Setbacks are an integral part of a scientific career, yet little is known about their long-term effects. Here we examine junior scientists applying for National Institutes of Health R01 grants. By focusing on proposals fell just below and just above the funding threshold, we compare near-miss with narrow-win applicants, and find that an early-career setback has powerful, opposing effects. On the one hand, it significantly increases attrition, predicting more than a 10% chance of disappearing permanently from the NIH system. Yet, despite an early setback, individuals with near misses systematically outperform those with narrow wins in the longer run. Moreover, this performance advantage seems to go beyond a screening mechanism, suggesting early-career setback appears to cause a performance improvement among those who persevere. Overall, these findings are consistent with the concept that “what doesn’t kill me makes me stronger,” which may have broad implications for identifying, training and nurturing junior scientists.

The connectivity pattern of networks based on ground level temperature records shows a dense stripe of links in the extra tropics of the southern hemisphere. We show that statistical categorization of these links yields a clear association with the pattern of an atmospheric Rossby wave, one of the major mechanisms associated with the weather system and with planetary scale energy transport. It is shown that alternating densities of negative and positive links are arranged in half Rossby wave distances around 3500, 7000, and 10 000 km and are aligned with the expected direction of energy flow, distribution of time delays, and the seasonality of these waves. In addition, long distance links that are associated with Rossby waves are the most dominant in the climate network.

The reanalysis data sets provide important sources for investigating the climate in Antarctica where stations are sparse. In this paper, we compare the 2 m near‐surface temperature data from five major reanalysis data sets with observational Antarctic stations data over the last 36 years: (i) the National Centers for Environmental Prediction and the National Center for Atmospheric Research Reanalysis (NCEP1), (ii) NCEP‐DOE Reanalysis 2 (NCEP2), (iii) the European Centre for Medium‐Range Weather Forecasts Interim Reanalysis (ERA‐Interim), (iv) the Japanese 55 year Reanalysis (JRA‐55), and (v) the National Aeronautics and Space Administration Modern‐Era Retrospective‐analysis for Research and Applications. In our assessment, we compare (a) the annual and seasonal trends obtained by linear regression analysis, (b) the standard deviation around the annual trends, (c) the detrended lag‐1‐autocorrelation C(1), (d) the Hurst exponent α that characterizes the long‐term memory in a record, and (e) the significance levels of the warming/cooling trends. We find that all five reanalysis data sets are able to reproduce quite well the long‐term memory in the instrumental data. In contrast, C(1), which is needed as input for the conventional significance analysis, shows fully erratic behavior. The observational warming/cooling trends in East and West Antarctica are not reproduced well by all reanalysis data sets, in particular, NCEP1, NCEP2, and JRA‐55 show spurious warming trends in many parts of East Antarctica, even in those parts where cooling has been observed. In contrast, the standard deviation around the trends is quite well reproduced by all reanalysis data sets. In the peninsula where the station density is quite high, the performance of the reanalysis data is considerably better. It is remarkable that all reanalysis data sets as well as the observational data show (under the assumption of a long‐term persistent process) that in the considered time period since 1979 the warming in the peninsula is not significant with p values well above 0.1.

Many complex systems can be represented as networks, and how a network breaks up into subnetworks or communities is of wide interest. However, the development of a method to detect nodes important to communities that is both fast and accurate is a very challenging and open problem. In this manuscript, we introduce a new approach to characterize the node importance to communities. First, a centrality metric is proposed to measure the importance of network nodes to community structure using the spectrum of the adjacency matrix. We define the node importance to communities as the relative change in the eigenvalues of the network adjacency matrix upon their removal. Second, we also propose an index to distinguish two kinds of important nodes in communities, i.e., “community core” and “bridge”. Our indices are only relied on the spectrum of the graph matrix. They are applied in many artificial networks as well as many real-world networks. This new methodology gives us a basic approach to solve this challenging problem and provides a realistic result.