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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoning
+Dataset for the paper "[Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoning](s)"
+
+# MMSci Dataset Collection
+
+The MMSci dataset collection consists of three complementary datasets designed for scientific multimodal table understanding and reasoning: MMSci-Pre, MMSci-Ins, and MMSci-Eval.
+
+## Dataset Summary
+
+- **MMSci-Pre**: A domain-specific pre-training dataset containing 52K scientific table structure recognition samples
+- **MMSci-Ins**: An instruction tuning dataset with 12K samples across three table-based tasks
+- **MMSci-Eval**: A benchmark with 3,114 testing samples for numerical reasoning evaluation
+
+## Dataset Details
+
+### MMSci-Pre
+- **Size**: 52K samples
+- **Format**: Table image-to-HTML pairs
+- **Source**: Scientific papers from SciGen dataset
+- **Purpose**: Table structure learning and alignment of visual features with textual representations
+- **Features**:
+  - High-quality HTML format tables
+  - Rendered table images preserving structural integrity
+  - Complex layouts and relationships from scientific papers
+  - Focus on tables with significant numerical values
+
+### MMSci-Ins
+- **Size**: 12K samples
+- **Format**: Instruction-following samples with reasoning steps
+- **Tasks**:
+  - Table Question Answering (TQA)
+  - Table Fact Verification (TFV)
+  - Table-to-Text Generation (T2T)
+- **Features**:
+  - Detailed step-by-step reasoning processes
+  - Balanced distribution across three tasks
+  - Each table paired with one TQA, TFV, and T2T task
+  - Built upon scientific domain tables
+
+### MMSci-Eval
+- **Size**: 3,114 samples
+- **Purpose**: Comprehensive evaluation of numerical reasoning capabilities
+- **Features**:
+  - Testing samples across TQA, TFV, and T2T tasks
+  - Focus on numerical reasoning assessment
+  - Based on SciGen dataset test set
+  - Diverse reasoning types and complexity levels
+
+## Dataset Creation
+
+The datasets were created through a rigorous process:
+1. Collection of raw tabular data from SciGen dataset
+2. Transformation of textual tables into HTML format
+3. Rendering of HTML tables into high-quality images
+4. Generation of instruction-following samples with reasoning steps
+5. Quality assurance through balanced task distribution
+
+## Intended Uses
+
+- Pre-training multimodal language models for table understanding
+- Fine-tuning models for specific table-based tasks
+- Evaluating numerical reasoning capabilities in scientific contexts
+- Benchmarking table understanding and reasoning systems
+
+## Citation
+
+If you found this repository or paper is helpful to you, please cite our paper.
+```
+
+```
+}