The document discusses reinventing the research article for online science publishing. It outlines seven "known knowns" in online science publishing, including that the internet has caused an information overload, science papers contain facts, and the traditional narrative research article format is outdated. The document then discusses each point in more detail, focusing on how science content can be improved for online delivery by better modeling user interests and extracting structured facts from research articles.

1. Reinventing the Research Article -
Seven Questions on
Science Publishing
Anita de Waard
Researcher Disruptive Technologies,
Elsevier Labs
NWO - Casimir Grantee,
Utrecht University
ELPUB 2008

2. Seven ’known knowns’ in online science publishing:

3. Seven ’known knowns’ in online science publishing:
1. The internet has caused an information overload.

4. Seven ’known knowns’ in online science publishing:
1. The internet has caused an information overload.
2. Science papers contain facts.

5. Seven ’known knowns’ in online science publishing:
1. The internet has caused an information overload.
2. Science papers contain facts.
3. The narrative research article is outdated and needs to be
replaced.

6. Seven ’known knowns’ in online science publishing:
1. The internet has caused an information overload.
2. Science papers contain facts.
3. The narrative research article is outdated and needs to be
replaced.
4. Since words contain meaning,

7. Seven ’known knowns’ in online science publishing:
1. The internet has caused an information overload.
2. Science papers contain facts.
3. The narrative research article is outdated and needs to be
replaced.
4. Since words contain meaning,
5. And words (and logic) contain scientific fact,

8. Seven ’known knowns’ in online science publishing:
1. The internet has caused an information overload.
2. Science papers contain facts.
3. The narrative research article is outdated and needs to be
replaced.
4. Since words contain meaning,
5. And words (and logic) contain scientific fact,
6. We just need to model them with xml + rdf;

9. Seven ’known knowns’ in online science publishing:
1. The internet has caused an information overload.
2. Science papers contain facts.
3. The narrative research article is outdated and needs to be
replaced.
4. Since words contain meaning,
5. And words (and logic) contain scientific fact,
6. We just need to model them with xml + rdf;
7. And the publishers should stop making all these papers.

10. 1. The internet has caused an information overload

11. 1. The internet has caused an information overload
- My own experience (as a researcher):

12. 1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists

13. 1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists
- OK: Finding things I expect hope exist

14. 1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists
- OK: Finding things I expect hope exist
- Hard: making sure I haven’t missed anything

15. 1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists
- OK: Finding things I expect hope exist
- Hard: making sure I haven’t missed anything
- However, none of these make me feel overwhelmed.

16. 1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists
- OK: Finding things I expect hope exist
- Hard: making sure I haven’t missed anything
- However, none of these make me feel overwhelmed.
- Infuriating:

17. 1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists
- OK: Finding things I expect hope exist
- Hard: making sure I haven’t missed anything
- However, none of these make me feel overwhelmed.
- Infuriating:
- Trying to respond to people who ask me something

18. 1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists
- OK: Finding things I expect hope exist
- Hard: making sure I haven’t missed anything
- However, none of these make me feel overwhelmed.
- Infuriating:
- Trying to respond to people who ask me something
- Managing three email accounts on 4 computers

19. 1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists
- OK: Finding things I expect hope exist
- Hard: making sure I haven’t missed anything
- However, none of these make me feel overwhelmed.
- Infuriating:
- Trying to respond to people who ask me something
- Managing three email accounts on 4 computers
- Following up on plans and projects

20. 1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists
- OK: Finding things I expect hope exist
- Hard: making sure I haven’t missed anything
- However, none of these make me feel overwhelmed.
- Infuriating:
- Trying to respond to people who ask me something
- Managing three email accounts on 4 computers
- Following up on plans and projects
- However, we can improve the delivery of science content online.

21. 1. The internet has caused an information overload

22. 1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:

23. 1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate

24. 1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
- Understand

25. 1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
- Understand
- Believe (Be convinced)

26. 1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
- Understand
- Believe (Be convinced)
- Explore

27. 1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
- Understand
- Believe (Be convinced)
- Explore
- But this does not address WHAT you want to Locate, Understand, ..

28. 1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
- Understand
- Believe (Be convinced)
- Explore
- But this does not address WHAT you want to Locate, Understand, ..
- Semantic network in pharmacology: ‘Grey out what I already know’

29. 1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
- Understand
- Believe (Be convinced)
- Explore
- But this does not address WHAT you want to Locate, Understand, ..
- Semantic network in pharmacology: ‘Grey out what I already know’
1. How can we model a user’s interest?

30. 1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
- Understand
- Believe (Be convinced)
- Explore
- But this does not address WHAT you want to Locate, Understand, ..
- Semantic network in pharmacology: ‘Grey out what I already know’
1. How can we model a user’s interest?

