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
Proposed and implemented is the language CoReJava (Constraint Optimization Regression in Java), which extends the programming language Java with regression analysis, i.e., the capability to do parameter estimation for a function. CoReJava is unique in that functional forms for regression analysis are expressed as first-class citizens, i.e., as Java programs, in which some parameters are not a priori known, but need to be learned from training sets provided as input. Typical applications of CoReJava include calibration of parameters of computational processes, described as 00 programs. To implement regression learning, the CoReJava compiler (1) analyses the structure of the parameterized Java program that represent a functional form, (2) automatically generates a constraint optimization problem, in which constraint variables are the unknown parameters, and the objective function to be minimized is the sum of squares of errors w.r.t. the training set, and (3) solves the optimization problem using an external non-linear optimization solver. CoReJava then executes as a regular Java program, in which the initially unknown parameters are replaced with the found optimal values. CoReJava syntax and semantics are formally defined and exemplified using a simple supply chain example.
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