Mirror Mirror: reflections of FAIR Data evolution across a national portfolio of projects

Dr Richard Ferrers1, Mr  Keith Russell1

1Australian Research Data Commons, Melbourne, Australia

Introduction.

ARDC in late 2019 invested in a programme of 42 Data and Services Discovery projects  focusing on discovering elements required to create Transformative Data collections (32 projects) and the Institutional Role in a Data Commons (10 projects). One aspect in these projects was discovering how the data involved in the projects could be made more FAIR (Findable, Accessible, Interoperable and Reusable).

Method.

Projects were asked to self-assess the FAIRness of the data at the project start, project end and expected state two years after the projects. The results were included in the final reports and have now been collated. This poster provides a summary of the findings from those survey responses. FAIR was assessed through 14 questions. For each question a scale rated the level of FAIRness.

Results.

Key findings include: (1) while FAIR maturity varied substantially across projects, discipline did not appear a substantial indicator of FAIR maturity, and (2) across all projects, types of FAIRness in practice were much more evenly rated than expected.

The poster will provide graphs indicating the spread of FAIRness by project and FAIR category, and how through the projects their FAIRness improved. Some projects had mature FAIRness at the beginning and added minor improvements, while others started with low FAIR maturity and added to their FAIRness substantially – as big improvers.

Conclusion.

The poster presents a national programme of data projects through the lens of FAIR, across a range of disciplines and project types.


Biography:

Richard is a Research Data Specialist at The Australian Research Data Commons, where he works in the Engagements team with Victorian and Tasmanian Universites, and Trusted Data Community.

Building capability for genomics research

Mr Jun Huh1

1New Zealand eScience Infrastructure (NeSI), Auckland, New Zealand

Introduction

In 2019, New Zealand eScience Infrastructure (NeSI) initiated work with Genomics Aotearoa to address requirements for storage and hosting of bioinformatics data. A prototype data repository was developed and launched (with assistance from University of Otago IT staff), followed by a production repository hosted on NeSI infrastructure.

Methods

Both repositories utilise Globus for group-based access management and data transfer functionality and fulfil iwi requirements for data to be hosted onshore. By year’s end, genomic data for five taonga species with cultural significance to Māori was securely stored and available for mediated access.

NeSI staff also began working with Genomics Aotearoa’s Vision Mātauranga Coordinator to develop a permissions workflow, which will include a review of all requests and a consent process through a kahui group of advisors.

Further development of the repository is currently in progress, including investigation of a metadata catalogue and work towards the goal of making the repository data F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) within a context of indigenous data governance.

Conclusion

This talk will share a brief overview of this project, some of the recent milestones achieved, and a look at further development planned over the next year.


Biography:

Jun Huh comes from a startup background with a focus around providing genuine value to users and steering organisations to be more user-driven.

Achieving hands-off data interoperability by interpreting arbitrary ontologies as relational tables.

Dr James Hester1

1ANSTO, Sydney, Australia

We aspire to a world where data described using differing standards can be ingested automatically based on machine-readable data description files: interoperability. A variety of machine-readable ontological languages for these data description files exist. Given this variety, building cross-community adoption of a single ontological standard is unlikely to be successful when integrating data from many domains, not least because fixing on a particular standard is likely to invalidate potentially decades of work developing and implementing otherwise solid intra-community standards in one or more target domains. This presentation explores one way to resolve this impasse: by making the ontologies interoperable, that is, by automatic transformations of the ontological descriptions themselves.

The relational model provides a universal basis for data description, in that the contents of any data file can be described as a set of tables. The ontologies that describe data and metadata from a given domain are simply “data about the data description” and also have a tabular representation.  Data dictionaries expressed in one machine-readable vocabulary and format can therefore be machine-transformed into data dictionaries expressed in a different vocabulary and format using a suitable relationally-aware language.

This presentation describes how a data dictionary for images written in the DDL2 ontological language is algorithmically transformed to a dictionary written in the newer DDLm language. The compact, relationally aware dREL language is used to specify the transformations, which are performed after translating from dREL to Julia.


Biography:

Dr James Hester has worked for over 20 years at both synchrotron and neutron facilities as an instrument scientist. At the same time as being involved in producing copious quantities of data, he has worked extensively on developing the next generation of IUCr CIF data standards, and is the current chair of the IUCr committee for the maintenance of the CIF standards.

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