Open science and AI
Open science principles: transparency, accessibility, and collaboration are essential when using AI in research. Sharing data, code, and models helps the scientific community verify, reproduce, and build upon your work.
Today’s AI insight
- Open datasets allow AI models to be trained and validated across diverse conditions
- Sharing code and pipelines ensures reproducibility and accelerates discovery
- Open science practices reduce the risk of bias and errors in AI research
Why this matters
- Closed AI pipelines can create black boxes, limiting trust and collaboration
- Open practices promote peer review, replication, and cumulative knowledge
- They support ethical research by making science accessible to all
A simple example
- In astronomy, AI models for detecting exoplanets are often trained on publicly available datasets
- Researchers can validate each other’s models and reproduce discoveries without relying on proprietary systems
Try this today
Share at least one component of your AI project:
- Dataset (anonymized if necessary)
- Model code
- Documentation of methods
Reflection
AI is more powerful when it is transparent and shared.
Open science ensures that AI contributes to collective knowledge and responsible research.
← Back to AI Advent 2025 overview