Notebook‐as‐a‐VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment. (17th May 2022)

• Record Type: Journal Article
• Authors: Zhao, Zhiming; Koulouzis, Spiros; Bianchi, Riccardo; Farshidi, Siamak; Shi, Zeshun; Xin, Ruyue; Wang, Yuandou; Li, Na; Shi, Yifang; Timmermans, Joris; Kissling, W. Daniel
• Is Part Of: Software, practice & experience. Volume 52:Number 9(2022)
• Page Start: 1947
• Page End: 1966
• Publication Date: 2022-05-17
• Subjects: cloud -- Jupyter -- virtual research environment; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3098
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: Virtual research environments (VREs) provide user‐centric support in the lifecycle of research activities, for example, discovering and accessing research assets or composing and executing application workflows. A typical VRE is often implemented as an integrated environment, including a catalog of research assets, a workflow management system, a data management framework, and tools for enabling user collaboration. In contrast, notebook environments like Jupyter allow researchers to rapidly prototype scientific code and share their experiments as online accessible notebooks. Jupyter can support several popular languages used by data scientists, such as Python, R, and Julia. However, such notebook environments do not have seamless support for running heavy computations on remote infrastructure or finding and accessing collaborative software code inside notebooks. This article investigates the gap between a notebook environment and a VRE and proposes an embedded VRE solution for the Jupyter environment called Notebook‐as‐a‐VRE (NaaVRE). The NaaVRE solution provides functional components via a component marketplace and allows users to create a customized VRE on top of the Jupyter environment. From the VRE, a user can search research assets (data, software, and algorithms), compose workflows, manage the lifecycle of an experiment, and share the results among users in the community. We demonstrate how such a solution can enhance a legacy workflow that uses Light Detection and Ranging (LiDAR) data from country‐wide airborne laser scanning surveys for deriving geospatial data products of ecosystem structure at high resolution over broad spatial extents. This enables users to scale out the processing of multi‐terabyte LiDAR point clouds for ecological applications to more data sources in a distributed cloud environment. Similar applications could be developed for workflows producing other essential biodiversity variables.