## 8 Conclusions  
In this paper, we presented a new open-source dataset for research on advanced positioning using Android smart devices, with a first review of and the first app for GNSS raw data on a smartwatch platform. We detailed our methodology for creating the dataset by describing the logging software development, the protocol definition and file structure in detail. We also provided a first analysis of the results by comparing five devices (four Android smartphones and a smartwatch) in terms of several performance metrics. It is to be reminded that the paper's focus and main novelty has been to introduce the methodology of the acquisition protocol and the new open-access app and datasets. The results are just examples of what can be achieved with the developed open-access tools, but they do not provide a comprehensive selection of performance metrics; for example, algorithms for user positioning starting from the acquired raw data are still to be implemented and the multi-system multi-frequency GNSS analysis is still a topic for future research. Nevertheless, our results have shown that GNSS raw data collected from mass-market devices is of good quality, with similar results between the smartwatch and smartphones. This strengthens the possibility of using smartwatch sensor data for broader research topics. It can enable significant research in the area, provided that suitable acquisition and processing tools are made available to the research community, such as our developed software \"Mimir\". Last but not least, we have also reviewed the current challenges associated with Android data processing and highlighted the potential research opportunities for the continuity of this work.  
**Author Contributions:** Conceptualization, A.G. and E.S.L.; methodology, A.G.; software, A.G.; validation, A.G. and E.S.L.; formal analysis, A.G.; investigation, A.G.; resources, A.G.; data curation, A.G.; writing--original draft preparation, A.G. and E.S.L.; writing--review and editing, A.G., E.S.L., A.O. and J.N.; visualization, A.G.; supervision, E.S.L., A.O. and J.N.; project administration, A.G.; funding acquisition, E.S.L., A.O. and J.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska Curie grant agreement number 956090 (APPROPOS: Approximate Computing for Power and Energy Optimisation, [[http://www.apropos-int.eu/](http://www.apropos-int.eu/)]([http://www.apropos-int.eu/](http://www.apropos-int.eu/)) (accessed on 21 November 2023)), by the LEAP-RE programme of the European Union's Horizon 2020 Research and Innovation Program, grant agreement number 963530 (LEDSOL project) and the Academy of Finland, project number 352364.  
**Data Availability Statement:** The data presented is published under CC-BY4.0 on Zenodo repository ([[https://zenodo.org/records/8340005](https://zenodo.org/records/8340005)]([https://zenodo.org/records/8340005](https://zenodo.org/records/8340005)) (accessed on 21 November 2023). Software developed are published under Apache 2.0 open-source license on GitHub (Mimir App: [[https://github.com/agrenier-gnss/mimir](https://github.com/agrenier-gnss/mimir)]([https://github.com/agrenier-gnss/mimir](https://github.com/agrenier-gnss/mimir)) (accessed on 21 November 2023), Mimir Analyzer: [[https://github.com/agrenier-gnss/MimirAnalyzer](https://github.com/agrenier-gnss/MimirAnalyzer)]([https://github.com/agrenier-gnss/MimirAnalyzer](https://github.com/agrenier-gnss/MimirAnalyzer)) (accessed on 21 November 2023).  
**Acknowledgments:** We would like to thank the following students at Tampere University who have helped with the software developments and with preliminary work on the Android devices: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.