Overview
SciCode represents a unique approach to evaluating code-generating AI models - one that bridges the gap between general programming and scientific research. Developed by domain scientists, this benchmark uses real laboratory problems that researchers actually encounter in their work.
Key Statistics
| Metric | Value |
|---|---|
| Total Sub-tasks | 338 |
| Source Problems | 80 genuine lab problems |
| Scientific Disciplines | 16 different fields |
| Evaluation Focus | Code + Scientific accuracy |
Discipline Coverage
SciCode spans 16 scientific disciplines, including:
| Category | Examples |
|---|---|
| Life Sciences | Molecular biology, genetics, biochemistry |
| Physical Sciences | Physics, chemistry, materials science |
| Earth Sciences | Geology, climate science, ecology |
| Computational Sciences | Bioinformatics, computational chemistry |
What Makes SciCode Unique
- Authenticity: Problems come from actual research scenarios
- Domain Expertise: Curated by practicing scientists
- Multi-step Tasks: Requires understanding context before coding
- Scientific Validation: Code must produce scientifically accurate results
Evaluation Criteria
SciCode doesn’t just test whether code runs - it tests whether code produces correct scientific outcomes:
- Functional Correctness: Does the code execute without errors?
- Scientific Accuracy: Do results match expected scientific outcomes?
- Methodological Soundness: Does the approach follow scientific best practices?
- Reproducibility: Can results be replicated?
Example Task Types
- Data analysis pipelines for experimental results
- Statistical analysis of scientific datasets
- Simulation code for physical/biological phenomena
- Data visualization for research publications
- Image processing for scientific imaging
Purpose
SciCode evaluates whether AI models can be useful assistants in scientific research - generating code that actually works for real scientific challenges rather than just algorithmic exercises.
Source: SciCode
