Community-Driven Metadata Standards for Agricultural Microbiome Research

Accelerating the pace of microbiome science to enhance crop productivity and agroecosystem health will require transdisciplinary studies, comparisons among datasets, and synthetic analyses of research from diverse crop management contexts. However, despite the widespread availability of crop-associated microbiome data, variation in field sampling and laboratory processing methodologies, as well as metadata collection and reporting, significantly constrains the potential for integrative and comparative analyses. Here we discuss the need for agriculture-specific metadata standards for microbiome research, and propose a list of “required” and “desirable” metadata categories and ontologies essential to be included in a future minimum information metadata standards checklist for describing agricultural microbiome studies. We begin by briefly reviewing existing metadata standards relevant to agricultural microbiome research, and describe ongoing efforts to enhance the potential for integration of data across research studies. Our goal is not to delineate a fixed list of metadata requirements. Instead, we hope to advance the field by providing a starting point for discussion, and inspire researchers to adopt standardized procedures for collecting and reporting consistent and well-annotated metadata for agricultural microbiome research

More Than the Sum of Its Parts: Unlocking the Power of Network Structure for Understanding Organization and Function in Microbiomes

Plant and soil microbiomes are integral to the health and productivity of plants and ecosystems, yet researchers struggle to identify microbiome characteristics important for providing beneficial outcomes. Network analysis offers a shift in analytical framework beyond who is present to the organization or patterns of coexistence between microbes within the microbiome. Because microbial phenotypes are often significantly impacted by coexisting populations, patterns of coexistence within microbiomes are likely to be especially important in predicting functional outcomes. Here, we provide an overview of the how and why of network analysis in microbiome research, highlighting the ways in which network analyses have provided novel insights into microbiome organization and functional capacities, the diverse network roles of different microbial populations, and the eco-evolutionary dynamics of plant and soil microbiomes

Scaling Research Support for Early-Stage Researchers with Crowdsourcing

Support from peers and experts, such as feedback on research artefacts, is an important component of developing research skills. The support is especially helpful for early-stage researchers (ESRs), typically PhD students at the critical stage of learning research skills. Currently, such support mainly comes from a small circle of advisors and colleagues. Gaining access to quality and diverse support outside a research group is challenging for most ESRs. This thesis presents several studies to advance the fundamental and practical understanding of designing systems to scale support for research skills development for ESRs. First, we conduct a systematic literature review on crowdsourcing for education that summarizes existing efforts in the research and application domain. This study also highlights the need for studies on crowdsourcing support for research skills development. Then, based on findings from the first study, we conducted another systematic literature review study on crowdsourcing support for project-based learning and research skills development. The third study explores the qualitative empirical understanding of how ESRs leverage current socio-technical affordances for distributed support in their research activities. This study reveals opportunities afforded by socio-technical systems and challenges faced by ESRs when seeking and adopting support from online research communities. The fourth study explores quantitative empirical understandings of the most desired types of feedback from external researchers that need to be prioritized to offer, and the challenges that need to be prioritized to solve. Building on the findings from the four studies above, we proposed a theoretical framework -- Researchersourcing -- that guides the understanding and designing of socio-technical systems that scale the support for research skills development. Accordingly, in the fifth study, we design and evaluate a crowdsourcing pipeline and a system to scale feedback on research drafts and ease the burdens of reviewing research drafts