Toward a Framework for Integrative, FAIR, and Reproducible Management of Data on the Dynamic Balance of Microbial Communities

TL;DR

This paper presents a framework designed to improve the management, discoverability, and reproducibility of microbial community data across various scientific disciplines, emphasizing FAIR principles and provenance tracking.

Contribution

It introduces an integrative research data management framework tailored for microbial community studies, incorporating best practices and provenance recording to enhance data reuse.

Findings

Framework facilitates data discovery and integration

Supports reproducibility through provenance tracking

Aligns with FAIR data principles

Abstract

The increasing volumes of data produced by high-throughput instruments coupled with advanced computational infrastructures for scientific computing have enabled what is often called a {\em Fourth Paradigm} for scientific research based on the exploration of large datasets. Current scientific research is often interdisciplinary, making data integration a critical technique for combining data from different scientific domains. Research data management is a critical part of this paradigm, through the proposition and development of methods, techniques, and practices for managing scientific data through their life cycle. Research on microbial communities follows the same pattern of production of large amounts of data obtained, for instance, from sequencing organisms present in environmental samples. Data on microbial communities can come from a multitude of sources and can be stored in different formats. For example, data from metagenomics, metatranscriptomics, metabolomics, and biological imaging are often combined in studies. In this article, we describe the design and current state of implementation of an integrative research data management framework for the Cluster of Excellence Balance of the Microverse aiming to allow for data on microbial communities to be more easily discovered, accessed, combined, and reused. This framework is based on research data repositories and best practices for managing workflows used in the analysis of microbial communities, which includes recording provenance information for tracking data derivation.