A guide for social science journal editors on easing into open science.

Journal editors have a large amount of power to advance open science in their respective fields by incentivising and mandating open policies and practices at their journals. The Data PASS Journal Editors Discussion Interface (JEDI, an online community for social science journal editors: www.dpjedi.org ) has collated several resources on embedding open science in journal editing ( www.dpjedi.org/resources ). However, it can be overwhelming as an editor new to open science practices to know where to start. For this reason, we created a guide for journal editors on how to get started with open science. The guide outlines steps that editors can take to implement open policies and practices within their journal, and goes through the what, why, how, and worries of each policy and practice. This manuscript introduces and summarizes the guide (full guide: https://doi.org/10.31219/osf.io/hstcx ).

What do participants think of our research practices? An examination of behavioural psychology participants' preferences.

What research practices should be considered acceptable? Historically, scientists have set the standards for what constitutes acceptable research practices. However, there is value in considering non-scientists' perspectives, including research participants'. 1873 participants from MTurk and university subject pools were surveyed after their participation in one of eight minimal-risk studies. We asked participants how they would feel if (mostly) common research practices were applied to their data: p-hacking/cherry-picking results, selective reporting of studies, Hypothesizing After Results are Known (HARKing), committing fraud, conducting direct replications, sharing data, sharing methods, and open access publishing. An overwhelming majority of psychology research participants think questionable research practices (e.g. p-hacking, HARKing) are unacceptable (68.3-81.3%), and were supportive of practices to increase transparency and replicability (71.4-80.1%). A surprising number of participants expressed positive or neutral views toward scientific fraud (18.7%), raising concerns about data quality. We grapple with this concern and interpret our results in light of the limitations of our study. Despite the ambiguity in our results, we argue that there is evidence (from our study and others') that researchers may be violating participants' expectations and should be transparent with participants about how their data will be used.

The crisis from above: Gatekeepers need better standards.

Improvements to the validity of psychological science depend upon more than the actions of individual researchers. Editors, journals, and publishers wield considerable power in shaping the incentives that have ushered in the generalizability crisis. These gatekeepers must raise their standards to ensure authors' claims are supported by evidence. Unless gatekeepers change, changes made by individual scientists will not be sustainable.

Initial evidence of research quality of registered reports compared with the standard publishing model.

In registered reports (RRs), initial peer review and in-principle acceptance occur before knowing the research outcomes. This combats publication bias and distinguishes planned from unplanned research. How RRs could improve the credibility of research findings is straightforward, but there is little empirical evidence. Also, there could be unintended costs such as reducing novelty. Here, 353 researchers peer reviewed a pair of papers from 29 published RRs from psychology and neuroscience and 57 non-RR comparison papers. RRs numerically outperformed comparison papers on all 19 criteria (mean difference 0.46, scale range -4 to +4) with effects ranging from RRs being statistically indistinguishable from comparison papers in novelty (0.13, 95% credible interval [-0.24, 0.49]) and creativity (0.22, [-0.14, 0.58]) to sizeable improvements in rigour of methodology (0.99, [0.62, 1.35]) and analysis (0.97, [0.60, 1.34]) and overall paper quality (0.66, [0.30, 1.02]). RRs could improve research quality while reducing publication bias and ultimately improve the credibility of the published literature.