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
Agricultural data generated from designed experiments are also prone to occurrence outliers. It is well known that Least Squares (LS) model can be distorted even by a single outlying observation. An outlier is one that appears to deviate markedly from the other members of the sample in which it occurs. The sources of influential subsets are diverse. Rousseeuw (1984) introduced a robust method known as Least Median of Squares (LMS) for linear regression models. By this method, the median of squares errors is minimized in order to obtain parameter estimates. It turns out that this estimator is very robust with respect to outliers. Since it focuses on the median residual, up to half of the observations can disagree without masking a model that fits the rest of the data. Therefore, the breakdown point of this estimator is 50%, the highest possible value. In the present investigation, this method is applied to analyze the data set containing outlying observations generated from agricultural field experiments. The data sets for the present investigation have been taken from Agricultural Field Experiments Information System, IASRI, New Delhi.
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