Keynote delivered at PRDLA 2008, Singapore

1. From Data to Data: One Version
of a History of Scholarly
Communication
PRDLA 2008 Closing Keynote
Dr Andrew Treloar – andrew.treloar.net
Australian National Data Service –
ands.org.au

3. Data led to early writing
http://www.utexas.edu/features/archive/2003/vase.html

4. But early preservation technologies
were a bit problematic…
http://www.earth-history.com/_images/ms2340.jpg

5. Time passes…

6. Doomed data
http://www.learnin
gcurve.gov.uk/foc
uson/domesday/ta
ke-a-closer-look/
In the vill in which St. Peter’s Church is situated [Westminster] the abbot of the same
place holds 13½ hides. There is land for 11 ploughs. To the demesne belongs 9 hides
and 1 virgate, and there are 4 ploughs. The villeins have 6 ploughs, and there could be 1
plough more. There are 9 villeins each on 1 virgate and 1 villein on 1 hide, and 9 villeins
on each half a virgate and 1 cottar on 5 acres, and 41 cottars who pay 40 shillings a
year for their gardens. [There is] Meadow for 11 ploughs, pasture for the livestock of the
vill, woodland for 100 pigs, and 25 houses of the abbot’s knights and other men who pay
8 shillings a year. In all it is worth £10; when received, the same; TRE £12. This manor

7. More time passes…

8. Scholarly communication for the
last 350 years

9. (a data-centric view, that is)

10. “A Correct Tide-
Table, Shewing the True
Times of the High-Waters
at London-Bridge, to Every
Day in the Year 1683. By
Mr. Flamstead”
Philosophical
Transactions, Vol.
13, (1683), pp. 10-15

11. 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

12. Data problems in published
literature

13. Inconvenient data
DOI: 10.1098/rsta.2005.1569

14. Imprisoned data
DOI 10.1098/rsta.2006.1793

15. Invisible data
DOI 10.1098/rsta.2006.1793

16. Inaccessible data

17. Missing negative data
• Need title capture for negative results

18. “Selective Publication of Antidepressant Trials and Its
Influence 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 that
the evidence base is complete and unbiased. Selective
publication of clinical trials - and the outcomes within
those trials - can lead to unrealistic estimates of drug
effectiveness and alter the apparent risk-benefit ratio”

19. 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
(http://www.pfc.org.au/bin/view/Main/Data)
19

20. 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

21. 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

22. 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

23. 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

24. A partial solution:
data in institutional repositories

25. ARROW

26. ARROW

27. ARROW Discovery Service

28. ARROW Discovery Service

29. Another partial solution:
researcher workflow integration

30. Repository domains
Treloar, A. and Harboe-Ree, C. (2008). "Data management and the curation continuum:
how the Monash experience is informing repository relationships". Proceedings of VALA
2008, Melbourne, February.

31. Service Provider
ARCHER’s Data-centric Model Shib Protected
Federation
IdP
IdP Web Access
Automated Instrument
Data Deposition
Content Management Private/Shared
System Research Repository Analysis Workflow
PKI
Automation
IdP
Desktop Access
31
IdP

32. ARCHER portal screenshot
32

34. Another partial solution:
discipline self-organisation

35. TARDIS overview

36. TARDIS partners

37. A national solution:
ANDS

38. 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

39. ARDC diagram

40. 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

41. 41

42. 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!

43. Questions?
• andrew.treloar@its.monash.edu.au
• http://andrew.treloar.net/
• http://arrow.edu.au/
• http://archer.edu.au/
• http://ands.org.au/