diff --git a/docs/scens/data_science.rst b/docs/scens/data_science.rst
index 6341389b..edb3aa8a 100644
--- a/docs/scens/data_science.rst
+++ b/docs/scens/data_science.rst
@@ -69,6 +69,32 @@ The Data Science Agent is an agent that can automatically perform feature engine
       dotenv set DS_CODER_ON_WHOLE_PIPELINE True
       dotenv set DS_CODER_COSTEER_ENV_TYPE docker
 
+- 🚀 **Run the Application**
+
+  - You can directly run the application by using the following command:
+    
+    .. code-block:: sh
+
+        rdagent data_science --competition <Competition ID>
+
+  - Then, you can run the test set score corresponding to each round of the loop.
+
+    .. code-block:: sh
+
+        dotenv run -- python rdagent/log/mle_summary.py grade <url_to_log>
+
+    Here, <url_to_log> refers to the parent directory of the log folder generated during the run.
+
+- 📥 **Visualize the R&D Process**
+
+  - We provide a web UI to visualize the log. You just need to run:
+
+    .. code-block:: sh
+
+        streamlit run rdagent/log/ui/dsapp.py
+
+  - Then you can input the log path and visualize the R&D process.
+
 🔍 MLE-bench Guide: Running ML Engineering via MLE-bench
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/rdagent/log/mle_summary.py b/rdagent/log/mle_summary.py
index 97aced02..aa6232b9 100644
--- a/rdagent/log/mle_summary.py
+++ b/rdagent/log/mle_summary.py
@@ -44,7 +44,7 @@ def save_grade_info(log_trace_path: Path):
 
 
 def save_all_grade_info(log_folder):
-    for log_trace_path in log_folder.iterdir():
+    for log_trace_path in Path(log_folder).iterdir():
         if is_valid_session(log_trace_path):
             try:
                 save_grade_info(log_trace_path)