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 sheep (Ovis aries) plays a major socio-economic role in the world. Copy number variations (CNVs) are increasingly recognized as a key and potent source of genetic variation and phenotypic diversity, but little is known about the extent to which CNVs contribute to genetic variation in Chinese sheep breeds. Analyses of CNVs in the genomes of eight sheep breeds were performed using the sheep SNP50 BeadChip genotyping array. A total of 111 CNV regions (CNVRs) were obtained from 160 Chinese sheep breeds. These CNVRs covered 13.75 Mb of the sheep genome sequence. A total of 22 Go terms and 17 candidate genes were obtained from the functional analysis. Ten CNVRs were selected for validation, of which 7 CNVRs were further experimentally confirmed by quantitative PCR. Four candidate genes were selected to confirm the results of the functional analysis. These results provide a resource for furthering understanding of ruminant biology, and for further improving the genetic quality of sheep breeds. (C) 2015 Elsevier Inc. All rights reserved.
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