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What Bioinformaticians Need to
Know About Digital Publishing
Beyond the PDF
Philip E. Bourne PhD
pbourne@ucsd.edu

?
CBIIT October 30, 2013

1
Where My Biased Perspective Comes
From..
• Computational biologist – interests in systems
pharmacology, evolution, protein structure
• Developer of the RCSB PDB
• Founding Editor in Chief of PLOS
Computational Biology
• Got interested in scholarly communication

CBIIT October 30, 2013

2
Scholarly Communication is Being Disrupted
– Witness The Story of Meredith

http://fora.tv/2012/04/20/Congress_Unplugged_Phil_Bourne
CBIIT October 30, 2013

3
The Era of Open Has The Potential
to Deinstitutionalize

Daniel Hulshizer/Associated Press

CBIIT October 30, 2013

4
The Era of Open Has The Potential
to Deinstitutionalize

Daniel Hulshizer/Associated Press

CBIIT October 30, 2013

5
Most Academic Institutions Have Yet
to Realize This

Funding Agencies Could Provide the
Wake Up Call

CBIIT October 30, 2013

6
Publishing is Also Being
Deinstitutionalized
• Today:
• Approx 10,000 publishers
• Publishing approx 25,000 journals
• Which publish approx 1.5 million articles per year
(almost 1 million of which appear in PubMed)

CBIIT October 30, 2013

7
Witness the ‘Open Access Mega
Journal'
1. Very very large
– Publishing thousands of articles per year
– and benefiting from economies of scale

2. Open Access
– Because no one will pay a subscription fee for a journal that
large (and growing that fast)
– and using an OA Business Model where each article pays for its
own costs

3. (Preferably) without any ‘artificial’ constraints on
its ability to grow
– For example, a desire to only publish ‘high impact; papers
[Pete Binfield]
CBIIT October 30, 2013

8
3500

3000

Publications by PLOSONE per quarter since launch
Publications by PLoS ONE per quarter
since launch

2500

2000

1500

1000

500

[Pete Binfield]

Q
1

20
Q 07
2
20
Q 07
3
20
Q 07
4
20
Q 07
1
20
Q 08
2
20
Q 08
3
20
Q 08
4
20
Q 08
1
20
Q 09
2
20
Q 09
3
20
Q 09
4
20
Q 09
1
20
Q 10
2
20
Q 10
3
20
Q 10
4
20
Q 10
1
20
Q 11
2
20
11

0
“Open Access Mega Journals”
– One Name, Two Flavours
• ‘Clones’ of PLoS ONE (not selective)
–
–
–
–
–
–

SAGE Open
BMJ Open
Scientific Reports (Nature)
AIP Advances (Am Inst Physics)
G3 (Genetics Soc of America)
Biology Open (Company of Biologists)

• ‘Pseudo-Clones’ of PLoS ONE (probably selective)
– Physical Review X (Am Physical Society)
– Open Biology (Royal Society)
– Cell Reports (Elsevier, Cell Press)
[Pete Binfield]
CBIIT October 30, 2013

10
This Still Places the Research Article as
the Central Focus of the Academic
Enterprise…

Maybe the Article is Only One View

CBIIT October 30, 2013

11
Paper as Portal
0. Full text of PLoS papers stored
in a database

4. The composite view has
links to pertinent blocks
of literature text and back to the PDB

4.

1.
1. A link brings up figures
from the paper

2.

3. A composite view of
journal and database
content results

3.

1. User clicks on thumbnail
2. Metadata and a
webservices call provide
a renderable image that
can be annotated
3. Selecting a features
provides a
database/literature
mashup
4. That leads to new
papers

PLoS Comp. Biol. 2005 1(3) e34

2. Clicking the paper figure retrieves
data from the PDB which is
analyzed

12
Given This Disruption It is
Worth Thinking About…
• A paper as only one form of knowledge
discovery
• The use of interaction and rich media from
which to learn and actually do science
• Reproducibility
• Reward structures
• Better management of the research lifecycle
P.E. Bourne 2005 In the Future will a Biological Database Really be Different
from a Biological Journal? PLOS Comp. Biol. 1(3) e34
CBIIT October 30, 2013

