Kristian Zarebski

There is a myriad of variables that must be considered when designing a superconducting magnet. Two of these are: the local magnetic field, dictating the response of the superconductor, and the mutual inductance matrix, dictating the generated voltage. These issues may be resolved with the help of finite element analysis tools, but this is not practical at the very beginning of coil design or when many concepts must be analysed quickly. Then comes the problem of robustly constructing the coil's winding pack such that it will operate as required. Analytical Python Electromagnetic Coil Simulator (APECS) offers one solution to the above. It uses accepted analytical and semi-numerical methods to calculate, at any point in space, the magnetic field generated by circular loop coils, such as a tokamak's poloidal field magnets, as well as straight-edge loop coils, such as a rectangular toroidal field magnet in a spherical tokamak. Combined with knowledge about the performance of the superconducting material and a user-specified cable design, APECS can construct a “minimum viable” winding pack for each poloidal field magnet to aid further analysis and design iteration. Despite that it cannot presently optimize the cable design or the thermo-hydraulics, superconductor/copper content, or the stress and failure, it is an invaluable tool used extensively in the design and analysis of the poloidal field (PF) magnet system on U.K.’s Spherical Tokamak for Energy Production (STEP).

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of ‘following the science’ are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policymakers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.