SSP 2012 presentation on trends that we need to be mindful of when thinking about scholarship, publishing and use.

1. Trends Influencing Future Scholarship
Tim Babbitt
May 31, 2012
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2. CHANGES FROM TECHNOLOGY

3. Growth of Mobile data usage

4. Technology is changing how we structure work
Then
 Notepads, notebooks
 Books
 cd, dvd, itunes
 Focus groups
 Classrooms/lectures
Now
 Evernote
 eBooks
 Spotify, youtube (streamed)
 Affectiva
 Khan Academy

5. CHANGES IN ACADEMIA

6. Towards reproducible research
 Reproducible research
 means
context, quality, trust
 means easy access to the
sources
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7. Changing Policy
 Emerging trend of journals and publishers linking to openaccess data repositories
 Journals and funding agencies setting policy to preserve
and associate data supporting research results
 Open Access
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8. Changing Global Research Patterns
The center of gravity of the world system of scholarship
is moving from west to east.
NOTES: Asia-10 includes
China, Japan, India,
Indonesia, Malaysia,
Philippines, Singapore,
South Korea, Taiwan, and
Thailand.
SOURCE: National
Science Board, Science
and Engineering Indicators
2010
8

9. Citations of U.S. research articles in non-U.S.
literature, by region/country: 1998–2010

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South
Korea, Taiwan, Thailand; EU = European Union

10. Share of citations to
international literature: 2000–10
Asia-8 =
India, Indonesia, Malaysia, Philippines, Singapore, South
Korea, Taiwan, Thailand; EU = European Union

11. Citations from Asia 10 Articles
NOTES: Asia-10 includes China, Japan, India, Indonesia, Malaysia, Philippines,
Singapore, South Korea, Taiwan, and Thailand. Asia-8 excludes China and Japan.
SOURCE: National Science Board, Science and Engineering Indicators 2010
11

12. US Academic Expenditures on Research by
Area, FY2008
(Millions of current dollars)
SOURCE: National Science
Foundation/Division of Science
Resources Statistics, Survey of
Research and Development
Expenditures at Universities and
Colleges: FY 2008.
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13. Master’s degrees conferred
Modern Languages
1%
Other
12%
Biological sciences
2%
Visual/Performing Arts
2%
Computer science
3%
Education
29%
Social Sciences
3%
Psychology
5%
Public Administration
5%
Engineering
5%
Health profession
9%
Business
25%
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14. Doctorate Placement
4000
3500
3000
2500
Other
2000
Government
1500
Business
Academia
1000
500
0
Life
sciences
Physical
sciences
Social
sciences
Engineering
Education
Humanities Other fields
SOURCE: Survey of
Earned Doctorates.
14

15. Average Age of References
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16. Citation Format by Discipline
Secondary Source Citations
Journals
6%
3%
5%
9%
Books/Monographs
Reports
Conference Proceedings
4%
15%
9%
Other
4%
2%
0%
12%
10%
38%
42%
33%
12%
18%
73%
79%
56%
54%
53%
45%
18%
Education
Chemistry
Civil Engineering
Economics
History
Educational
Psychology

17. Academic work is social
 2006 Univ. of Minn.
Study
 68% - Faculty work
collaboratively
 52% - Collaborate with
colleagues at other
institutions
 46% - Find the distance
from colleagues is a
collaboration obstacle
 “One group of experts
can’t do everything”
SOURCE: Newman M E J PNAS 2001;98:404-409

18. Sharing
Low threshold (at good
enough)
 Astrophysicists
 arXiv
 Political scientists
 SSRN
 Economists
High threshold (competitive)
 Historians
 Molecular and cell
biologists
 Archaeologists
 Biochemists
 SSRN, NBER,
 Performers/Composers
Junior faculty in all fields are especially
cautious for fear of theft and/or
misinterpretation.
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19. CHANGES AT THE LIBRARY