13
MOOCs As Research

CBIIT October 30, 2013

14
Pubcast – Video Integrated with the
Full Text of the Paper

CBIIT October 30, 2013

15
http://www.scivee.tv
https://www.coursera.org/course/drugdiscovery
Spontaneous Groups Formed from All Over the World

CBIIT October 30, 2013

16
Given This Disruption It is
Worth Thinking About…
• A paper as only one form of knowledge
discovery
• The use of interaction and rich media from
which to learn and actually do science
• Reproducibility
• Reward structures
• Better management of the research lifecycle
P.E. Bourne 2005 In the Future will a Biological Database Really be Different
from a Biological Journal? PLOS Comp. Biol. 1(3) e34
CBIIT October 30, 2013

17
Attitudes are Changing

datasets
data collections
algorithms
configurations
tools and apps
codes
workflows
scripts
code libraries
services,
system software
infrastructure,
compilers
hardware
[Carole Goble]

“An article about computational
science in a scientific publication
is not the scholarship itself, it is
merely advertising of the
scholarship. The actual
scholarship is the complete
software development
environment, [the complete data]
and the complete set of
instructions which generated the
figures.”
David Donoho, “Wavelab and Reproducible
Research,” 1995

Morin et al Shining Light into Black Boxes
Science 13 April 2012: 336(6078) 159-160

Ince et al The case for open computer
18
programs, Nature 482, 2012
47/53 “landmark” publications
could not be replicated
[Begley, Ellis Nature, 483, 2012]

19

[Carole Goble]
Nekrutenko & Taylor, Next-generation sequencing data interpretation: enhancing,
reproducibility and accessibility, Nature Genetics 13 (2012)

59% of papers in the 50 highest-IF journals comply with (often
weak) data sharing rules.
Alsheikh-Ali et al Public Availability of Published Research Data in High-Impact
Journals. PLoS ONE 6(9) 2011
[Carole Goble]

CBIIT October 30, 2013

20
170 Journals, 2011-2012
Required as condition of publication
Required but may not affect decisions
Explicitly encouraged
Implied
No mention

[Carole Goble]

Required as condition of publication
Required but may not affect decisions
Explicitly encouraged
Implied
No mention

Stodden V, Guo P, Ma Z (2013) Toward Reproducible Computational Research: An
Empirical Analysis of Data and Code Policy Adoption by Journals. PLoS ONE 8(6):
21
e67111. doi:10.1371/journal.pone.0067111
Flaws Are Becoming More
Obvious

Out of 18 microarray papers, results
from 10 could not be reproduced

More retractions:
>15X increase in last decade
At current % > by 2045 as many papers published as retracted
[Carole Goble]

1. Ioannidis et al., 2009. Repeatability of published microarray gene expression analyses. Nature Genetics 41: 14
2. Science publishing: The trouble with retractions http://www.nature.com/news/2011/111005/full/478026a.html
22
3. Bjorn Brembs: Open Access and the looming crisis in science https://theconversation.com/open-access-and-the-looming-crisis-in-science-14950
Given This Disruption It is
Worth Thinking About…
• A paper as only one form of knowledge
discovery
• The use of interaction and rich media from
which to learn and actually do science
• Reproducibility
• Reward structures
• Better management of the research lifecycle
P.E. Bourne 2005 In the Future will a Biological Database Really be Different
from a Biological Journal? PLOS Comp. Biol. 1(3) e34
CBIIT October 30, 2013

23
Unfortunately the Metrics of
Success Remain…

[Carole Goble]

CBIIT October 30, 2013

24
This makes no sense when you ask
yourself the question:
What is more valuable a dataset used
and cited by 100 scientists or a paper
you wrote that only you cite?
Case in point…

CBIIT October 30, 2013

25
What can you do today to change the
situation?

CBIIT October 30, 2013

26
What Can You Do?
• Support emergent community commons/portals
• Be involved in the support and development of
metadata standards
• Contribute to workflow development etc. to drive
an open research lifecycle
• Educate your mentors on the importance of
open science and scholarly communication
• Write software thinking of an App model

