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Easy as ABC A triumph of re-useable metadata Julia Hickie Mark Raadgever 1 Trove Support.

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Presentation on theme: "Easy as ABC A triumph of re-useable metadata Julia Hickie Mark Raadgever 1 Trove Support."— Presentation transcript:

1 Easy as ABC A triumph of re-useable metadata Julia Hickie Mark Raadgever 1 Trove Support

2 2

3 3 https://plot.ly/~wragge/6/trove-newspaper-articles-by-state/ 1955

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5 1.A web crawling bot to pickup records 2. Transformers to change the records 3. A loader to dump them in Trove 5 Dragline loading a dump truck at German Creek brown coal open mine, Queensland, 1985 Sievers, Wolfgang http://nla.gov.au/nla.pic-vn4801485 Tonka, Brian Auer https://flic.kr/p/4qKzQE Mi colección de Transformers (17/Dic/2007) Gustavo Vargas https://flic.kr/p/4ee2Nh

6 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) 6

7 7 http://www.abc.net.au/radionational/feed/2887252/podcast.xml

8 8 http://www.abc.net.au/radionational/programs/healthreport/

9 9 http://www.abc.net.au/radionational/programs/healthreport/past-programs/index=2013

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12 12 Radio National Website NLA Harvester HTML XML HTML Trove XML PHP Script Regular expressions XSLT stylesheets Java modules

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17 17 Radio National Website NLA Harvester HTML XML HTML Trove XML PHP Script Regular expressions XSLT stylesheets Java modules

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22 22 Radio National Website NLA Harvester HTML XML HTML Trove XML PHP Script Regular expressions XSLT stylesheets Java modules

23 23 http://trove.nla.gov.au/version/209294503

24 WHY? 24

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28 Michael Neubert From wheels to bikes - http://wheelbike.blogspot.com.au/2012/02/starting-search-for-bikes-in-trove.html 28

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31 Why Radio National 31

32 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 32

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34 34 http://inaword.dhistory.org

35 https://github.com/wragge/radio-national-data 35

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39 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 39

40 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 40

41 Questions? 41


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