This document summarizes digital science and the future of online research. It discusses how technology is changing the research workflow by enabling more efficient sharing of ideas, literature reviews, results, materials and data. However, there are still roadblocks to overcome, including specialization of tools, lack of interoperability, and accessibility issues. The key constituencies that must be considered are machines/tools, researchers, and decision makers. While the future of research is digital, adoption remains uneven and cultural shifts are needed to fully realize the benefits of new technologies.

1. digital science and the
future of online research
kaitlin thaney
policy lunchbox, 3 aug 2011
charles darwin house, london

2. xi. background

4. technology company
publisher link
london, nyc, tokyo

5. investment arm
incubator role
in-house dev

6. tiered approach
build to scale
researcher-focused

7. 1. science is changing

8. 1. science is changing
(and the research workflow)

9. research
idea
publish lit review
share results materials
retest
analyze experiment
collect data

10. blocking points
(to name a few ... )
idea
publish lit review
share results materials
retest
analyze experiment
collect data

11. types of information
(will revisit later)
idea
articles
content
proceedings prof activities
mentorship
patents
share results the non-digital “stuff”
retest
analysis protocols
synthesis parameters
datasets

12. text text
text

14. roadblocks
(to name a few) ...
specialisation of tools (+/-)
interoperability
accessibility
design decisions
the “social issue”

15. 2. key constituencies

16. (3)

17. machines
researchers
decision makers

18. machines
researchers
decision makers

19. ...
annotation
markup
search
discovery
“behind the
scenes”
...

20. still the starting point

21. patents are no better
(in many cases, worse)

27. can streamline

28. name disambiguation

29. 10,11-dihydro-5-methyl-5H-dibenzo[b,e][1,4]diazepin-11-one
(still strains the minds of the best)

32. machines
researchers
decision makers

33. CC-BY-2.0 - Plaxco Lab - http://www.flickr.com/photos/34857812@N04/

35. tracking
expiration

36. ordering
+
processing
the non-digital

37. protocols
parameters
calibration
misc. lit

38. managing information
different types of “data”

39. “i invented a folder based
system ...”

40. “i invented a folder based
system ...”
“yeah, we had a LIMS. it only
ever got used to store photos
from lab nights out.”

42. why?
experimentation reliance
data moves, grows legs
funder/instit’n pressure

44. machines
researchers
decision makers

45. how we measure
impact, reputation etc.

46. data capture
(of a different sort)

47. the “social issue”
best practices
behaviour roadblocks
discipline / researcher specific

48. paper’s still the currency

49. imperfect system

50. “Right now we're going through a
Cambrian explosion of metrics.”
- Johan Bollen
Nature 465, 864-866 (2010) | doi:10.1038/465864a

51. there’s been a drastic spike in terms of
sheer volume and type

52. citation / impact factor
h - index
weighted citations (eigenfactor, sjr)
“betweenness centrality”
alt-metrics, etc.

53. difficult to ...
harmonise
track /maintain / map
understand
(even still measure)

57. tools for decision makers
(research admin / funders)
using technology to spur
cultural shift

58. “ what do we want
on the back of our
(science) baseball
Top Trumps cards? “
- paul groth (et al.)

59. 3. the reality

60. “the future is here ...
just not evenly
distributed yet.”
- William Gibson

61. changing
understandings,
paradigms

62. technology can help
design decisions are key
plan for the irrational

63. more efficient research
increase productivity
enable reproducibility

64. thank you.
k.thaney@digital-science.com
www.digital-science.com
@kaythaney