Data Steward Service Center (DSSC): FAIRagro RDM-Expertise Hub

The Data Steward Service Center (DSSC) is the central institution within FAIRagro to develop data management tools based on the needs of the scientific community. The DSSC organizes the continuous exchange of RDM knowledge and experience with other institutions, channels user requests from the community, and transfers knowledge from the FAIRagro task areas to the FAIRagro data stewards. FAIRagro data stewards are experts in the field of RDM for agrosystems research supervising and will train data curators in our community. Data stewards have core competencies in research data management (e.g., cross-scale from genes, phenomics, management to region; sensitive data, remote sensing, time series, plant, soil and related FAIRagro data). Knowledge and expertise is pooled to provide the full range of expertise to the community in one place to foster the coalescence of the community. The DSSC is headed by a coordinator and will house five data stewards, who are active in the community e.g. train data curators, give legal support. In the course of the project, further institutional or project data stewards will be integrated and the pool of experts will be further expanded. The network to the other NFDI consortia is continuously growing

The Data Steward Service Center (DSSC)

<p>The Data Steward Service Center (DSSC) is the central institution within FAIRagro to develop data management tools based on the needs of the scientific community. The DSSC organizes the contin-uous exchange of RDM knowledge and experience with other institutions, channels user requests from the community, and transfers knowledge from the FAIRagro task areas to the FAIRagro data stewards. FAIRagro data stewards are experts in the field of RDM for agrosystems research super-vising and will train data curators in our community. Data stewards have core competencies in re-search data management (e.g., cross-scale from genes, phenomics, management to region; sensitive data, remote sensing, time series, plant, soil and related FAIRagro data). Knowledge and expertise is pooled to provide the full range of expertise to the community in one place to foster the coalescence of the community. The DSSC is headed by a coordinator and will house five data stewards, who are active in the community e.g. train data curators, give legal support. In the course of the project, further institutional or project data stewards will be integrated and the pool of experts will be further expanded. The network to the other NFDI consortia is continuously growing.</p&gt

Single-parent expression complementation contributes to phenotypic heterosis in maize hybrids

The dominance model of heterosis explains the superior performance of F1-hybrids via the complementation of deleterious alleles by beneficial alleles in many genes. Genes active in one parent but inactive in the second lead to single-parent expression (SPE) complementation in maize (Zea mays L.) hybrids. In this study, SPE complementation resulted in approximately 700 additionally active genes in different tissues of genetically diverse maize hybrids on average. We established that the number of SPE genes is significantly associated with mid-parent heterosis (MPH) for all surveyed phenotypic traits. In addition, we highlighted that maternally (SPE_B) and paternally (SPE_X) active SPE genes enriched in gene co-expression modules are highly correlated within each SPE type but separated between these two SPE types. While SPE_B-enriched co-expression modules are positively correlated with phenotypic traits, SPE_X-enriched modules displayed a negative correlation. Gene ontology term enrichment analyses indicated that SPE_B patterns are associated with growth and development, whereas SPE_X patterns are enriched in defense and stress response. In summary, these results link the degree of phenotypic MPH to the prevalence of gene expression complementation observed by SPE, supporting the notion that hybrids benefit from SPE complementation via its role in coordinating maize development in fluctuating environments.This article is published as Baldauf, Jutta A., Meiling Liu, Lucia Vedder, Peng Yu, Hans-Peter Piepho, Heiko Schoof, Dan Nettleton, and Frank Hochholdinger. "Single-parent expression complementation contributes to phenotypic heterosis in maize hybrids." Plant Physiology 189 (2022): 1625-1638. doi:10.1093/plphys/kiac180. Posted with permission. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited

Boxplot of the log10 transformed expression values for selected GO terms.

Each dot represents the expression level of a single gene, while the boxplot summarizes these single points. Root and shoot are separated by color, while the species are spatially divided. Two statistical tests were performed, between the tissues and between the species for each GO term individually. The results of the tissue-wise comparison are printed below the GO term name between the green and purple boxplots, while the species-related comparison is placed between the spatially separated boxplots. (TIF)</p

Workflow of the gene expression and functional analysis on three levels.

Each level is framed by a yellow, orange, or green square. (TIF)</p

Relative chlorophyll content assessed by an multispeQ gadget for ten replicated seedlings of barley (cultivar Scarlett) and tomato (cultivar Moneymaker) in two different photoperiods (8h, 16h day light).

Values above boxplots indicate the result of a t-test comparison.</p

S2 Fig -

The number of reads (in millions), mapped reads, and mapped in genes in Barley (A) and tomato gene annotation (B). (TIF)</p

DATA FACT SHEETS of the Data Steward Service Center (DSSC) from the NFDI consortium FAIRagro

These data fact sheets introduce data types and provide an overview about typically used data in the agrosystem research community. Specifics regarding to research data management and where data can be found and published are elaborated for soil data, field data in general, genetic data ("omics-data"), phenotyping data, field robotics &amp; sensor data and big geodata. We also lay out the legal framework for data use with specifics to our domain. FAIRagro is the agrosystem science consortium within the German National Research Data Infrastructure (NFDI), offering a helpdesk for any stages of the data life cycle