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OBIS PowerPoint Presentation

OBIS

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OBIS

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Presentation Transcript

  1. OBIS

  2. Current situation • Working on new IT platform • Present technology 8 years old • Data ingestion going fine • Including data quality • Position, time • Taxonomy • Web site well visited

  3. Number of records (M)

  4. Number of datasets

  5. Average size dataset (K)

  6. Web statistics

  7. Data statistics

  8. Analysis of content • First preliminary analyses • Has to take into account huge bias • Geography • Mostly coastal • Mostly northern hemisphere • Taxonomy • Presence-only • ‘Safety in numbers’

  9. Number of records For known species most important to your project, what major discoveries have been made about their range or distribution? What is least known with regards to their distribution that you would like to know?

  10. Number of species

  11. Hurlbert’s index (es(50))

  12. Large marine ecosystems

  13. ‘Age’ of record – trends study

  14. Latitudinal gradient ES(50)

  15. Marine fish to be discovered Mora et al (2007). The completeness of taxonomic inventories for describing the global diversity and distribution of marine fishes. Proc. R. Soc. B, published on line Percentage completeness 1 100

  16. How good is the data? • Data are from many sources • Inconsistent become apparent • Differences in names used • Mistakes in transformations • Decimalising lat/lon • Needs quality control • Data collection driven by priorities • Sampling bias; resolution

  17. Quality control • Check formal record structure • Check date/time • Check position • In the ocean? • In dataset bounding box? • Check taxonomy • Problem: no reference list

  18. New species are discovered Data from http://marinespecies.org

  19. Problems with taxonomic names • Misspellings • Mixed with other information • Gadus sp.; Gadus sp. A; Gadus sp. a… • Gadus morhua?; Gadus cfr morhua; Gadus aff. morhua… • Gadus morhua juv.; Gadus morhua juvenile; Gadus morhua juveniles… • Mixed with ecological/sampling information • Also variation in classification and author string

  20. Examples of variation • Callorhinchus callorynchus • Cirrhinus or Cirrhina • Cirrhinus cirrhosa or C. cirrhosus • Cirrhina cirrhosa or C. cirrhosus • Microsoft helping a bit: • Calinectes ornatus Ordway, 1863 Calinectes ornatus Ordway, 1864 … Calinectes ornatus Ordway, 1891

  21. Number of ‘species’ in OBIS • 147K unique ‘scientific names’ • 132K ‘clean names’ • Approx 10% reduced (from 147K) • 80K match with WoRMS • 11K known synonyms or misspellings • Non-matches assumed valid • 121K ‘valid names’ • Approx 20% reduced

  22. Reduction of es(50) per 5d square

  23. Same for fish

  24. General patterns indistinguishable All Fish Dirty Clean

  25. Completeness

  26. How to get OBIS data? • Web site • DiGIR provider • OGC-compliant web services • Exist on experimental basis • Google base • Ask us! • Custom data extraction

  27. Data from field projects • Not always easy to ‘trace’ • Not well documented what is CoML data, aand which field project it belongs to • Needs mechanism to better document • Part of the metadata? • Exercise was done at iOBIS • Spreadsheet will be made available • Please check • In general, good agreement with our understanding and information from annual reports

  28. Field projects

  29. How to get data in OBIS? • Dialogue ongoing with all major providers • All field projects • Regional OBIS Nodes (RONs) • FishBase, OBIS SEAMAP… • iOBIS needs time to ingest data • Quality control… • Data cycle • Lag in data availability ~3 months • Depending on quality of the data