20. Changing Environment of Research
Past
Present
Future
Book
Aggregation
Book & Journal
Aggregation
Electronic Information
Aggregation
Other sources
Other sources
Other sources
LIBRARY
The “center” of research is shifting from libraries to other sources
Books
on shelves
LIBRARY
LIBRARY
Databases
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21. Resource Usage Trends
250,000,000
200,000,000
Items
150,000,000
100,000,000
50,000,000
Circulation
Interlibrary loans
2002
2004
2006
2008
2010
188,601,008
200,203,943
187,236,440
138,102,762
136,003,396
7,843,649
8,545,417
10,265,385
10,695,342
SOURCES: National
Center for Education
Statistics. Academic
Libraries.
10,157,182
http://nces.ed.gov/pubsearch/get
pubcats.asp?sid=041#)
,
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22. Changing Resource Expenditures
SOURCE: NCES 2010, 2008, 2006, 2004, 2002 Supplemental tables for Academic Libraries
Books, serial backfiles and other
materials
Overall % by content category
80%
70%
$700,000,000
$600,000,000
$500,000,000
$400,000,000
$300,000,000
$200,000,000
$100,000,000
$-
60%
50%
Electronic
Audiovisual
Print
Books, serial backfiles
and other materials
40%
30%
Current Serial
Subscriptions
20%
10%
0%
2002
2004
2006
2008
2010
2002
Current Serial Subscriptions
2004
2006
2008
2010
Overall % of purchased and
licenced content
$1,400,000,000
$1,200,000,000
100%
$1,000,000,000
80%
$800,000,000
Electronic
$400,000,000
60%
Print
$600,000,000
40%
$200,000,000
Electronic
20%
$-
Print
0%
2002
2004
2006
2008
2010
2002
2004
2006
2008
2010
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23. Changing Discovery Methods
Data from Evans (2008

24. Context
 Activities are the
context for when our
content is used in
research
 Research mash-ups
 “whole is greater than
the parts”
 Critical for ecosystem
of research

25. Research Network Connections
 Era of connections





Social networks
Professional networks
Connected information
Connected concepts
Connected meaning

26. The Interconnected Article
Blogs
Related
Articles
Notebooks
Wikis
Comments
& Reviews
Models
Codes
Presentations
Algorithms
Preprints
Podcasts
Models
Methods
Video
Plans
Data
Intermediate
Results
Ontologies
Has a content edge over
print: more of it and more
timely
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27. Network of Research Areas

28. Network of Ideas (citations)

29. Network of datasets

30. Evolution of search
Catalog/index
Database/Search Engine
Xpath/XQuery
We are here
Subject
indexing
of objects
Full text
search
A&I
search
Search Technology
Semantic
and
machine
search
Structural
and
network
search
Information Structure
Hand
crafted
Metadata
Content
Text
Content
XML

31. Search features
 Going from metadata about objects and text search to
ideas, context and mining
 Semantic Search
 Greater granularity of discovery
 Structural analysis of content
 Precision search
 Semantic search
 Internationalization of search
 Translated search

