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
Artificial Neural Network Approach for Revealing Individuality, Group Membership and Age Information in Goat Kid Contact Calls
Machine learning techniques are becoming an important tool for studying animal vocal communication. The goat (Capra hircus) is a very social species, in which vocal communication and recognition are important. We tested the reliability of a Multi-Layer Perceptron (feed-forward Artificial Neural Network, ANN) to automate the process of classification of calls according to individual identity, group membership and maturation in this species. Vocalisations were obtained from 10 half-sibling (same father but different mothers) goat kids, belonging to 3 distinct social groups. We recorded 157 contact calls emitted during first week, and 164 additional calls recorded from the same individuals at 5 weeks. For each call, we measured 27 spectral and temporal acoustic parameters using a custom built program in Praat software. For each classification task we built stratified 10-fold cross-validated neural networks. The input nodes corresponded to the acoustic parameters measured on each signal. ANNs were trained with the error-back-propagation algorithm. The number of hidden units was set to the number of attributes + classes. Each model was trained for 350 epochs (learning rate 0.2; momentum 0.2). To estimate a reliable error for the models, we repeated 10-fold cross-validation iterations 10 times and calculated the average predictive performance. The accuracy was 71.13 +/- 1.16% for vocal individuality, 79.59 +/- 0.75% for social group and 91.37 +/- 0.76% for age of the vocalising animal. Our results demonstrate that ANNs are a powerful tool for studying vocal dues to individuality, group membership and maturation in contact calls. The performances we achieved were higher than those obtained for the same classification tasks using classical statistical methods such as Discriminant Function Analysis. Further studies, investigating the reliability of these algorithms for the real-time classification of contact calls and comparing ANNs with other machine learning techniques are important to develop technology to remotely monitor the vocalisations of domestic livestock.
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