The editors of Behavioral Neuroscience have been discussing several recent developments in the landscape of scientific publishing. The discussion was prompted, in part, by reported issues of reproducibility and concerns about the integrity of the scientific literature. Although enhanced rigor and transparency in science are certainly important, a related issue is that increased competition and focus on novel findings has impeded the extent to which the scientific process is cumulative. We have decided to join the growing number of journals that are adopting new reviewing and publishing practices to address these problems. In addition to our standard research articles, we are pleased to announce 3 new categories of articles: replications, registered reports, and null results. In joining other journals in psychology and related fields to offer these publication types, we hope to promote higher standards of methodological rigor in our science. This will ensure that our discoveries are based on sound evidence and that they provide a durable foundation for future progress. (PsycINFO Database Record)

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests

In recent years, the psychological and behavioral sciences have increased efforts to strengthen methodological practices and publication standards, with the ultimate goal of enhancing the value and reproducibility of published reports. These issues are especially important in the multidisciplinary field of psychophysiology, which yields rich and complex data sets with a large number of observations. In addition, the technological tools and analysis methods available in the field of psychophysiology are continually evolving, widening the array of techniques and approaches available to researchers. This special issue presents articles detailing rigorous and systematic evaluations of tasks, measures, materials, analysis approaches, and statistical practices in a variety of subdisciplines of psychophysiology. These articles highlight challenges in conducting and interpreting psychophysiological research and provide data-driven, evidence-based recommendations for overcoming those challenges to produce robust, reproducible results in the field of psychophysiology.

Reproducible research is a concept that has emerged in data and computationally intensive sciences in which the code used to conduct all analyses, including generation of publication quality figures, is directly available, and preferably in open source manner. This perspective outlines the processes and attributes, and illustrates the execution of reproducible research via a simple exposure assessment of air pollutants in metropolitan Philadelphia.

The way science journals present research must be rehabilitated or risk becoming obsolete, causing foreseeable negative consequences to research funding and pro-ductivity. Researchers are dealing with ever- increasing complexities, and as techniques and solutions become more involved, so too does the task of describing them. Unfortunately, simply explaining a technique with text does not always paint a clear enough picture. Scientific publishing has followed essentially the same model since the original scientific journal was published in the mid-seventeenth century. Thanks to advances in technology, we have seen some minor improvements such as the addition of color printing and better dissemination and search functionality through online cataloging. But what has actually changed? In truth, not all that much. Articles are still published as text heavy-tomes with the occasional pho-tograph or chart to demonstrate a point.

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.