32. Semantic Search
 Semantic Search utilizes robust data structures like
ontologies to apply domain knowledge to otherwise twodimensional terms.
 The application of word context provides a dynamic
aspect to semantic search, allowing the user’s real-time
intent to guide results.
 Contrast with static thesauri and controlled vocabularies
which miss nuances of context and intent.
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33. Automated processing of library
content
 PubMed contains ~17,787,763 articles to
date
 Manually searching is tedious and
frustrating
 Can be hard finding links between data
and articles
 Conclusion? Machines will be reading the
library.
 Using MyExperiment
Workflows, researcher Paul Fisher found
Link between cholesterol, patient trauma
and parasite resistance in cattle.
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34. Arrowsmith LBD: the ABC Model
Articles about an AB relationship
A
Raynaud’s syndrome
AB
B
blood viscosity
etc.
BC
C
dietary fish oil
Articles about a BC relationship
 AB and BC are complementary but disjoint : They can reveal an implicit
relationship between A and C in the absence of any explicit relation.
 The researcher assesses titles in the B literature identified by the system
for fit or contribution to problem.
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35. Content Presented as Data
Incidence of “Malvinas”)
Something happened !
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36. Purpose of Content Analytics
Consumptive use
 informs
 delivers ideas.
Analytical use
 inspires or proves ideas
 The true center of
research.
They occupy different points in the
scholarly information lifecycle.
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37. What does Content Analytics do?
 Collaborative man-machine exploration
 highlights trends, clues or anomalies [visualization – leverages
cognitive skills].
 On demand Analysis.
 identify and quantify trends, relationships, concepts and
correlations. (tools: SEASR, nltk , autonomy, … )
 Continuous Analytics
 generate new ‘facets’ or annotations for discovery [augments
content].
 Preserves value
 Older content is read lessii, but remains important for trend
analysis and statistical significance. [value shifts]
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38. Examples of text as data
 Changes in word sense ( e.g. consumption( TB )
, moot, oratio1 ) and spelling (e.g. 18th C. ſ to s , *re 
*er )
 Bibliometrics and other usage analyses
 Citation patterns
 Institution vs. discipline
 Author demographics
 Pharma: Drug / Symptom correlation.
 Biology: Species / date / location observations.
 Social Sci: Work/life habits of undergrads based on
access patterns at different institutions [ usage data based]
 …
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39. Text Mining
Unstructured text to queryable data structures
WHY?
 TOO MUCH TEXT TO HAND ANALYZE.
 Improved discovery ( better ‘metadata’ )
 Business Intelligence

e.g. content stats -> content acquisitions
 Saleable datasets
E.g. Distribution of authors vs. disciplines vs. grants
 End User research agendas
 High-End : Custom (user specified) mining as a service
 Simple : Visualization of results ( frequency / co-occurrence … )
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40. Options for use of text mining
Many, many options – it is about capability
 Generic ‘improvements’
 disambiguation of people, places and events ()
 concept labeling ( different terms, same ‘thing’)
 Corpus specific
 E.g. extract institutions from theses (demographics).
 Discipline specific
 E.g. taxon labeling in biology.
 Discovery tools , e.g.
 topic labeling (`natural` disciplines)
 reading level (grade, UG, PG … )
 Structural analysis ( important parts of doc )
 Boilerplate vs. ‘meat’
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41. E.G. “White Plague”
TUBERCULOSI
S
TB and
Antibioti
cs
Heroin , TB
and
Aurantimona
daceae
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42. Datasets: Factoids & point data












ca. 1.4M Faculty ( 50% full-time ) in US HE, ~75M people enrolled in US HE
ca. 100k Faculty in UK HE
44% of Researchers use online (other people’s) datasets for their research
48% of Researchers use datasets > 1GB
10.8% store their data outside their institution ( 50% store it in their “lab”)
1 - 5% of datasets are formally moved into the curation process.
66% of faculty have requested other people’s data ( and 49% of those got it).
[ 26.5% have the expertise to analyze their own data.
[ 80.3% do not have sufficient expertise to manage their own data
Institutional storage costs ~ $600 / TB / year
[ 58% is the annual increase in the amount of data being generated
[ 20-40% is annual growth in the amount of storage deployed (est.)


< 1% of ecological data is accessible after publication.
> 85% of all information is in text form


2.7 times more citations accrue to papers with accessible data
3 to 6 times more papers emerge if the data is accessible.
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43. Drivers of change

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





Nearly ubiquitous high-speed wireless globally
Inexpensive devices/apps/services
Global technology innovation
Policy shifts in Academia
Internationalization of scholarship
Growth in primary source datasets
Fearless and connected entrepreneurs
Fearless and connected researchers
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44. Thank you!
Questions?
Tim Babbitt
timothy.babbitt@proquest.com
(734) 997-4593
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