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
Bioinformatics is the application of computer technology to the management of biological information. In Bioinformatics, Motif finding is one of the most popular problems, which has many applications. It is the process of locating the meaningful patterns in the sequence of Deoxyribo Nucleic Acid (DNA), Ribo Nucleic Acid (RNA) or Proteins. Motifs vary in lengths, positions, redundancy, orientation and bases. Finding these short sequences (motifs or signals) is a fundamental problem in molecular biology and computer science with important applications such as knowledge-based drug design, forensic DNA analysis, and agricultural biotechnology. In this work, the clustering system is used to predict local protein sequence Motifs. Since clustering algorithms can provide an automatic, unsupervised discovery process for sequence motifs, the K-Means clustering algorithm and Rough-K-means algorithm proposed are chosen as the motif discovery method for this study and the results are compared. The structural similarity of the clusters discovered by the proposed approach is studied to analyze how the recurring patterns correlate with its structure. Also, some biochemical references are included in our evaluation. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ICCTSD 2011
Inappropriate format for Document type, expected simple value but got array, please use list format