Data about soil processes and field experiments are essential for understanding natural systems. Despite the high level of scientific sophistication in many studies, data standardization and FAIR principles are rarely prioritized. Reusability is often not achieved, as most datasets are published - if at all - without a standardized table structure or metadata describing included variables. In SoilPulse we explore workflows and develop tools to support the creation and curation of reusable datasets. We propose a systematic processing chain along 5 steps: Data ingestion to our tool, Converting data structures into machine-readable formats, Defining variables of the dataset by standardized vocabulary covering parameters/concepts (e.g. bulk density, infiltration rate), units, andmeasurement methods, Automated assessment of data reusability using FAIR metrics and Preparation of FAIRified datasets for publication and exchange between research groups. Those steps are ongoingly implemented within the python package soilpulsecore. We demonstrate these processing steps using existing datasets of erosion experiments to highlight practical pathways toward FAIR data implementation. While our focus is on soil process data, many of the challenges we encountered and solutions we developed are relevant to the broader earth system sciences community. By combining general and domain-specific vocabularies for clear reference to variables (methods, parameters/concepts and units) with tools that assist data curation, SoilPulse supports theFAIRification process.

This work has been funded by the German Research Foundation (DFG) through the project NFDI4Earth (TA1 M1.1, DFG project no. 460036893, https://www.nfdi4earth.de/) within the German National Research Data Infrastructure (NFDI, https://www.nfdi.de/).