The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
The rich collection of known genetic information and the recent completion of rice genome sequencing projcct provided the cereal plant researchers a useful tool to investigate the roles of genes and genomic organization that contribute to numerous agronomic traits. Gramene (http://www.gramene.org) is a unique database where users are allowed to query and explore the power of genomic colinearity and comparative genomics for genetic and genomic studies on plant genomes. Gramene presents a wholesome perspective by assimilating data from a broad range of publicly available data sources for cereals like rice, sorghum, maize, wild rice, wheat, oats, barley, and other agronomically important crop plants such as poplar and grape, and the model plant Arabidopsis. As part of the process, it preserves the original data, but also reanalyzes for integration into several knowledge domains of maps, markers, genes, proteins, pathways, phenotypes, including Quantitative Trait Loci (QTL) and genetic diversity/natural variation. This allows researchers to use this information resource to decipher the known and predicted interactions between the components of biological systems, and how these interactions regulate plant development. Using examples from rice, this article describes how the database can be helpful to researchers representing an array of knowledge domains ranging from plant biology, plant breeding, molecular biology, genomics, biochemistry, genetics, bioinformatics, and phylogenomics.
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