CBIIT October 30, 2013

27
Portals

http://www.3dvcell.org/
http://www.force11.org/
CBIIT October 30, 2013

28
What Can You Do?
• Support emergent community commons/portals
• Be involved in the support and development of
metadata standards
• Contribute to workflow development etc. to drive
an open research lifecycle
• Educate your mentors on the importance of
open science and scholarly communication
• Write software thinking of an App model

CBIIT October 30, 2013

29
We Need Innovative Contributions
to the Research Lifecycle
Authoring
Tools

Data
Capture

Lab
Notebooks

Software
Repositories

Analysis
Tools

Scholarly
Communication
Visualization

IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION

Commercial &
Public Tools

DisciplineBased Metadata
Standards

Community Portals
Git-like
Resources
By Discipline

Data Journals

New Reward
Systems

Training
Institutional Repositories
Commercial Repositories

30
We Need Innovative Contributions
to the Research Lifecycle
Authoring
Tools

Data
Capture

Lab
Notebooks

Software
Repositories

Analysis
Tools

Scholarly
Communication
Visualization

IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION

Commercial &
Public Tools

DisciplineBased Metadata
Standards

Community Portals
Git-like
Resources
By Discipline

Data Journals

New Reward
Systems

Training
Institutional Repositories
Commercial Repositories

31
What Can You Do?
• Support emergent community commons/portals
• Be involved in the support and development of
metadata standards
• Contribute to workflow development etc. to drive
an open research lifecycle
• Educate your mentors on the importance of
open science and scholarly communication
• Write software thinking of an App model

CBIIT October 30, 2013

32
Pressure Your Institutions to Play a
Greater Role
• We need institutional data/knowledge sharing
plans
• We need digital universities
• We need data/information scientists to be
better recognized by institutions – its not all
about papers – this implies new metrics
CBIIT October 30, 2013

33
A View from the Digital University
Jane scores well in parts of her advanced on-line biology class. Professors who
undertake research in the areas where Jane did well are automatically notified of her
potential based on a computer analysis of her scores and background interests and
Professor Smith interviews her and offers her a research internship for the summer.
Over the summer, as she enters details of her experiments related to understanding a
widespread neurodegenerative disease in an on-line laboratory notebook, the
underlying computer system automatically puts Jane into contact with another
student, Jack, in a different department whose notebook reveals he is working on
using bacteria for purposes of toxic waste cleanup. Why the connection? It turns out
the same gene, which they both reference a number of times in their notes, is linked
to two very different disciplines – mental health and the environment. In the analog
university they would never have discovered each other, but at the Digital University
pooled knowledge can lead to a distinct advantage. The collaboration later results in a
patent filing and triggers a notification to a number of biotech companies who might
be interested in licensing the technology. A company licenses the technology and
hires Jane and Jack to continue working on the project. Professor Smith hires another
student using the revenue from the license and this in turn leads to a large federal
grant. The students get good jobs, further research is supported and societal benefit
arises from the technology. A hypothetical example for why the Digital University
makes sense.
CBIIT October 30, 2013

34
Committee on Academic
Promotions
• What Counts
–
–
–
–
–

Money
Grants
Papers
Teaching
Service

• What Does Not
–
–
–
–
–
–

Sharing data
Sharing software
Open access
Collaboration
Patents
Startups

Ten Simple Rules for Getting Ahead as a Computational Biologist in Academia
2011 PLOS Comp Biol 7(1) e1002001
CBIIT October 30, 2013

35
What Can You Do?
• Support emergent community commons/portals
• Be involved in the support and development of
metadata standards
• Contribute to workflow development etc. to drive
an open research lifecycle
• Educate your mentors on the importance of
open science and scholarly communication
• Write software thinking of an App model

CBIIT October 30, 2013

36
What Do We Need to Do to
Get There? An App+ Store?
• The App model
– Think of it operating on a content base rather
than a mobile device
– Simple and consistent user interface
– Needs to pass some quality control
– Has a reward

• The App+ Model
– Apps interoperate through a generic workflow
interface

CBIIT October 30, 2013

37
Summary
• Disruption is occurring
• As bioinformaticians we have the skill set to
leverage change and make a difference
• Go for it

CBIIT October 30, 2013

38
pbourne@ucsd.edu

Questions?
CBIIT October 30, 2013

39

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What Bioinformaticians Need to Know About Digital Publishing Beyond the PDF

  • 1. What Bioinformaticians Need to Know About Digital Publishing Beyond the PDF Philip E. Bourne PhD pbourne@ucsd.edu ? CBIIT October 30, 2013 1
  • 2. Where My Biased Perspective Comes From.. • Computational biologist – interests in systems pharmacology, evolution, protein structure • Developer of the RCSB PDB • Founding Editor in Chief of PLOS Computational Biology • Got interested in scholarly communication CBIIT October 30, 2013 2
  • 3. Scholarly Communication is Being Disrupted – Witness The Story of Meredith http://fora.tv/2012/04/20/Congress_Unplugged_Phil_Bourne CBIIT October 30, 2013 3
  • 4. The Era of Open Has The Potential to Deinstitutionalize Daniel Hulshizer/Associated Press CBIIT October 30, 2013 4
  • 5. The Era of Open Has The Potential to Deinstitutionalize Daniel Hulshizer/Associated Press CBIIT October 30, 2013 5
  • 6. Most Academic Institutions Have Yet to Realize This Funding Agencies Could Provide the Wake Up Call CBIIT October 30, 2013 6
  • 7. Publishing is Also Being Deinstitutionalized • Today: • Approx 10,000 publishers • Publishing approx 25,000 journals • Which publish approx 1.5 million articles per year (almost 1 million of which appear in PubMed) CBIIT October 30, 2013 7
  • 8. Witness the ‘Open Access Mega Journal' 1. Very very large – Publishing thousands of articles per year – and benefiting from economies of scale 2. Open Access – Because no one will pay a subscription fee for a journal that large (and growing that fast) – and using an OA Business Model where each article pays for its own costs 3. (Preferably) without any ‘artificial’ constraints on its ability to grow – For example, a desire to only publish ‘high impact; papers [Pete Binfield] CBIIT October 30, 2013 8
  • 9. 3500 3000 Publications by PLOSONE per quarter since launch Publications by PLoS ONE per quarter since launch 2500 2000 1500 1000 500 [Pete Binfield] Q 1 20 Q 07 2 20 Q 07 3 20 Q 07 4 20 Q 07 1 20 Q 08 2 20 Q 08 3 20 Q 08 4 20 Q 08 1 20 Q 09 2 20 Q 09 3 20 Q 09 4 20 Q 09 1 20 Q 10 2 20 Q 10 3 20 Q 10 4 20 Q 10 1 20 Q 11 2 20 11 0
  • 10. “Open Access Mega Journals” – One Name, Two Flavours • ‘Clones’ of PLoS ONE (not selective) – – – – – – SAGE Open BMJ Open Scientific Reports (Nature) AIP Advances (Am Inst Physics) G3 (Genetics Soc of America) Biology Open (Company of Biologists) • ‘Pseudo-Clones’ of PLoS ONE (probably selective) – Physical Review X (Am Physical Society) – Open Biology (Royal Society) – Cell Reports (Elsevier, Cell Press) [Pete Binfield] CBIIT October 30, 2013 10
  • 11. This Still Places the Research Article as the Central Focus of the Academic Enterprise… Maybe the Article is Only One View CBIIT October 30, 2013 11
  • 12. Paper as Portal 0. Full text of PLoS papers stored in a database 4. The composite view has links to pertinent blocks of literature text and back to the PDB 4. 1. 1. A link brings up figures from the paper 2. 3. A composite view of journal and database content results 3. 1. User clicks on thumbnail 2. Metadata and a webservices call provide a renderable image that can be annotated 3. Selecting a features provides a database/literature mashup 4. That leads to new papers PLoS Comp. Biol. 2005 1(3) e34 2. Clicking the paper figure retrieves data from the PDB which is analyzed 12
  • 13. Given This Disruption It is Worth Thinking About… • A paper as only one form of knowledge discovery • The use of interaction and rich media from which to learn and actually do science • Reproducibility • Reward structures • Better management of the research lifecycle P.E. Bourne 2005 In the Future will a Biological Database Really be Different from a Biological Journal? PLOS Comp. Biol. 1(3) e34 CBIIT October 30, 2013 13
  • 14. MOOCs As Research CBIIT October 30, 2013 14
  • 15. Pubcast – Video Integrated with the Full Text of the Paper CBIIT October 30, 2013 15 http://www.scivee.tv
  • 16. https://www.coursera.org/course/drugdiscovery Spontaneous Groups Formed from All Over the World CBIIT October 30, 2013 16
  • 17. Given This Disruption It is Worth Thinking About… • A paper as only one form of knowledge discovery • The use of interaction and rich media from which to learn and actually do science • Reproducibility • Reward structures • Better management of the research lifecycle P.E. Bourne 2005 In the Future will a Biological Database Really be Different from a Biological Journal? PLOS Comp. Biol. 1(3) e34 CBIIT October 30, 2013 17
  • 18. Attitudes are Changing datasets data collections algorithms configurations tools and apps codes workflows scripts code libraries services, system software infrastructure, compilers hardware [Carole Goble] “An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment, [the complete data] and the complete set of instructions which generated the figures.” David Donoho, “Wavelab and Reproducible Research,” 1995 Morin et al Shining Light into Black Boxes Science 13 April 2012: 336(6078) 159-160 Ince et al The case for open computer 18 programs, Nature 482, 2012
  • 19. 47/53 “landmark” publications could not be replicated [Begley, Ellis Nature, 483, 2012] 19 [Carole Goble]
  • 20. Nekrutenko & Taylor, Next-generation sequencing data interpretation: enhancing, reproducibility and accessibility, Nature Genetics 13 (2012) 59% of papers in the 50 highest-IF journals comply with (often weak) data sharing rules. Alsheikh-Ali et al Public Availability of Published Research Data in High-Impact Journals. PLoS ONE 6(9) 2011 [Carole Goble] CBIIT October 30, 2013 20
  • 21. 170 Journals, 2011-2012 Required as condition of publication Required but may not affect decisions Explicitly encouraged Implied No mention [Carole Goble] Required as condition of publication Required but may not affect decisions Explicitly encouraged Implied No mention Stodden V, Guo P, Ma Z (2013) Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals. PLoS ONE 8(6): 21 e67111. doi:10.1371/journal.pone.0067111
  • 22. Flaws Are Becoming More Obvious Out of 18 microarray papers, results from 10 could not be reproduced More retractions: >15X increase in last decade At current % > by 2045 as many papers published as retracted [Carole Goble] 1. Ioannidis et al., 2009. Repeatability of published microarray gene expression analyses. Nature Genetics 41: 14 2. Science publishing: The trouble with retractions http://www.nature.com/news/2011/111005/full/478026a.html 22 3. Bjorn Brembs: Open Access and the looming crisis in science https://theconversation.com/open-access-and-the-looming-crisis-in-science-14950
  • 23. Given This Disruption It is Worth Thinking About… • A paper as only one form of knowledge discovery • The use of interaction and rich media from which to learn and actually do science • Reproducibility • Reward structures • Better management of the research lifecycle P.E. Bourne 2005 In the Future will a Biological Database Really be Different from a Biological Journal? PLOS Comp. Biol. 1(3) e34 CBIIT October 30, 2013 23
  • 24. Unfortunately the Metrics of Success Remain… [Carole Goble] CBIIT October 30, 2013 24
  • 25. This makes no sense when you ask yourself the question: What is more valuable a dataset used and cited by 100 scientists or a paper you wrote that only you cite? Case in point… CBIIT October 30, 2013 25
  • 26. What can you do today to change the situation? CBIIT October 30, 2013 26
  • 27. What Can You Do? • Support emergent community commons/portals • Be involved in the support and development of metadata standards • Contribute to workflow development etc. to drive an open research lifecycle • Educate your mentors on the importance of open science and scholarly communication • Write software thinking of an App model CBIIT October 30, 2013 27
  • 29. What Can You Do? • Support emergent community commons/portals • Be involved in the support and development of metadata standards • Contribute to workflow development etc. to drive an open research lifecycle • Educate your mentors on the importance of open science and scholarly communication • Write software thinking of an App model CBIIT October 30, 2013 29
