README.md

---
language:
- en
- si
license: cc-by-4.0
task_categories:
- text-generation
- question-answering
pretty_name: Open Math Dataset
tags:
- mathematics
- math
- education
- reasoning
- problem-solving
- multilingual
- sinhala
- sri-lanka
- algebra
- geometry
- calculus
- arithmetic
- word-problems
- step-by-step
- llm-training
- synthetic
size_categories:
- 1K<n<10K
---

## Dataset Description

**Open Math Dataset** is an open mathematics corpus designed for **mathematical AI, reasoning, education, and research**.

The project is being developed from **Sri Lanka** with the goal of creating an internationally useful mathematics dataset for developers, researchers, educators, and AI systems.

### Mathematics Corpus

The dataset is designed to contain structured mathematical problems and solutions across different mathematical domains and education levels.

**Planned content includes:**

- Arithmetic
- Algebra
- Geometry
- Trigonometry
- Calculus
- Statistics
- Probability
- Number theory
- Linear algebra
- Discrete mathematics
- Mathematical reasoning
- Word problems
- Step-by-step solutions
- Mathematical explanations
- Error correction
- Multiple solution methods
- Mathematical proofs

The dataset supports both **general mathematics** and **curriculum-specific mathematics**.

### Languages

Current languages:

- **English** (`en`)
- **Sinhala** (`si`)

Sinhala mathematics is an important initial focus because high-quality Sinhala mathematical training data is limited.

However, the project is **not limited to Sri Lankan mathematics**. The dataset is designed for international expansion and may include additional languages, countries, and curricula in future releases.

### Sri Lankan Mathematics

Sri Lankan mathematical content is one of the initial areas of development. This may include:

- Sri Lankan school mathematics
- Sinhala mathematical terminology
- O/L mathematics
- A/L mathematics
- Sri Lankan curriculum-related problems
- Localized mathematical explanations

Sri Lankan content is treated as part of the wider Open Math Dataset rather than the entire scope of the project.

## Dataset Structure

### Data Fields

The dataset is intended to use structured records containing information such as:

| Field              | Description                                      |
|--------------------|--------------------------------------------------|
| `id`               | Unique identifier                                |
| `problem`          | The mathematical problem statement               |
| `solution`         | Step-by-step solution                            |
| `answer`           | Final answer                                     |
| `language`         | Language code (`en`, `si`, etc.)                 |
| `topic`            | Mathematical topic (e.g. `algebra.linear_equations`) |
| `difficulty`       | Difficulty score (e.g. 0.0–1.0)                  |
| `education_level`  | Education level (e.g. `middle_school`)           |
| `curriculum`       | Curriculum identifier (optional)                 |
| `country`          | Country code (optional)                          |
| `source_type`      | Provenance type                                  |

### Example

```json
{
  "id": "omd_000001",
  "language": "en",
  "problem": "Solve 2x + 5 = 15.",
  "solution": "2x + 5 = 15\n2x = 10\nx = 5",
  "answer": "5",
  "topic": "algebra.linear_equations",
  "difficulty": 0.2,
  "education_level": "middle_school"
}
```

## Data Quality

Mathematical correctness is a major priority.

The project aims to prioritize:

- Correct answers
- Correct mathematical reasoning
- Clear explanations
- Consistent formatting
- Deduplication
- Accurate metadata
- Diverse difficulty levels
- Diverse mathematical domains
- Transparent data provenance

Automatically generated mathematical content should be treated as candidate data and verified before being considered high-quality training data.

## Data Pipeline

The planned data preparation process is:

```
Collection
    ↓
Cleaning
    ↓
Normalization
    ↓
Generation
    ↓
Mathematical Verification
    ↓
Deduplication
    ↓
Quality Filtering
    ↓
Metadata
    ↓
Dataset Release
```

## Intended Uses

The dataset may be useful for:

- Mathematical AI
- LLM training
- LLM fine-tuning
- Mathematical reasoning research
- Educational AI
- AI tutoring systems
- Question generation
- Mathematical evaluation
- Multilingual AI
- Curriculum-aware AI
- Mathematical search systems

## Data Provenance

The dataset may contain data from multiple sources and generation methods.

Possible source types include:

- `human_verified`
- `official_source`
- `public_domain`
- `synthetic_verified`
- `community_contributed`
- `translated_verified`

Source and licensing information should be preserved whenever possible.

Only data that can legally be redistributed should be included in public releases.

## Dataset Versions

The dataset will use versioned releases.

| Version | Description                     |
|---------|---------------------------------|
| `v0.1`  | Initial release                 |
| `v0.2`  | Expanded mathematical domains   |
| `v0.5`  | Multilingual expansion          |
| `v1.0`  | Stable major release            |

Dataset statistics and coverage information will be updated with future releases.

## Project Status

**Active Development**

Open Math Dataset is an ongoing project.

The initial focus is building a strong foundation of high-quality mathematical data before expanding into larger international and multilingual coverage.

## Long-term Goal

The goal is to create an open mathematics dataset that developers and researchers around the world can use.

- From basic arithmetic to advanced mathematics.
- From simple question-and-answer pairs to structured mathematical reasoning.
- From Sinhala and English to additional languages in the future.

**Mathematics is universal. The data should be too.**

## Maintainer

Created and maintained by **Tharustack**.

Built from Sri Lanka 🇱🇰 for the world 🌍.