_name_error").text("Please check the data - Some of the strains were not recognized")
+		pass = false
+	}
 
-	$("#strain_count_error").text("")
+	// Count number of strains
+	n_strains = strain_names.length;
+	$("#strain_count_error").text("");
 	if (n_strains < 5) {
 		$("#strain_count_error").text("Please check the data because fewer than five strains are present. Please measure trait values for at least five wild strains in at least three independent assays.")
 		pass = false
 	}
 
 	// Trait Name
-	trait_count = _.uniq(_.pluck(data, 2).filter((x, idx) => idx > 0 && x !== null)).length
+  trait_names = _.uniq(_.pluck(data, 2).filter((x, idx) => idx > 0 && x !== null && x.length > 0));
+	trait_count = trait_names.length;
 
 	$("#trait_name_error").text("")
-	if (trait_count > 1) {
+	if (trait_count >= 2) {
+    pass = false
 		$("#trait_name_error").text("Please check the data. The TraitName has multiple unique values. Only data for a single trait allowed.")
+    var most_common_trait = _.chain(_.pluck(data, 2)).countBy().pairs().max(_.last).head().value();
+    const index = trait_names.indexOf(most_common_trait);
+    if (index !== -1) {
+      trait_names.splice(index, 1);
+    }
+	} else {
+    trait_names = []
+  }
+
+  // Data label
+  label_len = $("#calcLabel").val().length
+  $("#calc_label_error").text("")
+	if (label_len == 0) {
+		$("#calc_label_error").text("Please include a brief description of the data.")
 		pass = false
 	}
-	
+
 	return pass
 }
 
@@ -171,40 +292,26 @@
 	}
 });
 
+
+$("#calcLabel").on('change', function() {
+	onFormChange()
+})
+
 hot.addHook("afterChange", function() {
-	isValid = validate_data(hot.getData());
+	onFormChange()
+})
+
+function onFormChange() {
+  isValid = validate_data(hot.getData());
 	hot.render();
 
 	if (isValid) {
 		document.getElementById('hcalc').disabled = false;
-
-		// Fetch dataset statistics
-		$.ajax({type: "POST",
-		url: "{{ url_for('heritability.check_data') }}",
-		data: JSON.stringify(hot.getData()),
-		contentType: "application/json; charset=utf-8",
-		dataType: 'json',
-		success:function(result) {
-			$("#trait_summary").html(`
-			<br />
-			Input data summary:
-			<ul>
-				<li>Minimum: ${result['minimum']}</li>
-				<li>Maximum: ${result['maximum']}</li>
-				<li>25% Quartile: ${result['25']}</li>
-				<li>50% Quartile: ${result['50']}</li>
-				<li>75% Quartile: ${result['75']}</li>
-				<li>Variance: ${result['variance']}</li>
-			`)
-		}
-		});
-		
 	} else {
 		$("#trait_summary").html("")
 		document.getElementById('hcalc').disabled = true;
 	}
-
-})
+}
 
 // Enable setting example data
 $("#set-example").on('click', function() {
@@ -225,16 +332,24 @@
 
 // submit result
 $("#hcalc").on("click", function(e) {
+  $("#hcalc").addClass("disabled")
 	if (isValid) {
+    var data = new FormData($('form#form-submit')[0]);
+    data.append('table_data', JSON.stringify(hot.getData()));
+    data.set('label', $("#calcLabel").val());
 		$.ajax({
 				type: "POST",
+        processData: false,
+        contentType: false,
+        dataType: 'json',
 				url: "{{ url_for('heritability.submit_h2') }}",
-				data: JSON.stringify(hot.getData()),
-                contentType: "application/json; charset=utf-8",
-				dataType: 'json',
-				success:function(result){
-					window.location = `heritability/h2/${result.data_hash}`
-				}
+				data: data,
+				success:function(result) {
+					window.location = `../heritability/h2/${result.id}`
+				},
+        error:function(error) {
+          $("#hcalc").removeClass("disabled")
+        }
 		})
 	}
 });
diff --git a/base/templates/tools/heritability_results.html b/base/templates/tools/heritability_results.html
index 5293c734..b3d9e4fc 100644
--- a/base/templates/tools/heritability_results.html
+++ b/base/templates/tools/heritability_results.html
@@ -1,8 +1,8 @@
 {% extends "_layouts/default.html" %}
 
 {% block custom_head %}
-
 	<link href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-lightbox/0.7.0/bootstrap-lightbox.css" rel="stylesheet" />
+    <link href = "https://code.jquery.com/ui/1.10.4/themes/ui-lightness/jquery-ui.css"
     <link rel="stylesheet" href="/static/css/d3-exploding-boxplot.css" />
     <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.7/d3.js" charset="utf-8"></script>
     <script src="https://d3js.org/queue.v1.min.js"> </script>
@@ -11,6 +11,7 @@
 	<script src="https://cdnjs.cloudflare.com/ajax/libs/jspdf/1.4.1/jspdf.min.js"></script>
 	<script src="https://cdnjs.cloudflare.com/ajax/libs/html2canvas/0.4.1/html2canvas.min.js"></script>
 	<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/FileSaver.js/2.0.2/FileSaver.min.js"></script>
+  <script src="https://code.jquery.com/ui/1.11.4/jquery-ui.min.js" integrity="sha256-xNjb53/rY+WmG+4L6tTl9m6PpqknWZvRt0rO1SRnJzw=" crossorigin="anonymous"></script>
 	<style type="text/css">
 		.rounded {
 			font-family: Helvetica, Tahoma, Geneva, sans-serif;
@@ -36,85 +37,99 @@
 
 {% block content %}
 
-{% if data and result %}
-    <div class='pull-right'>
-        <button id="ppg" type="button" class="btn btn-secondary" onClick='ppage()'>Previous</button>
-        <button id="npg" type="button" class="btn btn-secondary"  onClick='npage()'>Next</button>
-    </div>
-    <button id="pdd" type="button" class="btn btn-secondary" onClick='getPDF("containerfluid")'>PDF</button>
-{% endif %}
-
-<div id="dataContainer" class="row">
-    <div class='col-md-8 col-md-offset-2'>
-    
-    <h4>Heritability Calculator</h4>
-
-    <p>
-        This tool will calculate the broad-sense heritability for your trait of interest using a set of <i>C. elegans</i> wild
-        isolates. The broad-sense heritability is the amount of trait variance that comes from genetic differences in the
-        assayed group of strains. Generally, it is the ratio of genetic variance to total (genetic plus environmental)
-        variance.
-    </p>
-
-    <div id="htresarea"></div>
+<div class="row">
+  <h2 class="text-center">
+    {{ trait }}
+  </h2>
+</div>{# /row #}
 
-    </div>{# /col-md-8 #}
+<div class="row">
+  <h4 class="text-center">
+    {{ hr.label }}
+  </h4>
 </div>{# /row #}
 
-{% if not result %}
+{% if data and not result %}
 
 <div class="row">
-    <div class='col-md-8 col-md-offset-2'>
-        <p class='text-info'>
+    <div class='col-md-2'></div>
+    <div class='col-md-8'>
+        <p class='text-info text-center'>
             <strong>
                 The heritability calculation is currently being run. Please check back in a few minutes for results.
                 This page will reload automatically.
             </strong>
         </p>
-    </div>
-</div>
-
-{% endif %}
+    </div> {# /col-md-8 #}
+</div>{# /row #}
 
-<div class="row">
-    <div class="col-md-12">
-        {% if data %}
-        <div id="svgchartarea"></div>
-        {% endif %}
+{% else %}
 
-        {% if result %}
-        <canvas id="canvas" ></canvas>
-        <div id="png-container"></div>
-        {% endif %}
 
-    </div>{# /col-md-8 #}
+<div id="dataContainer" class="row">
+  <div class='col-md-12'>
+    <div id="htresarea"></div>
+  </div>{# /col-md-12 #}
 </div>{# /row #}
 
 <div class="row">
-    <h2>Data</h2>
-    <a class='btn btn-text pull-right' href="https://storage.googleapis.com/elegansvariation.org/reports/heritability/{{ data_hash }}/data.tsv">Download Table</a>
-	<table class="table table-hover table-condensed table-striped">
+    {% if data %}
+      <div id="svgchartarea" class='col-md-12'></div>
+    {% endif %}
+</div>{# /row #}
+
+  <div class="row">
+
+    {% if result %}
+    
+    <div id='resultDiv' hidden>
+      <canvas id="canvas"></canvas>
+      <div id="png-container"></div>
+    </div>
+
+    {% endif %}
+  </div>{# /row #}
+
+
+<div id="accordionData">
+
+  <h4 class='expand-disabled' onClick='getPDF()'>Download PDF</h4>
+  <div></div>
+
+  <h4 class='expand-disabled' onClick="window.open('https://storage.googleapis.com/elegansvariation.org/reports/heritability/{{ data_hash }}/data.tsv')">
+    Download Data
+  </h4>
+  <div></div>
+
+  <h4>View Data</h4>
+  <div>
+      <table class="table table-condensed table-striped">
         <thead>
-            <tr>    
-                <th>AssayNumber</th>
-                <th>Strain</th>
-                <th>TraitName</th>
-                <th>Replicate</th>
-                <th>Value</th>
-            </tr>
+          <tr>    
+            <th>AssayNumber</th>
+            <th>Strain</th>
+            <th>TraitName</th>
+            <th>Replicate</th>
+            <th>Value</th>
+          </tr>
         </thead>
-		<tbody>
-		{% for row in data %}
-            <tr><td> {{ row['AssayNumber'] }} </td>
-                <td> {{ row['Strain'] }} </td>
-                <td> {{ row['TraitName'] }} </td>
-                <td> {{ row['Replicate'] }} </td>
-                <td> {{ row['Value']|string|truncate(8, end= "") }} </td>
-            </tr>
+        <tbody>
+        {% for row in data %}
+          <tr>
+            <td> {{ row['AssayNumber'] }} </td>
+            <td> {{ row['Strain'] }} </td>
+            <td> {{ row['TraitName'] }} </td>
+            <td> {{ row['Replicate'] }} </td>
+            <td> {{ row['Value']|string|truncate(8, end= "") }} </td>
+          </tr>
         {% endfor %}
-		</tbody>
-    </table>
-	</div>
+        </tbody>
+      </table>
+  </div>
+
+</div>
+{% endif %}
+
 {% endblock %}
 
