Best Practices Guide for Ecological Data Curation and Management

Data curation and management are essential for maintaining the integrity, longevity, and usability of information generated by environmental monitoring programs, particularly those involving multiple teams, sampling events, and longitudinal timeframes. Historically, the absence of unified standard operating procedures creates isolated data silos, limiting scientific collaboration and compromising cross-study synthesis in the era of Big Data. This guide presents a scalable, replicable framework for ecological datasets, combining tidy data principles with standardized metadata and established biodiversity data standards such as Darwin Core (DwC). We provide practical guidelines spanning the entire data lifecycle, from field capture and standardized tabular organization to documented analytical workflows and long-term repository preservation. Furthermore, we highlight how transparent, machine-readable data management serves as an environmental governance asset, aligning research outputs with international FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority to control, Responsibility, Ethics) principles. Together, these practices improve data traceability, interoperability, and long-term utility, supporting reproducible science, evidence-informed decision-making, and adaptive environmental management.