Computing Environments for Reproducibility: Capturing the "Whole Tale"
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Abstract (arXiv)
The act of sharing scientific knowledge is rapidly evolving away from traditional articles and presentations to the delivery of executable objects that integrate the data and computational details (e.g., scripts and workflows) upon which the findings rely. This envisioned coupling of data and process is essential to advancing science but faces technical and institutional barriers. The Whole Tale project aims to address these barriers by connecting computational, data-intensive research efforts with the larger research process--transforming the knowledge discovery and dissemination process into one where data products are united with research articles to create "living publications" or "tales". The Whole Tale focuses on the full spectrum of science, empowering users in the long tail of science, and power users with demands for access to big data and compute resources. We report here on the design, architecture, and implementation of the Whole Tale environment.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 2.
6 papers.
- 2026The Last Human-Written Paper: Agent-Native Research Artifactscited by 16
- 2026Agora: Git as Shared Memory for Collective AutoResearchcited by 3
- 2024An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Servicescited by 2
- 2021A Guide to Computational Reproducibility in Signal Processing and Machine Learningcited by 1
- 2022Sharing and Caring: Creating a Culture of Constructive Criticism in Computational Legal Studiescited by 1
- 2024Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Modelscited by 1
- 2026Agora: Git as Shared Memory for Collective AutoResearch16 Sep 2026
- 2026The Last Human-Written Paper: Agent-Native Research Artifacts27 Apr 2026
- 2024An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services5 Nov 2024
- 2024Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Models27 Jun 2024
- 2022Sharing and Caring: Creating a Culture of Constructive Criticism in Computational Legal Studies19 Apr 2022
- 2021A Guide to Computational Reproducibility in Signal Processing and Machine Learning27 Aug 2021