diff --git a/lectures/_static/lecture_specific/inequality/data.ipynb b/lectures/_static/lecture_specific/inequality/data.ipynb
index 97aea6522..1bf3234c6 100644
--- a/lectures/_static/lecture_specific/inequality/data.ipynb
+++ b/lectures/_static/lecture_specific/inequality/data.ipynb
@@ -34,7 +34,7 @@
     "import wbgapi as wb\n",
     "import plotly.express as px\n",
     "\n",
-    "url = 'https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/SCF_plus/SCF_plus_mini.csv'\n",
+    "url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/SCF_plus_mini.csv'\n",
     "df = pd.read_csv(url)\n",
     "df_income_wealth = df.dropna()"
    ]
diff --git a/lectures/heavy_tails.md b/lectures/heavy_tails.md
index 8d85d60ac..defa5c1e2 100644
--- a/lectures/heavy_tails.md
+++ b/lectures/heavy_tails.md
@@ -824,7 +824,7 @@ mystnb:
     name: firm-size-dist
 tags: [hide-input]
 ---
-df_fs = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/forbes-global2000.csv')
+df_fs = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/forbes-global2000.csv')
 df_fs = df_fs[['Country', 'Sales', 'Profits', 'Assets', 'Market Value']]
 fig, ax = plt.subplots(figsize=(6.4, 3.5))
 
@@ -851,8 +851,8 @@ mystnb:
 tags: [hide-input]
 ---
 # import population data of cities in 2023 United States and 2023 Brazil from world population review
-df_cs_us = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/cities_us.csv')
-df_cs_br = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/cities_brazil.csv')
+df_cs_us = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/cities_us.csv')
+df_cs_br = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/cities_brazil.csv')
 
 fig, axes = plt.subplots(1, 2, figsize=(8.8, 3.6))
 
@@ -876,7 +876,7 @@ mystnb:
     name: wealth-dist
 tags: [hide-input]
 ---
-df_w = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/forbes-billionaires.csv')
+df_w = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/forbes-billionaires.csv')
 df_w = df_w[['country', 'realTimeWorth', 'realTimeRank']].dropna()
 df_w = df_w.astype({'realTimeRank': int})
 df_w = df_w.sort_values('realTimeRank', ascending=True).copy()
diff --git a/lectures/inequality.md b/lectures/inequality.md
index 1f56e7597..d066b7ab8 100644
--- a/lectures/inequality.md
+++ b/lectures/inequality.md
@@ -246,7 +246,7 @@ The following code block imports a subset of the dataset `SCF_plus` for 2016,
 which is derived from the [Survey of Consumer Finances](https://en.wikipedia.org/wiki/Survey_of_Consumer_Finances) (SCF).
 
 ```{code-cell} ipython3
-url = 'https://github.com/QuantEcon/high_dim_data/raw/main/SCF_plus/SCF_plus_mini.csv'
+url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/SCF_plus_mini.csv'
 df = pd.read_csv(url)
 df_income_wealth = df.dropna()
 ```
diff --git a/lectures/mle.md b/lectures/mle.md
index 9576d979a..a740b0355 100644
--- a/lectures/mle.md
+++ b/lectures/mle.md
@@ -90,7 +90,7 @@ The following code imports this data  and reads it into an array called `sample`
 ```{code-cell} ipython3
 :tags: [hide-input]
 
-url = 'https://github.com/QuantEcon/high_dim_data/raw/main/SCF_plus/SCF_plus_mini_no_weights.csv'
+url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/SCF_plus_mini_no_weights.csv'
 df = pd.read_csv(url)
 df = df.dropna()
 df = df[df['year'] == 2016]