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
Automatic indexing and creating semantic networks for agricultural science papers in the Polish language
This paper presents an automatic indexing system, created on the basis of text analysis, which involves grouping words and reducing them to their dictionary form. The system, developed with the help of an inflection dictionary of the Polish language, is designed to store and retrieve scientific papers dedicated to agriculture. During the analysis, auxiliary words such as pronouns, conjunctions, etc. were omitted. The words which are not present in the inflection dictionary, were used to create a dictionary of new terms. The words stored in the dictionary of new terms were used for the extraction of agricultural terms, which then could be located in the AGROVOC thesaurus. For each of the analyzed papers, a set of concepts with assigned weights was created. For each of the stored scientific papers, an "artificial sentence" was generated. An "artificial sentence" was created on the basis of the frequency of occurrence of dictionary forms of a word appearing in the texts and the word's grammatical category. This "artificial sentence" as well as sets of terms were used to find relationships between the papers stored in the system. These dependencies are used in an algorithm of searching for articles matching a query. It was observed that the number of correct results depends on the number of words in the paper. If a work consisted of at least a thousand words, the probability of misdiagnosis of content was not higher than 5%. In the case of short texts, such as abstracts, the probability of misdiagnosis was much higher, approximately 23%. Results obtained in the presented system are more accurate than those obtained by standard search engines. This method can also be applied to other natural languages with extensive inflection systems. The presented solution is a continuation of the work carried out under a grant [N N310 038538].
Inappropriate format for Document type, expected simple value but got array, please use list format