JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1153 10.6339/24-JDS1153 Computing in Data Science RCloud – Collaborative Visualization and Analysis Platform UrbanekSimonurbanek@R-project.org1 Department of Statistics, University of Auckland, Auckland, New Zealand 202512122024232389398 Supplementary Material

Source code repository and documentation: https://github.com/att/rcloud

Public instance and tutorials: https://rcloud.social

3172023692024 2025 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2025 Open access article under the CC BY license.

The last decade has seen a vast increase of the abundance of data, fuelling the need for data analytic tools that can keep up with the data size and complexity. This has changed the way we analyze data: moving from away from single data analysts working on their individual computers, to large clusters and distributed systems leveraged by dozens of data scientists. Technological advances have been addressing the scalability aspects, however, the resulting complexity necessitates that more people are involved in a data analysis than before. Collaboration and leveraging of other’s work becomes crucial in the modern, interconnected world of data science. In this article we propose and describe an open-source, web-based, collaborative visualization and data analysis platform RCloud. It de-couples the user from the location of the data analysis while preserving security, interactivity and visualization capabilities. Its collaborative features enable data scientists to explore, work together and share analyses in a seamless fashion. We describe the concepts and design decisions that enabled it to support large data science teams in the industry and academia.

 cloud computing collaboration data analysis data science distributed computing reproducible research visualization Current work and public instance is supported by the University of Auckland and the Centre for eResearch. References Chacon S, Straub B (2014). Pro Git. Apress. Garman J (2003). Kerberos: The Definitive Guide. O’Reilly Media. GitHub (2020). https://github.com/. Gupta M, George JF (2016). Toward the development of a big data analytics capability. Information & Management, 53(8): 1049–1064. Hilbert M, López P (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332: 60–65. Jupyter Development Team (2015). Messaging in Jupyter. Kluyver T, Ragan-Kelley B, Pérez F, Granger B, Bussonnier M, Frederic J, et al. (2016). Jupyter notebooks – a publishing format for reproducible computational workflows. In: Positioning and Power in Academic Publishing: Players, Agents and Agendas (F Loizides, B Schmidt, eds.), 87–90. IOS Press. Knuth DE (1984). Literate programming. The Computer Journal, 27: 97–111. Miller MS (2006). Robust composition: Towards a unified approach to access control and concurrency control, Ph.D. thesis, Johns Hopkins University, Baltimore, Maryland, USA. North S, Scheidegger C, Urbanek S, Woodhull G (2015). Collaborative visual analysis with RCloud. In: 2015 IEEE Conference on Visual Analytics Science and Technology (VAST), 25–32. Odersky M, Spoon L, Venners B (2008). Programming in Scala. Artima. Pérez F, Granger BE (2007). IPython: a system for interactive scientific computing. Computing in Science & Engineering, 9(3): 21–29. R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Redis (2020). https://redis.io/. RStudio Team (2020). RStudio: Integrated Development Environment for R. RStudio, PBC., Boston, MA. Sandve GK, Nekrutenko A, Taylor J, Hovig E (2013). Ten simple rules for reproducible computational research. PLoS Computational Biology, 9(10): e1003285. Stagg GW Henry L, (2024). webr: The statistical language R compiled to WebAssembly via Emscripten. The Apache Software Foundation (2020a). Apache Lucene. https://lucene.apache.org/. The Apache Software Foundation (2020b). Apache Solr. https://solr.apache.org/. Tuloup J, Tandon M, Renou M, Beier T (2021). Jupyterlite: Wasm powered Jupyter running in the browser. Urbanek S (2003). Rserve – a fast way to provide R functionality to applications. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). Vaidyanathan R, Xie Y, Allaire J, Cheng J, Sievert C, Russell K (2021). htmlwidgets: HTML Widgets for R. R package version 1.5.4. Van Rossum G, Drake FL (2009). Python 3 Reference Manual. CreateSpace, Scotts Valley, CA.