Open data from physical model tests: Lessons learned from related initiatives

Sutherland, J. and Awre, C. and Malvarez, G. and Van Gils, A. and Harpham, Q. (2017) Open data from physical model tests: Lessons learned from related initiatives. In: IAHR World Congress 2017, 13 - 18 August 2017, Kuala Lumpur, Malaysia.

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Abstract

The HYDRALAB network of European physical model laboratories (www.hydralab.eu) has a range of facilities that includes flumes, basins, ice facilities, rotating tanks and environmental facilities. Each institution had its own data collection system, there are many proprietorial data formats, a shortage of meta-data and no central effort to curate or preserve this data in a findable, accessible, interoperable and reusable (FAIR) way. HYDRALAB+ (2015-2019) is a European Commission Horizon 2020 project to support this network, which requires FAIR data management. HYDRALAB is reviewing the steps taken to make data openly accessible in related disciplines, so that lessons learned can be applied to HDRALAB+. The chosen communities were: (i) the University of Hull’s digital repository, (ii) EMODnet Baltic Checkpoint, (iii) OpenEarth and (iv) the FP7 projects PEGASO and MEDINA and the EU MED project COASTGAP. It is clear that no one solution can deal with all situations: different data types and requirements can best be dealt with by different approaches. Standards for meta-data should be applied, but no existing standard covers the range of situations faced by HYDRALAB. All can be extended in a bespoke manner (which can potentially be included in an update of the standard) but it is highly likely that more than one standard (and none) will be used in such a diverse community. This is perfectly acceptable, so long as the standard is published. There is also a clear need for guidance on the development of repositories where large volumes of data are collected and an understanding of how much needs to be made available on-line. Although there can be conflicts of interest between institutions that are developing policies for data management and projects that want a uniform approach to data management across all partners, systems today can generally accommodate this.

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