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In economics, this same concept is known as the J Curve (https://www.wikiwand.com/en/J_curve)

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The normal distribution in r

The continuity between these new modular features and the rest of the DAW’s workflow probably has unexpected consequences. Before getting information about BWS3, one might have thought that the “Native Modular System” promised since the first version might still be an add-on. What the marketing copy around this “killer feature” makes clear, it’s the result of a very deliberate process from the start and it’ll make for a qualitatively different workflow.

I wrote this sentence. Two coauthors, three peer reviewers, and an editor all read it multiple times. No one ever asked for it to be changed.

I think this paper and these data could be extremely useful for psychologists, but I also think this paper needs at least one more analysis: estimating effect sizes by research area, controlling for publication bias.

It's very hard to interpret these estimates given good evidence and arguments that researchers and journals select for p < .05. I think it's safe to assume that all these estimates reported in this preprint are bigger than the true averages (Simonsohn, Nelson, & Simmons, 2014).

One approach to estimating "selection bias adjusted" effects would be to estimate the effect size for each research area using the code provided in the p-curve effect size paper supplements (http://www.p-curve.com/Supplement/). You could estimate confidence intervals or percentiles using bootstrapping procedures or write code to estimate the lower and upper bounds using the same methods to estimate the average effect.

This approach assumes the p-values all test the hypothesis of interest and don't suffer from unique selection biases (see Selecting p Values in Simonsohn, Nelson, & Simmons, 2014, p. 540).

Hope this helps make the paper better!