Many scientific workflows are data-driven and need to be continuously executed for the large volume of datasets transferred from distributed data sources. The overhead arising from data transfers must be considered when optimizing workflow performance. Many workflow systems support various data transfer protocols (DTPs) and file systems. However, challenges that hinder wide protocol adoption are mainly the need for more feasibility of adapting new solutions, such as decentralized ones. In this paper, we prototype a container-native data layer that supports multiple DTPs, e.g., FTP, WebDAV, and IPFS, for Cloud workflows. Based on this tool, we demonstrated the feasibility of using combinations of Docker, CWL, and Argo to deploy and execute several application scenarios adaptably. Besides, we analyzed the performance of data transfers and workflow execution time between IPFS and WebDAV, which can help users decide which one to handle data. Our results show that IPFS outperforms WebDAV in uploading large files, and the makespan via IPFS executed in Argo is comparable with WebDAV.

Computational notebook environments such as the Jupyter play an increasingly important role in data-centric research for prototyping computational experiments, documenting code implementations, and sharing scientific results. Effectively discovering and reusing notebooks available on the web can reduce repetitive work and facilitate scientific innovations. However, general-purpose web search engines (e.g., Google Search) do not explicitly index the contents of notebooks, and notebook repositories (e.g., Kaggle and GitHub) require users to create domain-specific queries based on the metadata in the notebook catalogs, which fail to capture the working contexts in the notebook environment. This poster presents a Context-aware Notebook Search Framework (CANSF) to enable a researcher to seamlessly discover external notebooks based on semantic contexts of the literate programming activities in the Jupyter environment.

The cover image is based on the Research Article Notebook-as-a-VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment by Zhiming Zhao et al., https://doi.org/10.1002/spe.3098.

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.

We present ACE, the Art, Color and Emotion browser. ACE is a data driven web based platform for exploring the visual sentiment and emotion in artistic paintings over time. To that end, we train our own visual artistic sentiment extraction model by leveraging the artworks from the OmniArt dataset. With our model we are able to estimate the overall sentiment dominating in groups of artworks belonging to a specific time interval. To make the results interactive and explorable we designed an intuitive interface with a carefully considered shape, color and element placement enforcing a top-down interaction scheme. Moreover, we perform extensive control on resource utilisation to provide the smoothest possible user experience and quality of service while using ACE.