 {% block script %}
@@ -190,48 +205,48 @@ <h2>Data</h2>
             for (var cc=0; cc<UniqueNames.length; cc++){cpal.push(getRandomColor())};
         }
 
+        width = document.getElementById("svgchartarea").offsetWidth - 100; /* 100px is from the chart legend*/
+        height = 9 * width / 16;
+        console.log(width)
         var chart = d3.exploding_boxplot(df, {y:'Value', group:'Strain', color:'AssayNumber', label:'TraitName', pal:cpal})
+                      .width(width)
+                      .height(height);
         document.getElementById("svgchartarea").innerHTML = "";
-        chart('#svgchartarea')
+        chart('#svgchartarea');
     }
 
     async function drawImg(canvas, image, DOMURL, w, h){
-
         return new Promise(resolve => {
-
-            image.onload = function () {
-                    var ctx = canvas.getContext("2d");
-                    ctx.fillStyle = "#FFFFFF";
-                    ctx.fillRect(0, 0, w, h);
-                    ctx.drawImage(image, 0, 0, w, h);
-                    var png = canvas.toDataURL("image/jpg",1);
-                    imag = png
-                    document.getElementById('png-container').innerHTML = '<img id="img1" src="'+png+'"/>';
-                    DOMURL.revokeObjectURL(png);
-        if (this.complete){
-        imag = this.src
-
-        }
-                    resolve('resolved');
-                }
-
+          image.onload = function () {
+            var ctx = canvas.getContext("2d");
+            ctx.fillStyle = "#FFFFFF";
+            ctx.fillRect(0, 0, w, h);
+            ctx.drawImage(image, 0, 0, w, h);
+            var png = canvas.toDataURL("image/jpg",1);
+            imag = png
+            document.getElementById('png-container').innerHTML = '<img id="img1" src="'+png+'"/>';
+            DOMURL.revokeObjectURL(png);
+            if (this.complete){
+              imag = this.src
+            }
+            resolve('resolved');
+          }
         });
-
-    }
+      }
     
     var loadCanvas = function(){
         var svgString = new XMLSerializer().serializeToString(document.querySelector('svg'));
 
         var canvas = document.getElementById("canvas");
-        canvas.width = 1000; 
-        canvas.height = 480; 
+        /*canvas.width = 1000; 
+        canvas.height = 480;*/
         var ctx = canvas.getContext("2d");
         var DOMURL = self.URL || self.webkitURL || self;
         image = document.createElement("IMG"); 
         var svg = new Blob([svgString], {type: "image/svg+xml;charset=utf-8"});
         var url = DOMURL.createObjectURL(svg);
 
-        drawImg(canvas, image, DOMURL, 1000, 480).then(() => {
+        drawImg(canvas, image, DOMURL, canvas.width, canvas.height).then(() => {
         
         var doc = new jsPDF('p', 'px', 'a4');
         var source = document.getElementById('dataContainer'); 
@@ -262,52 +277,54 @@ <h2>Data</h2>
             a.click();
         })
         image.src = url;
-        document.getElementById("svgchartarea").remove();
-        document.getElementById('canvas').remove();
     }
 
     var getPDF = function(){
-        loadCanvas();
+      loadCanvas();
     }
 
     $(document).ready(function(){
-        if (chartJson.length != 0){
-            var node= document.getElementById("svgchartarea");
-            $("#svgchartarea").html("")
-            //node.querySelectorAll('*').forEach(n => n.remove());
-            newPt = document.createElement("p")
-            newPt.innerHTML = "Broad-sense heritability (<i> H<sup>2</sup> </i>) = {{ (result['H2']*100)|round(2) }}% (range {{ (result['ci_l']*100)|round(2) }}% to {{ (result['ci_r']*100)|round(2) }}%)";
-            document.getElementById("htresarea").appendChild(newPt);
-            newPt = document.createElement("p")
-            newPt.appendChild(document.createTextNode("Trait: " + "{{ trait }}"));
-            document.getElementById("htresarea").appendChild(newPt);
-        
-            // chart(data,aes)
-            // aesthetic :
-            // y : point's value on y axis Value
-            // group : how to group data on x axis Strain
-            // color : color of the point / boxplot AssayNumber
-            // label : displayed text in toolbox AssayNumber_Strain_Replicate : Value
-            console.log(chartJson)
-
-            chartJson.sort(sortByProperty("Strain"));
-		    chartJson.forEach(function(d,i){
-                if (strn.indexOf(d['Strain']) == -1) {
-                    strn.push(d['Strain']);}
-                });
-		    maxP = ((strn.length % 25) > 0) ? parseInt(strn.length / 25)+1 : parseInt(strn.length / 25);
-
-            loadPlot(1);
-
-        } else {
-            newPt.appendChild(document.createTextNode("Chart data is empty!"));
-        }
-    })
+      if (chartJson.length != 0){
+        var node= document.getElementById("svgchartarea");
+        $("#svgchartarea").html("")
+        //node.querySelectorAll('*').forEach(n => n.remove());
+        newPt = document.createElement("p")
+        newPt.innerHTML = "Broad-sense heritability (<i> H<sup>2</sup> </i>) = {{ (result['H2']*100)|round(2) }}% (range {{ (result['ci_l']*100)|round(2) }}% to {{ (result['ci_r']*100)|round(2) }}%)";
+        document.getElementById("htresarea").appendChild(newPt);
+        newPt = document.createElement("p")
+        newPt.appendChild(document.createTextNode("Trait: " + "{{ trait }}"));
+        document.getElementById("htresarea").appendChild(newPt);
+    
+        // chart(data,aes)
+        // aesthetic :
+        // y : point's value on y axis Value
+        // group : how to group data on x axis Strain
+        // color : color of the point / boxplot AssayNumber
+        // label : displayed text in toolbox AssayNumber_Strain_Replicate : Value
+        console.log(chartJson)
+        chartJson.sort(sortByProperty("Strain"));
+        chartJson.forEach(function(d,i){
+          if (strn.indexOf(d['Strain']) == -1) {
+            strn.push(d['Strain']);}
+          });
+        maxP = ((strn.length % 25) > 0) ? parseInt(strn.length / 25)+1 : parseInt(strn.length / 25);
 
-    $( document ).ready(function() {
-        $("a[href^='http://'], a[href^='https://'], a[href$='pdf']").attr("target","_blank");
-    });
+        loadPlot(1);
+      } else {
+        newPt.appendChild(document.createTextNode("Chart data is empty!"));
+      }
+
+      $("a[href^='http://'], a[href^='https://'], a[href$='pdf']").attr("target","_blank");
+      $( "#accordionData" ).accordion({ collapsible: true, active: false });
+
+    })
+    
+    $("#accordionData").on("accordionbeforeactivate", function (){
+      return ! arguments[1].newHeader.hasClass("expand-disabled");
+    })
+  
     </script>
-{% endif %}
+{% endif %}        
+
 
 {% endblock %}
\ No newline at end of file
diff --git a/base/templates/user/profile.html b/base/templates/user/profile.html
index b12744b1..e661916a 100644
--- a/base/templates/user/profile.html
+++ b/base/templates/user/profile.html
@@ -26,7 +26,12 @@
   </div> <!-- /col-md-3 -->
   <div class='col-md-6' align="center">
     <button id='edit-btn' type='submit' class="btn btn-primary btn-block"
-      onClick="window.location.href='{{ url_for( 'user.user_update') }}';" >
+      onClick="window.location.href='{{ url_for( 'user.user_update') }}';" 
+    {% if user['user_type'] != 'LOCAL' %}
+      disabled>
+    {% else %} 
+      >
+    {% endif %}
       <span class="glyphicon glyphicon-edit" aria-hidden="true"></span> 
         Edit 
     </button>
diff --git a/base/utils/gcloud.py b/base/utils/gcloud.py
index bb6c2123..316caf2a 100644
--- a/base/utils/gcloud.py
+++ b/base/utils/gcloud.py
@@ -6,11 +6,13 @@
 from gcloud import datastore, storage
 from logzero import logger
 from google.oauth2 import service_account
+from google.cloud import tasks_v2
+from google.protobuf import timestamp_pb2
+import requests
 
-from base.constants import GOOGLE_CLOUD_BUCKET, GOOGLE_CLOUD_PROJECT_ID
+from base.constants import GOOGLE_CLOUD_BUCKET, GOOGLE_CLOUD_PROJECT_ID, GOOGLE_CLOUD_LOCATION
 from base.utils.data_utils import dump_json
 
