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Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses(1). The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset(2-5). Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.

Colonization, speciation and extinction are dynamic processes that influence global patterns of species richness(1-6). Island biogeography theory predicts that the contribution of these processes to the accumulation of species diversity depends on the area and isolation of the island(7,8). Notably, there has been no robust global test of this prediction for islands where speciation cannot be ignored(9), because neither the appropriate data nor the analytical tools have been available. Here we address both deficiencies to reveal, for island birds, the empirical shape of the general relationships that determine how colonization, extinction and speciation rates co-vary with the area and isolation of islands. We compiled a global molecular phylogenetic dataset of birds on islands, based on the terrestrial avifaunas of 41 oceanic archipelagos worldwide (including 596 avian taxa), and applied a new analysis method to estimate the sensitivity of island-specific rates of colonization, speciation and extinction to island features (area and isolation). Our model predicts-with high explanatory power-several global relationships. We found a decline in colonization with isolation, a decline in extinction with area and an increase in speciation with area and isolation. Combining the theoretical foundations of island biogeography(7,8) with the temporal information contained in molecular phylogenies(10) proves a powerful approach to reveal the fundamental relationships that govern variation in biodiversity across the planet.

Using a global molecular phylogenetic dataset of birds on islands, the sensitivity of island-specific rates of colonization, speciation and extinction to island features (area and isolation) is estimated.