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----
-dataset_info:
-- config_name: collaboration_patterns
-  features:
-  - name: pattern_name
-    dtype: string
-  - name: collaboration_count
-    dtype: float64
-  - name: collaboration_pct
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 154
-    num_examples: 5
-  download_size: 2002
-  dataset_size: 154
-- config_name: india
-  features:
-  - name: data_type
-    dtype: string
-  - name: metric_name
-    dtype: string
-  - name: value
-    dtype: float64
-  - name: item_name
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 7685
-    num_examples: 62
-  download_size: 6660
-  dataset_size: 7685
-- config_name: occupations
-  features:
-  - name: soc_group
-    dtype: string
-  - name: percentage
-    dtype: float64
-  - name: facet
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1272
-    num_examples: 22
-  download_size: 2600
-  dataset_size: 1272
-- config_name: task_collaboration_intersections
-  features:
-  - name: task_name
-    dtype: string
-  - name: collaboration_pattern
-    dtype: string
-  - name: onet_task_collaboration_count
-    dtype: float64
-  - name: onet_task_collaboration_pct
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 643865
-    num_examples: 4528
-  download_size: 150663
-  dataset_size: 643865
-- config_name: tasks
-  features:
-  - name: task_name
-    dtype: string
-  - name: onet_task_count
-    dtype: float64
-  - name: onet_task_pct
-    dtype: float64
-  - name: has_automation_data
-    dtype: bool
-  - name: has_augmentation_data
-    dtype: bool
-  - name: has_usage_data
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 330701
-    num_examples: 2616
-  download_size: 169539
-  dataset_size: 330701
-configs:
-- config_name: collaboration_patterns
-  data_files:
-  - split: train
-    path: collaboration_patterns/train-*
-- config_name: india
-  data_files:
-  - split: train
-    path: india/train-*
-- config_name: occupations
-  data_files:
-  - split: train
-    path: occupations/train-*
-- config_name: task_collaboration_intersections
-  data_files:
-  - split: train
-    path: task_collaboration_intersections/train-*
-- config_name: tasks
-  data_files:
-  - split: train
-    path: tasks/train-*
----
+# Economic Index - Structured & Cleaned Dataset
+
+This dataset is a cleaned, structured version of the [Anthropic Economic Index](https://huggingface.co/datasets/Anthropic/EconomicIndex), organized for easy integration with persona-based scenario generation pipelines.
+
+## Dataset Description
+
+The Anthropic Economic Index tracks how people use Claude AI for work-related tasks. This structured version extracts and organizes the key information into easy-to-use tables.
+
+**Original Data Period**: August 4-11, 2025  
+**Source**: Anthropic Economic Index Release 2025-09-15  
+**Processing**: Extracted from enriched_claude_ai.csv with comprehensive structuring
+
+## Dataset Structure
+
+This dataset contains 5 splits:
+
+### 1. `tasks` (2,616 rows)
+All unique tasks people do with Claude AI, with usage metrics.
+
+**Columns:**
+- `task_name` (string): Description of the task
+- `onet_task_count` (float): Number of conversations using this task
+- `onet_task_pct` (float): Percentage of total usage
+- `onet_task_pct_index` (float): Specialization index
+- `automation_pct` (float): Automation percentage (where available)
+- `augmentation_pct` (float): Augmentation percentage (where available)
+- `has_automation_data` (bool): Whether automation data exists
+- `has_augmentation_data` (bool): Whether augmentation data exists
+- `has_usage_data` (bool): Whether usage data exists
+
+**Example:**
+```python
+from datasets import load_dataset
+ds = load_dataset("anna-sarvam/economic-index-structured")
+print(ds['tasks'][0])
+# {'task_name': 'write new programs or modify existing programs...', 
+#  'onet_task_count': 6618.0, 'onet_task_pct': 0.52, ...}
+```
+
+### 2. `collaboration_patterns` (5 rows)
+How users interact with Claude AI.
+
+**Patterns:**
+1. **directive** (38.8%) - Direct instructions
+2. **task iteration** (22.2%) - Step-by-step refinement
+3. **learning** (20.3%) - Educational assistance
+4. **feedback loop** (10.3%) - Iterative improvement
+5. **validation** (4.5%) - Verification
+
+**Columns:**
+- `pattern_name` (string): Name of collaboration pattern
+- `collaboration_count` (float): Number of uses
+- `collaboration_pct` (float): Percentage of total
+
+### 3. `task_collaboration_intersections` (4,528 rows)
+Which collaboration patterns are used for which tasks.
+
+**Columns:**
+- `task_name` (string): Task description
+- `collaboration_pattern` (string): Pattern used
+- `onet_task_collaboration_count` (float): Count for this combination
+- `onet_task_collaboration_pct` (float): Percentage within task
+
+### 4. `occupations` (22 rows)
+SOC (Standard Occupational Classification) occupation groups.
+
+**Top Occupations:**
+1. Computer and Mathematical (35.9%)
+2. Educational Instruction and Library (12.3%)
+3. Arts, Design, Entertainment, Sports, and Media (8.2%)
+
+**Columns:**
+- `soc_group` (string): Occupation group name
+- `percentage` (float): Percentage of classified tasks
+- `facet` (string): Data facet
+
+### 5. `india` (65 rows)
+India-specific usage patterns and top tasks.
