International mobility facilitates the exchange of scientific, institutional and cultural knowledge. Yet whether globalization and advances in virtual communication technologies have altered the impact of researcher mobility is a relevant and open question that we address by analysing a broad international set of 26 170 physicists from 1980 to 2009, focusing on the 10-year period centred around each mobility event to assess the impact of mobility on research outcomes. We account for secular globalization trends by splitting the analysis into three periods, measuring for each period the effect of mobility on researchers' citation impact, research topic diversity, collaboration networks and geographical coordination. In order to identify causal effects we leverage statistical matching methods that pair mobile researchers with non-mobile researchers that are similar in research profile attributes prior the mobility event. We find that mobile researchers gain up to a 17% increase in citations relative to their non-mobile counterparts, which can be explained by the simultaneous increase in their diversity of co-authors, topics and geographical coordination in the period immediately following migration. Nevertheless, we also observe that researcher's completely curtail prior collaborations with their source country in 11% of the cross-border mobility events. As such, these individual-level perturbations fuel multiscale churning in scientific networks, e.g. rewiring the connectivity of individuals and ideas and affecting international integration. Together these results provide additional clarity on the complex relationship between human capital mobility and the dynamics of social capital investment, with implications for immigration and national innovation system policy.

Abstract: We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties – i.e., off-market real estate entering the habitation market, just not for purchase and hence free of speculation – as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making.

A fascinating story is unfolding at the interface between mitochondria and the ER. Two new papers, one in this issue of The EMBO Journal (Wu et al, 2016) and one in the journal Autophagy (Chen et al, 2016), further clarify the role of mitochondrial outer membrane protein FUNDC1 in autophagy and connect it to mitochondrial fission occurring at the interface between mitochondria and the ER.

To what degree has the vast space of higher-order knowledge combinations been explored and how has it evolved over time? To address these questions, we first develop a systematic approach to measuring combinatorial innovation in the biomedical sciences based upon the comprehensive ontology of Medical Subject Headings (MeSH) developed and maintained by the US National Library of Medicine. As such, this approach leverages an expert-defined knowledge ontology that features both breadth (27,875 MeSH analyzed across 25 million articles indexed by PubMed that were published from 1902 onwards) and depth (we differentiate between Major and Minor MeSH terms to identify differences in the knowledge network representation constructed from primary research topics only). With this level of uniform resolution, we differentiate between three different modes of innovation contributing to the combinatorial knowledge network: (i) conceptual innovation associated with the emergence of new concepts and entities (measured as the entry of new MeSH) and (ii) recombinant innovation, associated with the emergence of new combinations, which itself consists of two types: peripheral (i.e. combinations involving new knowledge) and core (combinations comprised of pre-existing knowledge only). Another relevant question we seek to address is whether examining triplet and quartet combinations, in addition to the more traditional dyadic or pairwise combinations, provides evidence of any new phenomena associated with higher-order combinations. Analysis of the size, growth, and coverage of combinatorial innovation yield results that are largely independent of the combination order, with some subtle caveats identified at higher order (i.e. beyond the common dyadic or pairwise representation of combinations). Our main results are two-fold: (a) despite the persistent addition of new MeSH terms, the network is densifying over time meaning that scholars are increasingly exploringand realizing the vast space of all knowledge combinations and (b) conceptual innovation is increasingly concentrated within single research articles, a harbinger of the recent paradigm shift towards convergence science.

We analyzed the longitudinal activity of nearly 7,000 editors at the mega-journal PLOS ONE over the 10-year period 2006-2015. Using the article-editor associations, we develop editor-specific measures of power, activity, article acceptance time, citation impact, and editorial renumeration (an analogue to self-citation). We observe remarkably high levels of power inequality among the PLOS ONE editors, with the top-10 editors responsible for 3,366 articles -- corresponding to 2.4% of the 141,986 articles we analyzed. Such high inequality levels suggest the presence of unintended incentives, which may reinforce unethical behavior in the form of decision-level biases at the editorial level. Our results indicate that editors may become apathetic in judging the quality of articles and susceptible to modes of power-driven misconduct. We used the longitudinal dimension of editor activity to develop two panel regression models which test and verify the presence of editor-level bias. In the first model we analyzed the citation impact of articles, and in the second model we modeled the decision time between an article being submitted and ultimately accepted by the editor. We focused on two variables that represent social factors that capture potential conflicts-of-interest: (i) we accounted for the social ties between editors and authors by developing a measure of repeat authorship among an editor's article set, and (ii) we accounted for the rate of citations directed towards the editor's own publications in the reference list of each article he/she oversaw. Our results indicate that these two factors play a significant role in the editorial decision process. Moreover, these two effects appear to increase with editor age, which is consistent with behavioral studies concerning the evolution of misbehavior and response to temptation in power-driven environments.

We develop a systematic approach to measuring combinatorial innovation in the biomedical sciences based upon the comprehensive ontology of Medical Subject Headings (MeSH). This approach leverages an expert-defined knowledge ontology that features both breadth (27,875 MeSH analyzed across 25 million articles indexed by PubMed from 1902 onwards) and depth (we differentiate between Major and Minor MeSH terms to identify differences in the knowledge network representation constructed from primary research topics only). With this level of uniform resolution we differentiate between three different modes of innovation contributing to the combinatorial knowledge network: (i) conceptual innovation associated with the emergence of new concepts and entities (measured as the entry of new MeSH); and (ii) recombinant innovation, associated with the emergence of new combinations, which itself consists of two types: peripheral (i.e., combinations involving new knowledge) and core (combinations comprised of pre-existing knowledge only). Another relevant question we seek to address is whether examining triplet and quartet combinations, in addition to the more traditional dyadic or pairwise combinations, provide evidence of any new phenomena associated with higher-order combinations. Analysis of the size, growth, and coverage of combinatorial innovation yield results that are largely independent of the combination order, thereby suggesting that the common dyadic approach is sufficient to capture essential phenomena. Our main results are twofold: (a) despite the persistent addition of new MeSH terms, the network is densifying over time meaning that scholars are increasingly exploring and realizing the vast space of all knowledge combinations; and (b) conceptual innovation is increasingly concentrated within single research articles, a harbinger of the recent paradigm shift towards convergence science.

We present a simple generalization of Hirsch's h-index, Z≡h2+C/5, where C is the total number of citations. Z is aimed at correcting the potentially excessive penalty made by h on a scientist's highly cited papers, because for the majority of scientists analyzed, we find the excess citation fraction (C-h2)/C to be distributed closely around the value 0.75, meaning that 75% of the author's impact is neglected. Additionally, Z is less sensitive to local changes in a scientist's citation profile, namely perturbations which increase h while only marginally affecting C. Using real career data for 476 physicists careers and 488 biologist careers, we analyze both the distribution of Z and the rank stability of Z with respect to the Hirsch index h and the Egghe index g. We analyze careers distributed across a wide range of total impact, including top-cited physicists and biologists for benchmark comparison. In practice, the Z-index requires the same information needed to calculate h and could be effortlessly incorporated within career profile databases, such as Google Scholar and ResearcherID. Because Z incorporates information from the entire publication profile while being more robust than h and g to local perturbations, we argue that Z is better suited for ranking comparisons in academic decision-making scenarios comprising a large number of scientists. © 2013 Elsevier Ltd.