Exploring Levels of Reproducibility with Whole Tale

Project Description

Whole Tale is a platform to simplify computational reproducibility[^SBBL13], i.e., a researcher A can easily create a tale that bundles data, code, and one or more compute environments into an executable research object that another researcher B can simply re-execute in the cloud to check and validate the results provided by A. In addition to re-executing someone else’s research tale, the Whole Tale platform also makes it easier to conduct replication studies, i.e., in which several elements of the original experiment by A can be varied to test how robust the original methods are under change of, e.g., data, parameter settings, implementation choices, or principal methods used[^Barb18].

In this summer internship project we will (1) pick one or more published studies whose computational elements are readily available for re-execution; (2) reproduce those original study results as tales, and (3) vary one or more other elements (e.g., applying parameter sweeps; migrating code from R to Python; or changing from one machine learning method to another.)

The main goals of the project are (i) to explore and evaluate the utility of Whole Tale as a platform for replication studies, and (ii) to better understand the different levels of computational and scientific reproducibility that occur in practice.

Necessary Prerequisites:

Desirable Skills / Qualifications:

Expected Outcomes:

Primary Mentors: Bertram Ludäscher and Victoria Stodden, University of Illinois at Urbana-Champaign

Secondary Mentor(s): Timothy McPhillips


[^SBBL13]: Setting the Default to Reproducible: Reproducibility in Computational and Experimental Mathematics. In: Stodden, V. ; Bailey, D. H. ; Borwein, J. ; LeVeque, R. ; Rider, B. ; Stein, W. (eds.) ICERM Workshop on Reproducibility in Computational and Experimental Mathematics, 2013. (download)

[^Barb18]: Barba, Lorena A.: Terminologies for Reproducible Research. arXiv:1802.03311 (2018).

[^RBDF16]: Rauber, Andreas et al. PRIMAD: Information gained by different types of reproducibility. Reproducibility of Data-Oriented Experiments in e-Science (Dagstuhl Seminar 16041). Leibniz-Zentrum für Informatik, Schloss Dagstuhl, Germany, 2016. (download)