Collaborative Workbench to Accelerate Science Algorithm Development

Issues related to Earth science collaboration are twofold. First, data, algorithms, and analysis results are scattered at different locations, usually behind complex security and firewall. Secondly, handful scientific collaboration platforms that offer sharing require scientists to learn new tools. There is high cost and big learning curve associated with these tools. To address these two issues, Earth science Collaborative Workbench (CWB) is being developed. CWB leverages Eclipse Rich Client Platform (RCP) to provide an extensible framework that can be customized easily via Eclipse plugins. Earth science analysis tools such as IDL and Python have been available as Eclipse IDE (Integrated Development Environment), which are essentially Eclipse plugins with views. CWB enhances scientist's existing research environment by extending the tools that they are already familiar with, via plugins. These plugins allow CWB to provide collaboration capabilities directly but transparently within scientist's existing research tools using cloud. Cloud integration from CWB is seamless. CWB interfaces to various cloud services, mainly cloud storage and compute resources. Additionally, CWB also interfaces with a cloud-based middleware that includes a catalog that tracks and mediates the collaboration. The catalog comprises of data management and user management information required for collaboration. Cloud-based collaboration via familiar tools means that many of the initial obstacles to entry for collaboration, such as expensive initial investment in training and infrastructure for sharing are eliminated. Instead, it allows for scientists to keep using the tools they are already used to; only with add on transparent features for cloud-based sharing. Furthermore, cloud based collaboration service supports the availability and consistency of the shared data, algorithms, and analysis results among scientists. Various levels of sharing resources are planned using the CWB. We envision that using CWB, scientists can be rolled into science algorithm development more easily and exposed to existing science algorithms and workflows. Accelerated science algorithm development will be possible due to the discovery and reuse of these algorithms and workflows within a collaborative environment. Initial implementation considers a case study that includes researchers collaborating towards improving spaceborne radar precipitation retrieval in Global Precipitation Measurement. The collaboration consists of a collaborative workflow that comprises of ice crystal modeling to generate scattering properties, precipitation modeling, and evaluation of the models.