RECOGNITION FOR REPRODUCIBILITY IN 10-YEAR ANNIVERSARY OF PLOS

April 6, 2017

An article co-authored by Yolanda Gil, Research Professor in Computer Science, and Daniel Garijo, a post-doctoral researcher in her group at the Information Sciences Institute, has been selected to represent Computational Biology in the PLOS ONE 10-Year Anniversary Datasets Collection that highlights articles with underlying datasets that have proven to be important or widely used or are particularly well reported.

PLOS, the Public Library of Science, is a non-profit company that has championed peer-reviewed open access scientific publishing since its inception in 2003. PLOS ONE is its flagship journal with articles covering all areas of science and medicine. PLOS ONE has published over 170,000 articles to date, with more than 11,000 of those articles in Computational Biology. A total of 20 articles covering 16 areas of science were selected for inclusion in the PLOS ONE 10-Year Anniversary Datasets Collection.

The selection was done by members of the PLOS ONE Editorial Staff, PLOS ONE Editorial Board, and PLOS-wide data advisory board. Details on how the articles were chosen are described in a blog post.

The article by Gil and Garijo is titled "Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome", and resulted from a collaboration with Dr. Phil E. Bourne and his group at the University of California San Diego. Published in 2013, it reproduces an earlier publication by Bourne and colleagues, proposes an approach to quantify the effort involved in reproducing prior work, and puts forward recommendations for reproducible publications. All the datasets, software, and workflows used to do the computations were made available by the authors in an accompanying web site, and were published in public repositories with persistent identifiers and open licenses.

Gil and Garijo promote this approach to scientific publications through the Scientific Papers of the Future initiative, giving training sessions on best practices in reproducible research, open science, and digital scholarship.