Unknown Unknowns

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Talk from Elpub2008 in Toronto on 'known unknowns' in science publishing

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Unknown Unknowns

  1. 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. 2. Seven ’known knowns’ in online science publishing:
  3. 3. Seven ’known knowns’ in online science publishing: 1. The internet has caused an information overload.
  4. 4. Seven ’known knowns’ in online science publishing: 1. The internet has caused an information overload. 2. Science papers contain facts.
  5. 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 The narrative research article is outdated and needs to be replaced.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. 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 The narrative research article is outdated and needs to be replaced. 4.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. 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 The narrative research article is outdated and needs to be replaced. 4.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. 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 The narrative research article is outdated and needs to be replaced. 4.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. 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 The narrative research article is outdated and needs to be replaced. 4.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. 10. 1. The internet has caused an information overload
  11. 11. 1. The internet has caused an information overload- My own experience (as a researcher):
  12. 12. 1. The internet has caused an information overload- My own experience (as a researcher): - Easy: find what I know exists
  13. 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. 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. 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. 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. 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. 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. 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. 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. 21. 1. The internet has caused an information overload
  22. 22. 1. The internet has caused an information overload- Pick (carve out) a first set of user needs, e.g.:
  23. 23. 1. The internet has caused an information overload- Pick (carve out) a first set of user needs, e.g.: - Locate
  24. 24. 1. The internet has caused an information overload- Pick (carve out) a first set of user needs, e.g.: - Locate - Understand
  25. 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. 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. 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. 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. 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. 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. 31. 2. Science papers contain facts
  32. 32. 2. Science papers contain facts- With FEBS Letters Editorial Office in Heidelberg/ MINT Database in Rome
  33. 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. 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. 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. 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. 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. 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. 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. 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. 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. 42. 2. Science papers contain facts
  43. 43. 2. Science papers contain facts- Issue: authors cannot be curators!
  44. 44. 2. Science papers contain facts- Issue: authors cannot be curators!- Fact is not claim, but created by consensus post-hoc
  45. 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. 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. 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. 48. 3. The narrative RA should be replaced
  49. 49. 3. The narrative RA should be replacedAristotle Quintilian Cell APA Style Guide The introduction of a speech, where one announces the subject and purposeprooimion 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 ofprothesis 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 whats 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 ones opponent. Following the refutatio and concluding the classical oration, the peroratioepilogos peroratio conventionally employed appeals through pathos, and often included a Discussion Discussion summing up (see the figures of summary, below).
  50. 50. The Story of Goldilocks Story Grammar Paper The AXH Domain of Ataxin-1 Mediatesand the Three BearsOnce 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 Stylea 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 purposeShe 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 Introductionforest. 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-1sanswered, 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 AbstractAt the table in the kitchen, there Name EpisodeconciselyNameforth the charges or accusation. Induce Similar Phenotypes When 1 puts dAtX-1 and hAtx-1were 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 whats been stated as the status, or point at issue in the case. Quintilian ContentsShe 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 fliesthe 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 speechThis 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, Resultsexclaimed. emphasized here.expression in the differentiated R1-R6 photoreceptor cells (Mollereau et al., 2000 and OTousa 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 ones opponent. obvious morphological changes in the Atx-1 does not showSo, she tasted the porridge   Data (data not shown), photoreceptor cellsfrom the second bowl. Following the refutatio and concluding the classical oration, the peroratioThis 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 asaid summing up (see the figures of summary, below). 28 daysSo, 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 induceshe said happily and eye abnormalities. The external structures of the eyes that overexpress dAtx-1 show disorganized ommatidia and loss of
  51. 51. 3. The narrative RA should be replaced
  52. 52. 3. The narrative RA should be replacedDiscourse Segments:
  53. 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. 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. 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. 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. 57. 3. The narrative RA should be replaced
  58. 58. 3. The narrative RA should be replaced
  59. 59. 3. The narrative RA should be replaced
  60. 60. 3. The narrative RA should be replaced
  61. 61. 3. The narrative RA should be replaced- Narrative is how stories are told; ‘the truth can only be told in stories’....
  62. 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. 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. 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. 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. 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. 67. 3. The narrative RA should be replaced
  68. 68. 3. The narrative RA should be replaced PHC undergo Growth arrest
  69. 69. 3. The narrative RA should be replaced PHC undergo Growth arrestPaper A: implication method fact goal fact results
  70. 70. 3. The narrative RA should be replaced PHC undergo Growth arrestPaper A: implication method fact goal fact results data 1 data 2 data 3
