Timothy Dunn

Han Zhang, Anshuman Sabath, Timothy W. Dunn et al.

Contemporary scientometric indicators remain anchored in paradigms and axioms from when academic research was conducted in small scholarly communities. With the global proliferation of scientific research, academia is now organized in large communities with high rates of information incompleteness regarding work impact and individual contributions. This has significant implications for how research output is measured and quality controlled, especially as the rate of academic publishing continues to rise. Exploits of complex systems are typically found at discrete transition points where rules turn on or off, and academia is not immune to this pattern. Exploitative career boosting strategies are a growing problem, largely enabled by misaligned incentives and traditional metrics that force discretization of credit to authors and prior works despite their fundamentally continuous nature. This article introduces Liberata's scientometrics, a share based framework for academic publishing and quality control. In this system, authorship positions are replaced with contribution shares that sum to unity and encode both ordinality and relative contribution distances. These shares can be traded on Liberata's academic marketplaces for quality control services such as peer review and replication, rewarding contributors based on the long term success of the work. Citations are weighted to guard against frivolous referencing and credit inflation, and modular correction factors allow multiple measures of impact. Liberata's metrics are formalized through two fundamental graphs, Shares and References, from which the system constructs academic capital and derives scientometrics capturing impact, risk, collaboration, collusion, value of quality control, and diversification. These metrics represent academic contributions and extend naturally to institutions, regions, time periods, and research fields.

Xing Yao, Vinayak Pathak, Haoran Xi et al.

This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.