diff --git a/doc/python/bio-alignment-chart.md b/doc/python/bio-alignment-chart.md
index 52806d83931..1810857140f 100644
--- a/doc/python/bio-alignment-chart.md
+++ b/doc/python/bio-alignment-chart.md
@@ -44,7 +44,7 @@ Note that the AlignmentChart only returns a chart of the sequence, while Alignme
 import plotly.express as px
 import pandas as pd
 
-df = (pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/Dash_Bio/Genetic/gene_conservation.csv')
+df = (pd.read_csv('https://git.io/gene_conservation.csv')
         .set_index('0')
         .loc[['consensus','conservation']]
         .T
@@ -56,7 +56,7 @@ fig.show()
 
 ## Alignment Chart in dash_bio
 
-```python no_display=true
+```python hide_code=true
 from IPython.display import IFrame
 snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
 IFrame(snippet_url + 'bio-alignmentchart', width='100%', height=630)
diff --git a/doc/python/bio-clustergram.md b/doc/python/bio-clustergram.md
index d98b6a58268..67d84601b75 100644
--- a/doc/python/bio-clustergram.md
+++ b/doc/python/bio-clustergram.md
@@ -40,10 +40,7 @@ import pandas as pd
 import dash_bio
 
 
-df = pd.read_csv(
-    'https://raw.githubusercontent.com/plotly/datasets/master/Dash_Bio/Chromosomal/' +
-    'clustergram_brain_cancer.csv', 
-)
+df = pd.read_csv('https://git.io/clustergram_brain_cancer.csv')
 
 dash_bio.Clustergram(
     data=df,
@@ -62,10 +59,8 @@ Change the colors of the dendrogram traces that are used to represent clusters,
 import pandas as pd
 import dash_bio
 
-df = pd.read_csv(
-    'https://raw.githubusercontent.com/plotly/datasets/master/Dash_Bio/Chromosomal/' +
-    'clustergram_brain_cancer.csv', 
-)
+
+df = pd.read_csv('https://git.io/clustergram_brain_cancer.csv')
 
 dash_bio.Clustergram(
     data=df,
@@ -90,10 +85,8 @@ Change the relative width and height of, respectively, the row and column dendro
 import pandas as pd
 import dash_bio
 
-df = pd.read_csv(
-    'https://raw.githubusercontent.com/plotly/datasets/master/Dash_Bio/Chromosomal/' +
-    'clustergram_brain_cancer.csv', 
-)
+
+df = pd.read_csv('https://git.io/clustergram_brain_cancer.csv')
 
 dash_bio.Clustergram(
     data=df,
@@ -107,7 +100,7 @@ dash_bio.Clustergram(
 
 ## Clustergram with Dash
 
-```python no_display=true
+```python hide_code=true
 from IPython.display import IFrame
 snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
 IFrame(snippet_url + 'bio-clustergram', width='100%', height=630)
diff --git a/doc/python/bio-manhattanplot.md b/doc/python/bio-manhattanplot.md
index 5fa417cc021..381da0b9622 100644
--- a/doc/python/bio-manhattanplot.md
+++ b/doc/python/bio-manhattanplot.md
@@ -40,7 +40,8 @@ ManhattanPlot allows you to visualize genome-wide association studies (GWAS) eff
 import pandas as pd
 import dash_bio
 
-df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/manhattan_data.csv')
+
+df = pd.read_csv('https://git.io/manhattan_data.csv')
 
 
 dash_bio.ManhattanPlot(
@@ -56,7 +57,7 @@ import pandas as pd
 import dash_bio
 
 
-df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/manhattan_data.csv')
+df = pd.read_csv('https://git.io/manhattan_data.csv')
 
 dash_bio.ManhattanPlot(
     dataframe=df,
@@ -68,7 +69,7 @@ dash_bio.ManhattanPlot(
 
 ## ManhattanPlot with Dash
 
-```python no_display=true
+```python hide_code=true
 from IPython.display import IFrame
 snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
 IFrame(snippet_url + 'bio-manhattanplot', width='100%', height=630)
diff --git a/doc/python/bio-volcano-plot.md b/doc/python/bio-volcano-plot.md
index 7089b1ae969..9d1bf6a2b09 100644
--- a/doc/python/bio-volcano-plot.md
+++ b/doc/python/bio-volcano-plot.md
@@ -41,10 +41,7 @@ import pandas as pd
 import dash_bio
 
 
-df = pd.read_csv(
-    'https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/' +
-    'volcano_data1.csv'
-)
+df = pd.read_csv('https://git.io/volcano_data1.csv')
 
 dash_bio.VolcanoPlot(
     dataframe=df,
@@ -59,7 +56,8 @@ Change the size of the points on the scatter plot, and the widths of the effect
 import pandas as pd
 import dash_bio
 
-df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/volcano_data1.csv')
+
+df = pd.read_csv('https://git.io/volcano_data1.csv')
 
 dash_bio.VolcanoPlot(
     dataframe=df,
@@ -71,7 +69,7 @@ dash_bio.VolcanoPlot(
 
 ## VolcanoPlot with Dash
 
-```python no_display=true
+```python hide_code=true
 from IPython.display import IFrame
 snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
 IFrame(snippet_url + 'bio-volcano', width='100%', height=630)