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Da molin databases_ecn_2012

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  • 1. Biodiversity DataBiodiversity Data vs. the Web 2.0vs. the Web 2.0 OR How I learned to stop worrying and love the “systems” Ana Dal Molin J. B. Woolley Texas A&M University
  • 2. Source: Opte.org Jan 2005
  • 3. [ Why this talk ]
  • 4. • Data providers • Aggregators • Tools • etc “growth in bioinformatics data exceeded Moore’s Law, the well- known observation that the number of transistors on a chip doubles every 18 months.” (Butte, 2001, TRENDS in Biotechnology 19(5)) • Johnson, N. 2007. Annual Rev. Entomology • http://www.ala.org.au/about-the-atlas/downloadable-tools/tools-review/ • IDigBio 47*
  • 5. [ what do I use? ]
  • 6. • Museums often have already decided on a model/database system • Each researcher, on the other hand, may not have, so questions – Content management systems (CMS)? – Which output? – Stability? – Best practices?
  • 7. ‘systems’ available • First Generation: desktop-based (MS Access, FileMaker) • Second Generation: desktop-based with web output • Third Generation: content management systems (PHP, Ruby, MySql, etc.)
  • 8. Data Accessibility
  • 9. Your data on the ‘net • Reach • Model GBIF species distribution data coverage (2010)
  • 10. [ ? ] Metadata Data Metadata repository Name IndexOccurrence Index Yellow PagesRegional Atlas Annotation Tools Biosecurity Portal Analysis Tools Products LaSalle, 2008. Atlas of Living Australia, ICE2008 presentation
  • 11. [ where do I stand? ]
  • 12. • Taxonomy as 2-natured science • Shifts in media format
  • 13. Web 1.0 -> Web 3.0  1.0: Static HTML, e-mail, forums, chat  2.0: Dynamic HTML, Wikis, blogging, commenting, social networking  3.0: … *You and your work are not invisible before publication*
  • 14. • Web 3.0: – “Social” – Tags – Cloud computing – Ubiquitous connectivity – Open technologies, open data formats (and open identity too) – Publishing in languages specifically designed for data (databases, markup) – Semantic web – Marketing
  • 15. http://www.tdwg.org
  • 16. • What the user wants • What you have to deal with * *not done!
  • 17. Think it through
  • 18. Books  Gutenberg  Gutenberg Project  WordCat  Hashi Trust
  • 19. The way we collect information is different The way we accumulate information is different The way we understand information is different
  • 20. … or not Jan/2012 33%USA, 20% Brazil, 26% Europe (Germany, Sweden, Spain, Greece, UK)
  • 21. 1.0 2.0
  • 22. • Web 3.0 1. People lie 2. People are lazy 3. People are stupid 4. Mission: impossible – know thyself 5. Schemas aren’t neutral 6. Metrics influence results 7. There’s more than one way to describe something C. Doctorow, Metacrap, 2001
  • 23. Issues • “Unification”* is not going to happen – curators and researchers will always have their own – (although often largely overlapping) set of crucial information fields which can be cross-linked • These days, it is imperative that databases communicate with each other • ‘unitary taxonomy’ is also not possible and any big database needs to allow the system to display conflicting ideas * Thomas, C. “Biodiversity databases spread, prompting unification call”, Science v. 325 (2009) ** http://hymao.org
  • 24. Data ephemerality • Local vs. Web data ?! Source: Wikipedia, “Science 2.0”
  • 25. Data ephemerality • Digital data preservation: Internet Archive, IIPC • Library of Congress discussions and recommendations – Disclosure, Adoption, Transparency , External dependency, Technical protection • http://www.digitalpreservation.gov/formats
  • 26.  User perspective  “Incomplete” sites  Dynamic information  Selective information?
  • 27. Why I am not a luddite:
  • 28.  Online databases are taxonomic product and marketing for your work  Online biodiversity databases complement your work  But it’s up to you to be able to make the user understand that your work is more than that  The user of online databases is probably not the same as the person who will get your paper
  • 29. summing up • Choose the system based on reports you want/need to deliver
  • 30. … or work with a journal/team that can help you • Make sure the system is flexible enough in your hands • Decide who will do the maintenance of your data – How big is your team? – Fluidity (positive and negative) • Think about stability and backup strategies
  • 31. Thanks!!

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