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Metadata for New Zealand's National Vegetation Plot Databank

Metadata for New Zealand's National Vegetation Plot Databank. Nick Spencer and Susan Wiser Landcare Research New Zealand. What is NVS?. NVS (National Vegetation Survey) – New Zealand’s largest archive facility for plot-based vegetation data . http://nvs.landcareResearch.co.nz.

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Metadata for New Zealand's National Vegetation Plot Databank

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  1. Metadata for New Zealand's National Vegetation Plot Databank Nick Spencer and Susan Wiser Landcare Research New Zealand

  2. What is NVS? • NVS (National Vegetation Survey) – New Zealand’s largest archive facility for plot-based vegetation data http://nvs.landcareResearch.co.nz

  3. NVS - coverage • Best in grassland and indigenous forest • Collection intensity has varied over 50+ years • 14 000 permanent and 52 000 relevé plots • NVS has many uses

  4. Why metadata management? • In the past – good for organising data • Expanding content and function – makes metadata critical • e.g. Kyoto protocol reporting • Metadata system redeveloped to meet new demands

  5. What is metadata? • Metadata is ‘information about information’ • Who, What, Where, When, Why and How …

  6. Consequence of missing metadata • Knowledge about a dataset is lost overtime Time of publication From Michener et al (1997) Specific details are lost rapidly e.g. Dates General details are lost through time Retirement or career change makes access difficult Accident may destroy data or documentation Death of investigator and loss of remaining records Time

  7. Why is metadata useful? • Search and locate datasets • Assess suitability of use • Reduces the effort required to use data • metadata leads to better information efficiency (Michener et al 1997) Caveat... • A balance needed • more metadata means less research (Michener et al 1997)

  8. Recent developments • Goals • Comprehensive • Standards based • Versatile • Approach • 1. XML based storage structures (‘Schema’)

  9. What is XML? • eXtensible Mark-up Language • Similar to HTML – but consists of user-defined tags to structure textual information • Promotes universal data access • Machine and human-readable • Open standard • Written in plain-text (ASCII)

  10. Recent developments • Goals • Comprehensive • Standards based • Versatile • Approach • 1. XML based storage structures (‘Schema’)

  11. Recent developments • Goals • Comprehensive • Standards based • Versatile • Approach • 1. XML based storage structures (‘Schema’) • 2. Separate the metadata and data systems (see the demonstration following this talk)

  12. Developing the schema • Looked to external metadata standards and profiles ISO 19115 – Geographic metadata standards DC – Dublin Core EML – Ecological Metadata Language • Adopted universal elements

  13. Our Metadata Schema • 34 primary metadata elements + 105 distinct sub-elements • 68% match with source standards • Grouped broadly as • Identity (title, Id) • Content (information types, methods) • Context (location, time, purpose) • Admin (ownership, access, availability, status)

  14. Notable features of our schema • Resources or related material • Internal (e.g. a child) • Managed (e.g. photographs) • External (e.g. bird counts) • Metadata containers and versioning

  15. Outcomes • Improved accessibility and consistency • XML document approach • Portable, flexible and extendable • Readily reformated for different uses (e.g. web, text, apps) • But… • Few mandatory metadata elements • Relational database  structured XML • XML tools and languages are less familiar c.f. SQL (20+ year standard)

  16. Acknowledgements Plant ecologists Peter Bellingham Susan Wiser Larry Burrows Rob Allen Data entry and administration Michelle Breach Dept. of Conservation Liaison Elaine Wright Funded by Foundation for Research Science & Technology Department of Conservation Terrestrial & Freshwater Biodiversity Information System IT strategists and developers Jerry Cooper Nick Spencer Mark Fuglestad

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