14:45 〜 15:00
[MGI30-05] How the use of controlled, accepted vocabularies could be improved to achieve open-science outcomes
キーワード:vocabularies, multi-faceted research, thesauri, ontologies, open science, data reuse
Environmental science teams are characteristically multi-disciplinary, multi-national, and multi-organisational. The environmental and ecological challenges that face us make these characteristics unavoidable if the best open science is to be achieved. The importance of ensuring the terms that describe shared data, and indeed information, are well-understood and properly defined cannot be under-estimated, not just within the teams themselves, but also when sharing the results with the world.
In a session run at SciDataCon in late 20211, several data-science specialists were asked to talk about their experience of how scientists engage with earth and environmental vocabularies and ontologies. This session highlighted the question of whether scientists truly appreciate the reason for using standardised vocabularies, and whether there were ways to improve their acceptance and hence practice. This would require several approaches. One might be to demonstrate the effectiveness of using a community-recognised, controlled vocabulary in enabling work to be discovered. Another might be to get researchers to identify and engage with a CoreTrustSeal2 accredited repository for their project data at the start of the project. Another is to create a range of educational packages, such as those introducing them to established vocabularies and ontologies. Overlay these challenges with language differences, we need to be more and more inventive. We must improve the situation for researchers and demonstrate to them that there is value in using standardised and accepted vocabularies and that this is an achievable part of their work, we miss fully benefitting from the wealth of expertise available to solve global problems and learn from each other.
In this talk we shall expand on these options, showing where they might be most effectively introduced.
1Laporte, M.A., Guru, S., Archambeau, et al., 2021. Earth and Environmental vocabularies and ontologies today: how are they managed? How are they used by scientists? https://doi.org/10.5281/zenodo.5594693
2Lin, D., Crabtree, J., Dillo, et al. 2020. The TRUST Principles for digital repositories. Scientific Data 7, 1–5. https://doi.org/10.1038/s41597-020-0486-7
In a session run at SciDataCon in late 20211, several data-science specialists were asked to talk about their experience of how scientists engage with earth and environmental vocabularies and ontologies. This session highlighted the question of whether scientists truly appreciate the reason for using standardised vocabularies, and whether there were ways to improve their acceptance and hence practice. This would require several approaches. One might be to demonstrate the effectiveness of using a community-recognised, controlled vocabulary in enabling work to be discovered. Another might be to get researchers to identify and engage with a CoreTrustSeal2 accredited repository for their project data at the start of the project. Another is to create a range of educational packages, such as those introducing them to established vocabularies and ontologies. Overlay these challenges with language differences, we need to be more and more inventive. We must improve the situation for researchers and demonstrate to them that there is value in using standardised and accepted vocabularies and that this is an achievable part of their work, we miss fully benefitting from the wealth of expertise available to solve global problems and learn from each other.
In this talk we shall expand on these options, showing where they might be most effectively introduced.
1Laporte, M.A., Guru, S., Archambeau, et al., 2021. Earth and Environmental vocabularies and ontologies today: how are they managed? How are they used by scientists? https://doi.org/10.5281/zenodo.5594693
2Lin, D., Crabtree, J., Dillo, et al. 2020. The TRUST Principles for digital repositories. Scientific Data 7, 1–5. https://doi.org/10.1038/s41597-020-0486-7