By Mike Lynch and Peter Sefton Mike Lynch and Peter Sefton attended the 2019 eResearch Australasia conference in Brisbane from 22-24 October 2019, where we presented a few things - and a pre-conference summit on the 21st held by the Australian Research Data Commons, where Mike presented our report from our small discovery project on scalable repository technology. UTS paid for the trip. What we presented - our work on Simple Scalable Research Data Repositories We've posted fleshed-out versions of our conference papers as usual.
This presentation was given by Peter Sefton & Michael Lynch at the eResearch Australasia 2019 Conference in Brisbane, on the 24th of October 2019. Welcome - we’re going to share this presentation. Peter/Petie will talk through the two major standards we’re building on, and Mike will talk about the software stack we ended up with. ' title='The project in a nutshell A static, file-based research data repository platform using open standards and off-the-shelf web technology OCFL – versioned file storage RO-Crate – dataset / object metadata Solr – index and discovery nginx – baked in access control
By Peter Sefton This presentation was given by Peter Sefton at the eResearch Australasia 2019 Conference in Brisbane, on the 24th of October 2019. ' title='Meet RO-Crate ' border='1’ width='85%'/> This presentation is part of a series of talks delivered here at eResearch Australasia - so it won’t go back over all of the detail already covered - see the introduction of datacrate in 2017 and and the 2018 update. The standard formerly known as DataCrate has been subsumed into a new standard called Research Object Crate - RO-Crate for short.
ARDC funding - Data and Services Discovery projects - Institutional Role in a Data Commons There are a lot of specialized repository applications, from small (Omeka) to large (Hydra, Fedora), all designed as special-purpose homes for datasets and metadata which provide APIs for getting things in and out. Experience has shown that these solutions don’t scale. Eventually, an institution will have to store a dataset that’s too big either to get in or out, or to store, and will have to look at a workaround like putting the data on disk and pointing to it from a record in the repository.