Update README.md

* explain 'data_profile_uri'
* add disclaim that a new viewer (codatta's data profiler) supports the view of whole image slide.

README.md

@@ -34,6 +34,7 @@ This presents a challenge for AI pathology models, as reported high accuracy mig
 This dataset includes two primary files:
 1. Slide-Level Labels ([PRAD.csv](https://huggingface.co/datasets/Codatta/Refined-TCGA-PRAD-Prostate-Cancer-Pathology-Dataset/blob/main/dataset/PRAD/PRAD.csv))
    * Contains comprehensive metadata and diagnostic details:
+      * `data_profile_uri`: A URI that links to Codatta's web application, offering a detailed view of the slide's metadata and its associated data lineage.
       * `slide_id`: Unique slide identifier.
       * `slide_name`: TCGA Whole Slide Image (WSI) name.
       * `label`: Corrected Gleason grade (e.g., 4+3, 5+4).
@@ -78,8 +79,7 @@ Some labels can be improved by adding alternative opinions to enhance the labels
 ### For AI Training Pipelines
 Combine Whole Slide Images (WSI) from TCGA PRAD with this dataset's slide-level labels (PRAD.csv) and ROI annotations (.geojson) to generate high-quality [X, y] pairs.
 ### For Pathology Research
-Use the ROI annotations in WSI viewers (e.g., QuPath) to interactively visualize labeled tumor regions.
-Explore detailed reasoning behind Gleason grade decisions to understand tumor composition.
+Use the ROI annotations in Whole Slide Images (WSIs) to interactively visualize labeled tumor regions. The slides can be viewed through Codatta's data profile (e.g., https://data.codatta.io/[slide_id]) or other compatible viewers like QuPath. Additionally, explore detailed reasoning behind Gleason grade decisions to gain insights into tumor composition.
 ### How to Load the Dataset
 1. **CSV File**
 Use pandas to explore slide-level metadata: