Xiaoran Yan

TL;DR: Network analyses suggest that the open science and reproducibility literatures are emerging relatively independently of each other, sharing few common papers or authors, and whether the literatures differentially incorporate collaborative, prosocial ideals that are known to engage members of underrepresented groups more than independent, winner-taken approaches.

TL;DR: In this article, the authors leverage Granger causality in a novel method of identifying teleconnections, which is explicitly defined as a statistical test between two time series, allowing for immediate interpretation of causal relationships between any two fields and providing an estimate of the timescale of the teleconnection response.

TL;DR: This article proposes an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user’s query in an intelligent way and uses meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time.

TL;DR: This article describes the many facets of the problem faced by American academic libraries and researchers wanting to work with big datasets and proposes a practical solution based on the five pillars: The Collaborative Archive and Data Research Environment.

TL;DR: This paper proposes a principled framework of network composition based on a unified dynamical process that uses transformations of dynamics to unify heterogeneous layers under a common dynamics.