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-# DiagramQG Dataset
-
-![Dataset Examples](example.pdf)
-*Figure 1: Four different examples of different subjects in DiagramQG dataset.*
-
-![Domain Distribution](course.pdf)
-*Figure 2: Domain diversity in DiagramQG. Each color corresponds to one subject: Natural Science (blue), Earth Science (yellow), Applied Science (green), and Social Science (orange).*
-
-## Overview
-DiagramQG is a comprehensive educational dataset focused on scientific diagram question generation. It contains:
-
-- 19,475 unique questions
-- 8,372 diagrams  
-- 44,472 combinations of (target & concept text constraint, diagram, question)
-- Coverage across 4 subjects, 15 courses, and 169 concepts
-
-## Dataset Structure
-
-### Subject Areas
-The dataset covers four main subject areas:
-- Natural Science
-- Earth Science  
-- Applied Science
-- Social Science
-
-### Hierarchical Organization
-Data is organized hierarchically:
-1. Subject (e.g., Natural Science)
-2. Course (e.g., Biology)  
-3. Concept (e.g., Ecological interactions)
-
-## Data Collection Process
-
-### Phase 1: Initial Data Gathering
-- Sources: Existing datasets and Google Image Search
-- Raw dataset: 20,000+ diagrams and 40,000+ questions
-
-### Phase 2: Organization
-- Classification into 4 subjects and 15 courses
-- Mapping questions to 169 distinct concepts
-
-### Phase 3: Annotation
-- Trained crowd workers annotate:
-  - Target & concept text constraints
-  - Diagram elements and texts
-- Produced 70,000+ unique combinations
-
-### Phase 4: Quality Assurance
-- Secondary crowd worker evaluation (0-100 scale)
-- Filtered combinations below 60 points
-- Final dataset: 44,472 validated combinations
-
-## Dataset Analysis
-
-### Question Distribution
-![Question Distribution](sunburst_chart_hd.png)
-*Figure 3: Question distribution in DiagramQG.*
-
-### Concept Distribution
-![Concept Distribution](proportions_plot_v6.png)
-*Figure 4: Distribution of diagrams, questions, and questions per diagram ratios across different concepts in DiagramQG.*
-
-### Dataset Comparison
-| Dataset | Questions | Images | Objects/Image | Image Type | Constraints | Knowledge Type |
-|---------|-----------|---------|---------------|------------|-------------|----------------|
-| VQAv2.0 | 1.1M | 20k | 3.5 | natural | answer | N/A |
-| FVQA | 5,826 | 2k | 2.9 | natural | answer | common-sense |
-| VQG-COCO | 25,000 | 5k | 3.3 | natural | image, caption | common-sense |
-| K-VQG | 16,098 | 13K | 2.7 | natural | knowledge triple | common-sense |
-| DiagramQG | 19,475 | 8,372 | 11.2 | diagram | target, concept | subject knowledge |
-
-## Unique Challenges
-
-1. **Domain-specific Knowledge Requirement**
-   - Requires understanding of specialized subject concepts
-   - Goes beyond common sense reasoning
-
-2. **Long-tail Distribution**
-   - Uneven concept coverage
-   - Challenges in model generalization
-
-3. **High Information Density**
-   - Complex diagram interpretation
-   - Dense visual information processing