Hello Darren, Thanks for reaching out and for the question. There are a few different reasons for individuals to take this step. I think the clearest benefit is greater impact and citations- openly available data is associated with an increased citation rate. Estimates varied between about 9 and 50 percentage points greater citation rates for articles with open data compared to those that do not (see Open Data under citation advantages here: https://osf.io/kgnva/wiki/Open%20Science%20Literature/). The other reason is another benefit that individuals accrue, but is mostly seen in anecdotal experiences so far: The act of preparing a dataset for curation and sharing takes a bit of extra time for cleaning, explaining variable names, and checking for errors. This makes the data more available for the sharer to reuse themselves in future years as they work to build upon work, the details of which are (often!) forgotten or held by individuals that are not longer in the lab. I hope that helps! Best, David

Hi, This is David Mellor from the Center for Open Science (we develop the OSF). I wanted to chime in with a few points. On the point of null hypothesis significance testing, preregistration of statistical tests is a key step to maintaining the integrity of your p-values. When the statistical test is specified ahead of time before seeing the data, the utility of the test is assured. Without preregistration, our biases lead us to alter our tests as we see the data and are led to the rewarding p-vaules. With preregistration, the test specified ahead of time is clear, and anything else you do is welcome and encouraged as part of the exploratory data analysis, whose purpose is to generate new hypotheses for a new set of data. This process is well established in the clinical sciences, but most basic research does not use it- we're trying to change that by offering $1000 prizes for publishing the results of preregistered research. Give it a try, you could get a prize! (https://cos.io/prereg). If the journal described above takes off, please consider adopting guidelines designed to guide editors, reviewers, and authors toward more transparent and reproducible research. The TOP Guidelines (https://cos.io/top) were developed to meet this purpose. I'm happy to answer any questions you about these or other steps you may be interested in pursuing. (david [at] cos [dot] io)