Keywords
Virtual Research Environment, e-Infrastructure, distributed computing, Agro-Informatics, Crop Modelling
Start Date
17-9-2020 10:40 AM
End Date
17-9-2020 11:00 AM
Abstract
Tackling some of the grand global challenges, agro-environmental research has turned more and more into international collaboration, where distributed research teams work together to solve complex research questions. Moreover, the interdisciplinary character of these challenges requires that a large diversity of different data sources and information is combined in new and innovative ways. There is a pressing need to support scientists with environments that allow them to efficiently work together and co-develop research. As research is often data-intensive, and big data becomes a common part of a fair amount of research, such environments should also offer the resources, tools, and workflows that allow the processing of data at scale if needed. Virtual Research Environments (VREs), which combine working in the Cloud with collaborative functions and state of the art data science tools, can be a potential solution. This paper describes how such a VRE, configured and run by the D4Science organisation, was successfully used in the AGINFRA PLUS European Horizon 2020 project to: (i) support exploratory modelling; (ii) run the WOFOST crop simulation model at scale using the standard service oriented interfaces of the available e-Infrastructure; (iii) present data in a web based dashboard; and (iv) sharing of data, notebooks, and models between VRE users. The VRE has meanwhile been evaluated by a substantial group of scientists from the targeted agro-environmental modelling communities with positive feedback in general. Organisational support and interoperability with systems already in use are mentioned as the main concerns. Achieving broad uptake and adoption however can be a challenge, as researchers currently can choose from or are already using quite a few similar tools and environments. Continued, iterative, development driven by user feedback, towards clear key advantages for researchers could prove helpful.
Towards Virtual Research Environments for Open Science: The Case of Agro-Environmental Modelling
Tackling some of the grand global challenges, agro-environmental research has turned more and more into international collaboration, where distributed research teams work together to solve complex research questions. Moreover, the interdisciplinary character of these challenges requires that a large diversity of different data sources and information is combined in new and innovative ways. There is a pressing need to support scientists with environments that allow them to efficiently work together and co-develop research. As research is often data-intensive, and big data becomes a common part of a fair amount of research, such environments should also offer the resources, tools, and workflows that allow the processing of data at scale if needed. Virtual Research Environments (VREs), which combine working in the Cloud with collaborative functions and state of the art data science tools, can be a potential solution. This paper describes how such a VRE, configured and run by the D4Science organisation, was successfully used in the AGINFRA PLUS European Horizon 2020 project to: (i) support exploratory modelling; (ii) run the WOFOST crop simulation model at scale using the standard service oriented interfaces of the available e-Infrastructure; (iii) present data in a web based dashboard; and (iv) sharing of data, notebooks, and models between VRE users. The VRE has meanwhile been evaluated by a substantial group of scientists from the targeted agro-environmental modelling communities with positive feedback in general. Organisational support and interoperability with systems already in use are mentioned as the main concerns. Achieving broad uptake and adoption however can be a challenge, as researchers currently can choose from or are already using quite a few similar tools and environments. Continued, iterative, development driven by user feedback, towards clear key advantages for researchers could prove helpful.
Stream and Session
false