The design of biocatalytic reaction systems is highly complex due to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modelling method. Consequently, reproducibility of enzymatic experiments and re-usability of enzymatic data are challenging. We developed the XML–based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, time course of substrate and product, kinetic parameters, and kinetic model, thus making enzymatic data findable, accessible, interoperable, and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, where data and metadata of different enzymatic reactions are collected and analysed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modelling of enzyme kinetics, publication platforms, and enzymatic reaction databases. EnzymeML is open, transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org/.

Here, we present Machine learning Assisted, Interferometric Structured Illumination Microscopy, MAI-SIM, as an easy-to-implement method for fast live cell super-resolution imaging in multiple colours.

Triboelectrification excited by breathing continuously replenishes electrostatic charges, endowing the self-charging face mask with an ultralong service lifespan