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Toward Enabling Reproducibility for Data-Intensive Research using the Whole Tale Platform

Authors :
Chard, Kyle
Gaffney, Niall
Hategan, Mihael
Kowalik, Kacper
Ludaescher, Bertram
McPhillips, Timothy
Nabrzyski, Jarek
Stodden, Victoria
Taylor, Ian
Thelen, Thomas
Turk, Matthew J.
Willis, Craig
Source :
Advances in Parallel Computing 2020
Publication Year :
2020

Abstract

Whole Tale http://wholetale.org is a web-based, open-source platform for reproducible research supporting the creation, sharing, execution, and verification of "Tales" for the scientific research community. Tales are executable research objects that capture the code, data, and environment along with narrative and workflow information needed to re-create computational results from scientific studies. Creating reproducible research objects that enable reproducibility, transparency, and re-execution for computational experiments requiring significant compute resources or utilizing massive data is an especially challenging open problem. We describe opportunities, challenges, and solutions to facilitating reproducibility for data- and compute-intensive research, that we call "Tales at Scale," using the Whole Tale computing platform. We highlight challenges and solutions in frontend responsiveness needs, gaps in current middleware design and implementation, network restrictions, containerization, and data access. Finally, we discuss challenges in packaging computational experiment implementations for portable data-intensive Tales and outline future work.

Details

Database :
arXiv
Journal :
Advances in Parallel Computing 2020
Publication Type :
Report
Accession number :
edsarx.2005.06087
Document Type :
Working Paper
Full Text :
https://doi.org/10.3233/APC200107