31. 2. Science papers contain facts

32. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome

33. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome
- Structured Digital Abstract [Gerstein et. al]: ‘machine-readable
XML summary of pertinent facts’

34. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome
- Structured Digital Abstract [Gerstein et. al]: ‘machine-readable
XML summary of pertinent facts’
- For FEBS: provide proteins, methods, protein-protein interactions,
as given in MINT:

35. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome
- Structured Digital Abstract [Gerstein et. al]: ‘machine-readable
XML summary of pertinent facts’
- For FEBS: provide proteins, methods, protein-protein interactions,
as given in MINT:
- 2008: authors provide, editors check

36. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome
- Structured Digital Abstract [Gerstein et. al]: ‘machine-readable
XML summary of pertinent facts’
- For FEBS: provide proteins, methods, protein-protein interactions,
as given in MINT:
- 2008: authors provide, editors check
- 2009: Word Plug-in tool suggests, authors (and editors) check

37. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome
- Structured Digital Abstract [Gerstein et. al]: ‘machine-readable
XML summary of pertinent facts’
- For FEBS: provide proteins, methods, protein-protein interactions,
as given in MINT:
- 2008: authors provide, editors check
- 2009: Word Plug-in tool suggests, authors (and editors) check

38. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome
- Structured Digital Abstract [Gerstein et. al]: ‘machine-readable
XML summary of pertinent facts’
- For FEBS: provide proteins, methods, protein-protein interactions,
as given in MINT:
- 2008: authors provide, editors check
- 2009: Word Plug-in tool suggests, authors (and editors) check

39. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome
- Structured Digital Abstract [Gerstein et. al]: ‘machine-readable
XML summary of pertinent facts’
- For FEBS: provide proteins, methods, protein-protein interactions,
as given in MINT:
- 2008: authors provide, editors check
- 2009: Word Plug-in tool suggests, authors (and editors) check

40. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome
- Structured Digital Abstract [Gerstein et. al]: ‘machine-readable
XML summary of pertinent facts’
- For FEBS: provide proteins, methods, protein-protein interactions,
as given in MINT:
- 2008: authors provide, editors check
- 2009: Word Plug-in tool suggests, authors (and editors) check

41. 2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/
MINT Database in Rome
- Structured Digital Abstract [Gerstein et. al]: ‘machine-readable
XML summary of pertinent facts’
- For FEBS: provide proteins, methods, protein-protein interactions,
as given in MINT:
- 2008: authors provide, editors check
- 2009: Word Plug-in tool suggests, authors (and editors) check

42. 2. Science papers contain facts

43. 2. Science papers contain facts
- Issue: authors cannot be curators!

44. 2. Science papers contain facts
- Issue: authors cannot be curators!
- Fact is not claim, but created by consensus post-hoc

45. 2. Science papers contain facts
- Issue: authors cannot be curators!
- Fact is not claim, but created by consensus post-hoc
- How do we model the process of consenses-building, of
disagreement, of fact creation, of mistrust and doubt?

46. 2. Science papers contain facts
- Issue: authors cannot be curators!
- Fact is not claim, but created by consensus post-hoc
- How do we model the process of consenses-building, of
disagreement, of fact creation, of mistrust and doubt?
2. Can we create (tools for) an ontology of doubt?

47. 2. Science papers contain facts
- Issue: authors cannot be curators!
- Fact is not claim, but created by consensus post-hoc
- How do we model the process of consenses-building, of
disagreement, of fact creation, of mistrust and doubt?
2. Can we create (tools for) an ontology of doubt?

48. 3. The narrative RA should be replaced

49. 3. The narrative RA should be replaced
Aristotle Quintilian Cell APA Style Guide
The introduction of a speech, where one announces the subject and purpose
prooimion Introduction exordium of the discourse, and where one usually employs the persuasive appeal of Introduction Introduction
ethos in order to establish credibility with the audience.
The second part of a classical oration, following the introduction or exordium.
The speaker here provides a narrative account of what has happened and
Statement of
prothesis narratio generally explains the nature of the case. Quintilian adds that the narratio is Introduction Introduction
Facts
followed by the propositio, a kind of summary of the issues or a statement of
the charge.
Coming between the narratio and the partitio of a classical oration, the
Summary propostitio propositio provides a brief summary of what one is about to speak on, or Abstract Abstract
concisely puts forth the charges or accusation.
Following the statement of facts, or narratio, comes the partitio or divisio. In
Division/ this section of the oration, the speaker outlines what will follow, in accordance Table of
partitio Article Outline
outline with what's been stated as the status, or point at issue in the case. Quintilian Contents
suggests the partitio is blended with the propositio and also assists memory.
Following the division / outline or partitio comes the main body of the speech
pistis Proof confirmatio where one offers logical arguments as proof. The appeal to logos is Results Methods, Results
emphasized here.
Following the the confirmatio or section on proof in a classical oration, comes
Refutation refutatio the refutation. As the name connotes, this section of a speech was devoted to Discussion Discussion
answering the counterarguments of one's opponent.
Following the refutatio and concluding the classical oration, the peroratio
epilogos peroratio conventionally employed appeals through pathos, and often included a Discussion Discussion
summing up (see the figures of summary, below).

50. The Story of Goldilocks Story Grammar Paper The AXH Domain of Ataxin-1 Mediates
and the Three Bears
Once upon a time
3. The narrative RA should be replaced
Time Setting Background
Neurodegeneration through Its Interaction with Gfi-1/
Senseless Proteins
The mechanisms mediating SCA1 pathogenesis are still not fully
Aristotle Quintilian understood, but some general principles have emerged. Guide
Cell APA Style
a little girl named Goldilocks Characters Objects of study the Drosophila Atx-1 homolog (dAtx-1) which lacks a polyQ tract,
The introduction of a speech, where one announces the subject and purpose
She went for a Introduction
prooimion walk in the exordium
Location of the discourse, and where one usually employs the persuasive appeal of effects and interactions to those of
Experimental studied and compared in vivo Introduction Introduction
forest. Pretty soon, she came ethos in order to establish credibility human protein
setup the with the audience.
upon a house.
She knocked and, when no one Goal The second part of a classical oration, following the introduction or exordium.function contributes to SCA1
Theme Research Gain insight into how Atx-1's
answered, Statement of
pathogenesis. How these interactions might contribute to the
The speaker here provides a narrative account of what has happened and
goal
prothesis narratio generally explains the nature of the case. Quintilian process andnarratio is might cause toxicity in only a subs
disease adds that the how they Introduction Introduction
Facts
followed by the propositio, a kind of summary neurons in SCA1 is not fully understood.
of of the issues or a statement of
the charge.
she walked right in. Attempt Hypothesis Atx-1 may play a role in the regulation of gene expression
Coming between the narratio and the partitio of a classical oration, the
Summary propostitio propositio provides a brief summary of what one is about to speak on, or Abstract Abstract
At the table in the kitchen, there Name EpisodeconciselyNameforth the charges or accusation. Induce Similar Phenotypes When
1 puts dAtX-1 and hAtx-1
were three bowls of porridge. Overexpressed in Files
Following the statement of facts, or narratio, comes the partitio or divisio. In
Division/
Goldilocks was hungry. Subgoalthis section of the oration, the speaker outlines what function of the AXH domain
Subgoal test the will follow, in accordance Table of
partitio Article Outline
outline with what's been stated as the status, or point at issue in the case. Quintilian Contents
She tasted the porridge from Attemptsuggests the partitio is blended with the propositio and also assists memory. using the GAL4/UAS system (Brand
Method overexpressed dAtx-1 in flies
the first bowl. and Perrimon, 1993) and compared its effects to those of hAtx-1.
Following the division / outline or partitio comes the main body of the speech
This porridge is too hot! sheconfirmatio
pistis Proof Outcome where one offers logical arguments asOverexpression of logos is by Rhodopsin1(Rh1)-GAL4, which drive
Results proof. The appeal to dAtx-1 Results Methods, Results
exclaimed. emphasized here.expression in the differentiated R1-R6 photoreceptor cells
(Mollereau et al., 2000 and O'Tousa et al., 1985), results in
Following the the confirmatio or section on proof in a classical oration, comes as does overexpression of hAtx-1
neurodegeneration in the eye,
Refutation refutatio the refutation. As the name connotes, this section of a speech at 2 days after eclosion, overexpression of either
[82Q]. Although was devoted to Discussion Discussion
answering the counterarguments of one's opponent. obvious morphological changes in the
Atx-1 does not show
So, she tasted the porridge Data (data not shown),
photoreceptor cells
from the second bowl. Following the refutatio and concluding the classical oration, the peroratio
This porridge is too cold, sheperoratio
epilogos Outcome conventionally employed appeals through pathos, and often included a large Discussion loss ofDiscussion
Results both genotypes show many holes and cell integrity a
said summing up (see the figures of summary, below).
28 days
So, she tasted the last bowl of Data (Figures 1B-1D).
porridge.
Ahhh, this porridge is just right, Outcome Results Overexpression of dAtx-1 using the GMR-GAL4 driver also induce
she said happily and eye abnormalities. The external structures of the eyes that
overexpress dAtx-1 show disorganized ommatidia and loss of

51. 3. The narrative RA should be replaced

52. 3. The narrative RA should be replaced
Discourse Segments:

53. 3. The narrative RA should be replaced
Discourse Segments:
- “A text is made up of Discourse Segments and the relations
between them” - Grosz and Sidner, Mann-Thomson, Marcu,
Swales

54. 3. The narrative RA should be replaced
Discourse Segments:
- “A text is made up of Discourse Segments and the relations
between them” - Grosz and Sidner, Mann-Thomson, Marcu,
Swales
- Discourse Segment Purpose: element that has a consistent
rhetorical/pragmatic goal.

55. 3. The narrative RA should be replaced
Discourse Segments:
- “A text is made up of Discourse Segments and the relations
between them” - Grosz and Sidner, Mann-Thomson, Marcu,
Swales
- Discourse Segment Purpose: element that has a consistent
rhetorical/pragmatic goal.
- Define for Biological Research Article:

56. 3. The narrative RA should be replaced
Discourse Segments:
- “A text is made up of Discourse Segments and the relations
between them” - Grosz and Sidner, Mann-Thomson, Marcu,
Swales
- Discourse Segment Purpose: element that has a consistent
rhetorical/pragmatic goal.
- Define for Biological Research Article:
<EXPERIMENTS>
<Experiment>
<Header header="h1">p53-Independent Initiation of G1 Arrest Induced by IR</Header>
<Fact fact="fa1" factref="br26">Since the transcriptional response by p53 is a relatively slow process,</Fact>
<Problem problem="p1">we asked whether initiation of a G1 arrest following genotoxic stress requires p53.
<Problem>
<Method method="m1">We generated an MCF-7 derivative </Method>
<Fact fact="fa2" factref="br24">that expresses the HPV16 E6 protein, which mediates degradation of p53
(<Bibref bib="br24">[24]</Bibref>).</Fact>
<Result result="r1">In the presence of E6, p53 stabilization in response to IR was almost completely prevented in
MCF-7 cells (<Figref figref="agami1.gif">Figure 1A).</Figref></Result>
<Result result="r2">Consistent with this, no induction of p21cip1 by IR was seen in the E6-expressing MCF-7 cells

57. 3. The narrative RA should be replaced

58. 3. The narrative RA should be replaced

59. 3. The narrative RA should be replaced

60. 3. The narrative RA should be replaced

61. 3. The narrative RA should be replaced
- Narrative is how stories are told; ‘the truth can only be told in
stories’....

62. 3. The narrative RA should be replaced
- Narrative is how stories are told; ‘the truth can only be told in
stories’....
- Scientific rhetoric is contained within the narrative

63. 3. The narrative RA should be replaced
- Narrative is how stories are told; ‘the truth can only be told in
stories’....
- Scientific rhetoric is contained within the narrative
- Main goal of article is to persuade: ‘ The author is a medium that
enables the article to get itself published (a la selfish gene/meme)’

64. 3. The narrative RA should be replaced
- Narrative is how stories are told; ‘the truth can only be told in
stories’....
- Scientific rhetoric is contained within the narrative
- Main goal of article is to persuade: ‘ The author is a medium that
enables the article to get itself published (a la selfish gene/meme)’
- Science happens in language - science is done by creating successful
persuasive texts IN ENGLISH! (empowerment rests on mastery of
this genre)

65. 3. The narrative RA should be replaced
- Narrative is how stories are told; ‘the truth can only be told in
stories’....
- Scientific rhetoric is contained within the narrative
- Main goal of article is to persuade: ‘ The author is a medium that
enables the article to get itself published (a la selfish gene/meme)’
- Science happens in language - science is done by creating successful
persuasive texts IN ENGLISH! (empowerment rests on mastery of
this genre)
- How to disentangle good science from good writing?

66. 3. The narrative RA should be replaced
- Narrative is how stories are told; ‘the truth can only be told in
stories’....
- Scientific rhetoric is contained within the narrative
- Main goal of article is to persuade: ‘ The author is a medium that
enables the article to get itself published (a la selfish gene/meme)’
- Science happens in language - science is done by creating successful
persuasive texts IN ENGLISH! (empowerment rests on mastery of
this genre)
- How to disentangle good science from good writing?
3. How can we better represent online narrative? ...

67. 3. The narrative RA should be replaced

68. 3. The narrative RA should be replaced
PHC undergo Growth arrest

69. 3. The narrative RA should be replaced
PHC undergo Growth arrest
Paper A:
implication
method fact
goal fact
results

70. 3. The narrative RA should be replaced
PHC undergo Growth arrest
Paper A:
implication
method fact
goal fact
results
data 1
data 2 data 3

71. 3. The narrative RA should be replaced
PHC undergo Growth arrest
Paper A: Paper B:
implication implication
method fact method fact
goal fact goal fact
results
results
data 1
data 4
data 2 data 3
data 5 data 6

72. 3. The narrative RA should be replaced
PHC undergo Growth arrest
Paper A: Paper B:
implication implication
method fact method fact
goal fact goal fact
results
results
data 1
data 4
data 2 data 3
data 5 data 6

73. 3. The narrative RA should be replaced
PHC undergo Growth arrest
Paper A: Paper B:
implication implication
method fact method fact
goal fact goal fact
results
results
data 1
data 4
data 2 data 3
data 5 data 6

74. 3. The narrative RA should be replaced
PHC undergo Growth arrest
Paper A: Paper B:
implication implication
g
nnin
method fact rpi method
de fact
un
goal fact goal fact
results
results
data 1
data 4
data 2 data 3
data 5 data 6

75. 3. The narrative RA should be replaced
PHC undergo Growth arrest
Paper A: Paper B:
implication implication
method fact method fact
goal fact goal fact
results
results
data 1
data 4
data 2 data 3
data 5 data 6

76. 3. The narrative RA should be replaced
PHC undergo Growth arrest
Paper A: Paper B:
implication implication
method method link
fact method fact
goal fact goal fact
results
results
data 1
data 4
data 2 data 3
data 5 data 6

77. 3. The narrative RA should be replaced

78. 3. The narrative RA should be replaced
- How to develop systems that ‘reconstruct the salami’

79. 3. The narrative RA should be replaced
- How to develop systems that ‘reconstruct the salami’
- Claim-evidence networks: identify nr. of experiments supporting a
claim, vs. nr. of papers containing two words in a sentence?

80. 3. The narrative RA should be replaced
- How to develop systems that ‘reconstruct the salami’
- Claim-evidence networks: identify nr. of experiments supporting a
claim, vs. nr. of papers containing two words in a sentence?
3. How can we better represent collections of online
narratives?

81. 3. The narrative RA should be replaced
- How to develop systems that ‘reconstruct the salami’
- Claim-evidence networks: identify nr. of experiments supporting a
claim, vs. nr. of papers containing two words in a sentence?
3. How can we better represent collections of online
narratives?

82. 4. Words contain meaning

83. 4. Words contain meaning
Sicilian?

84. 4. Words contain meaning
Sicilian?

85. 4. Words contain meaning
Sicilian?

86. 4. Words contain meaning
Sicilian?

87. 4. Words contain meaning
Sicilian?

88. 4. Words contain meaning

89. 4. Words contain meaning
- ‘A word is worth a thousand pictures’ (Don Loritz)

90. 4. Words contain meaning
- ‘A word is worth a thousand pictures’ (Don Loritz)
- The meaning of words occurs in context and is dependent
on knowledge and experience

91. 4. Words contain meaning
- ‘A word is worth a thousand pictures’ (Don Loritz)
- The meaning of words occurs in context and is dependent
on knowledge and experience
- This is even more so in science:
PSA = Prostate-Specific Antigen or Pot Smokers
Association of America?

92. 4. Words contain meaning

93. 4. Words contain meaning
- Cognitive linguistics: language and cognition cannot be separated -
language acts are cognitive acts

94. 4. Words contain meaning
- Cognitive linguistics: language and cognition cannot be separated -
language acts are cognitive acts
- Lakoff, metaphor: ‘anger is heat’

95. 4. Words contain meaning
- Cognitive linguistics: language and cognition cannot be separated -
language acts are cognitive acts
- Lakoff, metaphor: ‘anger is heat’
- Meaning is created in the mind:
a word is not (only) a ‘particle’ but (also) a ‘wave’:
Hearing/reading is not unpacking a package, but resonating at a
specific frequency - context is its medium - context-free language
does not exist!

96. 4. Words contain meaning
- Cognitive linguistics: language and cognition cannot be separated -
language acts are cognitive acts
- Lakoff, metaphor: ‘anger is heat’
- Meaning is created in the mind:
a word is not (only) a ‘particle’ but (also) a ‘wave’:
Hearing/reading is not unpacking a package, but resonating at a
specific frequency - context is its medium - context-free language
does not exist!
4. How do we model cognitive context?

97. 4. Words contain meaning
- Cognitive linguistics: language and cognition cannot be separated -
language acts are cognitive acts
- Lakoff, metaphor: ‘anger is heat’
- Meaning is created in the mind:
a word is not (only) a ‘particle’ but (also) a ‘wave’:
Hearing/reading is not unpacking a package, but resonating at a
specific frequency - context is its medium - context-free language
does not exist!
4. How do we model cognitive context?

98. 5. Words (and logic) contain scientific fact

99. 5. Words (and logic) contain scientific fact
• “[Y]ou can transform a fact into fiction or a fiction into fact just by
adding or subtracting references [and data]”
– Bruno Latour, ‘Science in Action’,1987

100. 5. Words (and logic) contain scientific fact
• “[Y]ou can transform a fact into fiction or a fiction into fact just by
adding or subtracting references [and data]”
– Bruno Latour, ‘Science in Action’,1987
“We generated an MCF-7
derivative that expresses the
HPV16 E6 protein, which
mediates degradation of p53
([24]).”

101. 5. Words (and logic) contain scientific fact
• “[Y]ou can transform a fact into fiction or a fiction into fact just by
adding or subtracting references [and data]”
– Bruno Latour, ‘Science in Action’,1987
24. M. Scheffner, B.A. Werness, J.M. Huibregtse, A.J. Levine and
“We generated an MCF-7 P.M. Howley, The E6 oncoprotein encoded by human
papillomavirus types 16 and 18 promotes the degradation of
derivative that expresses the p53. Cell 63 (1990), pp. 1129–1136. SummaryPlus | Full Text
+ Links | PDF (1728 K) | Abstract + References in Scopus |
HPV16 E6 protein, which Cited By in Scopus
mediates degradation of p53
([24]).”

102. 5. Words (and logic) contain scientific fact
• “[Y]ou can transform a fact into fiction or a fiction into fact just by
adding or subtracting references [and data]”
– Bruno Latour, ‘Science in Action’,1987
24. M. Scheffner, B.A. Werness, J.M. Huibregtse, A.J. Levine and
“We generated an MCF-7 P.M. Howley, The E6 oncoprotein encoded by human
papillomavirus types 16 and 18 promotes the degradation of
derivative that expresses the p53. Cell 63 (1990), pp. 1129–1136. SummaryPlus | Full Text
+ Links | PDF (1728 K) | Abstract + References in Scopus |
HPV16 E6 protein, which Cited By in Scopus
mediates degradation of p53
([24]).”
“In the presence of E6, p53
stabilization in response to IR
was almost completely
prevented in MCF-7 cells
(Figure 1A).”

103. 5. Words (and logic) contain scientific fact
• “[Y]ou can transform a fact into fiction or a fiction into fact just by
adding or subtracting references [and data]”
– Bruno Latour, ‘Science in Action’,1987
24. M. Scheffner, B.A. Werness, J.M. Huibregtse, A.J. Levine and
“We generated an MCF-7 P.M. Howley, The E6 oncoprotein encoded by human
papillomavirus types 16 and 18 promotes the degradation of
derivative that expresses the p53. Cell 63 (1990), pp. 1129–1136. SummaryPlus | Full Text
+ Links | PDF (1728 K) | Abstract + References in Scopus |
HPV16 E6 protein, which Cited By in Scopus
mediates degradation of p53
([24]).”
“In the presence of E6, p53
stabilization in response to IR
was almost completely
prevented in MCF-7 cells
(Figure 1A).” Figure 1. Initiation and Maintenance of G1 Arrest Induced by
IR(A) Stable MCF-7 clones containing either pCDNA3.1 (Neo)
or pCDNA3.1-E6 were irradiated (20 Gy), and cellular protein
extracts were made 2 hr later, separated on 10% SDS PAGE,
and immunoblotted to detect p53 and cyclin D1 proteins.

104. 5. Words (and logic) contain scientific fact

105. 5. Words (and logic) contain scientific fact
- Essential persuasive elements are non-textual

106. 5. Words (and logic) contain scientific fact
- Essential persuasive elements are non-textual
- Open Data, how to incorporate into ‘text mining’?

107. 5. Words (and logic) contain scientific fact
- Essential persuasive elements are non-textual
- Open Data, how to incorporate into ‘text mining’?
- Bioimage consortium (Shotton, Oxford): access biology images
across a variety of sources (PLoS, Nature, Elsevier...) and create
common metadata format

108. 5. Words (and logic) contain scientific fact
- Essential persuasive elements are non-textual
- Open Data, how to incorporate into ‘text mining’?
- Bioimage consortium (Shotton, Oxford): access biology images
across a variety of sources (PLoS, Nature, Elsevier...) and create
common metadata format
- SPIDER: Allowing shared access to epidemiology data (meta-
epidemiology)

109. 5. Words (and logic) contain scientific fact
- Essential persuasive elements are non-textual
- Open Data, how to incorporate into ‘text mining’?
- Bioimage consortium (Shotton, Oxford): access biology images
across a variety of sources (PLoS, Nature, Elsevier...) and create
common metadata format
- SPIDER: Allowing shared access to epidemiology data (meta-
epidemiology)
- Tie in to Open Data initiative, generalise, get buy in, sustainability:

110. 5. Words (and logic) contain scientific fact
- Essential persuasive elements are non-textual
- Open Data, how to incorporate into ‘text mining’?
- Bioimage consortium (Shotton, Oxford): access biology images
across a variety of sources (PLoS, Nature, Elsevier...) and create
common metadata format
- SPIDER: Allowing shared access to epidemiology data (meta-
epidemiology)
- Tie in to Open Data initiative, generalise, get buy in, sustainability:
5. How do we represent (and access) non-textual
elements?

111. 5. Words (and logic) contain scientific fact
- Essential persuasive elements are non-textual
- Open Data, how to incorporate into ‘text mining’?
- Bioimage consortium (Shotton, Oxford): access biology images
across a variety of sources (PLoS, Nature, Elsevier...) and create
common metadata format
- SPIDER: Allowing shared access to epidemiology data (meta-
epidemiology)
- Tie in to Open Data initiative, generalise, get buy in, sustainability:
5. How do we represent (and access) non-textual
elements?

112. 6. Just model the facts with xml + rdf

113. 6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?

114. 6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?
- Versioning in DTDs/Schemas? Principle of hierarchical trees - not
always best model of a content set

115. 6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?
- Versioning in DTDs/Schemas? Principle of hierarchical trees - not
always best model of a content set
- First pass at relations in RDF (Resource Description Framework:

116. 6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?
- Versioning in DTDs/Schemas? Principle of hierarchical trees - not
always best model of a content set
- First pass at relations in RDF (Resource Description Framework:
- Cohere: Open University - (open!) system of creating and
linking claims

117. 6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?
- Versioning in DTDs/Schemas? Principle of hierarchical trees - not
always best model of a content set
- First pass at relations in RDF (Resource Description Framework:
- Cohere: Open University - (open!) system of creating and
linking claims

118. 6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?
- Versioning in DTDs/Schemas? Principle of hierarchical trees - not
always best model of a content set
- First pass at relations in RDF (Resource Description Framework:
- Cohere: Open University - (open!) system of creating and
linking claims

119. 6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?
- Versioning in DTDs/Schemas? Principle of hierarchical trees - not
always best model of a content set
- First pass at relations in RDF (Resource Description Framework:
- Cohere: Open University - (open!) system of creating and
linking claims
- More experiments with RDF:

120. 6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?
- Versioning in DTDs/Schemas? Principle of hierarchical trees - not
always best model of a content set
- First pass at relations in RDF (Resource Description Framework:
- Cohere: Open University - (open!) system of creating and
linking claims
- More experiments with RDF:
- DOPE: Semantic access to heterogeneous data in pharmacology

121. 6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?
- Versioning in DTDs/Schemas? Principle of hierarchical trees - not
always best model of a content set
- First pass at relations in RDF (Resource Description Framework:
- Cohere: Open University - (open!) system of creating and
linking claims
- More experiments with RDF:
- DOPE: Semantic access to heterogeneous data in pharmacology
- OKKAM: Entity-centric web (EU-funded)

122. 1. DOPE (2003) the facts with xml + rdf
6. Just model
deduplicate thesaurus term
- Content in XML - but what about overlapping tags?
- Versioning in DTDs/Schemas? Principle of hierarchical trees - not
always best model of a content set visualise overlap results
- First pass at relations in RDF (Resource Description Framework:
- Cohere: Open University - (open!) system of creating and
select co-occurrence terms
linking claims
- More experiments with RDF:
- DOPE: Semantic access to heterogeneous data in pharmacology
see results set + link to full-text
- OKKAM: Entity-centric web (EU-funded)
8

123. 6. Just model the facts with xml + rdf

124. 6. Just model the facts with xml + rdf
- Yes, but:

125. 6. Just model the facts with xml + rdf
- Yes, but:
- In practice: ScienceDirect does not use our XML... (shhh....)

126. 6. Just model the facts with xml + rdf
- Yes, but:
- In practice: ScienceDirect does not use our XML... (shhh....)
- At Elsevier: Project Harpoon: ‘stab’ the document with
metadata, asynchronous, linked in (XPath/XQuery), distributed

127. 6. Just model the facts with xml + rdf
- Yes, but:
- In practice: ScienceDirect does not use our XML... (shhh....)
- At Elsevier: Project Harpoon: ‘stab’ the document with
metadata, asynchronous, linked in (XPath/XQuery), distributed
- Not solved in XML - how to access a phrase inside an article:

128. 6. Just model the facts with xml + rdf
- Yes, but:
- In practice: ScienceDirect does not use our XML... (shhh....)
- At Elsevier: Project Harpoon: ‘stab’ the document with
metadata, asynchronous, linked in (XPath/XQuery), distributed
- Not solved in XML - how to access a phrase inside an article:
- access inside a PDF by coordinates? Format, content changes

129. 6. Just model the facts with xml + rdf
- Yes, but:
- In practice: ScienceDirect does not use our XML... (shhh....)
- At Elsevier: Project Harpoon: ‘stab’ the document with
metadata, asynchronous, linked in (XPath/XQuery), distributed
- Not solved in XML - how to access a phrase inside an article:
- access inside a PDF by coordinates? Format, content changes
- add IDs to every single element? Format, content, version
changes?

130. 6. Just model the facts with xml + rdf
- Yes, but:
- In practice: ScienceDirect does not use our XML... (shhh....)
- At Elsevier: Project Harpoon: ‘stab’ the document with
metadata, asynchronous, linked in (XPath/XQuery), distributed
- Not solved in XML - how to access a phrase inside an article:
- access inside a PDF by coordinates? Format, content changes
- add IDs to every single element? Format, content, version
changes?
- How to represent relations, even if we know where they link?

131. 6. Just model the facts with xml + rdf
- Yes, but:
- In practice: ScienceDirect does not use our XML... (shhh....)
- At Elsevier: Project Harpoon: ‘stab’ the document with
metadata, asynchronous, linked in (XPath/XQuery), distributed
- Not solved in XML - how to access a phrase inside an article:
- access inside a PDF by coordinates? Format, content changes
- add IDs to every single element? Format, content, version
changes?
- How to represent relations, even if we know where they link?
6. How can we better model discourse elements (and
relations)?

132. 6. Just model the facts with xml + rdf
- Yes, but:
- In practice: ScienceDirect does not use our XML... (shhh....)
- At Elsevier: Project Harpoon: ‘stab’ the document with
metadata, asynchronous, linked in (XPath/XQuery), distributed
- Not solved in XML - how to access a phrase inside an article:
- access inside a PDF by coordinates? Format, content changes
- add IDs to every single element? Format, content, version
changes?
- How to represent relations, even if we know where they link?
6. How can we better model discourse elements (and
relations)?

133. 7. And publishers should stop making all those papers.

134. 7. And publishers should stop making all those papers.
- 6 uses of a RA:

135. 7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application

136. 7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card

137. 7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
- thesis

138. 7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
- thesis
- conference tickets

139. 7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
- thesis
- conference tickets
- research assessment

140. 7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
- thesis
- conference tickets
- research assessment
- and yes, by the way, reporting on scientific work.

141. 7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
- thesis
- conference tickets
- research assessment
- and yes, by the way, reporting on scientific work.
- Scientists are evaluated largely based on publications:
this enables their production to be evaluated by non-specialists

142. 7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
- thesis
- conference tickets
- research assessment
- and yes, by the way, reporting on scientific work.
- Scientists are evaluated largely based on publications:
this enables their production to be evaluated by non-specialists
- This places an undue stress on quantity, conformity (for risk of
being rejected), publishing for its own sake.

143. 7. And publishers should stop making all those papers.

144. 7. And publishers should stop making all those papers.
The real challenge:

145. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling

146. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power

147. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents

148. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:

149. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:
- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org

150. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:
- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org

151. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:
- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org
- Prof. Twalib Ngoma, Professor of Oncology from
Dar-Es-Salaam, Nigeria

152. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:
- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org
- Prof. Twalib Ngoma, Professor of Oncology from
Dar-Es-Salaam, Nigeria

153. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:
- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org
- Prof. Twalib Ngoma, Professor of Oncology from
Dar-Es-Salaam, Nigeria
- Prof. Zimitri Erasmus, Sociologist from Cape Town

154. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:
- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org
- Prof. Twalib Ngoma, Professor of Oncology from
Dar-Es-Salaam, Nigeria
- Prof. Zimitri Erasmus, Sociologist from Cape Town

155. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:
- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org
- Prof. Twalib Ngoma, Professor of Oncology from
Dar-Es-Salaam, Nigeria
- Prof. Zimitri Erasmus, Sociologist from Cape Town
How can we access their science?

156. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:
- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org
- Prof. Twalib Ngoma, Professor of Oncology from
Dar-Es-Salaam, Nigeria
- Prof. Zimitri Erasmus, Sociologist from Cape Town
How can we access their science?
7. How can we disentangle communication and evaluation
(‘metric of attribution’ - virtual RFID)?

157. 7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
- in large companies, nr. of PhDs is inversely proportional to power
- direction of scientific research determined by managers for adolescents
For science to survive, we need:
- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org
- Prof. Twalib Ngoma, Professor of Oncology from
Dar-Es-Salaam, Nigeria
- Prof. Zimitri Erasmus, Sociologist from Cape Town
How can we access their science?
7. How can we disentangle communication and evaluation
(‘metric of attribution’ - virtual RFID)?

158. Seven ‘Known Unknowns’ in Online Science Publishing

159. Seven ‘Known Unknowns’ in Online Science Publishing
1. How can we model a user’s interest?

160. Seven ‘Known Unknowns’ in Online Science Publishing
1. How can we model a user’s interest?
2. Can we create an ontology of doubt?

161. Seven ‘Known Unknowns’ in Online Science Publishing
1. How can we model a user’s interest?
2. Can we create an ontology of doubt?
3. How can we better represent collections of online narrative?

162. Seven ‘Known Unknowns’ in Online Science Publishing
1. How can we model a user’s interest?
2. Can we create an ontology of doubt?
3. How can we better represent collections of online narrative?
4. How do we model cognitive context?

163. Seven ‘Known Unknowns’ in Online Science Publishing
1. How can we model a user’s interest?
2. Can we create an ontology of doubt?
3. How can we better represent collections of online narrative?
4. How do we model cognitive context?
5. How do we represent and access non-textual elements?

164. Seven ‘Known Unknowns’ in Online Science Publishing
1. How can we model a user’s interest?
2. Can we create an ontology of doubt?
3. How can we better represent collections of online narrative?
4. How do we model cognitive context?
5. How do we represent and access non-textual elements?
6. How can we better model discourse elements and relations?

165. Seven ‘Known Unknowns’ in Online Science Publishing
1. How can we model a user’s interest?
2. Can we create an ontology of doubt?
3. How can we better represent collections of online narrative?
4. How do we model cognitive context?
5. How do we represent and access non-textual elements?
6. How can we better model discourse elements and relations?
7. How can we disentangle communication and evaluation?