Update README.md

README.md

@@ -42,8 +42,9 @@ This dataset provides various Python, Javascript & Ruby solutions for advent of
 More languages and years will be included in future releases.
 
 ## Key Features
-**Enriched solutions**: Each part of every question include at least 5 different solutions.  
-**Test cases**: Each question come with 3 test cases
+**Multi-language support**: Python, JavaScript & Ruby solutions (more languages will be added in future updates).  
+**Enriched solutions**: Each part of every question includes at least 5 different solutions for diversity.  
+**Test cases**: Every problem comes with three test cases for validation.
 
 
 ## Statistics:
@@ -54,6 +55,15 @@ More languages and years will be included in future releases.
 2024 | Javascript | 1 to 25 | 245
 2024 | Ruby | 1 to 25 | 245
 
+## Data Fields
+• **Year** (String): The year of the Advent of Code challenge.  
+• **Day** (String): The specific day of the challenge.  
+• **Part** (String): Indicates whether the solution is for Part 1 or Part 2 of the daily challenge.  
+• **Question** (String): The full problem statement for the given day and part.  
+• **Answer** (String): The correct final output for the problem, as computed from the input.  
+• **Solution** (String): A verified code implementation that solves the problem.  
+• **Language** (String): The programming language in which the solution is written.  
+
 ## Data Instance Example:  
 
 ```json
@@ -154,9 +164,11 @@ dataset = pd.read_csv(
 )
 ```
 
-## Source
-The dataset is derived from a combination of various websites. We performed data cleaning and validation to ensure the quality of the dataset.  
-For full details on how we cleaned the data, visit our [blog post](https://blog.supa.so/2025/01/24/preparing-code-eval-datasets-data-cleaning-and-automated-code-execution-for-advent-of-code-with-docker-and-python/).
+## Data Preprocessing
+Our software engineering team collects and curates existing solutions from multiple sources, followed by thorough data cleaning and validation to ensure high quality.  
+The data cleaning involves an automated pipeline that utilizes docker containers to execute the codes and a python script to manage the process as well as to validate the correctness.  
+For full details on how we cleaned the data, visit our
+ [blog post](https://blog.supa.so/2025/01/24/preparing-code-eval-datasets-data-cleaning-and-automated-code-execution-for-advent-of-code-with-docker-and-python/).
 
 ## Versioning and Maintenance
 Current Version: 2.0.0