Post on 20-Jun-2015
description
Reinventing the Research Article -Seven Questions on Science Publishing
Anita de WaardResearcher Disruptive Technologies,
Elsevier LabsNWO - Casimir Grantee,
Utrecht University
ELPUB 2008
Seven ’known knowns’ in online science publishing:
Seven ’known knowns’ in online science publishing:
1. The internet has caused an information overload.
Seven ’known knowns’ in online science publishing:
1. The internet has caused an information overload.
2. Science papers contain facts.
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 The narrative research article is outdated and needs to be replaced.
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 The narrative research article is outdated and needs to be replaced.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,
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 The narrative research article is outdated and needs to be replaced.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,
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 The narrative research article is outdated and needs to be replaced.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;
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 The narrative research article is outdated and needs to be replaced.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.
1. The internet has caused an information overload
- My own experience (as a researcher):
1. The internet has caused an information overload
- My own experience (as a researcher):
- Easy: find what I know exists
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
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
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.
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:
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
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
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
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.
1. The internet has caused an information overload
1. The internet has caused an information overload
1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
- Understand
1. The internet has caused an information overload
- Pick (carve out) a first set of user needs, e.g.:
- Locate
- Understand
- Believe (Be convinced)
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
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, ..
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. 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?
1. The internet has caused an information overload1. 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?
2. Science papers contain facts
- With FEBS Letters Editorial Office in Heidelberg/MINT Database in Rome
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’
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:
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
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
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
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
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
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
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
2. Science papers contain facts
2. Science papers contain facts
- Issue: authors cannot be curators!
2. Science papers contain facts
- Issue: authors cannot be curators!
- Fact is not claim, but created by consensus post-hoc
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. 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?
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?
2. Science papers contain facts 2. Science papers contain facts
3. The narrative RA should be replaced
3. The narrative RA should be replaced Aristotle QuintilianQuintilianQuintilian Cell APA Style Guide
prooimion Introduction exordiumThe introduction of a speech, where one announces the subject and purpose
of the discourse, and where one usually employs the persuasive appeal of ethos in order to establish credibility with the audience.
Introduction Introduction
prothesis Statement of Facts narratio
The second part of a classical oration, following the introduction or exordium. The speaker here provides a narrative account of what has happened and
generally explains the nature of the case. Quintilian adds that the narratio is followed by the propositio, a kind of summary of the issues or a statement of
the charge.
Introduction Introduction
Summary propostitioComing between the narratio and the partitio of a classical oration, the
propositio provides a brief summary of what one is about to speak on, or concisely puts forth the charges or accusation.
Abstract Abstract
Division/outline partitio
Following the statement of facts, or narratio, comes the partitio or divisio. In this section of the oration, the speaker outlines what will follow, in accordance with what's been stated as the status, or point at issue in the case. Quintilian suggests the partitio is blended with the propositio and also assists memory.
Table of Contents Article Outline
pistis Proof confirmatioFollowing the division / outline or partitio comes the main body of the speech
where one offers logical arguments as proof. The appeal to logos is emphasized here.
Results Methods, Results
Refutation refutatioFollowing the the confirmatio or section on proof in a classical oration, comes the refutation. As the name connotes, this section of a speech was devoted to
answering the counterarguments of one's opponent.Discussion Discussion
epilogos peroratioFollowing the refutatio and concluding the classical oration, the peroratio conventionally employed appeals through pathos, and often included a
summing up (see the figures of summary, below).Discussion Discussion
3. The narrative RA should be replaced Aristotle QuintilianQuintilianQuintilian Cell APA Style Guide
prooimion Introduction exordiumThe introduction of a speech, where one announces the subject and purpose
of the discourse, and where one usually employs the persuasive appeal of ethos in order to establish credibility with the audience.
Introduction Introduction
prothesis Statement of Facts narratio
The second part of a classical oration, following the introduction or exordium. The speaker here provides a narrative account of what has happened and
generally explains the nature of the case. Quintilian adds that the narratio is followed by the propositio, a kind of summary of the issues or a statement of
the charge.
Introduction Introduction
Summary propostitioComing between the narratio and the partitio of a classical oration, the
propositio provides a brief summary of what one is about to speak on, or concisely puts forth the charges or accusation.
Abstract Abstract
Division/outline partitio
Following the statement of facts, or narratio, comes the partitio or divisio. In this section of the oration, the speaker outlines what will follow, in accordance with what's been stated as the status, or point at issue in the case. Quintilian suggests the partitio is blended with the propositio and also assists memory.
Table of Contents Article Outline
pistis Proof confirmatioFollowing the division / outline or partitio comes the main body of the speech
where one offers logical arguments as proof. The appeal to logos is emphasized here.
