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
Short-term forecasting of zonal and meridional Wave Energy Flux in the Bay of Biscay using random forests
Three types of statistical models have been used to create up to 24h forecasts of the zonal and meridional components of wave energy flux levels at three directional buoys located in the Bay of Biscay. Hourly observations of the mean wave period and the significant height covering the 1999-2012 period have been used for this purpose. Additionally, data from the ocean (WAM model) and atmospheric components of the ERA-Interim reanalysis of the ECMWF have also been used. Those data have been splitted into a training database (1999-2005) used to build the models, and a test database (2006-2012) reserved to test them and assess their performance. The models used have been built using three different techniques: analogues, random forests (a machine learning algorithm) and a combination of both. For evaluation purposes, the performance of the models at each location has been compared at a 95% confidence level with the simplest prediction -persistence of levels-and also with the nearest gridpoint WAM forecasts. For forecasting horizons between 3 and roughly 16 hours at locations near the coast (where wave farms can be installed), among the statistical models, those built on random forests outperform the rest, including WAM and persistence.
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