+
+**Columns:**
+- `data_type` (string): Type of data (overall_metric, top_task, collaboration_pattern)
+- `metric_name` (string): Name of metric
+- `value` (float): Metric value
+- `item_name` (string): Task or pattern name (if applicable)
+
+## Key Statistics
+
+- **Total Tasks**: 2,616 unique tasks
+- **Collaboration Patterns**: 5 main types
+- **Occupation Groups**: 22 SOC categories
+- **Task-Pattern Combinations**: 4,528
+- **Geographic Coverage**: 201 countries (including India)
+
+## Usage Examples
+
+### Load the entire dataset
+```python
+from datasets import load_dataset
+
+ds = load_dataset("anna-sarvam/economic-index-structured")
+```
+
+### Get top 10 tasks
+```python
+tasks = ds['tasks'].to_pandas()
+top_10 = tasks.nlargest(10, 'onet_task_count')
+print(top_10[['task_name', 'onet_task_count']])
+```
+
+### Find education-related tasks
+```python
+tasks = ds['tasks'].to_pandas()
+education_tasks = tasks[tasks['task_name'].str.contains('education', case=False)]
+```
+
+### Get India-specific top tasks
+```python
+india = ds['india'].to_pandas()
+india_top_tasks = india[india['data_type'] == 'top_task']
+top_5_india = india_top_tasks.nlargest(5, 'value')
+```
+
+### Find tasks for software developers
+```python
+tasks = ds['tasks'].to_pandas()
+software_tasks = tasks[tasks['task_name'].str.contains('software|program|code', case=False)]
+```
+
+### Analyze collaboration patterns
+```python
+patterns = ds['collaboration_patterns'].to_pandas()
+print(patterns[['pattern_name', 'collaboration_pct']].sort_values('collaboration_pct', ascending=False))
+```
+
+## India-Specific Insights
+
+### Usage Statistics
+- **Total Conversations**: 1,831
+- **Global Percentage**: 0.88%
+- **Automation**: 45.5%
+- **Augmentation**: 54.5%
+
+### Top 5 Tasks in India
+1. Write/modify programs (6,618 uses)
+2. Fix software errors (5,118 uses)
+3. Adapt software to new hardware (3,594 uses)
+4. Debug and correct errors (2,663 uses)
+5. Build/maintain websites (2,661 uses)
+
+### Top 3 Collaboration Patterns in India
+1. **directive** (44.7%) - Higher than global average
+2. **task iteration** (23.4%)
+3. **learning** (14.5%)
+
+## Use Cases
+
+### 1. Persona-Scenario Matching
+Match tasks from this dataset to expanded personas based on occupation:
+```python
+# Load tasks
+tasks = ds['tasks'].to_pandas()
+
+# Filter for teachers
+education_tasks = tasks[tasks['task_name'].str.contains('educat|teach|tutor', case=False)]
+
+# Match to teacher personas
+```
+
+### 2. Realistic Collaboration Patterns
+Use actual collaboration patterns in scenario generation:
+```python
+patterns = ds['collaboration_patterns'].to_pandas()
+
+# Sample by actual distribution
+sampled_pattern = patterns.sample(1, weights='collaboration_pct')
+```
+
+### 3. India-Specific Scenarios
+Generate scenarios using India's actual usage patterns:
+```python
+india = ds['india'].to_pandas()
+india_tasks = india[india['data_type'] == 'top_task'].nlargest(20, 'value')
+```
+
+## Data Processing
+
+This dataset was created from the [Anthropic Economic Index](https://huggingface.co/datasets/Anthropic/EconomicIndex) through:
+
+1. **Download**: Extracted enriched_claude_ai.csv (137K rows)
+2. **Filtering**: Selected global-level data (geo_id='GLOBAL')
+3. **Structuring**: Organized by facets (tasks, collaboration, occupations)
+4. **Flattening**: Converted nested metrics to flat tables
+5. **India Extraction**: Isolated India-specific patterns (3,874 rows)
+
+## Automation vs Augmentation
+
+**Global Averages:**
+- Automation: 51.1% (AI does the task)
+- Augmentation: 48.9% (AI assists human)
+
+**India:**
+- Automation: 45.5%
+- Augmentation: 54.5%
+
+India shows more augmentation-focused usage compared to global patterns.
+
+## Limitations
+
+- Data from only one week (Aug 4-11, 2025)
+- Filtered for privacy (>200 conversations per country)
+- "not_classified" and "none" categories removed for clarity
+- Some tasks may not have automation/augmentation data
+
+## Citation
+
+If you use this dataset, please cite both the structured version and the original:
+
+```
+@dataset{economic_index_structured,
+  title={Economic Index - Structured & Cleaned Dataset},
+  author={Your Name},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/anna-sarvam/economic-index-structured}
+}
+
+@dataset{anthropic_economic_index,
+  title={Anthropic Economic Index},
+  author={Anthropic},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/Anthropic/EconomicIndex}
+}
+```
+
+## Related Resources
+
+- [Original Economic Index](https://huggingface.co/datasets/Anthropic/EconomicIndex)
+- [Economic Index Paper](https://arxiv.org/abs/2503.04761)
+- [O*NET Database](https://www.onetcenter.org/)
+
+## License
+
+Same as the original Anthropic Economic Index dataset (MIT License).
+
+## Maintenance
+
+This is a snapshot of the Economic Index as of September 2025. For the most up-to-date data, refer to the [original dataset](https://huggingface.co/datasets/Anthropic/EconomicIndex).