Results Methods, Results
Refutation refutatioFollowing the the confirmatio or section on proof in a classical oration, comes the refutation. As the name connotes, this section of a speech was devoted to
answering the counterarguments of one's opponent.Discussion Discussion
epilogos peroratioFollowing the refutatio and concluding the classical oration, the peroratio conventionally employed appeals through pathos, and often included a
summing up (see the figures of summary, below).Discussion Discussion
The Story of Goldilocks and the Three Bears
Story Grammar Paper The AXH Domain of Ataxin-1 Mediates Neurodegeneration through Its Interaction with Gfi-1/Senseless Proteins
Once upon a time Time Setting Background The mechanisms mediating SCA1 pathogenesis are still not fully understood, but some general principles have emerged.
a little girl named Goldilocks Characters
Setting
Objects of study the Drosophila Atx-1 homolog (dAtx-1) which lacks a polyQ tract,
She went for a walk in the forest. Pretty soon, she came upon a house.
Location
Setting
Experimental setup
studied and compared in vivo effects and interactions to those of the human protein
She knocked and, when no one answered,
Goal Theme Researchgoal
Gain insight into how Atx-1's function contributes to SCA1 pathogenesis. How these interactions might contribute to the disease process and how they might cause toxicity in only a subset of neurons in SCA1 is not fully understood.
she walked right in. Attempt
Theme
Hypothesis Atx-1 may play a role in the regulation of gene expression
At the table in the kitchen, there were three bowls of porridge.
Name Episode 1 Name dAtX-1 and hAtx-1 Induce Similar Phenotypes When Overexpressed in Files
Goldilocks was hungry. Subgoal
Episode 1
Subgoal test the function of the AXH domain
She tasted the porridge from the first bowl.
Attempt
Episode 1
Method overexpressed dAtx-1 in flies using the GAL4/UAS system (Brand and Perrimon, 1993) and compared its effects to those of hAtx-1.
This porridge is too hot! she exclaimed.
Outcome
Episode 1
Results Overexpression of dAtx-1 by Rhodopsin1(Rh1)-GAL4, which drives expression in the differentiated R1-R6 photoreceptor cells (Mollereau et al., 2000 and O'Tousa et al., 1985), results in neurodegeneration in the eye, as does overexpression of hAtx-1[82Q]. Although at 2 days after eclosion, overexpression of either Atx-1 does not show obvious morphological changes in the photoreceptor cellsSo, she tasted the porridge
from the second bowl.
Episode 1
Data (data not shown),
This porridge is too cold, she said
Outcome
Episode 1
Results both genotypes show many large holes and loss of cell integrity at 28 days
So, she tasted the last bowl of porridge.
Episode 1
Data (Figures 1B-1D).
Ahhh, this porridge is just right, she said happily and
Outcome
Episode 1
Results Overexpression of dAtx-1 using the GMR-GAL4 driver also induces eye abnormalities. The external structures of the eyes that overexpress dAtx-1 show disorganized ommatidia and loss of interommatidial bristles
she ate it all up.
Episode 1
Data (Figure 1F),
3. The narrative RA should be replaced
Discourse Segments:
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
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.
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:
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
3. The narrative RA should be replaced
3. The narrative RA should be replaced
3. The narrative RA should be replaced
3. The narrative RA should be replaced
3. The narrative RA should be replaced
3. The narrative RA should be replaced
- Narrative is how stories are told; ‘the truth can only be told in stories’....
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
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)’
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)
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. 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? ...
3. The narrative RA should be replaced
PHC Growth arrestundergo
3. The narrative RA should be replaced
PHC Growth arrestundergo
Paper A:implication
results
method
goal
fact
fact
3. The narrative RA should be replaced
PHC Growth arrestundergo
Paper A:implication
results
method
goal
fact
fact
data 1
data 2 data 3
3. The narrative RA should be replaced
PHC Growth arrestundergo
Paper A:implication
results
method
goal
fact
fact
Paper B:
data 4
data 5 data 6
implication
results
method
goal
fact
fact
data 1
data 2 data 3
3. The narrative RA should be replaced
PHC Growth arrestundergo
Paper A:implication
results
method
goal
fact
fact
Paper B:
data 4
data 5 data 6
implication
results
method
goal
fact
fact
data 1
data 2 data 3
3. The narrative RA should be replaced
PHC Growth arrestundergo
Paper A:implication
results
method
goal
fact
fact
Paper B:
data 4
data 5 data 6
implication
results
method
goal
fact
fact
data 1
data 2 data 3
3. The narrative RA should be replaced
PHC Growth arrestundergo
Paper A:implication
results
method
goal
fact
fact
Paper B:
data 4
data 5 data 6
implication
results
method
goal
fact
fact
underpinning
data 1
data 2 data 3
3. The narrative RA should be replaced
PHC Growth arrestundergo
Paper A:implication
results
method
goal
fact
fact
Paper B:
data 4
data 5 data 6
implication
results
method
goal
fact
fact
data 1
data 2 data 3
3. The narrative RA should be replaced
PHC Growth arrestundergo
Paper A:implication
results
method
goal
fact
fact
Paper B:
data 4
data 5 data 6
implication
results
method
goal
fact
fact
data 1
data 2 data 3
method link
3. The narrative RA should be replaced
3. The narrative RA should be replaced
- How to develop systems that ‘reconstruct the salami’
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. 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?