-
 def google_datastore(open=False):
     """
         Fetch google datastore credentials
@@ -223,26 +225,84 @@ def google_analytics():
 
 def generate_download_signed_url_v4(blob_path, expiration=datetime.timedelta(minutes=15)):
     """Generates a v4 signed URL for downloading a blob. """
-    client = google_storage()
-    bucket = client.bucket(GOOGLE_CLOUD_BUCKET)
-    blob = bucket.blob(blob_path)
-
-    url = blob.generate_signed_url(
+    cendr_bucket = get_cendr_bucket()
+    try: 
+      blob = cendr_bucket.blob(blob_path)
+      url = blob.generate_signed_url(
         expiration=expiration,
         method="GET"
-    )
+      )
+    except:
+      return None
     return url
 
 
 def generate_upload_signed_url_v4(blob_name, content_type="application/octet-stream"):
     """Generates a v4 signed URL for uploading a blob using HTTP PUT. """
-    client = google_storage()
-    bucket = client.bucket(GOOGLE_CLOUD_BUCKET)
-    blob = bucket.blob(blob_name)
-
-    url = blob.generate_signed_url(
-      expiration=datetime.timedelta(minutes=15),
-      method="PUT",
-      content_type=content_type
-    )
+    cendr_bucket = get_cendr_bucket()
+    try:
+      blob = cendr_bucket.blob(blob_name)
+      url = blob.generate_signed_url(
+        expiration=datetime.timedelta(minutes=15),
+        method="PUT",
+        content_type=content_type
+      )
+    except:
+      return None
     return url
+
+
+def google_task(open=False):
+    """
+        Fetch google datastore credentials
+
+        Args:
+            open - Return the client without storing it in the g object.
+    """
+    client = tasks_v2.CloudTasksClient()
+    if open:
+      return client
+    if g:
+      if not hasattr(g, 'tc'):
+        g.tc = client
+      return g.tc
+    return client
+
+
+def add_task(queue, url, payload, delay_seconds=None, task_name=None):
+  client = google_task()
+  parent = client.queue_path(GOOGLE_CLOUD_PROJECT_ID, GOOGLE_CLOUD_LOCATION, queue)
+  
+  task = {
+    "http_request": { 
+      "http_method": tasks_v2.HttpMethod.POST,
+      "url": url,
+    }
+  }
+
+  if payload is not None:
+    if isinstance(payload, dict):
+      payload = json.dumps(payload)
+      task["http_request"]["headers"] = {"Content-type": "application/json"}
+
+    converted_payload = payload.encode()
+    task["http_request"]["body"] = converted_payload
+
+
+  if delay_seconds is not None:
+    # Convert "seconds from now" into an rfc3339 datetime string then into a Timestamp protobuf.
+    d = datetime.datetime.utcnow() + datetime.timedelta(seconds=delay_seconds)
+    timestamp = timestamp_pb2.Timestamp()
+    timestamp.FromDatetime(d)
+    task["schedule_time"] = timestamp
+
+  if task_name is not None:
+    task["name"] = f"{parent}/tasks/{task_name}"
+
+  response = client.create_task(request={"parent": parent, "task": task})
+  if response:
+    logger.debug("Created task {}".format(response.name))
+    return True
+  else :
+    logger.error("Failed to create task {}".format(response.name))
+    return False
diff --git a/base/utils/jwt.py b/base/utils/jwt_utils.py
similarity index 100%
rename from base/utils/jwt.py
rename to base/utils/jwt_utils.py
diff --git a/base/views/about.py b/base/views/about.py
index 1a8aa03a..88398808 100644
--- a/base/views/about.py
+++ b/base/views/about.py
@@ -20,7 +20,7 @@
 from base.utils.email import send_email, DONATE_SUBMISSION_EMAIL
 from base.utils.data_utils import load_yaml, chicago_date, hash_it
 from base.utils.plots import time_series_plot
-from base.utils.jwt import jwt_required, get_current_user
+from base.utils.jwt_utils import jwt_required, get_current_user
 
 about_bp = Blueprint('about',
                      __name__,
diff --git a/base/views/admin/admin.py b/base/views/admin/admin.py
index 2dc9ecae..90e8fe2c 100644
--- a/base/views/admin/admin.py
+++ b/base/views/admin/admin.py
@@ -1,7 +1,7 @@
 from flask import (render_template,
                   Blueprint)
 from base.config import config
-from base.utils.jwt import admin_required
+from base.utils.jwt_utils import admin_required
 
 # Admin blueprint
 admin_bp = Blueprint('admin',
diff --git a/base/views/admin/data.py b/base/views/admin/data.py
index 3970b52c..7622bb58 100644
--- a/base/views/admin/data.py
+++ b/base/views/admin/data.py
@@ -14,7 +14,7 @@
 from base.config import config
 from base.models import data_report_ds
 from base.forms import data_report_form
-from base.utils.jwt import get_jwt, admin_required, get_current_user
+from base.utils.jwt_utils import get_jwt, admin_required, get_current_user
 from base.utils.data_utils import unique_id
 from base.utils.gcloud import delete_by_ref
 
diff --git a/base/views/admin/users.py b/base/views/admin/users.py
index ee42b832..ee3b247c 100644
--- a/base/views/admin/users.py
+++ b/base/views/admin/users.py
@@ -11,7 +11,7 @@
 
 from base.models import user_ds
 from base.forms import admin_edit_user_form
-from base.utils.jwt import jwt_required, get_jwt, admin_required
+from base.utils.jwt_utils import jwt_required, get_jwt, admin_required
 from base.utils.gcloud import delete_by_ref
 
 
diff --git a/base/views/auth/auth.py b/base/views/auth/auth.py
index 08f2c860..1e683c9a 100644
--- a/base/views/auth/auth.py
+++ b/base/views/auth/auth.py
@@ -13,7 +13,7 @@
 
 from base.models import user_ds
 from base.forms import basic_login_form
-from base.utils.jwt import (create_access_token, 
+from base.utils.jwt_utils import (create_access_token, 
                             set_access_cookies,
                             get_jwt_identity,
                             jwt_required,
@@ -64,7 +64,8 @@ def basic_login():
       if user.check_password(password, config['PASSWORD_SALT']):
         user.last_login = arrow.utcnow().datetime
         user.save()
-        return assign_access_refresh_tokens(username, user.roles, '/')
+        referrer = session.get('login_referrer', '/')
+        return assign_access_refresh_tokens(username, user.roles, referrer)
     flash('Wrong username or password', 'error')
     return redirect(request.referrer)
   return render_template('basic_login.html', **locals())
diff --git a/base/views/auth/oauth.py b/base/views/auth/oauth.py
index 49e1f2ce..9ee4b06d 100644
--- a/base/views/auth/oauth.py
+++ b/base/views/auth/oauth.py
@@ -9,7 +9,7 @@
 from base.config import config
 from base.models import user_ds
 from base.utils.data_utils import unique_id
-from base.utils.jwt import assign_access_refresh_tokens
+from base.utils.jwt_utils import assign_access_refresh_tokens
 
 
 google_bp = make_google_blueprint(client_id=config['GOOGLE_CLIENT_ID'], 
@@ -36,6 +36,7 @@ def create_or_update_google_user(user_info):
   user.last_login = now
   user.set_properties(username=user_email, password=unique_id(), salt=config['PASSWORD_SALT'], full_name=user_name, email=user_email.lower())
   user.verified_email = True;
+  user.user_type = 'OAUTH'
   user.save()
   return user
 
diff --git a/base/views/auth/saml.py b/base/views/auth/saml.py
index 548971c4..99bf22ef 100644
--- a/base/views/auth/saml.py
+++ b/base/views/auth/saml.py
@@ -9,7 +9,7 @@
 									Blueprint)
 from slugify import slugify
 from base.models import user_ds
-from base.utils.jwt import assign_access_refresh_tokens
+from base.utils.jwt_utils import assign_access_refresh_tokens
 
 from urllib.parse import urlparse
 from onelogin.saml2.auth import OneLogin_Saml2_Auth
@@ -21,20 +21,17 @@
                     __name__,
                     template_folder='')
 
-pd = {
-  'eduPersonPrincipalName': 'urn:oid:1.3.6.1.4.1.5923.1.1.1.6',
-  'mail': 'urn:oid:0.9.2342.19200300.100.1.3',
-  'o': 'urn:oid:2.5.4.10',
-  'displayName': 'urn:oid:2.16.840.1.113730.3.1.241',
-  'uid':  'urn:oid:0.9.2342.19200300.100.1.1',
-}
+attr_epp = 'urn:oid:1.3.6.1.4.1.5923.1.1.1.6'
+attr_mail = 'urn:oid:0.9.2342.19200300.100.1.3'
+attr_o = 'urn:oid:2.5.4.10'
+attr_name = 'urn:oid:2.16.840.1.113730.3.1.241'
+attr_uid =  'urn:oid:0.9.2342.19200300.100.1.1'
 
 
 def get_or_register_user(saml_auth):
   try:
     attributes = saml_auth.get_attributes()
-    username = attributes[pd.get('eduPersonPrincipalName')]
-    username = username[0]
+    username = attributes.get(attr_epp, [''])[0]
     id = slugify(username)
     if id is None:
       return None
@@ -46,17 +43,11 @@ def get_or_register_user(saml_auth):
       user.roles = ['user']
 
     user.username = username
-    user.email = attributes[pd['mail']]
+    user.email = attributes.get(attr_mail, [''])[0]
     user.verified_email = True
-    user.o = attributes[pd['o']] if hasattr(attributes, pd['o']) else ['']
-    user.full_name = attributes[pd['displayName']] if hasattr(attributes, pd['displayName']) else ['']
-    user.uid = attributes[pd['uid']] if hasattr(attributes, pd['uid']) else ['']
-
-    # properties are initially arrays
-    user.email = user.email[0]
-    user.o = user.o[0]
-    user.full_name = user.full_name[0]
-    user.uid = user.uid[0]
+    user.o = attributes.get(attr_o, [''])[0]
+    user.name = attributes.get(attr_name, [''])[0]
+    user.uid = attributes.get(attr_uid, [''])[0]
 
