Will open science change chemistry?

The article argues that while open access to journal articles has improved, the true barrier to progress in chemistry is the inability to effectively reuse underlying research data. Current reliance on unreadable formats like PDFs hampers data discoverability and creates a significant bottleneck for machine learning advancements, which require high-quality, structured datasets to train algorithms. Without addressing these foundational issues, the field cannot fully leverage new computational technologies to drive innovation. To resolve this, the scientific community is shifting focus toward FAIR data principles—ensuring information is findable, accessible, interoperable, and reusable. Initiatives across the UK and Germany are building interconnected infrastructure and standardized metadata to unify disparate chemical data. This structural change aims to facilitate a more collaborative environment, similar to the success of protein structure databases, where open sharing accelerates discovery. By making data machine-readable, researchers can efficiently reuse existing findings, fostering rapid innovation and reducing redundant experimentation. Relevance to open_data lies in the urgent need for robust, standardized data frameworks that go beyond simple public availability. The article highlights that data must be not just open, but structured and interoperable to be useful for modern AI and global collaboration. It underscores that cultural and structural barriers, including intellectual property concerns and lack of incentives, must be overcome. Achieving true open data requires addressing global inequities in resource access and creating citation systems that reward data creators, ensuring the benefits of open science are distributed globally rather than being limited by geographic or economic constraints.

Source: chemistryworld.com
Published on 2024-09-07

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