Abstract
In recent years, science has relied more than ever on large-scale data as well as on distributed computing and human resources. Scientists and research engineers in fields such as climate science and computational seismology, constantly strive to make good use of remote and largely heterogeneous computing resources (HPC, Cloud, institutional or local resources, etc.), process, archive and analyse results stored in different locations and collaborate effectively with other scientists.
The DARE platform enables the seamless development and reusability of scientific workflows and applications, and the reproducibility of the experiments. Further, it provides Workflow-as-a-Service (WaaS) functionality and dynamic loading of execution contexts in order to hide technical complexity from its end users. This paper introduces the software implementing the DARE platform. More information on the H2020 DARE project is provided in Klampanos et al. (2019), Atkinson et al. (2019), and Atkinson et al. (2020).
The DARE platform enables the seamless development and reusability of scientific workflows and applications, and the reproducibility of the experiments. Further, it provides Workflow-as-a-Service (WaaS) functionality and dynamic loading of execution contexts in order to hide technical complexity from its end users. This paper introduces the software implementing the DARE platform. More information on the H2020 DARE project is provided in Klampanos et al. (2019), Atkinson et al. (2019), and Atkinson et al. (2020).
Original language | English |
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Article number | 2664 |
Number of pages | 4 |
Journal | The Journal of Open Source Software |
Volume | 5 |
Issue number | 54 |
DOIs | |
Publication status | Published - 17 Oct 2020 |