What constitutes progress in empirical social science research? It is easy to talk about “new knowledge,” but hard to measure it. To evaluate what constitutes new knowledge requires measuring not only what scholars think of an experiment after it is performed, but also what they forecast about the results in advance. This grant supports an initiative by a team led by Stefano DellaVigna at the University of California, Berkeley to build, test, and study an online platform for collecting and cataloguing forecasts and prior beliefs about the results of social science experiments. DellaVigna and his team will write up summaries of intended experiments that forecasters can quickly read and absorb, and will find interesting. Those experts would, in turn, use the developed platform to enter their best estimates about how the experiment will turn out, including their predictions of the effect sizes. These answers can then be compared to the actual results of the experiments. The platform DellaVigna and his collaborators will develop has the potential to allow researchers to capture forecasts, control publication bias, improve the transparency and reproducibility of research designs, and advance the use of Bayesian methods more generally by helping quantify prior probabilities.