Conference Paper: “Advancing Computational Reproducibility in the Dataverse Data Repository Platform” (Preprint)

The paper linked to below by researchers at Harvard was recently shared on arXiv. It will be presented at P-RECS’20, June 23, 2020, Stockholm, Sweden.

Title

Advancing Computational Reproducibility in the Dataverse Data Repository Platform

Authors

Ana Trisovic
Harvard University

Philip Durbin
Harvard University

Tania Schlatter
Harvard University

Gustavo Durand
Harvard University

Sonia Barbosa
Harvard University

Danny Brooke
Harvard University

Mercè Crosas
Harvard University

Source

via arXiv

Abstract

Recent reproducibility case studies have raised concerns showing that much of the deposited research has not been reproducible. One of their conclusions was that the way data repositories store research data and code cannot fully facilitate reproducibility due to the absence of a runtime environment needed for the code execution. New specialized reproducibility tools provide cloud-based computational environments for code encapsulation, thus enabling research portability and reproducibility. However, they do not often enable research discoverability, standardized data citation, or long-term archival like data repositories do. This paper addresses the shortcomings of data repositories and reproducibility tools and how they could be overcome to improve the current lack of computational reproducibility in published and archived research outputs.

Direct to Full Text Paper
6 pages; PDF.

See Also: Publishing Computational Research – A Review Of Infrastructures For Reproducible And Transparent Scholarly Communication (January 2, 2020)

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