  • 30. We Need Innovative Contributions to the Research Lifecycle Authoring Tools Data Capture Lab Notebooks Software Repositories Analysis Tools Scholarly Communication Visualization IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Commercial & Public Tools DisciplineBased Metadata Standards Community Portals Git-like Resources By Discipline Data Journals New Reward Systems Training Institutional Repositories Commercial Repositories 30
  • 31. We Need Innovative Contributions to the Research Lifecycle Authoring Tools Data Capture Lab Notebooks Software Repositories Analysis Tools Scholarly Communication Visualization IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Commercial & Public Tools DisciplineBased Metadata Standards Community Portals Git-like Resources By Discipline Data Journals New Reward Systems Training Institutional Repositories Commercial Repositories 31
  • 32. What Can You Do? • Support emergent community commons/portals • Be involved in the support and development of metadata standards • Contribute to workflow development etc. to drive an open research lifecycle • Educate your mentors on the importance of open science and scholarly communication • Write software thinking of an App model CBIIT October 30, 2013 32
  • 33. Pressure Your Institutions to Play a Greater Role • We need institutional data/knowledge sharing plans • We need digital universities • We need data/information scientists to be better recognized by institutions – its not all about papers – this implies new metrics CBIIT October 30, 2013 33
  • 34. A View from the Digital University Jane scores well in parts of her advanced on-line biology class. Professors who undertake research in the areas where Jane did well are automatically notified of her potential based on a computer analysis of her scores and background interests and Professor Smith interviews her and offers her a research internship for the summer. Over the summer, as she enters details of her experiments related to understanding a widespread neurodegenerative disease in an on-line laboratory notebook, the underlying computer system automatically puts Jane into contact with another student, Jack, in a different department whose notebook reveals he is working on using bacteria for purposes of toxic waste cleanup. Why the connection? It turns out the same gene, which they both reference a number of times in their notes, is linked to two very different disciplines – mental health and the environment. In the analog university they would never have discovered each other, but at the Digital University pooled knowledge can lead to a distinct advantage. The collaboration later results in a patent filing and triggers a notification to a number of biotech companies who might be interested in licensing the technology. A company licenses the technology and hires Jane and Jack to continue working on the project. Professor Smith hires another student using the revenue from the license and this in turn leads to a large federal grant. The students get good jobs, further research is supported and societal benefit arises from the technology. A hypothetical example for why the Digital University makes sense. CBIIT October 30, 2013 34
  • 35. Committee on Academic Promotions • What Counts – – – – – Money Grants Papers Teaching Service • What Does Not – – – – – – Sharing data Sharing software Open access Collaboration Patents Startups Ten Simple Rules for Getting Ahead as a Computational Biologist in Academia 2011 PLOS Comp Biol 7(1) e1002001 CBIIT October 30, 2013 35
  • 36. What Can You Do? • Support emergent community commons/portals • Be involved in the support and development of metadata standards • Contribute to workflow development etc. to drive an open research lifecycle • Educate your mentors on the importance of open science and scholarly communication • Write software thinking of an App model CBIIT October 30, 2013 36
  • 37. What Do We Need to Do to Get There? An App+ Store? • The App model – Think of it operating on a content base rather than a mobile device – Simple and consistent user interface – Needs to pass some quality control – Has a reward • The App+ Model – Apps interoperate through a generic workflow interface CBIIT October 30, 2013 37
  • 38. Summary • Disruption is occurring • As bioinformaticians we have the skill set to leverage change and make a difference • Go for it CBIIT October 30, 2013 38