Update README.md

README.md

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 ### Data format
 
-The telemetry data must be presented as `pandas.DataFrame`. We require the data to have a column named `time` that stores the timestep. A sample of valid data could be downloaded using the `download_data()` method that we will demonstrate shortly below.
+The telemetry data must be presented as `pandas.DataFrame`. We require the data to have a column named `time` that stores the timestep. A sample of valid data could be downloaded using the `download_data()` or `download_multi_source_data()` method that we will demonstrate shortly below.
 
 ### Basic usage example
 
-A basic example to use BARO, an RCA baseline, to perform RCA are presented as follows,
+A basic example to use BARO, a metric-based RCA baseline, to perform RCA using metrics are presented as follows,
 
 ```python
 # You can put the code here to a file named test.py
@@ -130,6 +130,9 @@ Downloading data.csv..: 100%|█████████████████
 Top 5 root causes: ['emailservice_mem', 'recommendationservice_mem', 'cartservice_mem', 'checkoutservice_latency', 'cartservice_latency']
 ```
 
+A tutorial of using Multi-source BARO to diagnose failure using multi-source telemetry data (metrics, logs, and traces) is presented in [docs/multi-source-rca-demo.ipynb](docs/multi-source-rca-demo.ipynb).
+
+
 ## Available Datasets
 
 RCAEval benchmark includes three datasets: RE1, RE2, and RE3, designed to comprehensively support benchmarking RCA in microservice systems. Together, our three datasets feature 735 failure cases collected from three microservice systems (Online Boutique, Sock Shop, and Train Ticket) and including 11 fault types. Each failure case also includes annotated root cause service and root cause indicator (e.g., specific metric or log indicating the root cause). The statistics of the datasets are presented in the Table below.