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Making GeoNet Data FAIRer: lessons learnt from scoring a variety of natural hazard datasets (and how to improve them)

Urheber*innen

Hanson,  Jonathan
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Sherburn,  Steve
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

D'Anastasio,  Elisabetta
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Madley,  Megan
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Rattenbury,  Mark
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Mavroeidi,  Maria
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Christophersen,  Annemarie
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Team,  GeoNet Programme
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Zitation

Hanson, J., Sherburn, S., D'Anastasio, E., Madley, M., Rattenbury, M., Mavroeidi, M., Christophersen, A., Team, G. P. (2023): Making GeoNet Data FAIRer: lessons learnt from scoring a variety of natural hazard datasets (and how to improve them), XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-2199


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018611
Zusammenfassung
The GeoNet programme at GNS Science, Aotearoa New Zealand is producer and custodian for datasets used for multi-hazard monitoring, assessment, and research. These datasets are of high national value and significance and range from highly sampled/highly automated to manual and ad-hoc datasets. FAIR is a cornerstone of GeoNet’s and GNS Sciences’ data principles and an area where we are always seeking to improve. To understand and demonstrate improvements FAIRness of its datasets, all GeoNet datasets were assessed in 2019. Here we will discuss that scoring effort and the outcomes across our datasets, and show how subsequent targeted work has delivered improvements, using a few key examples: - Our eruption history database was converted from unstructured document format (GNS report) to a csv format and made fully public; this significantly enhanced its FAIR score across the board. - Our acoustic/infrasound dataset was made more discoverable by minting a DOI, creating a dataset description record that is based on international web standards and utilising community webservices, leading to significant increases in Findability and Interoperability. FAIRness scoring is a powerful tool to guide dataset management improvements. It also revealed that some datasets have natural ceilings, particularly when there is a lack of clear international or community standards. More established and cross-peril datasets can achieve high compliance with FAIR data principles with relatively simple steps, while some key improvements generate nary a ripple in the score. FAIRness and open data are key policies for GeoNet, and will remain so for the foreseeable future.