There are few things more aggravating to a parent than siblings fighting over division of time to play with a toy, or portion of dessert, or whatnot. “But it’s so unfair!!” is a familiar refrain. In this case, it’s not about getting exactly the same, but what each person wants.
The same might be said about the observations and models that go into our research. We work hard to secure the funding, make those observations, set up those models, and publish results. Now we must provide the data too? It’s so unfair that another person just gets this without doing all that!
And even if we don’t feel strongly about that concern, certainly we all grumble about time spent on writing data management plans, organizing data files, figuring out metadata formats and repository instructions, and assembling URLs and DOIs for data acknowledgement statements. And while nothing is life is truly fair, we do have an obligation to seek to justice for fairness where it can be had. A recent publication in JGR: Biogeosciences [Dai et al., 2018] and companion commentary [Bond-Lamberty, 2018] show just what can be reaped if we do.
Fluxnet is a global network of eddy covariance (EC) flux measurements on carbon and water exchanges with the land surface. Dai et al.  conducted a meta-analysis of EC publications, authors, citation metrics, and accessibility of EC data.
They demonstrate that the more likely one shared EC data, the more likely one was a more influential researcher based on citation counts and co-authorship. While there are limitations to identifying causation in the study, it does demonstrate that data sharing has positive benefits both to the scientific community and the one doing the sharing.
Bond-Lamberty  further goes on to note additional challenges in data sharing, including non-systematic policies among journals, and the trickier case of sharing code and algorithms to replicate published findings based on those data. Additionally, in the case of EC, data access is tied the policies of the regional repositories that hold these EC data, which may be out of the authors’ control.
The endgame is science that is reproducible and extensible. It is an expectation of those that fund the science (often, the public) and increasingly a principle of journal publishers such as AGU. The challenge is making the process straightforward, consistent, and fair to both data providers and users.
Building on the earlier development of the FAIR (Findable, Accessible, Interoperable, and Reusable) principles for scientific data management and stewardship, AGU is spear-heading The Enabling FAIR data project, aiming to make data FAIR across the Earth and space science community. The goal is to bring FAIR to the large scale, developing common standards for publication submission systems, holding surveys and webinars of various initiatives, enhancing methods to direct data to repositories, and finding agreement on metadata standards among journals.
The question is, is all this data sharing worth it? Many scientists are skeptical, but also recognize that this is the future of reproducible science [Nosek et al., 2015]. At least in this one case, the exercise demonstrated that the benefits of sharing data do outweigh the costs for both the providers and the users. And from that perspective, everyone gets what they need, which is just and FAIR.
—Ankur Rashmikant Desai, Editor, Journal of Geophysical Research: Biogeosciences, and Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison; email: email@example.com
Desai, A. R. (2018), It’s so unFAIR!, Eos, 99, https://doi.org/10.1029/2018EO097389. Published on 02 May 2018.
Text © 2018. The authors. CC BY-NC-ND 3.0
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