     # store the rest of the saml info
     user.samlUserdata = attributes
@@ -66,6 +57,7 @@ def get_or_register_user(saml_auth):
     user.samlNameIdSPNameQualifier = saml_auth.get_nameid_spnq()
     user.samlSessionIndex = saml_auth.get_session_index()
     user.last_login = now
+    user.user_type = 'SAML'
     user.save()
     return user
   except:
diff --git a/base/views/data.py b/base/views/data.py
index 870a1aaf..63faf5f5 100644
--- a/base/views/data.py
+++ b/base/views/data.py
@@ -1,16 +1,17 @@
 import requests
 
 from datetime import timedelta
-from base.utils.jwt import jwt_required
 from simplejson.errors import JSONDecodeError
 from flask import make_response, render_template, Blueprint
 
-from base.config import config
 from base.constants import GOOGLE_CLOUD_BUCKET
+from base.config import config
 from base.extensions import cache
-from base.views.api.api_strain import get_isotypes, query_strains
 from base.models import Strain
 from base.utils.gcloud import list_release_files, generate_download_signed_url_v4
+from base.utils.jwt_utils import jwt_required
+from base.views.api.api_strain import get_isotypes, query_strains
+
 
 data_bp = Blueprint('data',
                     __name__,
@@ -160,11 +161,17 @@ def generate_bam_download_script(release):
   strain_listing = query_strains(release=release, is_sequenced=True)
 
   for strain in strain_listing:
+    script_content += f'\n\n# Strain: {strain}'
+
     bam_path = 'bam/{}.bam'.format(strain)
+    bam_signed_url = generate_download_signed_url_v4(bam_path, expiration=expiration)
+    if bam_signed_url:
+      script_content += '\nwget "{}"'.format(bam_signed_url)
+    
     bai_path = 'bam/{}.bam.bai'.format(strain)
-    script_content += f'\n\n# Strain: {strain}'
-    script_content += '\nwget "{}"'.format(generate_download_signed_url_v4(bam_path, expiration=expiration))
-    script_content += '\nwget "{}"'.format(generate_download_signed_url_v4(bai_path, expiration=expiration))
+    bai_signed_url = generate_download_signed_url_v4(bai_path, expiration=expiration)
+    if bai_signed_url:
+      script_content += '\nwget "{}"'.format(bai_signed_url)
 
   return script_content
 
diff --git a/base/views/order.py b/base/views/order.py
index 885a3bd5..e482b429 100644
--- a/base/views/order.py
+++ b/base/views/order.py
@@ -14,7 +14,7 @@
 from base.utils.email import send_email, ORDER_SUBMISSION_EMAIL
 from base.utils.google_sheets import add_to_order_ws, lookup_order
 from base.utils.data_utils import chicago_date
-from base.utils.jwt import jwt_required, get_current_user
+from base.utils.jwt_utils import jwt_required, get_current_user
 
 order_bp = Blueprint('order',
                      __name__,
diff --git a/base/views/tools/heritability.py b/base/views/tools/heritability.py
index e61a4aab..0fdaec73 100644
--- a/base/views/tools/heritability.py
+++ b/base/views/tools/heritability.py
@@ -1,23 +1,25 @@
 import io
-import requests
-import statistics as st
-
-import numpy as np
 import pandas as pd
+import json
 
-from base.utils.data_utils import hash_it
-from base.utils.gcloud import check_blob, upload_file
-from base.config import config
-from base.forms import heritability_form
-
-from flask import (request,
+from flask import (flash,
+                   request,
+                   redirect,
+                   url_for,
                    jsonify,
                    render_template,
                    Blueprint,
                    abort)
 from logzero import logger
 from datetime import datetime
-from threading import Thread
+
+from base.config import config
+from base.views.api.api_strain import get_strains
+from base.utils.data_utils import hash_it, unique_id
+from base.utils.jwt_utils import jwt_required, get_jwt, get_current_user
+from base.utils.gcloud import check_blob, upload_file, add_task
+from base.forms import heritability_form
+from base.models import h2calc_ds
 
 # ================== #
 #   heritability     #
@@ -28,113 +30,147 @@
                             __name__,
                             template_folder='tools')
 
-@heritability_bp.route('/heritability')
-def heritability():
-    VARS = {"title": "Heritability Calculator",
-            "form": heritability_form()}
-    return render_template('tools/heritability_calculator.html', **VARS)
 
+def create_h2_task(data_hash):
+  """
+      This is designed to be run in the background on the server.
+      It will run a heritability analysis on google cloud run
+  """
+  hr = h2calc_ds(data_hash)
+
+  # Perform h2 request
+  queue = config['HERITABILITY_CALC_TASK_QUEUE']
+  url = config['HERITABILITY_CALC_URL']
+  data = {'hash': data_hash}
+  result = add_task(queue, url, data, task_name=data_hash)
 
-def h2_task(data, data_hash):
-    """
-        This is designed to be run in the background on the server.
-        It will run a heritability analysis on google cloud run
-    """
-    # Perform h2 request
-    result = requests.post(config['HERITABILITY_URL'], data={'data': data,
-                                                             'hash': data_hash})
-    logger.debug(result)
+  # Update report status
+  hr.status = 'SCHEDULED' if result else 'FAILED'
+  hr.save()
 
 
+@heritability_bp.route('/heritability')
+def heritability():
+  title = "Heritability Calculator"
+  form = heritability_form()
+  hide_form = True
+  strain_list = []
+  return render_template('tools/heritability_calculator.html', **locals())
+
+
+@heritability_bp.route('/heritability/create', methods=["GET"])
+@jwt_required()
+def heritability_create():
+  """
+      This endpoint is used to create a heritability job.
+  """
+  title = "Heritability Calculator"
+  jwt_csrf_token = (get_jwt() or {}).get("csrf")
+  form = heritability_form()
+  strain_data = get_strains()
+  strain_list = []
+  for x in strain_data:
+    strain_list.append(x.strain)
+
+  hide_form = False
+  id = unique_id()
+  return render_template('tools/heritability_calculator.html', **locals())
+  
+
 @heritability_bp.route('/heritability/submit', methods=["POST"])
+@jwt_required()
 def submit_h2():
-    """
-        This endpoint is used to submit a heritability job.
-        The endpoint request is executed as a background task to keep the job alive.
-    """
-    data = request.get_json()
-    data = [x for x in data[1:] if x[0] is not None]
-    header = ["AssayNumber", "Strain", "TraitName", "Replicate", "Value"]
-    data = pd.DataFrame(data, columns=header)
-    data = data.to_csv(index=False, sep="\t")
-    
-    # Generate an ID for the data based on its hash
-    data_hash = hash_it(data, length=32)
-    logger.debug(data_hash)
-
-    # Upload data immediately.
-    data_blob = f"reports/heritability/{data_hash}/data.tsv"
-    upload_file(data_blob, data, as_string=True)
-
-    thread = Thread(target=h2_task, args=(data, data_hash,))
-    thread.daemon = True
-    thread.start()
-    return jsonify({'thread_name': str(thread.name),
-                    'started': True,
-                    'data_hash': data_hash})
-
-
-@heritability_bp.route('/heritability', methods=["POST"])
-def check_data():
-    """
-        This check is used to report on the:
-
-        Minimum
-        Maximum
-        Quartiles: 25, 50, 75
-        Variance
-
-        using an AJAX request - it appears at the bottom
-        before the user submits.
-    """
-    data = request.get_json()
-    data = [x for x in data[1:] if x[0] is not None]
-    header = ["AssayNumber", "Strain", "TraitName", "Replicate", "Value"]
-    data = pd.DataFrame(data, columns=header)
-
-    # filter missing
-    data = data[data.Value.apply(lambda x: x not in [None, "", "NA"])]
-
-    # Convert to list
-    data = data.Value.astype(float).tolist()
-
-    result = {}
-    result["variance"] = "{:.2f}".format(st.variance(data))
-    result["sd"] = "{:.2f}".format(st.stdev(data))
-    result["minimum"] = "{:.2f}".format(min(data))
-    result["maximum"] = "{:.2f}".format(max(data))
-    # Calculate quartiles
-    All_quartiles = np.percentile(data, [25, 50, 75])
-    result["25"] = "{:.2f}".format(All_quartiles[0])
-    result["50"] = "{:.2f}".format(All_quartiles[1])
-    result["75"] = "{:.2f}".format(All_quartiles[2])
-    return result
-
-
-@heritability_bp.route("/heritability/h2/<data_hash>")
-def heritability_result(data_hash):
-    title = "Heritability Results"
-    data = check_blob(f"reports/heritability/{data_hash}/data.tsv")
-    result = check_blob(f"reports/heritability/{data_hash}/result.tsv")
-    ready = False
-
-    if data is None:
-        return abort(404, description="Heritability report not found")
-    data = data.download_as_string().decode('utf-8')
-    data = pd.read_csv(io.StringIO(data), sep="\t")
-    data['AssayNumber'] = data['AssayNumber'].astype(str)
-    data['label'] = data.apply(lambda x: f"{x['AssayNumber']}: {x['Value']}", 1)
-    data = data.to_dict('records')
-    trait = data[0]['TraitName']
-    # Get trait and set title
-    title = f"Heritability Results: {trait}"
-
-    if result:
-        result = result.download_as_string().decode('utf-8')
-        result = pd.read_csv(io.StringIO(result), sep="\t")
-        result = result.to_dict('records')[0]
-
-        fnam=datetime.today().strftime('%Y%m%d.')+trait
-        ready = True
-
-    return render_template("tools/heritability_results.html", **locals())
+  """
+      This endpoint is used to submit a heritability job.
+      The endpoint request is executed as a background task to keep the job alive.
+  """
+  user = get_current_user()
+  label = request.values['label']
+
+  # Process data into tsv
+  data = json.loads(request.values['table_data'])
+  data = [x for x in data[1:] if x[0] is not None]
+  header = ["AssayNumber", "Strain", "TraitName", "Replicate", "Value"]
+  data = pd.DataFrame(data, columns=header)
+  data = data.to_csv(index=False, sep="\t")
+  
+  # Generate an ID for the data based on its hash
+  data_hash = hash_it(data, length=32)
+  logger.debug(data_hash)
+
+  # Store the report info for user in datastore
+  id = unique_id()
+  hr = h2calc_ds(id)
+  hr.label = label
+  hr.data_hash = data_hash
+  hr.username = user.name
+  hr.status = 'NEW'
+  hr.save()
+
+  # Upload data immediately.
+  data_blob = f"reports/heritability/{data_hash}/data.tsv"
+  upload_file(data_blob, data, as_string=True)
+  hr.status = 'RECEIVED'
+  hr.save()
+
+  # Schedule the task
+  create_h2_task(data_hash)
+  return jsonify({'started': True,
+                  'data_hash': data_hash,
+                  'id': id})
+
+
+@heritability_bp.route("/heritability/h2/<id>")
+@jwt_required()
+def heritability_result(id):
+  title = "Heritability Results"
+  user = get_current_user()
+  hr = h2calc_ds(id)
+  ready = False
+
+  if (not hr._exists) or (hr.username != user.name):
+    flash('You do not have access to that report', 'danger')
+    abort(401)
+
+  data_hash = hr.data_hash
+  data = check_blob(f"reports/heritability/{data_hash}/data.tsv")
+  result = check_blob(f"reports/heritability/{data_hash}/result.tsv")
+
+  if data is None:
+    hr.status = 'NOT FOUND'
+    hr.save()
+    return abort(404, description="Heritability report not found")
+  data = data.download_as_string().decode('utf-8')
+  data = pd.read_csv(io.StringIO(data), sep="\t")
+  data['AssayNumber'] = data['AssayNumber'].astype(str)
+  data['label'] = data.apply(lambda x: f"{x['AssayNumber']}: {x['Value']}", 1)
+  data = data.to_dict('records')
+  trait = data[0]['TraitName']
+  # Get trait and set title
+  title = f"Heritability Results: {trait}"
+
+  if result:
+    hr.status = 'COMPLETE'
+    hr.save()
+    result = result.download_as_string().decode('utf-8')
+    result = pd.read_csv(io.StringIO(result), sep="\t")
+    result = result.to_dict('records')[0]
+
+    fnam=datetime.today().strftime('%Y%m%d.')+trait
+    ready = True
+
+  return render_template("tools/heritability_results.html", **locals())
+
+
+@heritability_bp.route("/heritability/h2/all")
+@jwt_required()
+def heritability_result_list():
+  title = "Heritability Results"
+  user = get_current_user()
+  items = h2calc_ds().query_by_username(user.name)
+  for x in items:
+    data_hash = x['data_hash']
+    if check_blob(f"reports/heritability/{data_hash}/result.tsv"):
+      x.status = 'COMPLETE'
+      x.save()
+  return render_template('tools/h2_result_list.html', **locals())
diff --git a/base/views/user.py b/base/views/user.py
index 3e3b38df..2e8d917f 100644
--- a/base/views/user.py
+++ b/base/views/user.py
@@ -12,7 +12,7 @@
 from base.config import config
 from base.models import user_ds
 from base.forms import user_register_form, user_update_form
-from base.utils.jwt import jwt_required, get_jwt, get_current_user, assign_access_refresh_tokens
+from base.utils.jwt_utils import jwt_required, get_jwt, get_current_user, assign_access_refresh_tokens
 
 
 user_bp = Blueprint('user',
@@ -40,6 +40,7 @@ def user_register():
     id = slugify(username)
     user = user_ds(id)
     user.set_properties(username=username, password=password, salt=config['PASSWORD_SALT'], full_name=full_name, email=email, roles=roles)
+    user.user_type = 'LOCAL'
     user.save()
     return assign_access_refresh_tokens(username, user.roles, url_for("user.user_profile"))
   return render_template('user/register.html', **locals())
diff --git a/cloud_buckets/cors.json b/cloud_buckets/cors.json
new file mode 100644
index 00000000..da7a16c8
--- /dev/null
+++ b/cloud_buckets/cors.json
@@ -0,0 +1,8 @@
+[
+  {
+    "origin": ["https://www.elegansvariation.org/", "http://localhost:5000/"],
+    "responseHeader": ["Content-Type", "Access-Control-Allow-Origin"],
+    "method": ["GET", "HEAD", "DELETE", "PUT", "POST"],
+    "maxAgeSeconds": 3600
+  }
+]
\ No newline at end of file
diff --git a/cloud_functions/heritability_run/.envrc b/cloud_functions/heritability_run/.envrc
new file mode 100644
index 00000000..b15726f9
--- /dev/null
+++ b/cloud_functions/heritability_run/.envrc
@@ -0,0 +1,2 @@
+export GOOGLE_APPLICATION_CREDENTIALS=../../../../env_config/client-secret.json
+
diff --git a/cloud_functions/heritability_run/.gcloudignore b/cloud_functions/heritability_run/.gcloudignore
new file mode 100644
index 00000000..4da3d968
--- /dev/null
+++ b/cloud_functions/heritability_run/.gcloudignore
@@ -0,0 +1,2 @@
+*.csv
+server
\ No newline at end of file
diff --git a/cloud_functions/heritability_run/Dockerfile b/cloud_functions/heritability_run/Dockerfile
new file mode 100644
index 00000000..6f921439
--- /dev/null
+++ b/cloud_functions/heritability_run/Dockerfile
@@ -0,0 +1,41 @@
+FROM continuumio/miniconda3
+
+RUN apt-get update && apt-get install -y procps && \
+    apt-get clean
+RUN conda config --add channels defaults && \
+    conda config --add channels bioconda && \
+    conda config --add channels conda-forge
+RUN conda create -n heritability \
+				 conda-forge::go=1.13.15 \
+				 r=3.6.0 \
+				 r-lme4 \
+				 r-dplyr \
+				 r-tidyr \
+				 r-glue \
+				 r-boot \
+				 r-data.table \
+				 r-futile.logger \
+				 && conda clean -a
+ENV PATH /opt/conda/envs/heritability/bin:$PATH
+LABEL Name="heritability" Author="Daniel Cook"
+
+# Create and change to the app directory.
+WORKDIR /app
+
+# Copy local code to the container image.
+COPY invoke.go ./
+
+COPY H2_script.R ./
+
+# get strain data
+COPY strain_data.tsv ./
+
+# Install google cloud storage
+RUN CGO_ENABLED=0 GOOS=linux go get cloud.google.com/go/storage
+
+# Build the binary.
+# -mod=readonly ensures immutable go.mod and go.sum in container builds.
+RUN CGO_ENABLED=0 GOOS=linux go build -v -o server
+
+# Run the web service on container startup.
+CMD ["/app/server"]
diff --git a/cloud_functions/heritability_run/H2_script.R b/cloud_functions/heritability_run/H2_script.R
new file mode 100644
index 00000000..9d6b2bc3
--- /dev/null
+++ b/cloud_functions/heritability_run/H2_script.R
@@ -0,0 +1,476 @@
+#!/usr/bin/env Rscript H2_script.R
+library(boot)
+library(lme4)
+library(dplyr)
+library(futile.logger)
+library(tidyr)
+library(glue)
+library(data.table)
+
+########################
+### define functions ###
+########################
+# Heritability
+# data is data frame that contains strain and Value column
+# indicies are used by the boot function to sample from the 'data' data.frame
+H2.test.boot <- function(data, indicies){
+    
+    d <- data[indicies,]
+    
+    pheno <- as.data.frame(dplyr::select(d, Value))[,1]
+    
+    Strain <- as.factor(d$Strain)
+    
+    reffMod <- lme4::lmer(pheno ~ 1 + (1|Strain))
+    
+    Variances <- as.data.frame(lme4::VarCorr(reffMod, comp = "Variance"))
+    
+    Vg <- Variances$vcov[1]
+    Ve <- Variances$vcov[2]
+    H2 <- Vg/(Vg+Ve)
+    
+    # errors <- sqrt(diag(lme4::VarCorr(reffMod, comp = "Variance")$strain))
+    
+    return(H2)
+}
+
+# data is data frame that contains strain and Value column
+H2.test <- function(data){
+    
+    pheno <- as.data.frame(dplyr::select(data, Value))[,1]
+    Strain <- as.factor(data$Strain)
+    
+    reffMod <- lme4::lmer(pheno ~ 1 + (1|Strain))
+    
+    Variances <- as.data.frame(lme4::VarCorr(reffMod, comp = "Variance"))
+    
+    Vg <- Variances$vcov[1]
+    Ve <- Variances$vcov[2]
+    H2 <- Vg/(Vg+Ve)
+    
+    # errors <- sqrt(diag(lme4::VarCorr(reffMod, comp = "Variance")$strain))
+    
+    return(H2)
+}
+
+# df is data frame that contains strain and Value column
+H2.calc <- function(data, boot = TRUE, type = "broad", reps = 10000){
+    df <- dplyr::select(data, Strain, Value)
+    
+    flog.info("Running bootstrapping")
+    if(boot == TRUE){
+        # bootstrapping with 10000 replications
+        # can reduce value to save time (500 is reasonable most of the time).
+        # if you Error in bca.ci(boot.out, conf, index[1L], L = L, t = t.o, t0 = t0.o,  : estimated adjustment 'a' is NA, then you need to increase R value.
+        if(type == "broad") {
+            results <- boot(data = df, statistic = H2.test.boot, R = reps) 
+        } else {
+            results <- boot(data = df, statistic = narrowh2.boot, R = reps) 
+        }
+        
+        # get 95% confidence interval
+        ci <- boot.ci(results, type="bca")
+        
+        H2_errors <- data.frame(H2 = ci$t0,
+                                ci_l = ci$bca[4],
+                                ci_r = ci$bca[5])
+        
+        return(H2_errors)
+        
+    } else {
+        if(type == "broad") {
+            H2 <- data.frame(H2 = H2.test(data = df), ci_l = NA, ci_r = NA)
+        } else {
+            H2 <- data.frame(H2 = narrowh2(df_h = df), ci_l = NA, ci_r = NA)
+        }
+        return(H2)
+    }
+    
+}
+
+# extract strain, isotype dataframe from database
+generate_isotype_lookup <- function(species = "ce") {
+    
+    if ( species == "ce" ) {
+        isotype_lookup <- strain_data %>%
+            dplyr::mutate(strain_names = ifelse(!is.na( previous_names ),
+                                                paste( strain, previous_names, sep = "|" ),
+                                                strain )) %>%
+            tidyr::separate_rows(strain_names, sep = "\\|") %>%
+            dplyr::select(strain, previous_name = strain_names, isotype) %>%
+            dplyr::distinct()
+    }
+    
+    return( isotype_lookup )
+}
+
+# Resolve strain names to isotypes
+resolve_isotypes <- function(...) {
+    
+    isotype_lookup = generate_isotype_lookup()
+    strains <- unlist(list(...))
+    
+    purrr::map_chr(strains, function(x) {
+        isotype <- isotype_lookup %>%
+            dplyr::filter(
+                (x == strain) |
+                    (x == isotype) |
+                    (x == previous_name)
+            ) %>%
+            dplyr::pull(isotype) %>%
+            unique()
+        
+        if (length(isotype) == 0) {
+            message(glue::glue("{x} is not a known strain. Isotype set to NA; Please check CeNDR"))
+        } else if (length(isotype) > 1) {
+            message(glue::glue("{x} resolves to multiple isotypes. Isotype set to NA; Please check CeNDR"))
+        }
+        if (length(isotype) != 1) {
+            isotype <- NA
+        }
+        isotype
+    })
+    
+}
+
+# prune outliers
+# data = strain, trait, phenotype
+BAMF_prune <- function(data, remove_outliers = TRUE ){
+    
+    categorize1 <- function(data) {
+        with(data,
+             ( (sixhs >= 1 & ( (s6h + s5h + s4h ) / numst) <= .05))
+             | ( (sixls >= 1 & ( (s6l + s5l + s4l) / numst) <= .05))
+        )
+    }
