Aletti, G., Crimaldi, I., & Saracco, F. (2020). A model for the Twitter sentiment curve. ArXiv:2011.05933 [Physics]. http://arxiv.org/abs/2011.05933

As Hall points out in Where Is My Flying Car? there is a strong argument to be made that the industrial revolution was a social chain reaction that had already set off an exponential improvement process on its way to 'singularity', this was disrupted in the 20th century however. But prior to that it had been ongoing for 300 years at a steady average rate of 7% more usable power annually.

Lalwani, P., Fansher, M., Lewis, R., Boduroglu, A., Shah, P., Adkins, T. J., … Jonides, J. (2020, November 8). Misunderstanding “Flattening the Curve”. https://doi.org/10.31234/osf.io/whe6q

There was a steep learning curve involved with using ECCO Pro. Reminds me of Roam, which also has a steep learning curve, but then it feels like it's worth it.

Karatayev, Vadim A., Madhur Anand, and Chris T. Bauch. ‘Local Lockdowns Outperform Global Lockdown on the Far Side of the COVID-19 Epidemic Curve’. Proceedings of the National Academy of Sciences 117, no. 39 (29 September 2020): 24575–80. https://doi.org/10.1073/pnas.2014385117.

COVID-19: Doing It Right. (2020, July 3). Open Mind. https://tamino.wordpress.com/2020/07/03/covid-19-doing-it-right/

Barro, R. J. (2020). Non-Pharmaceutical Interventions and Mortality in U.S. Cities during the Great Influenza Pandemic, 1918-1919 (Working Paper No. 27049; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27049

Bethune, Z. A., & Korinek, A. (2020). Covid-19 Infection Externalities: Trading Off Lives vs. Livelihoods (Working Paper No. 27009; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27009

Harris, J. E. (2020). The Coronavirus Epidemic Curve is Already Flattening in New York City (Working Paper No. 26917; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26917

Modelling the Distributional Impact of the COVID-19 Crisis. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 5, 2020, from https://covid-19.iza.org/publications/dp13235/

Lockdown Strategies, Mobility Patterns and COVID-19. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13293/

Salvador, C., Berg, M., Yu, Q., Alvaro, S., & Kitayama, S. (2020, April 13). Relational mobility predicts the increased speed of the spread of COVID-19: A 37 country study. https://doi.org/10.31234/osf.io/gwpj3

Larry Brilliant on How Well We Are Fighting Covid-19. (n.d.). Wired. Retrieved July 18, 2020, from https://www.wired.com/story/larry-brilliant-on-how-well-are-we-fighting-covid-19/

Block, P., Hoffman, M., Raabe, I. J., Dowd, J. B., Rahal, C., Kashyap, R., & Mills, M. C. (2020). Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nature Human Behaviour, 4(6), 588–596. https://doi.org/10.1038/s41562-020-0898-6

Zickfeld, J., Schubert, T. W., Herting, A. K., Grahe, J. E., & Faasse, K. (2020, April 16). Predictors of Health-Protective Behavior and Changes Over Time During the Outbreak of the COVID-19 Pandemic in Norway. https://doi.org/10.31234/osf.io/6vgf4

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

Roland, L. T., Gurrola, J. G., Loftus, P. A., Cheung, S. W., & Chang, J. L. (2020). Smell and taste symptom‐based predictive model for COVID‐19 diagnosis. International Forum of Allergy & Rhinology, alr.22602. https://doi.org/10.1002/alr.22602

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Coronavirus infections have peaked in much of the rich world. (n.d.). The Economist. Retrieved April 27, 2020, from https://www.economist.com/graphic-detail/2020/04/17/coronavirus-infections-have-peaked-in-much-of-the-rich-world?fsrc=scn/tw/te/bl/ed/dailychartcoronavirusinfectionshavepeakedinmuchoftherichworldgraphicdetail

Thunstrom, L., Newbold, S., Finnoff, D., Ashworth, M., & Shogren, J. F. (2020). The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19 (SSRN Scholarly Paper ID 3561934). Social Science Research Network. https://doi.org/10.2139/ssrn.3561934

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!