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README.md

@@ -1,42 +1,16 @@
 ---
-dataset_info:
-  features:
-  - name: question_id
-    dtype: string
-  - name: question_type
-    dtype: string
-  - name: description
-    dtype: string
-  - name: content
-    dtype: string
-  - name: environment
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: test
-    dtype: string
-  - name: scoring_config
-    dtype: string
-  splits:
-  - name: msb_type
-    num_bytes: 15728640
-    num_examples: 244
-  - name: et_type
-    num_bytes: 709558272
-    num_examples: 1085
-  - name: scicobench_all
-    num_bytes: 666894336
-    num_examples: 1329
-  download_size: 1392181248
-  dataset_size: 1392181248
 configs:
-- config_name: default
+- config_name: msb_type
   data_files:
-  - split: msb_type
+  - split: train
     path: data/msb_type.jsonl
-  - split: et_type
+- config_name: et_type
+  data_files:
+  - split: train
     path: data/et_type.jsonl
-  - split: scicobench_all
+- config_name: scicobench_all
+  data_files:
+  - split: train
     path: data/scicobench_all.jsonl
   default: true
 ---
@@ -45,21 +19,15 @@ configs:
 
 ## Dataset Description
 
-This dataset contains scientific coding questions and benchmarks from multiple sources, organized into three splits:
+This dataset contains scientific coding questions and benchmarks from multiple sources, organized into three configurations:
 
 - **msb_type** (244 samples): Multi-source Scientific Benchmark questions focusing on software engineering tasks
 - **et_type** (1,085 samples): Einstein Toolkit related coding questions with extensive documentation
-- **scicobench_all** (1,329 samples): Combined and unified format of all scientific coding questions
+- **scicobench_all** (1,329 samples, **default**): Combined and unified format of all scientific coding questions
 
 ### Dataset Summary
 
-The Scientific Coding Benchmark dataset is designed for evaluating AI models on real-world scientific software development tasks. Each sample includes:
-
-- Question descriptions and context
-- Code environment specifications
-- Test cases
-- Expected answers/solutions
-- Scoring configurations
+The Scientific Coding Benchmark dataset is designed for evaluating AI models on real-world scientific software development tasks. The dataset provides both raw data formats and a unified format for comprehensive evaluation.
 
 ### Supported Tasks
 
@@ -70,24 +38,44 @@ The Scientific Coding Benchmark dataset is designed for evaluating AI models on
 
 ## Dataset Structure
 
-### Data Splits
+### Data Configurations
+
+The dataset has three configurations with different schemas:
+
+| Configuration | Samples | Size | Description |
+|---------------|---------|------|-------------|
+| msb_type | 244 | ~15 MB | Software engineering tasks (raw format) |
+| et_type | 1,085 | ~677 MB | Einstein Toolkit tasks (raw format) |
+| **scicobench_all** | 1,329 | ~636 MB | **Unified format (default)** |
+
+**Default Configuration**: `scicobench_all`
+
+### Data Loading
+
+```python
+from datasets import load_dataset
 
-The dataset has three splits:
+# Load default configuration (scicobench_all - unified format)
+dataset = load_dataset("xinshuo/test_jsonl")
+print(f"Loaded {len(dataset['train'])} samples")
 
-| Split | Samples | Size | Description |
-|-------|---------|------|-------------|
-| msb_type | 244 | ~15 MB | Software engineering tasks from scientific projects |
-| et_type | 1,085 | ~677 MB | Einstein Toolkit code generation tasks |
-| scicobench_all | 1,329 | ~636 MB | Unified format combining all question types |
+# Load specific configurations
+msb_data = load_dataset("xinshuo/test_jsonl", "msb_type")
+et_data = load_dataset("xinshuo/test_jsonl", "et_type")
+all_data = load_dataset("xinshuo/test_jsonl", "scicobench_all")
 
-**Default Split**: `msb_type`
+# Access a sample
+print(dataset['train'][0])
+```
 
 ### Data Fields
 
-Each sample in the unified format contains:
+#### Unified Format (scicobench_all)
+
+Each sample contains:
 
