Building a Framework for Reproducibility: The Case for Standardized Data Reporting and Metadata Integration in Zebrafish Research

Zebrafish (Danio rerio) is one of the main vertebrate model organisms, significantly advancing scientific discoveries and facilitating human disease modeling. These discoveries, encompassing publications and high-throughput datasets, contribute to a growing body of knowledge critical for future research and precision medicine. However, the explosion of large datasets driven by advances in genomics, imaging, and artificial intelligence presents challenges in curating, integrating, and interpreting data. The lack of globally adopted standardized data reporting methods by the zebrafish research community compromises data usability, interoperability, and reproducibility. Data standards are essential for consistent and accurate reporting across diverse domains. This includes standardized reporting of genes, alleles, anatomical structures, developmental stages, and experimental metadata, such as drug/toxicant concentrations, morpholino dilutions, and imaging parameters. Variability in developmental stage reporting and insufficient image metadata exemplify data integration and reuse challenges. While databases like the Zebrafish Information Network (ZFIN) promote ontology-driven standards, widespread adoption remains limited, partly due to the lack of awareness or engagement within the research community. Furthermore, artificial intelligence (AI)-driven research depends on well-structured and standardized datasets to enhance data integration, knowledge discovery, and predictive modeling. To address these gaps, we appeal to the zebrafish research community to adopt and contribute to developing robust data standards. This paper highlights the pressing need for the zebrafish research community to actively contribute to developing, refining, and adopting data standards to ensure reproducibility, foster interdisciplinary collaboration, and support AI-powered scientific advancements.