FAIR fission track analysis with geochron@home

Abstract. Fission track thermochronology is based on the visual analysis of optical images. This visual process is prone to observer bias. Fission track datasets are currently reported as numerical summary tables. The interpretation of these tables requires a high degree of trust between the fission track analyst and the user of the data. geochron@home is software that removes this requirement of trust. It combines a browser-based “virtual microscope” with an online database to provide FAIR (Findable, Accessible, Interoperable and Reproducible) access to fission track data. geochron@home serves four different purposes. It can be used (1) to count fission tracks in “private mode”, i.e. hidden from other users on the internet; (2) to archive fission track images and counts for inspection by other users; (3) to create tutorials for new students of the fission track method; and (4) to serve randomly selected selections of images to citizen scientists. We illustrate these four applications with examples that demonstrate (1) geochron@home's ability to compare and combine fission track counts for multiple users within a lab group; (2) the value of the geochron@home archive in the peer review system; (3) the use of simple tutorials in teaching novice users how to count fission tracks; and (4) the opportunities and challenges of crowd-sourced fission track analysis. geochron@home was written in Python and Javascript. Its code is freely available for inspection and modification, allowing users to set up their own geochron@home server. Alternatively, users who would like to upload data to the archive, but do not have the facilities to set up their own server, may use the server at University College London free of charge. The archive accepts image stacks acquired on any type of digital microscope, and accommodates fission track data (counts and length measurements) from external fission track analysis suites such as Fission Track Studio and TrackFlow. We anticipate that the introduction of FAIR workflows will make fission track data more accurate and more future proof. Storing fission track data online will benefit future developments in fission track thermochronology. For example, archival datasets of peer reviewed fission track counts can be used to train and improve machine learning algorithms for automated fission track analysis. We invite other geochronological methods to follow the fission track community's lead in FAIR data processing. This would benefit all the Earth Science disciplines that depend on geochronological data.