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
Comparison of classification algorithms to predict outcomes of feedlot cattle identified and treated for bovine respiratory disease
Bovine respiratory disease (BRD) continues to be the primary cause of morbidity and mortality in feedyard cattle. Accurate identification of those animals that will not finish the production cycle normally following initial treatment for BRD would provide feedyard managers with opportunities to more effectively manage those animals. Our objectives were to assess the ability of different classification algorithms to accurately predict an individual calf's outcome based on data available at first identification of and treatment for BRD and also to identify characteristics of calves where predictive models performed well as gauged by accuracy. Data from 23 feedyards in multiple geographic locations within the U.S. from 2000 to 2009 representing over one million animals were analyzed to identify animals clinically diagnosed with BRD and treated with an antimicrobial. These data were analyzed both as a single dataset and as multiple datasets based on individual feedyards and partitioned into training, testing, and validation datasets. Classifiers were trained and optimized to identify calves that did not finish the production cycle with their cohort. Following classifier training, accuracy was evaluated using validation data. Analysis was also done to identify sub-groups of calves within populations where classifiers performed better compared to other sub-groups. Accuracy of individual classifiers varied by dataset. The accuracy of the best performing classifier by dataset ranged from a low of 63% in one dataset up to 95% in a different dataset. Sub-groups of calves were identified within some datasets where accuracy of a classifiers were greater than 98%; however these accuracies must be interpreted in relation to the prevalence of the class of interest within those populations. We found that by pairing the correct classifier with the data available, accurate predictions could be made that would provide feedlot managers with valuable information. (C) 2014 Elsevier B.V. All rights reserved.
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