The Dream FAIRer project addresses the growing need for reproducible, reusable, and FAIR-compliant research software in localization systems research. Building upon previous work on the ProxyFAUG data augmentation method for fingerprint-based localization, the project focuses on improving the alignment of research software and associated datasets with the FAIR and FAIR4RS principles. The project introduces improvements in reproducibility, interoperability, and long-term usability through explicit environment specification, structured versioning, temporal dataset splitting strategies aligned with community practices, clearer documentation of input and execution workflows, and repository consolidation. By providing openly accessible and reproducible research artifacts, Dream FAIRer contributes to more transparent and evidence-based evaluation practices in localization research while supporting broader adoption of Open Research Data and FAIR research software principles.

The Dream FAIRer project was funded by the HES-SO Open Research Data programme dedicated to software and source code (2025 call).

Results at a glance

• Within Dream FAIRer, the project activities resulted in the FAIRification and consolidation of the ProxyFAUG research artifacts into a fully documented and reproducible open repository. The project introduced a reproducible Conda environment specification, clarified dependency and version management, documented the expected input structure and workflow of the ProxyFAUG method, and implemented a temporal train/validation/test data splitting strategy aligned with domain-relevant evaluation conventions. The project also provides a reproducible open implementation of a rule-based fingerprint augmentation baseline for localization systems research, enabling transparent and evidence-based comparison of future methods under FAIR-compliant conditions. The resulting repository improves interoperability, reproducibility, and long-term reusability of the research outputs while contributing to broader Open Research Data and FAIR4RS adoption in the Indoor Positioning research community.