  71. 71. 3. The narrative RA should be replaced PHC undergo Growth arrestPaper 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. 72. 3. The narrative RA should be replaced PHC undergo Growth arrestPaper 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. 73. 3. The narrative RA should be replaced PHC undergo Growth arrestPaper 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. 74. 3. The narrative RA should be replaced PHC undergo Growth arrestPaper 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. 75. 3. The narrative RA should be replaced PHC undergo Growth arrestPaper 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. 76. 3. The narrative RA should be replaced PHC undergo Growth arrestPaper 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. 77. 3. The narrative RA should be replaced
  78. 78. 3. The narrative RA should be replaced- How to develop systems that ‘reconstruct the salami’
  79. 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. 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. 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. 82. 4. Words contain meaning
  83. 83. 4. Words contain meaning Sicilian?
  84. 84. 4. Words contain meaning Sicilian?
  85. 85. 4. Words contain meaning Sicilian?
  86. 86. 4. Words contain meaning Sicilian?
  87. 87. 4. Words contain meaning Sicilian?
  88. 88. 4. Words contain meaning
  89. 89. 4. Words contain meaning- ‘A word is worth a thousand pictures’ (Don Loritz)
  90. 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. 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. 92. 4. Words contain meaning
  93. 93. 4. Words contain meaning- Cognitive linguistics: language and cognition cannot be separated - language acts are cognitive acts
  94. 94. 4. Words contain meaning- Cognitive linguistics: language and cognition cannot be separated - language acts are cognitive acts- Lakoff, metaphor: ‘anger is heat’
  95. 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. 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. 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. 98. 5. Words (and logic) contain scientific fact
  99. 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. 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. 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. 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. 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. 104. 5. Words (and logic) contain scientific fact
  105. 105. 5. Words (and logic) contain scientific fact- Essential persuasive elements are non-textual
  106. 106. 5. Words (and logic) contain scientific fact- Essential persuasive elements are non-textual- Open Data, how to incorporate into ‘text mining’?
  107. 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. 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. 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. 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. 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. 112. 6. Just model the facts with xml + rdf
  113. 113. 6. Just model the facts with xml + rdf- Content in XML - but what about overlapping tags?
  114. 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. 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. 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. 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. 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. 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. 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. 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. 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. 123. 6. Just model the facts with xml + rdf
  124. 124. 6. Just model the facts with xml + rdf- Yes, but:
  125. 125. 6. Just model the facts with xml + rdf- Yes, but: - In practice: ScienceDirect does not use our XML... (shhh....)
  126. 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. 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. 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. 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. 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. 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. 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. 133. 7. And publishers should stop making all those papers.
  134. 134. 7. And publishers should stop making all those papers. - 6 uses of a RA:
  135. 135. 7. And publishers should stop making all those papers. - 6 uses of a RA: - job application
  136. 136. 7. And publishers should stop making all those papers. - 6 uses of a RA: - job application - report card
  137. 137. 7. And publishers should stop making all those papers. - 6 uses of a RA: - job application - report card - thesis
  138. 138. 7. And publishers should stop making all those papers. - 6 uses of a RA: - job application - report card - thesis - conference tickets
  139. 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. 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. 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. 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. 143. 7. And publishers should stop making all those papers.
  144. 144. 7. And publishers should stop making all those papers.The real challenge:
  145. 145. 7. And publishers should stop making all those papers.The real challenge:- in Holland, chemistry departments are dwindling
  146. 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. 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. 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 adolescentsFor science to survive, we need:
  149. 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 adolescentsFor science to survive, we need:- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org
  150. 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 adolescentsFor science to survive, we need:- ‘Hanny’, who found a Voorwerp on GalaxyZoo.org
  151. 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 adolescentsFor 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. 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 adolescentsFor 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. 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 adolescentsFor 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. 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 adolescentsFor 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. 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 adolescentsFor 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 TownHow can we access their science?
  156. 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 adolescentsFor 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 TownHow can we access their science? 7. How can we disentangle communication and evaluation (‘metric of attribution’ - virtual RFID)?
  157. 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 adolescentsFor 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 TownHow can we access their science? 7. How can we disentangle communication and evaluation (‘metric of attribution’ - virtual RFID)?
  158. 158. Seven ‘Known Unknowns’ in Online Science Publishing
  159. 159. Seven ‘Known Unknowns’ in Online Science Publishing 1. How can we model a user’s interest?
  160. 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. 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. 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. 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. 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. 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?

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