Dealing with scientific information 'overwhelm'. This slide set has been converted to ppt from Apple Keynote, and looks different from the original, especially the animations. This seems better than the first upload.

1. Big Journal Literature
Big Usage
Jan Velterop – SSP – Arlington, May 28, 2015

2. 11,135,542
More than 2 added
every minute of 2014
Number of abstracts in PubMed

3. Information overload!

4. that
Overload?
Or rapidly increasing
knowledge…
…making a world of
difference that can
change the course
of scientific thought?

5. Dissemination
of knowledge

6. Optimal dissemination for

8. Lamp post research

9. Looking merely at the literature that
one can read – which is not
necessarily all the literature that is
potentially important to one’s research
Lamp post research:

22. Big Usage
But not in the way we’re used to

23. So, what to do?

24. problemEvery has its solution

25. Possible strategies:
1.Publish a smaller number of papers
2.Accept that an ever smaller proportion of the
available papers is actually being read
3.Capture the knowledge contained in all papers
and map it in such a way that you can navigate
that knowledge

26. Possible strategies:
1.Publish a smaller number of papers
Maybe, but if it means less information, it’s
ludicrous
2.Accept that an ever smaller proportion of the
available papers is actually being read
3.Capture the knowledge contained in all papers and
map it in such a way that you can navigate that
knowledge

27. Possible strategies:
1.Publish a smaller number of papers
2.Accept that an ever smaller proportion of the
available papers is actually being read
How to choose, though?
3.Capture the knowledge contained in all papers
and map it in such a way that you can navigate
that knowledge

30. In any event:
l’embarras du choix

31. Possible strategies:
1.Publish a smaller number of papers
2.Accept that an ever smaller proportion of the
available papers is actually being read
3.Capture the knowledge contained in all papers
and map it in such a way that you can navigate
that knowledge
Yes! Helps to see trends and what to
choose!

32. First
create an overview…

33. …only then
start digging

34. How might we create overviews?

35. “As the rate of publishing accelerates,
the need for computational support to
work out which articles to read, and
how to interpret, reproduce and validate
the claims they contain is growing.”
Quote from ‘Lazarus’:
http://www.bbsrc.ac.uk/pa/grants/AwardDetails.aspx?FundingReference=BB/L005298/1

36. Extract Key Insights
Extract Key Insights

37. Imagine you had a paper that concluded:
“On hot days, it turns out that aspirin
decreases the chances of blot clots, but
increases the chances of heart attack in
humans; the effect wasn't observed in rats
at all; simulations of dogs seem to
suggest that the effect is present but
independent of temperature unless the dog
is accompanied by a human”

38. Imagine you had a paper that concluded:
“On hot dayshot days, it turns out that aspirinaspirin
decreasesdecreases the chances of blot clotsblot clots, but
increasesincreases the chances of heart attackheart attack in
humanshumans; the effect wasn't observed in ratsrats
at all; simulations of dogsdogs seem to
suggest that the effect is present but
independent of temperaturetemperature unless the dogdog
is accompanied by a humanhuman”

39. Significant concepts:
[CHEMBL25] (aspirin)
[EFO_0001702] ('temperature' from the
experimental factors ontology)
[Canis lupus familiaris]
[Homo sapiens]
[Mus musculus]
Headline Interactions (in the form of Triples):
[ASPIRIN] [DECREASES] [THROMBOSIS]
[ASPIRIN] [INCREASES] [MYOCARDIAL INFARCTION]
Significant concepts:
[CHEMBL25] (aspirin)
[EFO_0001702] ('temperature' from the
experimental factors ontology)
[Canis lupus familiaris]
[Homo sapiens]
[Mus musculus]
Headline Interactions (in the form of Triples):
[ASPIRIN] [DECREASES] [THROMBOSIS]
[ASPIRIN] [INCREASES] [MYOCARDIAL INFARCTION]
Add this to the article’s abstract
(after it’s been validated by the author):

40. Most efficient:
If publishers were to do this
(doesn’t cost much, and makes articles far more useful)
In case publishers don’t, alternative
ways are being developed outside
publishers’ control

41. publishing data in articles
Currently:
equals burying data
R.I.P.R.I.P.

