@conference {1534991,
	title = {Advancing Computational Reproducibility in the Dataverse Data Repository Platform},
	booktitle = {P-RECS {\textquoteright}20: Proceedings of the 3rd International Workshop on Practical Reproducible Evaluation of Computer Systems},
	year = {2020},
	pages = {15{\textendash}20},
	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.},
	url = {https://dl.acm.org/doi/10.1145/3391800.3398173},
	author = {Trisovic, Ana and Philip Durbin and Tania Schlatter and Gustavo Durand and Sonia Barbosa and Danny Brooke and Merc{\`e} Crosas}
}
