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
Decision tree learning is among the most popular machine learning techniques used for ecological modelling. Decision trees can be used to predict the value of one or several target (dependent) variables. They are hierarchical structures, where each internal node contains a test on an attribute, each branch corresponding to an outcome of the test, and each leaf node giving a prediction for the value of the class variable. Depending on whether we are dealing with a classification (discrete target) or a regression problem (continuous target), the decision tree is called a classification or a regression tree, respectively. The common way to induce decision trees is the so-called Top-Down Induction of Decision Tress (TDIDT). In this chapter, we introduce different types of decision trees, present basic algorithms to learn them, and give an overview of their applications in ecological modelling. The applications include modelling population dynamics and habitat suitability for different organisms (e.g. soil fauna, red deer, brown bears, bark beetles) in different ecosystems (e.g. aquatic, arable and forest ecosystems) exposed to different environmental pressures (e.g. agriculture, forestry, pollution, global warming).
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