+    # If the 5 innermost bins are discontinuous by more than a 1 bin gap, the
+    # observation is in the fifth bin (between 7 and 10x IQR outside the
+    # distribution), and the four outermost bins make up less than 5% of the
+    # population, mark the observation an outlier
+    categorize2 <- function(data) {
+        with(data,
+             ( (fivehs >= 1 & ( (s6h + s5h + s4h + s3h) / numst) <= .05))
+             | ( (fivels >= 1 & ( (s6l + s5l + s4l + s3l) / numst) <= .05))
+        )
+    }
+    # If the 4 innermost bins are discontinuous by more than a 1 bin gap, the
+    # observation is in the fourth bin (between 5 and 7x IQR outside the
+    # distribution), and the four outermost bins make up less than 5% of the
+    # population, mark the observation an outlier
+    categorize3 <- function(data) {
+        with(data,
+             ( (fourhs >= 1 & (s5h + s4h + s3h + s2h) / numst <= .05))
+             | ( (fourls >= 1 & (s5l + s4l + s3l + s2l) / numst <= .05))
+        )
+    }
+    napheno <- data[is.na(data$phenotype), ] %>%
+        dplyr::mutate(bamfoutlier1 = NA, bamfoutlier2 = NA, bamfoutlier3 = NA)
+    
+    datawithoutliers <- data %>%
+        # Filter out all of the wash and/or empty wells
+        dplyr::filter(!is.na(strain)) %>%
+        # Group by trait, the, calculate the first and third
+        # quartiles for each of the traits
+        dplyr::group_by(trait) %>%
+        dplyr::summarise(iqr = IQR(phenotype, na.rm = TRUE),
+                         q1 = quantile(phenotype, probs = .25, na.rm = TRUE),
+                         q3 = quantile(phenotype, probs = .75,
+                                       na.rm = TRUE)) %>%
+        # Add a column for the boundaries of each of the bins
+        dplyr::mutate(cut1h = q3 + (iqr * 2),
+                      cut1l = q1 - (iqr * 2),
+                      cut2h = q3 + (iqr * 3),
+                      cut2l = q1 - (iqr * 3),
+                      cut3h = q3 + (iqr * 4),
+                      cut3l = q1 - (iqr * 4),
+                      cut4h = q3 + (iqr * 5),
+                      cut4l = q1 - (iqr * 5),
+                      cut5l = q1 - (iqr * 7),
+                      cut5h = q3 + (iqr * 7),
+                      cut6l = q1 - (iqr * 10),
+                      cut6h = q3 + (iqr * 10)) %>%
+        # Join the bin boundaries back to the original data frame
+        dplyr::left_join(data, ., by = c("trait")) %>%
+        dplyr::ungroup() %>%
+        dplyr::rowwise() %>%
+        # Add columns tallying the total number of points in each of the bins
+        dplyr::mutate(onehs = ifelse(cut2h > phenotype & phenotype >= cut1h,
+                                     1, 0),
+                      onels = ifelse(cut2l < phenotype & phenotype <= cut1l,
+                                     1, 0),
+                      twohs = ifelse(cut3h > phenotype & phenotype >= cut2h,
+                                     1, 0),
+                      twols = ifelse(cut3l < phenotype & phenotype <= cut2l,
+                                     1, 0),
+                      threehs = ifelse(cut4h > phenotype & phenotype >= cut3h,
+                                       1, 0),
+                      threels = ifelse(cut4l < phenotype & phenotype <= cut3l,
+                                       1, 0),
+                      fourhs = ifelse(cut5h > phenotype &  phenotype >= cut4h,
+                                      1, 0),
+                      fourls = ifelse(cut5l < phenotype &  phenotype <= cut4l,
+                                      1, 0),
+                      fivehs = ifelse(cut6h > phenotype & phenotype >= cut5h,
+                                      1, 0),
+                      fivels = ifelse(cut6l < phenotype & phenotype <= cut5l,
+                                      1, 0),
+                      sixhs = ifelse(phenotype >= cut6h, 1, 0),
+                      sixls = ifelse(phenotype <= cut6l, 1, 0)) %>%
+        # Group on condition and trait, then sum the total number of data points
+        # in each of the IQR multiple bins
+        dplyr::group_by(trait) %>%
+        dplyr::mutate(s1h = sum(onehs, na.rm = TRUE),
+                      s2h = sum(twohs, na.rm = TRUE),
+                      s3h = sum(threehs, na.rm = TRUE),
+                      s4h = sum(fourhs, na.rm = TRUE),
+                      s5h = sum(fivehs, na.rm = TRUE),
+                      s1l = sum(onels, na.rm = TRUE),
+                      s2l = sum(twols, na.rm = TRUE),
+                      s3l = sum(threels, na.rm = TRUE),
+                      s4l = sum(fourls, na.rm = TRUE),
+                      s5l = sum(fivels, na.rm = TRUE),
+                      s6h = sum(sixhs, na.rm = TRUE),
+                      s6l = sum(sixls, na.rm = TRUE)) %>%
+        # Group on condition and trait, then check to see if the number of
+        # points in each bin is more than 5% of the total number of data points
+        dplyr::group_by(trait) %>%
+        dplyr::mutate(p1h = ifelse(sum(onehs, na.rm = TRUE) / n() >= .05, 1, 0),
+                      p2h = ifelse(sum(twohs, na.rm = TRUE) / n() >= .05, 1, 0),
+                      p3h = ifelse(sum(threehs, na.rm = TRUE) / n() >= .05, 1, 0),
+                      p4h = ifelse(sum(fourhs, na.rm = TRUE) / n() >= .05 ,1, 0),
+                      p5h = ifelse(sum(fivehs, na.rm = TRUE) / n() >= .05 ,1 ,0),
+                      p6h = ifelse(sum(sixhs, na.rm = TRUE) / n() >= .05 ,1, 0),
+                      p1l = ifelse(sum(onels, na.rm = TRUE) / n() >= .05 ,1, 0),
+                      p2l = ifelse(sum(twols, na.rm = TRUE) / n() >= .05 ,1, 0),
+                      p3l = ifelse(sum(threels, na.rm = TRUE) / n() >= .05 ,1,0),
+                      p4l = ifelse(sum(fourls, na.rm = TRUE) / n() >= .05 , 1, 0),
+                      p5l = ifelse(sum(fivels, na.rm = TRUE) / n() >= .05, 1, 0),
+                      p6l = ifelse(sum(sixls,
+                                       na.rm = TRUE) / n() >= .05, 1, 0)) %>%
+        # Count the number of observations in each condition/trait combination
+        dplyr::mutate(numst = n()) %>%
+        # Group on trait, then filter out NAs in any of the added
+        # columns
+        dplyr::group_by(trait) %>%
+        dplyr::filter(!is.na(trait), !is.na(phenotype), !is.na(iqr), !is.na(q1),
+                      !is.na(q3), !is.na(cut1h), !is.na(cut1l), !is.na(cut2h),
+                      !is.na(cut2l), !is.na(cut3h), !is.na(cut3l),
+                      !is.na(cut4h), !is.na(cut4l), !is.na(cut5l),
+                      !is.na(cut5h), !is.na(cut6l), !is.na(cut6h),
+                      !is.na(onehs), !is.na(onels), !is.na(twohs),
+                      !is.na(twols), !is.na(threehs), !is.na(threels),
+                      !is.na(fourhs), !is.na(fourls), !is.na(fivehs),
+                      !is.na(fivels), !is.na(sixhs), !is.na(sixls),
+                      !is.na(s1h), !is.na(s2h), !is.na(s3h), !is.na(s4h),
+                      !is.na(s5h), !is.na(s1l), !is.na(s2l), !is.na(s3l),
+                      !is.na(s4l), !is.na(s5l), !is.na(s6h), !is.na(s6l),
+                      !is.na(p1h), !is.na(p2h), !is.na(p3h), !is.na(p4h),
+                      !is.na(p5h), !is.na(p6h), !is.na(p1l), !is.na(p2l),
+                      !is.na(p3l), !is.na(p4l), !is.na(p5l), !is.na(p6l),
+                      !is.na(numst)) %>%
+        # Add three columns stating whether the observation is an outlier
+        # based the three outlier detection functions below
+        dplyr::ungroup() %>%
+        dplyr::mutate(cuts = categorize1(.),
+                      cuts1 = categorize2(.),
