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
Community structure models are improved by exploiting taxonomic rank with predictive clustering trees
Community structure modelling studies the influence of biotic and abiotic factors on the abundance and composition of a given taxonomic group of organisms. With the advancement of measurement and sensor technology, the availability, precision and complexity of environmental data constantly increases. Nowadays, measurements of ecosystems provide a complete snapshot of the state of the system, including information about the community structure of organisms that are present in a given sample. These measurements include multi-species data that are typically analysed by constructing community models as collections of models built for each species separately (local models) without considering the possible (taxonomic) relationships among species. In this work, we propose to construct a single community structure model for all the species (global model) that is able to exploit the aforementioned relationships. Namely, we investigate whether inclusion of additional information in the form of taxonomic rank or multiple species helps to build better community structure models. More specifically, we use predictive clustering trees (a generalized form of decision trees) to build models for three practically relevant datasets from the task of community structure modelling: microarthopod community living in the agricultural soils of Denmark, organisms living in Slovenian rivers and vegetation found in the State of Victoria, Australia. On each dataset, we compare the performance of four types of community structure models, which correspond to four machine learning tasks: Single species models without taxonomic rank correspond to single-label classification; single species models with taxonomic rank correspond to hierarchical single-label classification; multi-species models without taxonomic rank correspond to multi-label classification; and multi-species models with taxonomic rank correspond to hierarchical multi-label classification. The results of the experimental evaluation reveal that by using the taxonomic rank and the multi-species aspect of the data, we are able to learn better community structure models. (C) 2014 Elsevier B.V. All rights reserved.
Inappropriate format for Document type, expected simple value but got array, please use list format