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How we improved FAIR for volcano monitoring data at GeoNet, Aotearoa New Zealand

Steven Sherburn2 , Jonathan B Hanson1, Elisabetta D'Anastasio1, Mel Duncan1, Science Operations and Data team1,2, GeoNet team1,2

  • Affiliations: 1 GNS Science Te Pū Ao, 1 Fairway Drive, Lower Hutt, Wellington, Aotearoa New Zealand 2 GNS Science Te Pū Ao, Wairakei Research Centre, Private Bag 2000, Taupo, Aotearoa New Zealand

  • Presentation type: Poster

  • Presentation time: Friday 16:30 - 18:00, Room Poster Hall

  • Poster Board Number: 69

  • Programme No: 7.5.17

  • Theme 7 > Session 5

Abstract

The GeoNet programme at GNS Science is the primary agency for collecting, managing, and delivering volcano monitoring data in Aotearoa New Zealand. It is also responsible for monitoring and collecting earthquake, landslide, and tsunami data. We actively work to improve the FAIR of our data. We evaluated FAIR compliance using the Australian Research Data Commons (ARDC) FAIR data self-assessment tool, modified for our use ([Mavroeidi and Rattenbury, 2022]{.underline}). We first scored our data in 2021, and repeated that process in 2024. Due to difficulty in comparing our FAIR scores with those from other assessment tools, our primary focus has been using results to drive improvements in GeoNet data management practices. In 2021, all five volcano-specific datasets that were assessed ranked in the bottom half of all GeoNet FAIR scores, sparking significant efforts for improvement. In 2024, volcano datasets scored substantially higher. The most valuable improvements were to add datasets to GNS Science's Dataset Catalogue and generate a Digital Object Identifier (DOI) for each dataset. These two efforts hugely increased dataset Findability scores. In other cases, we worked on datasets that were only available upon request, and provided an open access mechanism and documentation as well Dataset Catalogue entries and DOIs. We will compare our 2021 and 2024 FAIR scores, discuss our FAIR scoring procedure, talk about which activities improved FAIR scores, and which did not. Mavroeidi, M.; Rattenbury, M.S. 2022 FAIR Principles applied to high-value geoscience datasets. Lower Hutt, N.Z.: GNS Science. GNS Science report 2021/62. 39 p.; doi: 10.21420/88HQ-9792.