In this Zenodo repository, we store the solved PyPSA networks used in the analysis of the paper "A minimal methanol backstop for high-electrification scenarios" published in Joule (10.1016/j.joule.2026.102464). Additionally, we provide csv files for all scenarios containing the figures for the energy balances, installed capacities, CAPEX and OPEX. Summary Electrification of sectors such as land transport and building heating is a cost-effective pathway to deep decarbonization. However, some sectors still require energy-dense fuels — including aviation, shipping and backup power — or chemical feedstocks. While a ‘hydrogen economy’ is often proposed to fill these hard-to-electrify gaps, it faces challenges in transport, storage, and infrastructure coordination. We introduce a ‘minimal methanol backstop’ to supply residual demand in highly-electrified systems. As a liquid fuel, methanol is easy to store and transport, and avoids infrastructure lock-in. Produced from hydrogen and carbon monoxide, it can help integrate biogenic carbon from decentralized biomass wastes and residues. Using a European energy system model constrained to be carbon-neutral, we show that methanol-based systems increase total system costs by 2.4% relative to hydrogen-based systems, an increase that remains below 6.4% across sensitivities. We argue that this modest cost premium is justified by reduced infrastructure complexity. Files compressed networks of all settings: default setting with 200Mt/a sequestration limit and techno-economic assumptions for 2030 low biomass potentials high biomass potentials high biomass potential and inf. sequestration no biomass backup low electrification todays transmission capacity no power transmission no CO$_2$ transport 400 Mt/a & infinite sequestration limit CO$_2$ reduction targets 90/95 % green imports from outside Europe relocation of industry within Europe techno-economic assumptions for 2050 csvs of all settings Usage You can open the .nc files from the network_files.zip using the PyPSA python package (https://github.com/PyPSA/PyPSA).