Open Annotation in the CHARMe Project for the I Annotate Conference
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- The document discusses the CHARMe project, which uses open annotation to share knowledge about climate datasets and help users assess their fitness for purpose. - Open annotation is a natural fit for CHARMe as it allows recording of motivations, tags, and multiple annotation targets. - CHARMe will create connected repositories of commentary information stored as triples in "CHARMe nodes" that can be accessed through websites or services. - Some advanced use cases for annotating climate data include discussing the timing of events, tracing plankton blooms, and identifying features in subsets of multidimensional climate datasets.
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Open Annotation in the CHARMe Project for the I Annotate Conference
- 1. Open Annotation in The CHARMe Project Raquel Alegre I Annotate 2014 3rd and 4th April 2014 – San Francisco Scientific S/W Engineer University of Reading, UK
- 2. Summary
- 3. • Introduction to CHARMe Summary
- 4. • Introduction to CHARMe • What’s a climate dataset? Summary
- 5. • Introduction to CHARMe • What’s a climate dataset? • Use of Open Annotation in CHARMe Summary
- 6. • Introduction to CHARMe • What’s a climate dataset? • Use of Open Annotation in CHARMe • CHARMe’s basic use case Summary
- 7. • Introduction to CHARMe • What’s a climate dataset? • Use of Open Annotation in CHARMe • CHARMe’s basic use case • CHARMe’s advanced tools Summary
- 9. Introduction to CHARMe Post-fact annotations
- 10. Introduction to CHARMe Post-fact annotations External events
- 11. Introduction to CHARMe Post-fact annotations External events Data Provenance
- 12. Introduction to CHARMe Post-fact annotations External events Data Provenance User feedback
- 13. Introduction to CHARMe Post-fact annotations External events Data Provenance User feedback Data policy
- 14. Introduction to CHARMe Post-fact annotations External events Data Provenance User feedback Data policy Results of assessments
- 15. Introduction to CHARMe Post-fact annotations External events Data Provenance User feedback Data policy Results of assessments CHARMe
- 16. Post-fact annotations External events Data Provenance User feedback Data policy Results of assessments CHARMe Introduction to CHARMe
- 17. Post-fact annotations External events Data Provenance User feedback Data policy Results of assessments Sharing knowledge about Climate Data to help users judge fitness-for-purpose CHARMe Introduction to CHARMe
- 18. CHARMe Annotation Metadata Climate Dataset Overlapping volcanic eruption I recently published a paper about this dataset CHARMe basic OA use case Does anyone know about other related datasets? http://www.someURL.com/dataset
- 19. CHARMe Annotation Metadata Climate Dataset Overlapping volcanic eruption I recently published a paper about this dataset CHARMe basic OA use case Does anyone know about other related datasets? http://www.someURL.com/dataset W3C OA is a natural fit for CHARMe…
- 20. CHARMe Annotation Metadata Climate Dataset Overlapping volcanic eruption I recently published a paper about this dataset CHARMe basic OA use case Does anyone know about other related datasets? http://www.someURL.com/dataset W3C OA is a natural fit for CHARMe… …plus it lets us record motivation, tag, have multiple targets.
- 21. CHARMe node CHARMe node CHARMe node 3rd party system Data provider website • CHARMe will create connected repositories of commentary information • Annotations stored as triples in “CHARMe nodes” • Information can be read and entered through websites or web services CHARMe basic OA use case
- 22. CHARMe basic OA use case
- 23. CHARMe basic OA use case
- 24. CHARMe basic OA use case
- 25. CHARMe basic OA use case
- 26. What's a Climate Dataset?
- 27. Measurement < 1999 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 HPLC PABS CDOM SPM POC - CHN Chla(fluoro) Primary production FRRF Ed, Lu ac9 bb6 VSF,bb3 Above water RSR PAR Transmissometer Flow cytometry Coulter Counter Phytoplankton counts CTD What's a Climate Dataset?