+                      cuts2 = categorize3(.))
+    
+    if ( remove_outliers == T){
+        outliers_removed <- datawithoutliers %>%
+            dplyr::rename(bamfoutlier1 = cuts,
+                          bamfoutlier2 = cuts1,
+                          bamfoutlier3 = cuts2) %>%
+            dplyr::filter(!bamfoutlier1 & !bamfoutlier2 & !bamfoutlier3) %>%
+            dplyr::select(trait, strain, phenotype)
+        
+        return(outliers_removed)
+    } else {
+        with_outliers <- datawithoutliers %>%
+            dplyr::rename(bamfoutlier1 = cuts,
+                          bamfoutlier2 = cuts1,
+                          bamfoutlier3 = cuts2) %>%
+            dplyr::select(trait, strain, phenotype, bamfoutlier1, bamfoutlier2, bamfoutlier3) %>%
+            dplyr::mutate(outlier = ifelse( bamfoutlier1 | bamfoutlier2 | bamfoutlier3, TRUE, FALSE)) %>%
+            dplyr::select(strain, trait, phenotype, outlier)
+        
+        return(with_outliers)
+    }
+}
+
+# Process phenotypes for mapping and heritability
+process_phenotypes <- function(df,
+                               summarize_replicates = "mean",
+                               prune_method = "BAMF",
+                               remove_outliers = TRUE,
+                               threshold = 2){
+    
+    if ( sum(grepl(colnames(df)[1], "Strain", ignore.case = T)) == 0 ) {
+        message(glue::glue("Check input data format, strain should be the first column."))
+    }
+    
+    # ~ ~ ~ # resolve strain isotypes # ~ ~ ~ #
+    # get strain isotypes
+    strain_isotypes_db <- generate_isotype_lookup()
+    # identify strains that were phenotyped, but are not part of an isotype
+    non_isotype_strains <- dplyr::filter(df,
+                                         !(strain %in% strain_isotypes_db$strain),
+                                         !(strain %in% strain_isotypes_db$isotype))
+    # remove any strains identified to not fall into an isotype
+    if ( nrow(non_isotype_strains) > 0 ) {
+        
+        strains_to_remove <- unique(non_isotype_strains$strain)
+        
+        message(glue::glue("Removing strain(s) {strains_to_remove} because they do not fall into a defined isotype."))
+        
+        df_non_isotypes_removed <- dplyr::filter( df, !( strain %in% strains_to_remove) )
+    } else {
+        df_non_isotypes_removed <- df
+    }
+    
+    # ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ # Resolve Isotypes # ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ #
+    df_isotypes_resolved <- df_non_isotypes_removed %>%
+        dplyr::group_by(strain) %>%
+        dplyr::mutate(isotype = resolve_isotypes(strain)) %>%
+        dplyr::ungroup() %>%
+        tidyr::gather(trait, phenotype, -strain, -isotype) %>%
+        dplyr::filter(!is.na(phenotype))
+    
+    # deal with multiple strains per isotype group
+    test <- df_isotypes_resolved %>%
+        dplyr::group_by(isotype) %>%
+        dplyr::mutate(num = length(unique(strain))) %>% 
+        dplyr::mutate(ref_strain = strain == isotype)
+    no_issues <- test %>%
+        dplyr::filter(num == 1 & ref_strain == T)
+    issues <- test %>%
+        dplyr::filter(num > 1 | ref_strain == F) 
+    
+    fixed_issues <- no_issues %>%
+        dplyr::select(-num, -ref_strain)
+    
+    # go through each isotype issue and resolve it
+    for(i in unique(issues$isotype)) {
+        df <- issues %>%
+            dplyr::filter(isotype == i)
+        
+        # if only one strain is phenotyped, just rename strain to isotype ref strain and flag
+        if(nrow(df) == 1) {
+            fix <- df %>%
+                dplyr::mutate(strain = isotype)
+            message(glue::glue("Non-isotype reference strain {df$strain[1]} renamed to isotype {i}."))
+        } else {
+            # remove non-isotype strains
+            fix <- df %>%
+                dplyr::filter(ref_strain) %>%
+                dplyr::select(-ref_strain, -num)
+            
+            # warn the user
+            if(sum(df$ref_strain) > 0) {
+                message(glue::glue("Non-isotype reference strain(s) {paste(df %>% dplyr::filter(!ref_strain) %>% dplyr::pull(strain), collapse = ', ')} from isotype group {i} removed."))
+            } 
+            else {
+                message(glue::glue("Non-isotype reference strain(s) {paste(df %>% dplyr::filter(!ref_strain) %>% dplyr::pull(strain), collapse = ', ')} from isotype group {i} removed.
+                                   To include this isotype in the analysis, you can (1) phenotype {i} or (2) evaluate the similarity of these strains and choose one representative for the group."))
+            }
+            }
+        # add to data
+        fixed_issues <- rbind(fixed_issues, fix)
+        }
+    
+    # ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ # Summarize Replicate Data # ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ #
+    df_replicates_summarized <- fixed_issues %>%
+        dplyr::group_by(isotype, trait) %>% {
+            if (summarize_replicates == "mean") dplyr::summarise(., phenotype = mean( phenotype, na.rm = T ) )
+            else if (summarize_replicates == "median") dplyr::summarise(., phenotype = median( phenotype, na.rm = T ) )
+            else if (summarize_replicates == "none") dplyr::mutate(., phenotype = phenotype)
+            else  message(glue::glue("Please choose mean, median, or none as options for summarizeing replicate data.")) } %>%
+        dplyr::select(strain = isotype, trait, phenotype) %>%
+        dplyr::ungroup()
+    
+    # ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ # Outlier Functions # ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ #
+    is_out_tukey <- function(x, k = threshold, na.rm = TRUE) {
+        quar <- quantile(x, probs = c(0.25, 0.75), na.rm = na.rm)
+        iqr <- diff(quar)
+        
+        ( !(quar[1] - k * iqr <= x) ) | ( !(x <= quar[2] + k * iqr) )
+    }
+    is_out_z <- function(x, thres = threshold, na.rm = TRUE) {
+        ( !abs(x - mean(x, na.rm = na.rm)) <= thres * sd(x, na.rm = na.rm) )
+    }
+    is_out_mad <- function(x, thres = threshold, na.rm = TRUE) {
+        ( !abs(x - median(x, na.rm = na.rm)) <= thres * mad(x, na.rm = na.rm) )
+    }
+    
+    # ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ # Perform outlier removal # ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ ## ~ ~ ~ #
+    if ( prune_method == "BAMF" ) {
+        df_outliers <- BAMF_prune(df_replicates_summarized, remove_outliers = FALSE)
+    } else {
+        df_outliers <-  dplyr::ungroup(df_replicates_summarized) %>%
+            dplyr::group_by(trait) %>% {
+                if (prune_method == "MAD") dplyr::transmute_if(., is.numeric, dplyr::funs( outlier = is_out_mad ) )
+                else if (prune_method == "TUKEY") dplyr::transmute_if(., is.numeric, dplyr::funs( outlier = is_out_tukey ) )
+                else if (prune_method == "Z") dplyr::transmute_if(., is.numeric, dplyr::funs( outlier = is_out_z ) )
+                else  message(glue::glue("Please choose BAMF, MAD, TUKEY, or Z as options for summarizeing replicate data.")) } %>%
+            dplyr::ungroup() %>%
+            dplyr::bind_cols(., dplyr::ungroup(df_replicates_summarized)) %>%
+            dplyr::select(strain, trait, phenotype, outlier)
+    }
+    
+    if (remove_outliers == TRUE ) {
+        processed_phenotypes_output <- df_outliers %>%
+            dplyr::filter( !outlier ) %>%
+            dplyr::select( -outlier )
+        # tidyr::spread( trait, phenotype)
+    } else {
+        processed_phenotypes_output <- df_outliers 
+        # tidyr::spread( trait, phenotype)
