Understanding Marine Scientist Software Tool Use
Matthew Lakier, Andrew Irwin, Daniel Vogel
Abstract
Marine science researchers are heavy users of software tools and systems such as statistics packages, visualization tools, and online data catalogues. Following a constructivist grounded theory approach, we conduct a semi-structured interview study of 23 marine science researchers and research supports within a North American university, to understand their perceptions of and approaches towards using both graphical and code-based software tools and systems. We propose the concept of fragmentation to represent how various factors lead to isolated pockets of views and practices concerning software tool use during the research process. These factors include informal learning of tools, preferences towards doing things from scratch, and a push towards more code-based tools. Based on our fndings, we suggest design priorities for user interfaces that could more efectively help support marine scientists make and use software tools and systems.
• Human-centered computing → Empirical studies in collaborative and social computing.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 72230c59-0322-4a77-a847-3f1f1cc5cb63Builds on9
- How do Data Science Workers Collaborate? Roles, Workflows, and ToolsAmy X. Zhang, Michael J. Muller, Dakuo WangCSCW 2020 · 260 citations
- Data Integration as Coordination: The Articulation of Data Work in an Ocean Science CollaborationAndrew B. Neang, Will Sutherland, Michael W. Beach, Charlotte P. LeeCSCW 2020 · 74 citations
- Passing the Data Baton : A Retrospective Analysis on Data Science Work and WorkersAnamaria Crisan, Brittany Fiore-Gartland, Melanie ToryIEEE VIS 2020 · 65 citations
- Towards Creative Version ControlSarah Sterman, Molly Jane Nicholas, Eric PaulosCSCW 2022 · 34 citations
- How Domain Experts Work with Data: Situating Data Science in the Practices and Settings of CraftworkJu-Yeon Jung, Tom Steinberger, John L. King, Mark S. AckermanCSCW 2022 · 24 citations
Related papers
- How Scientists Use Large Language Models to ProgramGabrielle O'BrienCHI 2025 · 12 citations
- A Theory of Scientific Programming EfficacyElizaveta Pertseva, Melinda Chang, Ulia Zaman, Michael CoblenzICSE 2024 · 4 citations
- Starting From Scratch Again and Again: Tracing the Origins of High Schoolers' Negative Perceptions of Block-Based ProgrammingCaryn Tran, Kristin Fasiang, Max Kanwal, Eleanor O'RourkeCHI 2026 · 1 citation
- Beyond the Artifact: Power as a Lens for Creativity Support ToolsJingyi Li, Eric Rawn, Jacob Ritchie, Jasper Tran O'Leary et al.UIST 2023 · 79 citations
- A Need-Finding Study with Users of Geospatial DataParker Ziegler, Sarah E. ChasinsCHI 2023 · 17 citations