42. ocuments
Via Utopia Documents, LAZARUS ‘resurrects’
knowledge from being buried in articles:
• entities (‘concepts’, incl. synonyms, e.g. proteins)
• phrases, statements, assertions (e.g. triples)
• molecules (incl. Markush structure groups)
• graphs
• tables http://utopiadocs.com

43. • entities (‘concepts’, incl. synonyms, e.g. proteins)
• phrases, statements, assertions (e.g. triples)
• molecules (incl. Markush structure groups)
• graphs
• tables
These are captured – with their provenance, e.g.
DOI – in a ‘Knowledge Graph’ of their relationships
When assertions are captured, they are compared
to the Knowledge Graph and labelled as ‘new’ (to
the Graph) or ‘already found earlier’
should beshould be
interesting forinteresting for
the peerthe peer
reviewer of areviewer of a
newlynewly
submittedsubmitted
articlearticle

44. “Lazarus to harness the crowd reading life-
science articles to resurrect the swathes of
legacy data buried in charts, tables, diagrams
and free-text, to liberate processable data into a
shared resource that benefits the community.”

45. “Lazarus to harness the crowd reading life-
science articles to resurrect the swathes of
legacy data buried in charts, tables, diagrams
and free-text, to liberate processable data into a
shared resource that benefits the community.”
“…activities currently carried out anyway by
individuals for their own purposes (annotating,
cross-referencing articles with databases,
organising collections of articles).”

46. “Lazarus to harness the crowd reading life-
science articles to resurrect the swathes of
legacy data buried in charts, tables, diagrams
and free-text, to liberate processable data into a
shared resource that benefits the community.”
Works on any pdf, from paywalled
Works on any pdf, from paywalled
and open sources alike
and open sources alike
“…activities currently carried out anyway by
individuals for their own purposes (annotating,
cross-referencing articles with databases,
organising collections of articles).”

48. VHL protein binds to HIF-α which is ubiquitinated and tagged for degradation in the proteasome.

51. ‘Assertions’ and ‘significant concepts’ extracted
from articles (either by the publisher or by others,
like Utopia’s LAZARUS), are added to a growing
‘knowledge graph’ which can be analysed for
trends, clusters, areas of intensive activity, etc.

52. Getting the picture from a large number of data

53. What we need is information
extracted from as many articles as
possible
The more we have, the ‘sharper’
the knowledge picture

54. Getting a better picture from even more assertions

56. Homing in
i.e. making the
choice what to
read in detail

58. BRAIN — Bio Relations And Intelligence Network

62. “Recombinant Knowledge”

64. >>>>

66. Once researchers have identified the
articles they really need to read,
it should be made very easy to do so

67. Ergo, what publishers should do, too,
is to make all articles available in
all formats: HTML, XML, PDF and
ePub – even print, on demand.

68. Also on mobile devices

69. For instance:
Easier than you might think

70. (www.researchpad.co)

73. Build collection of favourites

74. Read full text

75. Inspect metrics

76. share with others

79. sales@newgen.co technical inquiries: patrick@newgen.co
In their words:

80. ResearchPad Launch Process
Project
Definition
Branding
Publishing
Go Live
Turnaround
Time
- 8 weeks
Slide borrowed from:

81. What ResearchPad can do for publishers who
want it, at no extra cost*, is to integrate a
publisher’s content with anything from
elsewhere that’s freely available with open
access, so that this open access material can
be accessed from within the publisher’s platform
* personal communication

82. sales@newgen.co technical inquiries: patrick@newgen.co

83. Thank you
Jan Velterop – 28 May 2015
velterop@me.com