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?
3. The narrative RA should be replaced 3. The narrative RA should be replaced
4. Words contain meaning
4. Words contain meaning
Sicilian?
4. Words contain meaning
Sicilian?
4. Words contain meaning
Sicilian?
4. Words contain meaning
Sicilian?
4. Words contain meaning
Sicilian?
4. Words contain meaning
4. Words contain meaning
- ‘A word is worth a thousand pictures’ (Don Loritz)
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
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?
4. Words contain meaning
4. Words contain meaning
- Cognitive linguistics: language and cognition cannot be separated - language acts are cognitive acts
4. Words contain meaning
- Cognitive linguistics: language and cognition cannot be separated - language acts are cognitive acts
- Lakoff, metaphor: ‘anger is heat’
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. 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?
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?
4. Words contain meaning
5. Words (and logic) contain scientific fact
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
5. Words (and logic) contain scientific fact
“We generated an MCF-7 derivative that expresses the
HPV16 E6 protein, which mediates degradation of p53
([24]).”
• “[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
5. Words (and logic) contain scientific fact
“We generated an MCF-7 derivative that expresses the
HPV16 E6 protein, which mediates degradation of p53
([24]).”
24. M. Scheffner, B.A. Werness, J.M. Huibregtse, A.J. Levine and P.M. Howley, The E6 oncoprotein encoded by human
papillomavirus types 16 and 18 promotes the degradation of p53. Cell 63 (1990), pp. 1129–1136. SummaryPlus | Full Text + Links | PDF (1728 K) | Abstract + References in Scopus |
Cited By in Scopus
• “[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
5. Words (and logic) contain scientific fact
“In the presence of E6, p53 stabilization in response to IR
was almost completely prevented in MCF-7 cells
(Figure 1A).”
“We generated an MCF-7 derivative that expresses the
HPV16 E6 protein, which mediates degradation of p53
([24]).”
24. M. Scheffner, B.A. Werness, J.M. Huibregtse, A.J. Levine and P.M. Howley, The E6 oncoprotein encoded by human
papillomavirus types 16 and 18 promotes the degradation of p53. Cell 63 (1990), pp. 1129–1136. SummaryPlus | Full Text + Links | PDF (1728 K) | Abstract + References in Scopus |
Cited By in Scopus
• “[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
5. Words (and logic) contain scientific fact
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.
“In the presence of E6, p53 stabilization in response to IR
was almost completely prevented in MCF-7 cells
(Figure 1A).”
“We generated an MCF-7 derivative that expresses the
HPV16 E6 protein, which mediates degradation of p53
([24]).”
24. M. Scheffner, B.A. Werness, J.M. Huibregtse, A.J. Levine and P.M. Howley, The E6 oncoprotein encoded by human
papillomavirus types 16 and 18 promotes the degradation of p53. Cell 63 (1990), pp. 1129–1136. SummaryPlus | Full Text + Links | PDF (1728 K) | Abstract + References in Scopus |
Cited By in Scopus
• “[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
5. Words (and logic) contain scientific fact
5. Words (and logic) contain scientific fact
- Essential persuasive elements are non-textual
5. Words (and logic) contain scientific fact
- Essential persuasive elements are non-textual
- Open Data, how to incorporate into ‘text mining’?
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
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)
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. 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?
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?
5. Words (and logic) contain scientific fact
6. Just model the facts with xml + rdf
6. Just model the facts with xml + rdf
- Content in XML - but what about overlapping tags?
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
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:
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
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
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
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:
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
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)
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)
8
1. DOPE (2003)deduplicate thesaurus term
select co-occurrence terms
see results set + link to full-text
visualise overlap results
6. Just model the facts with xml + rdf
6. Just model the facts with xml + rdf- Yes, but:
6. Just model the facts with xml + rdf- Yes, but:
- In practice: ScienceDirect does not use our XML... (shhh....)
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
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:
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
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?
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. 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)?
6. Just model the facts with xml + rdf6. 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)?
7. And publishers should stop making all those papers.
7. And publishers should stop making all those papers.
- 6 uses of a RA:
7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
- thesis
7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
- thesis
- conference tickets
7. And publishers should stop making all those papers.
- 6 uses of a RA:
- job application
- report card
- thesis
- conference tickets
- research assessment
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.
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
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.
7. And publishers should stop making all those papers.
The real challenge:
7. And publishers should stop making all those papers.
The real challenge:
- in Holland, chemistry departments are dwindling
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
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
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:
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
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
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
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
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
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
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. 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)?
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)?
7. And publishers should stop making all those papers. 7. And publishers should stop making all those papers.
Seven ‘Known Unknowns’ in Online Science Publishing
Seven ‘Known Unknowns’ in Online Science Publishing
1. How can we model a user’s interest?
Seven ‘Known Unknowns’ in Online Science Publishing
1. How can we model a user’s interest?
2. Can we create an ontology of doubt?
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?
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?
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?
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?
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?