- 1Lantmäteriet, Geodetic Infrastructure, Gävle, Sweden (holger.steffen@lm.se)
- 2Department of Geology & Geophysics, Texas A&M University, College Station, TX, USA
- 3Department of Earth & Planetary Sciences, University of California, Santa Cruz, USA
- 4Department of Earth Sciences, University at Buffalo, Buffalo, USA
- 5Delft University of Technology, Delft, The Netherlands
- 6Tidbit Software, California, USA
The glacial isostatic adjustment (GIA) and sea level modeling communities have historically lagged other fields in adhering to the FAIR principles of making model outputs findable, accessible, interoperable, and reusable – a delay that has slowed scientific discovery. While sharing model outputs has improved recently, usability of available outputs continues to be hindered by lack of standardization. Meanwhile, legacy model outputs can be lost as the technology storing them grows obsolete and their creators retire or leave academia.
The GIAmachine initiative addresses this problem. GIAmachine aims to make accessible as many published GIA and sea level model outputs as are retrievable by
- cataloguing and standardizing published GIA and sea level model outputs;
- contacting authors of published-but-inaccessible models to encourage them to upload their outputs to DOI-minting repositories;
- partnering with the GHub science gateway to make a long-term home for these newly available outputs;
- building Jupyter notebooks on GHub that make these models interoperable and easy to use; and
- encouraging the GIA and sea level modeling communities to follow the FAIR principles.
Our poster will introduce the GIAmachine online portal and outline outstanding challenges. We appreciate community input for designing a living resource that meets the specific needs of current and future scientists.
How to cite: Steffen, H., Creel, R. C., Kodama, S. T., Tulenko, J. P., Steffen, R., Riva, R. E. M., Quinn, J., and Briner, J. P.: GIAmachine: a community-driven rescue and recovery initiative for legacy sea level and glacial isostatic adjustment modeling data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16724, https://doi.org/10.5194/egusphere-egu26-16724, 2026.