A prototype software framework for transparent, reusable and updatable computational health economic models

Most health economic analyses are undertaken with the aid of computers. However, the ethical dimensions of implementing health economic models as software (or computational health economic models (CHEMs)) are poorly understood. We propose that developers and funders of CHEMs share ethical responsibilities to (i) establish socially acceptable user requirements and design specifications; (ii) ensure fitness for purpose; and (iii) support socially beneficial use. We further propose that a transparent (T), reusable (R) and updatable (U) CHEM is suggestive of a project team that has largely fulfilled these responsibilities. We propose six criteria for assessing CHEMs: (T1) software files are open access; (T2) project team contributions and judgments are easily identified; (R1) programming practices promote generalisability and transferability; (R2) licenses restrict only unethical reuse; (U1) maintenance infrastructure is in place; and (U2) new releases are systematically retested and appropriately deprecated. To facilitate CHEMs that meet TRU criteria, we have developed a prototype software framework in the open-source programming language R. The framework comprises six code libraries for authoring CHEMs, supplying CHEMs with data and undertaking analyses with CHEMs. The prototype software framework integrates with services for software development and research data archiving. We determine that an initial set of youth mental health CHEMs we developed with the prototype software framework wholly meet criteria T1-2, R1-2 and U1 and partially meet criterion U2. Our assessment criteria and prototype software framework can help inform and improve ethical implementation of CHEMs. Resource barriers to ethical CHEM practice should be addressed by research funders.Comment: 17 pages, 4 tables, 1 figur

The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps

Functional interpretation of single cell similarity maps.

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration