3. Three problems, some solutions,
and ideas for the future
• Three examples where scientific discourse
falls short: why we need more context
2
4. Three problems, some solutions,
and ideas for the future
• Three examples where scientific discourse
falls short: why we need more context
• Some projects that are modeling context now
2
5. Three problems, some solutions,
and ideas for the future
• Three examples where scientific discourse
falls short: why we need more context
• Some projects that are modeling context now
• What we really need: networked knowledge
2
21. Problem #1: Knowledge is not
connected or tracable
• How can we scale up the 1-to-1 interactions on
efficacy and side effects happening today?
7
22. Problem #1: Knowledge is not
connected or tracable
• How can we scale up the 1-to-1 interactions on
efficacy and side effects happening today?
• How do we know who is speaking in a patient
forum?
7
23. Problem #1: Knowledge is not
connected or tracable
• How can we scale up the 1-to-1 interactions on
efficacy and side effects happening today?
• How do we know who is speaking in a patient
forum?
• How to we get scientific knowledge in on this?
7
24. Problem #1: Knowledge is not
connected or tracable
• How can we scale up the 1-to-1 interactions on
efficacy and side effects happening today?
• How do we know who is speaking in a patient
forum?
• How to we get scientific knowledge in on this?
• How do we know who paid for knowledge?
7
25. Problem #1: Knowledge is not
connected or tracable
• How can we scale up the 1-to-1 interactions on
efficacy and side effects happening today?
• How do we know who is speaking in a patient
forum?
• How to we get scientific knowledge in on this?
• How do we know who paid for knowledge?
• If a study is sponsored, how do you refer back to
sources that link out to it?
7
28. 2. Drug-drug interactions
• Drug Interaction Knowledge Base
• Problem: how to integrate knowledge from
various repositories and data stores into a
single source
8
29. 2. Drug-drug interactions
• Drug Interaction Knowledge Base
• Problem: how to integrate knowledge from
various repositories and data stores into a
single source
• One of the main stumbling blocks: the way
we record experiments in prose:
8
31. E.g. Moltke et al, 1999:
After normalization for hepatic abundance, relative
contributions to net intrinsic clearance were 37% for
CYP2C19, 28% for CYP2D6, and 35% for CYP3A4.
All samples were of the CYP2D6 and CYP2C19 normal
metabolizer phenotype based on prior in vitro
phenotyping studies.
Average relative in vivo abundances, equivalent to the relative activity
factors, were estimated using methods described in detail
previously (Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999,
2000, 2001; von Moltke et al., 1999 a,b; Störmer et al., 2000).
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
Based on established index reactions, S-CT and S-DCT were negligible
inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
weakly inhibited CYP2D6 (IC50 = 70–80 µM)
S-CT was transformed to S-DCT by CYP2C19 (Km = 69 µM),
CYP2D6 (Km = 29 µM), and CYP3A4 (Km = 588 µM).
The potential inhibitory effect of the stereoisomers of CT, DCT, and DDCT on the activity of
six human cytochromes was evaluated using index reactions and methods as follows (Table
1): CYP1A2, phenacetin (100 µM) to acetaminophen (von Moltke et al.,1996a;
Venkatakrishnan et al., 1998b); CYP2C9, tolbutamide (100 µM) to hydroxytolbutamide
9
(Venkatakrishnan et al., 1998c); CYP2C19, S-mephenytoin (25 µM) to 4′-OH-mephenytoin
33. Problem #2:
Knowledge is not actionable
• Self-reference:
10
34. Problem #2:
Knowledge is not actionable
• Self-reference:
10
35. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
10
36. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
• Reference to external data sources:
10
37. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
• Reference to external data sources:
10
38. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
• Reference to external data sources:
Average relative in vivo abundances equivalent to the relative activity
factors, were estimated using methods described in detail previously
(Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
von Moltke et al., 1999 a,b; Störmer et al., 2000).
10
39. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
• Reference to external data sources:
Average relative in vivo abundances equivalent to the relative activity
factors, were estimated using methods described in detail previously
(Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
von Moltke et al., 1999 a,b; Störmer et al., 2000).
10
40. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
• Reference to external data sources:
Average relative in vivo abundances equivalent to the relative activity
factors, were estimated using methods described in detail previously
(Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
von Moltke et al., 1999 a,b; Störmer et al., 2000).
• Ways of describing meant for human eyes
10
41. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
• Reference to external data sources:
Average relative in vivo abundances equivalent to the relative activity
factors, were estimated using methods described in detail previously
(Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
von Moltke et al., 1999 a,b; Störmer et al., 2000).
• Ways of describing meant for human eyes
Based on established index reactions, S-CT and S-DCT were negligible
inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
weakly inhibited CYP2D6 (IC50 = 70–80 µM)
10
42. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
• Reference to external data sources:
Average relative in vivo abundances equivalent to the relative activity
factors, were estimated using methods described in detail previously
(Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
von Moltke et al., 1999 a,b; Störmer et al., 2000).
• Ways of describing meant for human eyes
Based on established index reactions, S-CT and S-DCT were negligible
inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
weakly inhibited CYP2D6 (IC50 = 70–80 µM)
10
43. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
• Reference to external data sources:
Average relative in vivo abundances equivalent to the relative activity
factors, were estimated using methods described in detail previously
(Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
von Moltke et al., 1999 a,b; Störmer et al., 2000).
• Ways of describing meant for human eyes
Based on established index reactions, S-CT and S-DCT were negligible
inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
weakly inhibited CYP2D6 (IC50 = 70–80 µM)
10
44. Problem #2:
Knowledge is not actionable
• Self-reference:
R-CT and its metabolites, studied using the same procedures, had
properties very similar to those of the corresponding S-enantiomers.
• Reference to external data sources:
Average relative in vivo abundances equivalent to the relative activity
factors, were estimated using methods described in detail previously
(Crespi, 1995; Venkatakrishnan et al., 1998 a,c, 1999, 2000, 2001;
von Moltke et al., 1999 a,b; Störmer et al., 2000).
• Ways of describing meant for human eyes
Based on established index reactions, S-CT and S-DCT were negligible
inhibitors (IC50> 100 µM) of CYP1A2, -2C9, -2C19, -2E1, and -3A, and
weakly inhibited CYP2D6 (IC50 = 70–80 µM)
• Many statements wrapped into one:
S-CT was transformed to S-DCT by CYP2C19 (Km = 69 µM), CYP2D6 (Km
= 29 µM), and CYP3A4 (Km = 588 µM). 10
45. 3. NIF Antibody Study
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
46. 3. NIF Antibody Study
• Pilot project to use text mining to identify antibodies used
in studies
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
47. 3. NIF Antibody Study
• Pilot project to use text mining to identify antibodies used
in studies
• Antibodies are a major source of experimental variability:
–Same antibody can give very different results
–Different antibodies to the same protein can give very
different results
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
48. 3. NIF Antibody Study
• Pilot project to use text mining to identify antibodies used
in studies
• Antibodies are a major source of experimental variability:
–Same antibody can give very different results
–Different antibodies to the same protein can give very
different results
• Neuroscientists spend a lot of time tracking down
antibodies and troubleshooting experiments that use
antibodies, e.g.:
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
49. 3. NIF Antibody Study
• Pilot project to use text mining to identify antibodies used
in studies
• Antibodies are a major source of experimental variability:
–Same antibody can give very different results
–Different antibodies to the same protein can give very
different results
• Neuroscientists spend a lot of time tracking down
antibodies and troubleshooting experiments that use
antibodies, e.g.:
Tissue sections were blocked with 5% serum and incubated
overnight at 4 °C with the following primary antibodies: anti-ChAT
(1:100; Millipore, Billerica, MA), anti-Bax (1:50; Santa Cruz), anti-
Bcl-xl (1:50; Cell Signaling), anti- neurofilament 200 kDa (1:200;
Millipore) ...
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
50. What studies used my monoclonal mouse
antibody against actin in humans?
• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
Santa Cruz Biotechnology)…
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
51. What studies used my monoclonal mouse
antibody against actin in humans?
Subject is Human
• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
Santa Cruz Biotechnology)…
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
52. What studies used my monoclonal mouse
antibody against actin in humans?
Subject is Human
• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies antibody
mAb=monoclonal
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
Santa Cruz Biotechnology)…
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
53. What studies used my monoclonal mouse
antibody against actin in humans?
Subject is Human
• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies antibody
mAb=monoclonal
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
Santa Cruz Biotechnology)…
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
54. What studies used my monoclonal mouse
antibody against actin in humans?
Subject is Human
• Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9) and
from subjects with AD (n11) were obtained from the Rapid Autopsy Program
• Immunoblot analysis and antibodies antibody
mAb=monoclonal
• The following antibodies were used for immunoblotting: -actin mAb (1:10,000 dilution,
Sigma-Aldrich); -tubulin mAb (1:10,000, Abcam); T46 mAb (specific to tau 404–441, 1:1000,
Invitrogen); Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig et al., 2007); AT8
mAb (phospho-tau Ser199, Ser202, and Thr205, 1:500, Innogenetics); PHF-1 mAb (phospho-tau
Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb (phospho-tau Ser262 and Ser356,
1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (C terminus, 1:1000,
Santa Cruz Biotechnology)…
•95 antibodies were identified in 8 articles
•52 did not contain enough information to
determine the antibody used
Maryann Martone, Jan 2012:
2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012)
56. Problem #3: Knowledge is not
connected to the real world
• No way to ensure connections between
experiments/real-world manipulations and record
13
57. Problem #3: Knowledge is not
connected to the real world
• No way to ensure connections between
experiments/real-world manipulations and record
• Specific characteristics of real-world objects
matter, a great deal (e.g. patients, genes, etc)
13
58. Problem #3: Knowledge is not
connected to the real world
• No way to ensure connections between
experiments/real-world manipulations and record
• Specific characteristics of real-world objects
matter, a great deal (e.g. patients, genes, etc)
• Sometimes essential details are lost in statistical
manipulation, pulling out certain features, etc.
13
60. So what is missing?
1.Lexapro example: we need to be able to trace claims
throughout the evidence base
14
61. So what is missing?
1.Lexapro example: we need to be able to trace claims
throughout the evidence base
2.Drug-drug interaction example: we need to access
actionable content from papers
14
62. So what is missing?
1.Lexapro example: we need to be able to trace claims
throughout the evidence base
2.Drug-drug interaction example: we need to access
actionable content from papers
3.Antibodies example: we need to know which real-world
objects the experiment was done on
14
63. So what is missing?
1.Lexapro example: we need to be able to trace claims
throughout the evidence base
Knowledge context
2.Drug-drug interaction example: we need to access
actionable content from papers
3.Antibodies example: we need to know which real-world
objects the experiment was done on
14
64. So what is missing?
1.Lexapro example: we need to be able to trace claims
throughout the evidence base
Knowledge context
2.Drug-drug interaction example: we need to access
actionable content from papers
Research context
3.Antibodies example: we need to know which real-world
objects the experiment was done on
14
65. So what is missing?
1.Lexapro example: we need to be able to trace claims
throughout the evidence base
Knowledge context
2.Drug-drug interaction example: we need to access
actionable content from papers
Research context
3.Antibodies example: we need to know which real-world
objects the experiment was done on
Real-World context
14
67. Some projects addressing this:
• Knowledge context: manually trace and link
evidence:
–Data2Semantics: trace evidence for clinical guidelines
–DIKB: trace heritage of Product Insert information
15
68. Some projects addressing this:
• Knowledge context: manually trace and link
evidence:
–Data2Semantics: trace evidence for clinical guidelines
–DIKB: trace heritage of Product Insert information
• Experimental context: share workflow
representations:
–Workflow4Ever: share workflows
–Yolanda Gil’s workflow design: share abstract workflows
15
69. Some projects addressing this:
• Knowledge context: manually trace and link
evidence:
–Data2Semantics: trace evidence for clinical guidelines
–DIKB: trace heritage of Product Insert information
• Experimental context: share workflow
representations:
–Workflow4Ever: share workflows
–Yolanda Gil’s workflow design: share abstract workflows
• Real-world context: manually look up the entities
–NIF Antibodies registry
–Pharmapendium
15
71. BUT:
• This is all manual: doesn’t scale
• It is all done after the data is already buried
• Papers act as if they are independent entities:
we are not using the social, semantic web!
• Problem: the myth of the standalone article.
16
72. What do we need?
[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
17
network-enabled-research/
73. What do we need?
[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
17
network-enabled-research/
74. What do we need?
Internet of things: (Bleecker, [1])
Interact with ‘objects that blog’ or ‘Blogjects’, that:
track where they are and where they’ve been;
have histories of their encounters and experiences
have agency - an assertive voice on the social web [2]
[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
17
network-enabled-research/
75. What do we need?
Internet of things: (Bleecker, [1])
Interact with ‘objects that blog’ or ‘Blogjects’, that:
track where they are and where they’ve been;
have histories of their encounters and experiences
have agency - an assertive voice on the social web [2]
[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
17
network-enabled-research/
76. What do we need?
Internet of things: (Bleecker, [1])
Interact with ‘objects that blog’ or ‘Blogjects’, that:
track where they are and where they’ve been;
have histories of their encounters and experiences
have agency - an assertive voice on the social web [2]
Research Objects: (Bechofer et al, [2])
Create semantically rich aggregations of resources,
that can possess some scientific intent or support
some research objective
[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
17
network-enabled-research/
77. What do we need?
Internet of things: (Bleecker, [1])
Interact with ‘objects that blog’ or ‘Blogjects’, that:
track where they are and where they’ve been;
have histories of their encounters and experiences
have agency - an assertive voice on the social web [2]
Research Objects: (Bechofer et al, [2])
Create semantically rich aggregations of resources,
that can possess some scientific intent or support
some research objective
[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
17
network-enabled-research/
78. What do we need?
Internet of things: (Bleecker, [1])
Interact with ‘objects that blog’ or ‘Blogjects’, that:
track where they are and where they’ve been;
have histories of their encounters and experiences
have agency - an assertive voice on the social web [2]
Research Objects: (Bechofer et al, [2])
Create semantically rich aggregations of resources,
that can possess some scientific intent or support
some research objective
Networked Knowledge: (Neylon, [3])
If we care about taking advantage of the web and
internet for research then we must tackle the building
of scholarly communication networks.
These networks will have two critical characteristics:
scale and a lack of friction. [3]
[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
17
network-enabled-research/
82. Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
interactions with them and their experiences
• Easy ways to author our experiences with these tools
18
83. Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
18
84. Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
18
85. Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
• Tools to create Research Objects and allow us to
describe our actions and observations
18
86. Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
• Tools to create Research Objects and allow us to
describe our actions and observations
• Repositories for Research Objects with unique IDs,
provenance, persistance
18
87. Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
• Tools to create Research Objects and allow us to
describe our actions and observations
• Repositories for Research Objects with unique IDs,
provenance, persistance
• Infrastructure to connect all of this, traverse it
18
88. Towards networked knowledge:
Real-World context:
• Networked Objects in the lab that store our
interactions with them and their experiences
• Easy ways to author our experiences with these tools
Research context:
• Tools to create Research Objects and allow us to
describe our actions and observations
• Repositories for Research Objects with unique IDs,
provenance, persistance
• Infrastructure to connect all of this, traverse it
• Meta-analyses and visualisations to make sense of it.
18
91. Knowledge context?
• We need:
– A way to represent ‘Research Thoughts’: Observational and
Interpretational assertions
– To link them together in a meaningfully, approaching the richness
of natural language
– Tools to comment on other people’s (networked) thoughts, vet
them, judge them, contradict/confirm
– Interfaces to link knowledge back and forth through time/
argument and oversee arguments
19
92. Knowledge context?
• We need:
– A way to represent ‘Research Thoughts’: Observational and
Interpretational assertions
– To link them together in a meaningfully, approaching the richness
of natural language
– Tools to comment on other people’s (networked) thoughts, vet
them, judge them, contradict/confirm
– Interfaces to link knowledge back and forth through time/
argument and oversee arguments
• We have:
– Some interfaces
– Some tools to pull out assertions
19
93. Knowledge context?
• We need:
– A way to represent ‘Research Thoughts’: Observational and
Interpretational assertions
– To link them together in a meaningfully, approaching the richness
of natural language
– Tools to comment on other people’s (networked) thoughts, vet
them, judge them, contradict/confirm
– Interfaces to link knowledge back and forth through time/
argument and oversee arguments
• We have:
– Some interfaces
– Some tools to pull out assertions
•
19
100. Knowledge context - we need
representation of text!
• Some tools exist, but...
• We need a) models and b) tools that create ROs and
graft a structured narrative on top of that
• Containing the richness of models, thoughts,
serendipity, associations...
• Mostly: we need reasons for people to do this and
rewards for them to do so
• Some examples of enforced structure: grant proposals,
data plans
22
103. Summary:
• Current way of publishing does not suffice!
• We need networked knowledge to provide:
–Real-world context
–Experimental context
–Knowledge context
23
104. Summary:
• Current way of publishing does not suffice!
• We need networked knowledge to provide:
–Real-world context
–Experimental context
–Knowledge context
• What do we have:
–Real-World/Experimental: technologies, but no practice
–Knowledge: some tools to encode this manually and
help pull out semi-automatically
23
105. Summary:
• Current way of publishing does not suffice!
• We need networked knowledge to provide:
–Real-world context
–Experimental context
–Knowledge context
• What do we have:
–Real-World/Experimental: technologies, but no practice
–Knowledge: some tools to encode this manually and
help pull out semi-automatically
• What do we need:
–A framework to tie this all together
–Understanding of the authors’ drivers and rewards, to
change their writing habits 23
107. Some ideas for next steps:
• Gathering of minds: Force11.org,
BeyondThePDF, ScienceOnline, ...
24
108. Some ideas for next steps:
• Gathering of minds: Force11.org,
BeyondThePDF, ScienceOnline, ...
• People and ideas are converging but there is
no real driver to collaborate
24
109. Some ideas for next steps:
• Gathering of minds: Force11.org,
BeyondThePDF, ScienceOnline, ...
• People and ideas are converging but there is
no real driver to collaborate
• Need to develop use cases, for instance: drug-
drug interaction experiments?
24
110. Some ideas for next steps:
• Gathering of minds: Force11.org,
BeyondThePDF, ScienceOnline, ...
• People and ideas are converging but there is
no real driver to collaborate
• Need to develop use cases, for instance: drug-
drug interaction experiments?
• Very interested to work on this: happy to
discuss!
24
111. Some ideas for next steps:
• Gathering of minds: Force11.org,
BeyondThePDF, ScienceOnline, ...
• People and ideas are converging but there is
no real driver to collaborate
• Need to develop use cases, for instance: drug-
drug interaction experiments?
• Very interested to work on this: happy to
discuss!
a.dewaard@elsevier.com
24
112. Acknowledgements:
• Collaborations:
–DOMEO: Paolo Ciccarese, Tim Clark, Harvard
–Data2Semantics: Rinke Hoekstra, Paul Groth, VU
–DIKB: Rich Boycer, Jodi Schneider, Maria Liakata
–Provenance and experimental modeling:
Gully Burns, Eduard Hovy, Yolanda Gil, ISI
–Linked Data Integration: Joanne Luciano, Deborah
McGuiness, John Erickson, RPI
–Claimed Knowledge Updates: Agnes Sandor, Xerox
• Discussions:
–Phil Bourne, Cameron Neylon, Dave De Roure,
Carole Goble, Brad Allen, Maryann Martone,
Sophia Ananiadou 25