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
Guiding the Introduction of Big Data in Organizations: A Methodology with Business- and Data-Driven Ideation and Enterprise Architecture Management-Based Implementation
Researchers and practitioners frequently assume that big data can be leveraged to create value for organizations implementing it. Decisions for big data idea generation and implementation need careful consideration of multiple factors. However, no scientifically grounded and unbiased method to structure such an assessment and to guide implementation exists yet. This paper describes a methodology based on IT value theory and workgroup ideation guiding big data idea generation, idea assessment and implementation management. Distinct business and data driven perspectives are distinguished to account for big data specifics. Enterprise Architecture Management and Business Model Generation techniques are used in individual steps for execution. A first prototypical application in the context of Supply Chain Management illustrates the applicability of the method.
Inappropriate format for Document type, expected simple value but got array, please use list format