Reproducibility is a foundational principle in scientific research. Yet in computational hydrology, the code and data that actually produces published results is not regularly made available, inhibiting the ability of the community to reproduce and verify previous findings. In order to overcome this problem we recommend that re-useable code and formal workflows, which unambiguously reproduce published scientific results, are made available for the community alongside data, so that we can verify previous findings, and build directly from previous work. In cases where reproducing large-scale hydrologic studies is computationally very expensive and time-consuming, new processes are required to ensure scientific rigour. Such changes will strongly improve the transparency of hydrological research, and thus provide a more credible foundation for scientific advancement and policy support.
Reproducible Science Promoting Open Science
In a recent Opinion article, Parker et al.  highlight a range of important issues and provide tangible solutions to improve transparency in ecology and evolution (E&E). We agree wholeheartedly with their points and encourage the E&E community to heed their advice. However, a key issue remains conspicuously unaddressed: Parker et al. assume that ‘deliberate dishonesty’ is rare in E&E, yet evidence suggests that occurrences of scientific misconduct (i.e., data fabrication, falsification, and/or plagiarism) are disturbingly common in the life sciences .
Early in my Ph.D. studies, my supervisor assigned me the task of running computer code written by a previous student who was graduated and gone. It was hell. I had to sort through many different versions of the code, saved in folders with a mysterious numbering scheme. There was no documentation and scarcely an explanatory comment in the code itself. It took me at least a year to run the code reliably, and more to get results that reproduced those in my predecessor's thesis. Now that I run my own lab, I make sure that my students don't have to go through that.
A scientific result is not truly established until it is independently confirmed. This is one of the tenets of experimental science. Yet, we have seen a rash of recent headlines about experimental results that could not be reproduced. In the biomedical field, efforts to reproduce results of academic research by drug companies have had less than a 50% success rate,a resulting in billions of dollars in wasted effort. In most cases the cause is not intentional fraud, but rather sloppy research protocols and faulty statistical analysis. Nevertheless, this has led to both a loss in public confidence in the scientific enterprise and some serious soul searching within certain fields. Publishers have begun to take the lead in insisting on more careful reporting and review, as well as facilitating government open science initiatives mandating sharing of research data and code. To support efforts of this type, the ACM Publications Board recently approved a new policy on Result and Artifact Review and Badging. This policy defines two badges ACM will use to highlight papers that have undergone independent verification. Results Replicated is applied when the paper's main results have been replicated using artifacts provided by the author, or Results Reproduced if done completely independently.
A comic illustrating the complexities and history of research reproducibility.
The scientific community is increasingly concerned with cases of published "discoveries" that are not replicated in further studies. The field of mouse phenotyping was one of the first to raise this concern, and to relate it to other complicated methodological issues: the complex interaction between genotype and environment; the definitions of behavioral constructs; and the use of the mouse as a model animal for human health and disease mechanisms. In January 2015, researchers from various disciplines including genetics, behavior genetics, neuroscience, ethology, statistics and bioinformatics gathered in Tel Aviv University to discuss these issues. The general consent presented here was that the issue is prevalent and of concern, and should be addressed at the statistical, methodological and policy levels, but is not so severe as to call into question the validity and the usefulness of the field as a whole. Well-organized community efforts, coupled with improved data and metadata sharing were agreed by all to have a key role to play in view of identifying specific problems, as well as promoting effective solutions. As replicability is related to validity and may also affect generalizability and translation of findings, the implications of the present discussion reach far beyond the issue of replicability of mouse phenotypes but may be highly relevant throughout biomedical research.
When graduate student Alyssa Ward took a science-policy internship, she expected to learn about policy — not to unearth gaps in her biomedical training. She was compiling a bibliography about the reproducibility of experiments, and one of the papers, a meta-analysis, found that scientists routinely fail to explain how they choose the number of samples to use in a study. "My surprise was not about the omission — it was because I had no clue how, or when, to calculate sample size," Ward says. Nor had she ever been taught about major categories of experimental design, or the limitations of P values. (Although they can help to judge the strength of scientific evidence, P values do not — as many think — estimate the likelihood that a hypothesis is true.)
The solution to science's replication crisis is a new ecosystem in which scientists sell what they learn from their research. In each pairwise transaction, the information seller makes (loses) money if he turns out to be correct (incorrect). Responsibility for the determination of correctness is delegated, with appropriate incentives, to the information purchaser. Each transaction is brokered by a central exchange, which holds money from the anonymous information buyer and anonymous information seller in escrow, and which enforces a set of incentives facilitating the transfer of useful, bluntly honest information from the seller to the buyer. This new ecosystem, capitalist science, directly addresses socialist science's replication crisis by explicitly rewarding accuracy and penalizing inaccuracy.
Wireless networks are the key enabling technology of the mobile revolution. However, experimental mobile and wireless research is still hindered by the lack of a solid framework to adequately evaluate the performance of a wide variety of techniques and protocols proposed by the community. In this talk, I will motivate the need for experimental reproducibility as a necessary aspect for healthy progress as accepted by other communities. I will illustrate how other research communities went through similar processes. I will then present the unique challenges of mobile and wireless experimentation, and discuss approaches, past, current, and future to address these challenges. Finally, I will discuss how reproducibility extends to mobile and wireless security research.
A number of proactive steps are underway to improve the rigor and reproducibility of the data reported in the Journal of Neurophysiology. The American Physiological Society's Publications Committee is currently devising implementation plans for the following recommendations from editors of the Society's journals.