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
Modelling of landscape variables at multiple extents to predict fine sediments and suitable habitat for Tubifex tubifex in a stream system
1. Aggregations of fine sediments are a suitable proxy for the presence and abundance of Tubifex tubifex, one of the obligate hosts in the parasitic life cycle that causes salmonid whirling disease (Myxobolus cerebralis). 2. To determine and evaluate practical approaches to predict fine sediments (<2 mm diameter) that could support Tubifex spp. aggregations, we measured habitat features in a catchment with field measures and metrics derived from digital data sets and geospatial tools at three different spatial extents (m(2)) within a hierarchical structure. 3. We used linear mixed models to test plausible candidate models that best explained the presence of fine sediments measured in stream surveys with metrics from several spatial extents. 4. The percent slow water habitat measured at the finest extent provided the best model to predict the likely presence of fine sediments. The most influential models to predict fine sediments using landscape metrics measured at broader extents included variables that measure the percentage land cover in conifer or agriculture, specifically, decreases in conifer cover and increases in agriculture. 5. The overall best-fitting model of the presence of fine sediments in a stream reach combined variables measured and operating at different spatial extents. 6. Landscape features modelled within a hierarchical framework may be useful tools to evaluate and prioritise areas with fine sediments that may be at risk of infection by Myxobolus cerebralis.
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