+    }
+    
+    return(processed_phenotypes_output)
+}
+
+
+
+# RUN
+args = commandArgs(trailingOnly=TRUE)
+
+# update to include geno matrix
+usage_cmd = "USAGE: Rscript H2_script.R input_file output_file"
+
+if (length(args) < 3){
+    print(usage_cmd)
+}
+
+### ARGS ###
+# 1 - input_file
+# 2 - output_file
+# 3 - hash_file
+# 4 - version
+# 5 - strain_data
+
+
+# Read in data
+data <- data.table::fread(args[1]) %>%
+    tidyr::spread(TraitName, Value) %>%
+    dplyr::select(-Replicate, -AssayNumber) %>%
+    dplyr::rename(strain = Strain)
+output_fname = args[2]
+heritability_version = args[4]
+hash <- readLines(args[3])
+strain_data <- data.table::fread(args[5])
+
+# process phenotypes
+processed_data <- process_phenotypes(data, summarize_replicates = "none") %>%
+    dplyr::rename(Strain = strain, Value = phenotype, TraitName = trait)
+
+# Run H2 calculation
+result <- H2.calc(processed_data, boot = T, type = "broad") 
+
+# add timepoint data
+result$hash <- hash
+result$trait_name <- data$TraitName[1]
+result$date <- Sys.time()
+result$heritability_version <- heritability_version
+
+# Write the result
+data.table::fwrite(result, output_fname, sep = '\t')
diff --git a/cloud_functions/heritability_run/README.md b/cloud_functions/heritability_run/README.md
new file mode 100644
index 00000000..7c2639cb
--- /dev/null
+++ b/cloud_functions/heritability_run/README.md
@@ -0,0 +1,8 @@
+This directory contains the code to start the Google Cloud Run Microservice for the Heritability Tool
+
+Build using:
+
+```bash
+gcloud builds submit --tag gcr.io/andersen-lab/h2 --timeout=3h
+gcloud run deploy --image gcr.io/andersen-lab/h2 --memory 512Mi --platform managed h2
+```
\ No newline at end of file
diff --git a/cloud_functions/heritability_run/cloudbuild.yaml b/cloud_functions/heritability_run/cloudbuild.yaml
new file mode 100644
index 00000000..7a898060
--- /dev/null
+++ b/cloud_functions/heritability_run/cloudbuild.yaml
@@ -0,0 +1,6 @@
+steps:
+- name: 'gcr.io/kaniko-project/executor:latest'
+  args:
+  - --destination=gcr.io/andersen-lab/h2
+  - --cache=true
+  - --cache-ttl=48h
\ No newline at end of file
diff --git a/cloud_functions/heritability_run/invoke.go b/cloud_functions/heritability_run/invoke.go
new file mode 100644
index 00000000..fb78ef3f
--- /dev/null
+++ b/cloud_functions/heritability_run/invoke.go
@@ -0,0 +1,203 @@
+package main
+
+// VERSION v1
+
+import (
+	"context"
+	"encoding/json"
+	"fmt"
+	"io"
+	"io/ioutil"
+	"log"
+	"net/http"
+	"os"
+	"os/exec"
+	"time"
+
+	"cloud.google.com/go/storage"
+)
+
+// change to v2 20210121
+const heritabilityVersion = "v2"
+const datasetName = "data.tsv"
+const resultName = "result.tsv"
+const bucketName = "elegansvariation.org"
+
+type Payload struct {
+	Hash string
+}
+
+func check(e error) {
+	if e != nil {
+		panic(e)
+	}
+}
+
+func downloadFile(w io.Writer, object string) ([]byte, error) {
+	// object := "object-name"
+	ctx := context.Background()
+	client, err := storage.NewClient(ctx)
+	if err != nil {
+		return nil, fmt.Errorf("storage.NewClient: %v", err)
+	}
+	defer client.Close()
+
+	ctx, cancel := context.WithTimeout(ctx, time.Second*50)
+	defer cancel()
+
+	rc, err := client.Bucket(bucketName).Object(object).NewReader(ctx)
+	if err != nil {
+		return nil, fmt.Errorf("Object(%q).NewReader: %v", object, err)
+	}
+	defer rc.Close()
+
+	data, err := ioutil.ReadAll(rc)
+	if err != nil {
+		return nil, fmt.Errorf("ioutil.ReadAll: %v", err)
+	}
+	fmt.Fprintf(w, "Blob %v downloaded.\n", object)
+	return data, nil
+}
+
+func copyBlob(bucket string, source string, blob string) {
+	log.Printf("%s --> %s", source, blob)
+	ctx := context.Background()
+	client, err := storage.NewClient(ctx)
+	check(err)
+
+	// source
+	f, err := os.Open(source)
+	if err != nil {
+		log.Fatal(err)
+	}
+	defer f.Close()
+
+	wc := client.Bucket(bucket).Object(blob).NewWriter(ctx)
+	_, err = io.Copy(wc, f)
+	check(err)
+	err = wc.Close()
+	check(err)
+}
+
+func fileExists(filename string) bool {
+	info, err := os.Stat(filename)
+	if os.IsNotExist(err) {
+		return false
+	}
+	return !info.IsDir()
+}
+
+func runTask(dataHash string, queueName string, taskName string) {
+	// Run the heritability analysis. This is used to run it in the background.
+	// Execute R script
+	cmd := exec.Command("Rscript", "H2_script.R", datasetName, resultName, "hash.txt", heritabilityVersion, "strain_data.tsv")
+	cmd.Stderr = os.Stderr
+	o, err := cmd.Output()
+	check(err)
+
+	// Copy results to google storage.
+	resultBlob := fmt.Sprintf("reports/heritability/%s/%s", dataHash, resultName)
+	copyBlob(bucketName, resultName, resultBlob)
+
+	// Log & output details of the task.
+	output := fmt.Sprintf("Completed task: task queue(%s), task name(%s), output(%s)",
+		queueName,
+		taskName,
+		o,
+	)
+	log.Println(output)
+}
+
+func h2Handler(w http.ResponseWriter, r *http.Request) {
+	/*
+		Handler for google cloud task which triggers a heritability calculation
+	*/
+	// Check header to verify the request is from a Cloud Task
+	taskName := r.Header.Get("X-Cloudtasks-Taskname")
+	if taskName == "" {
+		log.Println("Invalid Task: No X-Appengine-Taskname request header found")
+		http.Error(w, "Bad Request - Invalid Task", http.StatusBadRequest)
+		return
+	}
+
+	// Pull useful headers from Task request.
+	queueName := r.Header.Get("X-Cloudtasks-Queuename")
+
+	// Extract the request body for further task details.
+	body, err := ioutil.ReadAll(r.Body)
+	if err != nil {
+		log.Printf("ReadAll: %v", err)
+		http.Error(w, "Internal Error", http.StatusInternalServerError)
+		return
+	}
+
+	// Get POST data parse the body
+	var p Payload
+	err1 := json.Unmarshal([]byte(string(body)), &p)
+	if err1 != nil {
+		log.Printf("Error parsing payload JSON: %v", err)
+		http.Error(w, err.Error(), http.StatusBadRequest)
+		return
+	}
+	log.Printf("payload: %+v", p)
+
+	// Download the dataset
+	dataBlob := fmt.Sprintf("reports/heritability/%s/%s", p.Hash, datasetName)
+	dataset, err2 := downloadFile(os.Stdout, dataBlob)
+	check(err2)
+
+	// Store it locally
+	f, err3 := os.Create(datasetName)
+	check(err3)
+	f.Write(dataset)
+	f.Close()
+
+	// Write the hash
+	h, err4 := os.Create("hash.txt")
+	check(err4)
+	h.WriteString(p.Hash + "\n")
+	f.Close()
+
+	// Log & output details of the task.
+	output := fmt.Sprintf("Started task: task queue(%s), task name(%s), payload(%s)",
+		queueName,
+		taskName,
+		string(body),
+	)
+	log.Println(output)
+
+	// Execute the R script
+	runTask(p.Hash, queueName, taskName)
+
+	if err5 := json.NewEncoder(w).Encode("submitted h2"); err5 != nil {
+		log.Printf("Error sending response: %v", err5)
+	}
+
+	// Set a non-2xx status code to indicate a failure in task processing that should be retried.
+	// For example, http.Error(w, "Internal Server Error: Task Processing", http.StatusInternalServerError)
+
+	fmt.Fprintln(w, "200 OK")
+
+}
+
+// indexHandler responds to requests with our greeting.
+func indexHandler(w http.ResponseWriter, r *http.Request) {
+	if r.URL.Path != "/" {
+		http.NotFound(w, r)
+		return
+	}
+	fmt.Fprint(w, "200 OK")
+}
+
+func main() {
+	http.HandleFunc("/", indexHandler)
+	http.HandleFunc("/h2", h2Handler)
+
+	port := os.Getenv("PORT")
+	if port == "" {
+		port = "8080"
+	}
+
+	log.Printf("listening on %s", port)
+	log.Fatal(http.ListenAndServe(fmt.Sprintf(":%s", port), nil))
+}
diff --git a/requirements.txt b/requirements.txt
index aad3965b..bd7bbfdd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -42,6 +42,8 @@ cachelib
 pytabix
 python3-saml
 flask_jwt_extended
+google-cloud
+google-cloud-tasks
 google-cloud-storage
 google-cloud-datastore
 google_cloud_logging