1 = Monash University, Department of Econometrics and Business Statistics, Melbourne, Australia\ 2 = Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)\ 3 = Telethon Kids Institute, Perth Children's Hospital, Perth, Australia\ 4 = Berkeley Institute for Data Science, University of California, Berkeley, USA\
- = Both authors contributed equally to the work\
Correspondence to:\ Nicholas Tierney (nicholas.tierney@gmail.com) and Karthik Ram (karthik.ram@berkeley.edu)
Abstract
There are numerous arguments that strongly support practice of open science, with societal and individual benefits. For individual researchers, sharing research artifacts such as data can improve trust and transparency, reproducibility of one's own work, and catalyze new collaborations. Despite the general appreciation of the benefits of data sharing, research data are available only to the original investigators. For data that are shared, lack of useful metadata and documentation make them challenging to reuse. In this commentary we argue that incentives and infrastructure for making data useful are the biggest barrier to creating a culture of widespread data sharing. We compare data to code, and computational environments in the context of reproducible research and provide some practical guidance on how one can improve the chances of their data being reusable and partially bridge the incentive gap.