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
Acoustic monitoring of farm animals may serve as an efficient and species-appropriate management tool for enhancing animal health, welfare, and farm efficiency. In the course of evolution, nature developed manifold means of communication. Sound is one of the most important to convey information and to express emotional states and conditions. Farmers and ethologists are convinced that animal utterances provide valuable information about the animals' state-of-being and condition. Despite the complexity of human speech and the size of the human vocabulary, which is unique in the animal realm, the production and reception of sounds in vertebrates have much in common-with-human -processes.-This- encourages science to adapt methods and experiences from speech-recognition to recognise animal calls. The problem of animal independent call-recognition is comparable to speaker independent word spotting in speech-recognition. In speech-recognition, double stochastic processes such as Hidden Markov Models (HMMs) have proved very efficient. They are applied here to recognise animal calls, using utterances of cows as an example. Results presented here are based on the use of Hidden-Morkov-Models and features such as Mel-Frequency-Cepstral Coefficients. Beside the methods applied, the success of a call-recogniser very much depends on a representative and comprehensive data corpus. The results reveal that HMMs are well suited for animal call-recognition.
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