e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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pRPL 2.0: Improving the Parallel Raster Processing Library


This article presents an improved parallel Raster Processing Library - pRPL version 2.0. Since the release of version 1.0, a series of modifications has been made in pRPL to improve its usability, flexibility, and performance. While retaining some of the key features of pRPL, the new version has gained several new features: (1) a new DataManager class has been added for integrated data management, and to facilitate data decomposition, assignment mapping, data distribution, Transition execution, and load-balancing; (2) a GDAL-based raster data I/O mechanism has been added to support various geospatial raster data formats, and provide centralized and pseudo parallel I/O modes; and (3) a static load-balancing mode and a dynamic load-balancing mode using the task-farming technique are provided. A parallel zonal statistics tool and a parallel Cellular Automata model were developed to demonstrate the usability and performance of pRPL 2.0. The experiments using the California datasets showed that the performance altered when different pRPL options (i.e. load-balancing mode, I/O mode and writer mode) were used for different algorithms, datasets, and varying numbers of processes.

  • CN
  • China_Univ_Geosci (CN)
Data keywords
  • data management
Agriculture keywords
  • farming
Data topic
  • big data
  • information systems
  • modeling
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.