This dataset contains the data for the scientific article titled "Using symmetry to control viscoelastic waves in pillar arrays". Authors are Jason P. Beech, Oskar E. Ström, Enrico Turato and Jonas O. Tegenfeldt.

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Several rebel groups actively recruit children to serve among their ranks. While this constitutes one of the most egregious violations of children’s rights, it remains unclear what impact recruited children have on the fighting capacities of these armed groups. The existing research suggests that, on the one hand, armed groups drafting children might also be militarily effective, since it is cheaper to provide for children, they are more obedient and aggressive than adults, and easily manipulable. On the other hand, children may negatively affect rebel groups’ fighting capacities as they are less proficient combatants than adults and often difficult to control. We add to this debate by systematically analyzing the quantitative evidence on the impact of child soldiers on rebel groups’ fighting capacities. Based on the analysis of newly compiled data on child recruitment by rebel groups between 1989 and 2010, our analyses show that children may actually increase rebel groups’ fighting capacities. That said, rebels’ ability to procure arms and the access to resources seem to be more important determinants of fighting capacity. The authors discuss these findings in light of policy implications and avenues for future research.

This dataset is the data used in the CEQ Assessment in Sri Lanka (2009). This version can differ from others because the authors and the quality control team have made some changes to the original one.

The authors model trades-through, i.e. transactions that reach at least the second level of limit orders in an order book. Using tick-by-tick data on Euronext-traded stocks, they show that a simple bivariate Hawkes process fits nicely their empirical observations of trades-through. The authors show that the cross-influence of bid and ask trades-through is weak.

By encouraging and requiring that authors share their data in order to publish articles, scholarly journals have become an important actor in the movement to improve the openness of data and the reproducibility of research. But how many social science journals encourage or mandate that authors share the data supporting their research findings? How does the share of journal data policies vary by discipline? What influences these journals’ decisions to adopt such policies and instructions? And what do those policies and instructions look like? We discuss the results of our analysis of the instructions and policies of 291 highly-ranked journals publishing social science research, where we studied the contents of journal data policies and instructions across 14 variables, such as when and how authors are asked to share their data, and what role journal ranking and age play in the existence and quality of data policies and instructions. We also attempt to compare our results to the results of other studies that have analyzed the policies of social science journals, although differences in the journals chosen and how each study defines what constitutes a data policy limit this comparison. We conclude that a little more than half of the journals in our study have data policies. A greater share of the economics journals have data policies and mandate sharing, followed by political science/international relations and psychology journals. Finally, we use our findings to make several recommendations: Policies should include the terms “data”, “dataset” or more specific terms that make it clear what to make available; policies should include the benefits of data sharing; journals, publishers, and associations need to collaborate more to clarify data policies; and policies should explicitly ask for qualitative data.

This dataset is the data used in the CEQ Assessment in Panama (2016). This version can differ from others because the authors and the quality control team have made some changes to the original one.

Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author’s favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological literature are often grossly misinterpreted. We explain how to avoid these misinterpretations and propose a unified approach that makes it possible for researchers to preprocess data with matching (such as with the easy-to-use software we offer) and then to apply the best parametric techniques they would have used anyway. This procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences. See also: Causal Inference

This dataset is the data used in the CEQ Assessment in Iran (2011). This version can differ from others because the authors and the quality control team have made some changes to the original one.