Transgenic Bt corn expressing the Cry3Bb insecticidal protein active against corn rootworm (CRW) (Diabrotica spp.; Coleoptera: Chrysomelidae) was released for commercial use in 2003 and is expected to be widely adopted. Yet, the direct and indirect risks to soil microorganisms of growing this CRW-resistant Bt corn versus applying insecticides to control the rootworm have not been assessed under field conditions. The effects of CRW Bt corn and the insecticide tefluthrin [2,3,5,6-tetrafluoro-4-methylbenzyl (Z)-(1RS)-cis-3-(2-chloro-3,3,3-trifluoroprop-1-enyl)-2,2-dimethylcyclopropanecarboxylate] on soil microbial biomass, activity (N mineralization potential, short-term nitrification rate, and soil respiration), and bacterial community structure as determined by terminal restriction fragment length polymorphism (T-RFLP) analysis were assessed over two seasons in a field experiment. Bt corn had no deleterious effects on microbial activity or bacterial community measures compared with the non-transgenic isoline. The T-RFLP analysis indicated that amplifiable bacterial species composition and relative abundance differed substantially between years, but did not differ between rhizosphere and bulk soils. The application of tefluthrin also had no effect on any microbial measure except decreased soil respiration observed in tefluthrin-treated plots compared with Bt and non-transgenic isoline (NoBt) plots in 2002. Our results indicate that the release of CRW Bt corn poses little threat to the ecology of the soil microbial community based on parameters measured in this study.

Photosynthetic assimilation of CO2 is a primary source of carbon in soil and root exudates and can influence the community dynamics of rhizosphere organisms. Thus, if carbon partitioning is affected in transgenic crops, rhizosphere microbial communities may also be affected. In this study, the temporal effects of gene transformation on carbon partitioning in rice and rhizosphere microbial communities were investigated under greenhouse conditions using the 13C pulse‐chase labeling method and phospholipid fatty acid (PLFA) analysis. The 13C contents in leaves of transgenic (Bt) and nontransgenic (Ck) rice were significantly different at the seedling, booting and heading stages. There were no detectable differences in 13C distribution in rice roots and rhizosphere microorganisms at any point during rice development. Although a significantly lower amount of Gram‐positive bacterial PLFAs and a higher amount of Gram‐negative bacterial PLFAs were observed in Bt rice rhizosphere as compared with Ck at all plant development stages, there were no significant differences in the amount of individual 13C‐PLFA between Bt and Ck rhizospheres at any growing stage. These findings indicate that the insertion of cry1Ab and marker genes into rice had no persistent or adverse effect on the photosynthate distribution in rice or the microbial community composition in its rhizosphere.

Summary Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

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Scientific innovation is increasingly reliant on data and computational resources. Much of today’s life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data. COPO is a computational system that attempts to address some of these challenges by enabling scientists to describe their research objects (raw or processed data, publications, samples, images, etc.) using community-sanctioned metadata sets and vocabularies, and then use public or institutional repositories to share them with the wider scientific community. COPO encourages data generators to adhere to appropriate metadata standards when publishing research objects, using semantic terms to add meaning to them and specify relationships between them. This allows data consumers, be they people or machines, to find, aggregate, and analyse data which would otherwise be private or invisible, building upon existing standards to push the state of the art in scientific data dissemination whilst minimising the burden of data publication and sharing.