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Easy as ABCA triumph of re-useable metadata
Julia HickieMark Raadgever
Trove Support
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https://plot.ly/~wragge/6/trove-newspaper-articles-by-state/
1955
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1. A web crawling bot to pickup records
2. Transformers to change the records
3. A loader to dump them in Trove
Dragline loading a dump truck at German Creek brown coal open mine, Queensland, 1985 Sievers, Wolfganghttp://nla.gov.au/nla.pic-vn4801485
Tonka, Brian Auerhttps://flic.kr/p/4qKzQE
Mi colección de Transformers (17/Dic/2007)Gustavo Vargashttps://flic.kr/p/4ee2Nh
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Why didn’t they become a Trove contributor?
1. No resources, no money, no capability for technical change
2. Can’t meet a metadata standard (that no longer exists)
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http://www.abc.net.au/radionational/feed/2887252/podcast.xml
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http://www.abc.net.au/radionational/programs/healthreport/
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http://www.abc.net.au/radionational/programs/healthreport/past-programs/index=2013
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Radio National
Website NLA Harvester
HTML
XML
HTML
Trove
XML
PHPScript
• Regular expressions• XSLT stylesheets• Java modules
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Radio National
Website NLA Harvester
HTML
XML
HTML
Trove
XML
PHPScript
• Regular expressions• XSLT stylesheets• Java modules
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Radio National
Website NLA Harvester
HTML
XML
HTML
Trove
XML
PHPScript
• Regular expressions• XSLT stylesheets• Java modules
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WHY?
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Michael Neubert From wheels to bikes -http://wheelbike.blogspot.com.au/2012/02/starting-search-for-bikes-in-trove.html
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2013-07
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ABC Clickthroughs July 2013-September 2014
Clickthroughs
Content added
Promotion
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Why Radio National
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What else?
• Standardised records allow analysis of content• Digital historians can use the API to investigate
trends• E.g. Tim Sherratt’s In a Word
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34http://inaword.dhistory.org
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https://github.com/wragge/radio-national-data
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Lessons Learned• Adaptation of existing functions– Sitemap harvesting– RSS harvesting– XSLT Transformation
• Development of generic rather than specialised tools
• Staff learning opportunities – we became better at using core technology
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Future
• Re-examine contributors previously unable to meet technical requirements
• Encourage re-use of the dataset – including adding it to library catalogues as well as scholarly analysis
• Think beyond conventional data
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Questions?
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