The Importance Of Recording Zero In The Digital Age Of Science

By Nathalie Batoux, Product Manager (Discovery and Innovation) Digital Products Development, IDBS

Explore the value of negative results and how proper data management enables your digital maturity journey towards scientific advancement. Negative results are crucial for ML models to make accurate real-world predictions. However, scientists often avoid publishing negative results due to fear of showcasing failures. Comprehensive recording of all results, including negative ones, can create robust ML training datasets and accelerate scientific progress. The article also highlights the role of data accessibility and findability in successful ML training. Nathalie Batoux, a Product Manager at IDBS, advocates for comprehensive recording practices for potential time and cost savings in research and development.