SignificanceScientific and scholarly influence is multifaceted, shifts over time, and varies across disciplines. We present a dynamic topic model to credit documents with influence that shapes future discourse based on their content and contextual features. We trace discursive innovation in scholarship and identify the influence of particular articles along with their authors, affiliations, and journals. In collections of science, social science, and humanities research spanning over a century, our measure helps predict citations and reveals signals that recognize authors who make diverse contributions and whose contributions take longer to be appreciated, allowing us to compensate for bias in citation behavior.

Children born preterm have poorer outcomes than children born full-term, but the caregiving environment can ameliorate some of these differences. Recent research has proposed that preterm birth may be a plasticity factor, leading to better outcomes for preterm than full-term infants in higher quality environments. This analysis uses data from two waves of an Irish study of children (at 9 months and 3 years of age, n=11,134 children) and their caregivers (n=11,132 mothers, n=9,998 fathers) to investigate differences in how caregiving affects social, cognitive, and motor skills between full-term, late preterm, and very preterm children. Results indicate that parental emotional distress and quality of attachment are important for child outcomes. Both being born very preterm and late preterm continue to be risk factors for poorer outcomes at 3 years of age. Only fathers’ emotional distress significantly moderated the effect of prematurity on infants’ cognitive and social outcomes – no other interactions between prematurity and environment were significant. These interactions were somewhat in line with diathesis stress, but the effect sizes were too small to provide strong support for this model. There is no evidence that preterm birth is a plasticity factor.

Abstract-Distributed communities of researchers rely increasingly on valuable, proprietary, or sensitive datasets. Given the growth of such data, especially in fields new to data-driven, computationally intensive research like the social sciences and humanities, coupled with what are often strict and complex data-use agreements, many research communities now require methods that allow secure, scalable and cost-effective storage and analysis. Here we present CLOUD KOTTA: a cloud-based data management and analytics framework. CLOUD KOTTA delivers an end-to-end solution for coordinating secure access to large datasets, and an execution model that provides both automated infrastructure scaling and support for executing analytics near to the data. CLOUD KOTTA implements a fine-grained security model ensuring that only authorized users may access, analyze, and download protected data. It also implements automated methods for acquiring and configuring low-cost storage and compute resources as they are needed. We present the architecture and implementation of CLOUD KOTTA and demonstrate the advantages it provides in terms of increased performance and flexibility. We show that CLOUD KOTTA's elastic provisioning model can reduce costs by up to 16x when compared with statically provisioned models.

Abstract. Nearly all personal relationships exhibit a multiplexity where people relate to one another in many different ways. Using a set of faculty CVs from multiple research institutions, we mined a hypergraph of researchers connected by co-occurring named entities (people, places and organizations). This results in an edge-sparse, link-dense structure with weighted connections that accurately encodes faculty department structure. We introduce a novel model that generates dyadic proposals of how well two nodes should be connected based on both the mass and distributional similarity of links through shared neighbors. Similar link prediction tasks have been primarily explored in unipartite settings, but for hypergraphs where hyper-edges out-number nodes 25-to-1, accounting for link similarity is crucial. Our model is tested by using its proposals to recover link strengths from four systematically lesioned versions of the graph. The model is also compared to other link prediction methods in a static setting. Our results show the model is able to recover a majority of link mass in various settings and that it out-performs other link prediction methods. Overall, the results support the descriptive fidelity of our text-mined, named entity hypergraph of multi-faceted relationships and underscore the importance of link similarity in analyzing link-dense multiplexitous relationships.