Retrieving biodiversity data from multiple sources: making secondary data standardized and accessible

Biodiversity data, particularly species occurrence and abundance, are indispensable for testing empirical hypothesis in natural sciences. However, datasets built for research programs do not often meet FAIR (findable, accessible, interoperable, and reusable) principles, which raises questions about data quality, accuracy, and availability. The 21st century has markedly been a new era for data science and analytics, and every effort to aggregate, standardize, filter, and share biodiversity data from multiple sources have become increasingly necessary. In this study, we propose a framework for refining and conform secondary biodiversity data to FAIR standards to make them available for valuable use such as macroecological modeling and other studies. We relied on a Darwin Core base model to standardize and further facilitate the curation and validation of data related including the occurrence and abundance of multiple taxa of a region that encompasses estuarine ecosystems in an ecotonal area bordering the easternmost Amazonia. We further discuss the significance of feeding standardized public data repositories to advance scientific progress and highlight their role in contributing to the biodiversity management and conservation.