This is Witold and today we are building a bear.
In our recent paper with Erik van Zwet and Andrew, A statistical case for qualified scientific optimism, we fit metascientific models that use hundreds of thousands of z-values from empirical research. We will be blogging about this paper in the coming months, but the data that we needed for this paper seemed so interesting, that they spun off into a whole separate resource, Benchmarks of Empirical Accuracy in Research. I think readers interested in metascience will find it useful.
BEAR is an open-source compilation of 26 (and counting!) documented metascience datasets. Sometimes it just re-uses data from publications, like Kevin Lang’s work on false positives in economics—and dozen more. (I can’t stress this enough, huge thanks to the legion of researchers who created/maintain these datasets.) For some others I do much more myself, like processing outcomes from tens of thousands trials from clinicaltrials.gov.
And you can access all of that, 12.5 mln results in total, with a few clicks. Just go to https://witold.xyz/BEAR/. BEAR has bespoke datasets from medicine, neuroscience, psychology, ecology, education, economics, political science and more—all standardised and ready to use. Some sets have only z-values, but some have much richer structure: study types, subcategories, effect sizes, type of measures, meta-analytic groupings, etc. Of course quality varies, but I hope the documentation/standardisation will help people pick the right dataset for their research question.
Why does this exist? As I say on the website:
Quantitative metascience drives important debates about research standards. Most of its crucial contributions have been based on analysing individual datasets, often painstakingly constructed by researchers. Thanks to their work we now have better understanding of replicability, publication bias, p-hacking, reporting practices, pre-registration, significance rates, and more. But many canonical findings of metascience are based on individual datasets, each constructed in a specific way. How generalisable are these findings?
In other words, metascience itself can suffer from one of the crucial metascientific concerns: external validity. As someone who works on evidence synthesis I am very concerned with this. To give one example, Erik blogged about the virality of the scary publication bias histogram in biomedical journals. But different metascientific sets give completely different pictures of selection:
I am NOT particularly interested in selection in this post. We could talk about any other metascientific property, e.g. p-hacking, low power. I am also not trying to explain what drives these differences or say that these sets are comparable. (We get into that a little bit in the paper and if people are interested in that particular question, I can write a follow-up post.) The point is that we can learn much, much more when we go beyond one data point. OK, that’s pretty trivial thing to say, but Erik’s example clearly shows that it’s one that hasn’t fully sunk in. So I just hope that BEAR will make it easy to compare metascientific corpora and give people a better picture of the limits of generalisability of various claims. Or generally make metascientific research easier.
For completeness, the GitHub repo also includes the model that Erik, Andrew and I use in the paper I linked earlier. You can use it yourself to e.g. estimate (idealised) expected replication rates, but the model is optional and separate from BEAR data. Big kudos to Erik who got this process started and hunted down many of these sources for the paper. But most importantly the credit goes to teams of researchers who generated and maintain the linked sets.
I also have no doubt that in putting this together I made many mistakes. Even with automated checks, it’s not easy to work with so many data sources in parallel, so I’d be grateful for any corrections!