e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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Interpretation of microarray data: Trudging out of the abyss towards elucidation of biological significance


The recent development of tools for expression profiling in livestock has availed reproductive biologists of new opportunities to examine global changes in gene expression during key developmental events, in response to hormonal or other treatments, and as a tool for phenotyping or predicting developmental potential. Such experiments often yield lists of tens to thousands of modulated genes, transcripts of interest, or both. Some argue that such technological advances signal a move from hypothesis-driven research to descriptive discovery research, resulting in information overload at the expense of biological significance. One can easily spend hours staring into the abyss, wondering if the results are real and what they mean. However, microarrays can be more than a high throughput and expensive screening tool. Many factors contribute to the success of expression profiling experiments and the yield of interpretable data, including the nature of the hypothesis or objective of the study, the microarray platform, the complexity of the tissue of interest, the experimental design, and the incorporation of the best available strategies for data analysis and interpretation of the biological themes. Although challenging due to the lack of extensive annotation or ontology classification for genes in livestock species, functional categories of coregulated genes and gene pathways can be determined, and hypotheses about common regulatory elements or the functional significance can be formulated. We have applied cDNA microarray technology to studies of follicular growth, oocyte quality, and the periovulatory period in cattle. Lessons learned from such experiments and a review of the available literature form the basis for the strategies described to facilitate successful application of microarray technology to studies of reproductive biology of livestock species.

  • US
  • Michigan_State_Univ (US)
  • Univ_Wisconsin_Madison (US)
Data keywords
  • ontology
Agriculture keywords
  • cattle
  • livestock
Data topic
  • information systems
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
  • Michigan_State_Univ (US)
  • Univ_Wisconsin_Madison (US)
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e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.