A presentation by Traore et al. at the Workshop on Dealing with Drivers of Rapid Change in Africa: Integration of Lessons from Long-term Research on INRM, ILRI, Nairobi, June 12-13, 2008.

1. Why documenting research data? Is it worth the extra effort? Learnings from the Fakara metadata encoding exercise Presented by P.S. Traoré et al.at the Workshop on Dealing with Drivers of Rapid Change in Africa: Integration of Lessons from Long-term Research on INRM, ILRI, Nairobi, June 12-13, 2008

3. biophysical, socio-economic data

4. proprietary, 3d party data

5. specialized, disciplinary data

7. remote sensing share of data provision growing – just a matter of time (technology driven)

8. increase in connectivity = data mining opportunities

10. Fakara commissioned study (JIRCAS – ILRI – ICRISAT – INRAN – CILSS +) Objective: Document Fakara Data collected in the Fakara area by ILRI, ICRISAT and JIRCAS, according to recognized standards and software Workplan: 1) Completion of the inventory of available data, by September 2006 through interaction with concerned scientists and a workshop 2) Encoding of data, by December 2006 3) Finalization of the Fakara Metadatabase document and submission of final report with publication list to JIRCAS, by January 2007

12. privileged area for a series of studies at the landscape scale

13. earlier work initiated by ILRI team (Pierre Hiernaux, Matthew Turner)

15. area of 500 km2

16. early 2000, ICRISAT involvement: characterization and in-situ evaluation of technologies

18. African Monsoon Multidisciplinary Analysis (AMMA) (ICRISAT has recently signed a Data Agreement with AMMA/IRD allowing access to several data sets and satellite images collected within this project)

19. INRAN (Gandah et al.)

21. Difficult to capitalize on data collected by collaborating institutions

22. Data sharing is very limited

25. Help data producers publicize and support use of data

26. Increase the value of data as potential users are more likely to retrieve information about it and make proper use of it

27. Protect an organization’s investment in data throughout the years

28. Limit loss of value that affects undocumented data with staff changes

29. Reduce duplication of datasets arising from lack of confidence in existing dataDrivers of Change Workshop – ILRI, Nairobi – 13 June 2008

31. guidelines and tools can help implement metadata policy

32. but metadata encoding remains dependent upon efficient software tools

33. metadata policy = should apply not only to new datasets, but also previously created ones… by far the biggest burden for an organization, because info. required to describe past data often missed as data creators have left

34. postponing description of existing datasets will result in shinking knowledge about the datasets = NO GOOD!

35. highlights the need to plan metadata establishment ASAP for existing datasetsDrivers of Change Workshop – ILRI, Nairobi – 13 June 2008

37. but… no standardized, unique definition of geographical datasets  subjective and project/objective specific !

38. different themes belonging to same geographic area (e.g. Fakara)

39. similar themes belonging to different geographic areas (

40. different GIS datasets might show different content and hierarchical structure

41. granularity is a way to define hierarchy in a dataset, helps metadata implementation- understanding commonality between elements is key… “to correctly apply a metadata standard to a dataset, it helps to understand what the single elements share in common and how they could integrate inside the dataset” (CSI, 2005)  e.g. contact, distribution infos. - then, implementing inheritance is critical… “the efficient metadata management of a GIS dataset is implemented in such a way that most of the metadata info can flow from coarse level of granularity down to individual elements of the dataset” (CSI, 2005)  use of metadata templates Drivers of Change Workshop – ILRI, Nairobi – 13 June 2008

42. Suggested Metadata Checklist (see Schweitzer1998*.pdf handout for details) Metadata standards are specifications  they tend to emphasize fine details of geospatial data. Below are interview guidelines to fill FGDC type records 1.What does the dataset describe? 2.Who produced the dataset? 3. Why was the dataset created? 4. How was the dataset created? 5. How reliableare the data, andwhat problemsremain in the dataset? 6. How can one get a copyof the dataset? 7. Who wrotethe metadata? Drivers of Change Workshop – ILRI, Nairobi – 13 June 2008

43. GeoNetwork Drivers of Change Workshop – ILRI, Nairobi – 13 June 2008

44. GeoNetwork inside CGIAR Drivers of Change Workshop – ILRI, Nairobi – 13 June 2008

45. GeoNetwork in Africa Drivers of Change Workshop – ILRI, Nairobi – 13 June 2008

46. Metadata records on GeoNetwork nodes Drivers of Change Workshop – ILRI, Nairobi – 13 June 2008

48. metadata are too complicated. Private users will not create metadata because existing formats, especially MPEG-7, are too complicated. As long as there are no automatic tools for creating metadata, they will not be created.

49. metadata are subjective and depend on context. Most probably, two persons will attach different metadata to the same resource due to their different points of view. Moreover metadata can be misinterpreted due to its dependency on context.…/… (worse to come… :-) Drivers of Change Workshop – ILRI, Nairobi – 13 June 2008

51. metadata are useless. Many of today's search engines allow finding texts very efficiently. There are other techniques for finding pictures, videos and music, namely query-by-example that will become more and more powerful in the future. Thus there is no real need for metadata.Drivers of Change Workshop – ILRI, Nairobi – 13 June 2008

52. So…?? Adapted from S. Harris Drivers of Change Workshop – ILRI, Nairobi – 13 June 2008

65. Meta-experiments

66. Meta-partnerships

67. Beyond metadata =

68. Meta-information

69. Meta-knowledge

70. Meta-wisdom

71. Beyond the technical level:

72. Conceptual level

74. Little use without “true” trans-disciplinarity (invest more the education sector?)

75. Little use without “higher-level” research paradigms (e.g. roads or genes?)

76. Little use without “higher-level” knowledge confrontation (e.g. stochastic methods as quantitative basis for holistic reasoning) – implicit vs. explicit

77. Little use without different collaborative models (increase contact area)

78. Observe more? (go to the field, really. travel overland)