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From Data to Data: One version of a History of Scholarly Communication


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Keynote delivered at PRDLA 2008, Singapore

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From Data to Data: One version of a History of Scholarly Communication

  1. 1. From Data to Data: One Version of a History of Scholarly Communication PRDLA 2008 Closing KeynoteDr Andrew Treloar – Australian National Data Service –
  2. 2. Data led to early writing
  3. 3. But early preservation technologies were a bit problematic…
  4. 4. Time passes…
  5. 5. Doomed data http://www.learnin uson/domesday/ta ke-a-closer-look/In the vill in which St. Peter’s Church is situated [Westminster] the abbot of the sameplace holds 13½ hides. There is land for 11 ploughs. To the demesne belongs 9 hidesand 1 virgate, and there are 4 ploughs. The villeins have 6 ploughs, and there could be 1plough more. There are 9 villeins each on 1 virgate and 1 villein on 1 hide, and 9 villeinson each half a virgate and 1 cottar on 5 acres, and 41 cottars who pay 40 shillings ayear for their gardens. [There is] Meadow for 11 ploughs, pasture for the livestock of thevill, woodland for 100 pigs, and 25 houses of the abbot’s knights and other men who pay8 shillings a year. In all it is worth £10; when received, the same; TRE £12. This manor
  6. 6. More time passes…
  7. 7. Scholarly communication for the last 350 years
  8. 8. (a data-centric view, that is)
  9. 9. “A Correct Tide-Table, Shewing the TrueTimes of the High-Watersat London-Bridge, to EveryDay in the Year 1683. ByMr. Flamstead”PhilosophicalTransactions, Vol.13, (1683), pp. 10-15
  10. 10. Eclipse tables “An Observation of the Beginning of the Lunar Eclipse which Hapned Aug. 19. 1681. in the Morning, Made on the Island of St. Lawrence or Madagascar, by Mr. Tho. Heathcot, and Communicated by Mr. Flamstead” Philosophical Transactions, Vol. 13, (1683), p. 15
  11. 11. Data problems in published literature
  12. 12. Inconvenient data DOI: 10.1098/rsta.2005.1569
  13. 13. Imprisoned data DOI 10.1098/rsta.2006.1793
  14. 14. Invisible dataDOI 10.1098/rsta.2006.1793
  15. 15. Inaccessible data
  16. 16. Missing negative data• Need title capture for negative results
  17. 17. “Selective Publication of Antidepressant Trials and ItsInfluence on Apparent Efficacy”Turner, Erick, Matthews, Annette, Linardatos, Eftihia,Tell, Robert, Rosenthal, Robert.New England Journal of Medicine. 358(3):252-260,January 17, 2008.From the Abstract:“Evidence-based medicine is valuable to the extent thatthe evidence base is complete and unbiased. Selectivepublication of clinical trials - and the outcomes withinthose trials - can lead to unrealistic estimates of drugeffectiveness and alter the apparent risk-benefit ratio”
  18. 18. Why is data now so important?• We are in an era of increasing data-intensive research• Almost all data is now born digital• Increasing amount of data generated (semi-)automatically• “Consequently, increasing effort and therefore funding will necessarily be diverted to data and data management over time” – Towards the Australian Data Commons, p. 4 ( 19
  19. 19. Need for standardisation• Software and silicon-based hardware keep getting cheaper, carbon-based wetware keeps getting more expensive• Fixing data management problems is enormously labour intensive and costly• “Consequently, standardisation within forms of data and simplification in the frameworks around retention, storage, access and use of data, and the elimination of differences whose resolution requires labour, must be made, if the on-going keeping and reuse of data is to remain affordable” – Towards the Australian Data Commons, p. 5 20
  20. 20. Role of data federations• With more data online, more can be done• Possible now to answer questions unrelated to reasons why data was collected originally• Increasing focus on cross-disciplinary science• “Consequently greater clarity is needed over control and access to community-funded data, and the means of aggregating, federating and accessing such data are increasingly important” – Towards the Australian Data Commons, p. 5 21
  21. 21. Changing Data, Changing Research• New scientific instruments – Large Hadron Collider at CERN: 1.5 GB/sec – Square Kilometre Array telescope: 1 EB/day! • Exabyte = a thousand million gigabytes (1018 bytes)• New scientific Models – The mapping of the Human Genome: A billion DNA letters in a human sequence – Global climate models: ever finer time/space resolution• New knowledge from unlocked data – Hubble data has to be shared six months after collection – Majority of published research from Hubble telescope data was not “first use” 22
  22. 22. Data desiderata• Easy deposit for researchers• Greater (preferably open) access for all• Easier (or any!) citability• Easier discoverability, particularly outside generating discipline• More context for those outside the generating discipline
  23. 23. A partial solution:data in institutional repositories
  24. 24. ARROW
  25. 25. ARROW
  26. 26. ARROW Discovery Service
  27. 27. ARROW Discovery Service
  28. 28. Another partial solution:researcher workflow integration
  29. 29. Repository domainsTreloar, A. and Harboe-Ree, C. (2008). "Data management and the curation continuum:how the Monash experience is informing repository relationships". Proceedings of VALA2008, Melbourne, February.
  30. 30. Service ProviderARCHER’s Data-centric Model Shib Protected Federation IdP IdP Web Access Automated Instrument Data DepositionContent Management Private/Shared System Research Repository Analysis Workflow PKI Automation IdP Desktop Access 31 IdP
  31. 31. ARCHER portal screenshot 32
  32. 32. Another partial solution:discipline self-organisation
  33. 33. TARDIS overview
  34. 34. TARDIS partners
  35. 35. A national solution: ANDS
  36. 36. Australian National Data Service• Funded by Australian Government at A$21M from mid-2008 through mid-2011• Goal: to deliver greater access to Australia’s research data assets in forms that support easier and more effective data use and reuse• Approach: building the Australian Research Data Commons
  37. 37. ARDC diagram
  38. 38. ANDS Delivery Structure• ANDS has been structured as four inter- related and co-ordinated service delivery programs: – Developing Frameworks (policy, planning) – Providing Utilities (discovery, persistent ID) – Seeding the Commons (more data, better managed) – Building Capabilities (researcher and support)• Plus candidate service development activities funded through a discipline-driven 40
  39. 39. 41
  40. 40. Conclusion• Data is becoming steadily more important for research• Research results need to be communicated• Data is the next great challenge for scholarly communication• And so, it should be the next great challenge for libraries• Over to you!
  41. 41. Questions?•••••