Dr Peter Sefton1, Ms Rosanna Smith1, Dr Simon Musgrave1, Mr Michael Lynch2
1University Of Queensland, St Lucia, Australia, 2Univerity of Sydney, Camperdown, Australia
Biography:
Peter Sefton is an eResearch expert, specialising in software development, Research Data Management and metadata, currently a senior advisor for the Language Research Data Commons project at the University of Queensland.
Rosanna is a Language Technology Analyst in the LDaCA team. She completed Honours in linguistics at Monash University studying morphology in Scandinavian languages and has previously worked as a linguist and project manager in language technology.
Mike is a software engineer with over ten years experience providing specialised support for research, with expertise in research data management, open standards for data repositories and data publication and the application of modern IT development and deployment practices to research software. He has experience in full-stack web development in Python and JavaScript, machine learning, natural language processing and data visualisation, and is interested in digital humanities and functional programming.
Abstract:
Research Object Crate (RO-Crate) is a simple method for linked-data description and packaging of data. Since 2021, the Language Data Commons of Australia (LDaCA) project has onboarded a number of language data collections with thousands of files. These are all consistently described as RO-Crates using a human and machine-readable Metadata Profile, discoverable through an online portal, and available via an access-controlled API. This presentation will show how analytics workflows can be connected to data in the LDaCA repository and use linked data descriptions, such as the W3C “CSV for the web” (CSVW) standard, to automatically detect and load the right data for analytical workflows. We will show how the general-purpose flexible linked metadata and raw data is prepared for use with common tools implemented in Jupyter notebooks.
This work, funded by the Australian Research Data Commons ARDC, has enabled novel research by making data collected using sub-disciplinary norms of linguistics available to researchers working in other specialised areas – we will show examples of this and how this approach is relevant to other HASS and STEM disciplines, demonstrating work which would not have been possible without this co-investment between the Language Data Commons partners and ARDC
The presentation should be accessible to the broad audience of eResearch and be of particular relevance to those with an interest in workflows and analytics, as well as metadata, vocabulary and repository specialists. It shows a FAIR research system which runs on open specifications and code and can be redeployed for other domains.