From Field to Analysis: Two Models for Data Distribution in a Standardized and FAIR Way

Abstract:

Science is increasingly collaborative and data intensive. Data from the field and lab should be accessible to all collaborators in a Findable, Accessible, Interoperable, and Reusable (FAIR) way, but data transparency, reproducibility, and accessibility can be hard to achieve without a dedicated data specialist. Here we highlight the utility of two field manipulation data streams of small to medium scales, from two separate research projects. The first is an academic setting with an emphasis on within-lab data exchange, where a R data package provides a straightforward, standardized method for data sharing. In contrast, the second is a multi-institutional initiative of national collaborators with varying coding backgrounds, where a real-time data dashboard is most useful for visualization, quality checks, and download. Both approaches have trade-offs but aim to address the need for accountability throughout the scientific process.