Astrix LabWare Integration enabled a global company with operations across 54 countries to streamline their process for entering, documenting, and reporting their compliance with environmental regulations associated with water quality, including ISO/IEC 17025. In under six months... [Read More]

Clinical diagnostic laboratories must have high-quality software in order to run reliably and efficiently. But quality is about more than just the software code. Quality must also be the foundation of the software architecture.
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In this 2021 journal article published in Data Science Journal, Damerow et al. emphasize that while sample-based research is critical to a wide range of ecosystem sciences, the increasingly multidisciplinary approach to those sciences requires a better, more coordinated practice of sample identification and data management. "While there are widely adopted conventions within certain domains to describe sample data," they say, "these have gaps when applied in a multidisciplinary context." With this paper, the authors present a more practical approach to sample identification and management that takes into account the multidisciplinary requirements of ecosystems research. After a brief review of literature and existing sample identification methods, guidance, and standards, they describe their pilot program for standardizing sample metadata and propose several benefits to the program. They conclude that "user-friendly guidance and sample metadata templates are an essential step in promoting standard practices that make data publishing, integration, and reuse easier," though proper training, legacy data management tools, and information management systems are also important components towards those goals.