Verifiability in computer-aided research: the role of digital scientific notations at the human-computer interface

Most of today’s scientific research relies on computers and software for processing scientific information. Examples of such computer-aided research are the analysis of experimental data or the simulation of phenomena based on theoretical models. With the rapid increase of computational power, scientific software has integrated more and more complex scientific knowledge in a black-box fashion. As a consequence, its users do not know, and do not even have a chance of finding out, which assumptions and approximations their computations are based on. This black-box nature of scientific software has made the verification of much computer-aided research close to impossible. The present work starts with an analysis of this situation from the point of view of human-computer interaction in scientific research. It identifies the key role of digital scientific notations at the human-computer interface, reviews the most popular ones in use today, and describes a proof-of-concept implementation of Leibniz, a language designed as a verifiable digital scientific notation for models formulated as mathematical equations

Separation of Concerns in Epidemiological Modelling

International audienceModelling and simulation have been heavily used in epidemiology, for instance to study the transmission of infectious diseases, their pathogenicity and their propagation. A major hindrance to modelling in epidemiology is the mixing of concerns that ought to be separated. The most obvious one is the computer implementation that should not be mixed with domain aspects. But several domain concerns should also be separated from the core epidemiological ones. These include the distribution of the studied populations into spatial regions, age intervals, sexes, species, viral strains... We propose an approach that relies on a mathematical model of the dynamics of a compartment-based population. The separation of domain concerns is provided by expressing each one as a stochastic automaton and combining them with a tensor sum. A DSL, Kendrick, and a tool, support this approach that has been validated on several case studies