- 28. Measurement < 1999 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 HPLC PABS CDOM SPM POC - CHN Chla(fluoro) Primary production FRRF Ed, Lu ac9 bb6 VSF,bb3 Above water RSR PAR Transmissometer Flow cytometry Coulter Counter Phytoplankton counts CTD What's a Climate Dataset?
- 29. Measurement < 1999 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 HPLC PABS CDOM SPM POC - CHN Chla(fluoro) Primary production FRRF Ed, Lu ac9 bb6 VSF,bb3 Above water RSR PAR Transmissometer Flow cytometry Coulter Counter Phytoplankton counts CTD What's a Climate Dataset?
- 30. Measurement < 1999 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 HPLC PABS CDOM SPM POC - CHN Chla(fluoro) Primary production FRRF Ed, Lu ac9 bb6 VSF,bb3 Above water RSR PAR Transmissometer Flow cytometry Coulter Counter Phytoplankton counts CTD What's a Climate Dataset?
- 31. Measurement < 1999 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 HPLC PABS CDOM SPM POC - CHN Chla(fluoro) Primary production FRRF Ed, Lu ac9 bb6 VSF,bb3 Above water RSR PAR Transmissometer Flow cytometry Coulter Counter Phytoplankton counts CTD What's a Climate Dataset? http://svs.gsfc.nasa.gov/vis/a000000/a003800/a003821/flow_flat_sst_2048x1024.mp4
- 32. Some advanced use cases... Climate data users discuss about data:
- 33. Some advanced use cases... Climate data users discuss about data: - Intercompare datasets - Research on events timing - Focus on specific areas of the world
- 34. Some advanced use cases...
- 35. Plankton bloom: “when did it start?” Some advanced use cases...
- 36. Plankton bloom: “when did it start?” Sediment from rivers: “Where did it come from?” Some advanced use cases...
- 37. Plankton bloom: “when did it start?” Sediment from rivers: “Where did it come from?” Reflective bloom: “or is it an image artefact??” Some advanced use cases...
- 38. Plankton bloom: “when did it start?” Sediment from rivers: “Where did it come from?” Reflective bloom: “or is it an image artefact??” Plankton tracing currents: “how deep?” Some advanced use cases...
- 39. Plankton bloom: “when did it start?” Sediment from rivers: “Where did it come from?” Reflective bloom: “or is it an image artefact??” Plankton tracing currents: “how deep?” Clear blue ocean: “isn’t it nice?” Some advanced use cases...
- 40. CHARMe advanced use cases Climate data often comes in 2D, 3D and 4D formats How to refer to a specific subset of these? OA SVG Selectors are not enough
- 45. Climate Dataset Dataset’s subset CHARMe Annotation Metadata Let’s update our report. Could be sediment. Why these high values? Subset Selector Geographic Selector Temporal Selector hasTemporalSelector hasGeographicSelector
- 46. CHARMe is all about Climate Data, but its use cases are also applicable to many other fields. Even Art! Conclusions
- 47. CHARMe is all about Climate Data, but its use cases are also applicable to many other fields. Even Art! Previous efforts e.g.: MapHub. Conclusions
- 48. CHARMe is all about Climate Data, but its use cases are also applicable to many other fields. Even Art! Previous efforts e.g.: MapHub. A few questions: Conclusions
- 49. CHARMe is all about Climate Data, but its use cases are also applicable to many other fields. Even Art! Previous efforts e.g.: MapHub. A few questions: How to annotate 4D data? Conclusions
- 50. CHARMe is all about Climate Data, but its use cases are also applicable to many other fields. Even Art! Previous efforts e.g.: MapHub. A few questions: How to annotate 4D data? Is it possible to standardize this? Conclusions
- 51. CHARMe is all about Climate Data, but its use cases are also applicable to many other fields. Even Art! Previous efforts e.g.: MapHub. A few questions: How to annotate 4D data? Is it possible to standardize this? What RDF ontologies to use? Conclusions
- 52. CHARMe is all about Climate Data, but its use cases are also applicable to many other fields. Even Art! Previous efforts e.g.: MapHub. A few questions: How to annotate 4D data? Is it possible to standardize this? What RDF ontologies to use? Let’s discuss later! Conclusions