 - `question_id` (string): Unique identifier for the question
-- `question_type` (string): Type/category of the question
+- `question_type` (string): Type/category of the question (MSB or ET)
 - `description` (string): High-level description of the task
 - `content` (string): Detailed question content and requirements
 - `environment` (string): Environment setup and configuration details
@@ -95,22 +83,24 @@ Each sample in the unified format contains:
 - `test` (string): Test cases and validation logic
 - `scoring_config` (string): Configuration for scoring the solution
 
-### Data Loading
+#### MSB Type Format (msb_type)
 
-```python
-from datasets import load_dataset
-
-# Load default split (msb_type)
-dataset = load_dataset("xinshuo/test_jsonl")
+Raw format from Scientific Coding SWE dataset with 33 fields including:
+- `org`, `repo`, `number`, `state`, `title`, `body`
+- `fix_patch`, `test_patch`, `doc_patch`
+- `language`, `instance_id`
+- `fixed_tests`, `run_result`, `test_patch_result`, `fix_patch_result`
+- And more... (see samples for full schema)
 
-# Load specific split
-msb_data = load_dataset("xinshuo/test_jsonl", split="msb_type")
-et_data = load_dataset("xinshuo/test_jsonl", split="et_type")
-all_data = load_dataset("xinshuo/test_jsonl", split="scicobench_all")
+#### ET Type Format (et_type)
 
-# Access a sample
-print(msb_data[0])
-```
+Raw format from Einstein Toolkit with 15 fields including:
+- `thorn_name`, `url`, `configuration`
+- `interface`, `param`, `schedule`
+- `src_filename`, `src_code`
+- `context`, `doc_tex_content`, `readme_content`
+- `combined_doc_context`
+- And more... (see samples for full schema)
 
 ## Source Data
 
@@ -134,6 +124,52 @@ Einstein Toolkit code generation tasks including:
 
 Unified format combining both question types with standardized structure for comprehensive evaluation.
 
+## Usage Examples
+
+### Load and Iterate Through Questions
+
+```python
+from datasets import load_dataset
+
+# Load the unified format (recommended)
+dataset = load_dataset("xinshuo/test_jsonl")
+
+for sample in dataset['train']:
+    print(f"Question ID: {sample['question_id']}")
+    print(f"Type: {sample['question_type']}")
+    print(f"Description: {sample['description'][:100]}...")
+    print("-" * 80)
+```
+
+### Filter by Question Type
+
+```python
+# Filter MSB-type questions from the unified dataset
+msb_questions = dataset['train'].filter(lambda x: x['question_type'] == 'MSB')
+print(f"MSB questions: {len(msb_questions)}")
+
+# Filter ET-type questions
+et_questions = dataset['train'].filter(lambda x: x['question_type'] == 'ET')
+print(f"ET questions: {len(et_questions)}")
+```
+
+### Work with Raw Formats
+
+```python
+# Load raw MSB format for detailed metadata
+msb_raw = load_dataset("xinshuo/test_jsonl", "msb_type")
+sample = msb_raw['train'][0]
+print(f"Organization: {sample['org']}")
+print(f"Repository: {sample['repo']}")
+print(f"Fix patch:\n{sample['fix_patch'][:200]}...")
+
+# Load raw ET format for full documentation
+et_raw = load_dataset("xinshuo/test_jsonl", "et_type")
+sample = et_raw['train'][0]
+print(f"Thorn: {sample['thorn_name']}")
+print(f"Documentation: {sample['combined_doc_context'][:200]}...")
+```
+
 ## Considerations for Using the Data
 
 ### Use Cases
@@ -143,11 +179,24 @@ Unified format combining both question types with standardized structure for com
 - Research in automated scientific software development
 - Education in scientific programming practices
 
+### Recommended Configuration
+
+For most use cases, we recommend using the **default `scicobench_all` configuration**, which provides:
+- Unified schema across all questions
+- Standardized fields for easier processing
+- Complete coverage of both MSB and ET question types
+
+Use the raw configurations (`msb_type` or `et_type`) only if you need:
+- Original metadata and detailed execution results
+- Domain-specific fields
+- Full documentation context
+
 ### Limitations
 
 - Focus on specific scientific domains (physics, chemistry, quantum computing)
 - Primarily C++ and Python languages
 - May require domain-specific knowledge to evaluate correctly
+- Different configurations have different schemas
 
 ## Additional Information