1. Viewing universities as
landscapes of scholarship
Jodi Schneider
@jschneider
jschneider@pobox.com
2017-08-04
VIVO keynote
New York, NY

3. Heaney’s report proposes:
The information
landscape can be seen
as a contour map in
which there are
mountains, hillocks,
valleys, plains and
plateaux.
Heaney 2000,
“An Analytical Model of Collections and their Catalogues"

4. A large general collection
of information
– say a research library –
can be seen as a
plateau, raised above
the surrounding plain.
Heaney 2000

5. A specialized collection
of particular importance
is like a sharp peak.
Heaney 2000
flickr: pefectfutures/3299973538/

6. Upon a plateau there might be undulations
representing strengths and weaknesses.
Heaney 2000
Heaney 2000

7. The scholar surveying the
landscape is looking for
the high places. A high
point represents an area
where the potential for
gleaning desired
information by visiting that
spot (physically or by
remote means) is greater
than that of other areas.
Heaney 2000
flickr: pefectfutures/3299973538/

8. To continue the analogy, the
scholar is concerned at the
initial survey to identify
areas rather than specific
features – to identify
rainforest rather than to
retrieve an analysis of the
canopy fauna of the
Amazon basin. This model
attempts to characterise that
initial part of the process of
information retrieval.
Heaney 2000,
“An Analytical Model of Collections and their Catalogues"

9. The landscape is,
however,
multidimensional. Where
one scholar may see a
peak another may see a
trough. The task is to
devise mapping
conventions which
enable scholars to read
the map of the landscape
fruitfully, at the
appropriate level of
generality or specificity.
Heaney 2000

10. What might the information
landscape look like?

11. VosViewer
http://www.vosviewer.com/
University
collaboration map

12. What might the
information landscape
of a university cover?

13. The information landscape of the
university would have to consider
• People & Organizations
• Spaces & Places
• Activities & Resources
• Ideas
• …Maybe More?

14. VosViewer
http://www.vosviewer.com/
Cardiology 2006-2010

15. Slice into topical landscapes
• The information landscape of a reearch
group
• … of a program
• … of a department
• … of a college

16. It would interlock with increasingly
larger landscapes
• The information landscape of a university
• … of a region
• … of a nation
• … of the world

17. Also sliced into topical landscapes
• The information landscape of a subfield
• … of a field
• … of a mega-field

18. VosViewer
http://www.vosviewer.com/
Patient safety
2006-2010

19. How do fields differ?
Patient safety
2006-2010
Cardiology
2006-2010

20. Where are the opportunities in
the field?
Patient safety
2006-2010
Cardiology
2006-2010

21. Where are the opportunities in
the field?
Patient safety
2006-2010
Cardiology
2006-2010
And who is at the pinch points?

22. What stakeholder questions
could this map help answer?

23. Your systems have GREAT data!

24. Does it provide a pathfinder for
your stakeholders?

25. Who are YOUR stakeholders?
What do they want?

26. Student & scholar questions
• If I want to study topic X, where should I
go?
• Where are the best holdings
(library/archive) for a given topic?
• How can I track and map research for a
literature review?

27. PI questions
• I want to find a collaborator who
understands topic X/paper Y. Who has co-
cited between work in my field and that?
• Who is working in topic X, either here or
somewhere I’ve been.
• Who do I know in common with person Z?
• Who at my institution has already been
funded on this grant program?

28. Collection & research
management questions
• What were the papers in top 10 journals
published by our people last year?
• What books have faculty published?

29. Strategic questions
• What are the key areas for strategic
investment?
• Is the area growing? shrinking? How will
external events impact that?
• Are there disjoint groups working in this
topic? Could and should they be bridged?
• What are this unit’s peers?

30. Serving stakeholders
• Scholarship is the unique business of the
university.
• Stakeholders have specific questions that
come from their interactions with scholarship.
• To serve stakeholders, the research
information system community needs to
envision what’s possible & what’s desirable
for SCHOLARSHIP.
• Different roles for librarians, systems
developers, repository managers, ontologists.

31. “the scholar is concerned at the initial survey to
identify areas rather than specific features”
“enable scholars to read the
map”

32. http://scimaps.org/mapdetail/a_chart_illustrating_124

34. http://scimaps.org/mapdetail/being_a_map_of_physi_171

35. What questions do YOUR stakeholders want to
“read off the map”?

36. Needs an ECOSYTEM of data
• Not just native system data
• Not just MY institution

37. Data you own vs. data you get from
others
• People & Organizations
• Spaces & Places
• Activities & Resources
• Ideas
• …Maybe More?

38. Needs to be user-centered
• Linked Data Principles
• The Del.ici.ous lesson

39. Linked Data Principles
• Linked Data Principles
https://www.w3.org/DesignIssues/LinkedData.html

40. Linked Data Principles
• Linked Data Principles
https://www.w3.org/DesignIssues/LinkedData.html
1. Use URIs as names for things
2. Use HTTP URIs so that people can
look up those names.
3. When someone looks up a URI,
provide useful information, using
the standards (RDF*, SPARQL)
4. Include links to other URIs. so that
they can discover more things.
- Tim Berners-Lee

41. Linked Data Principles
• Linked Data Principles
https://www.w3.org/DesignIssues/LinkedData.html
1. Use URIs as names for things
2. Use HTTP URIs so that people can
look up those names.
3. When someone looks up a URI,
provide useful information, using
the standards (RDF*, SPARQL)
4. Include links to other URIs. so
that they can discover more things.
- Tim Berners-Lee

42. Linked Data Principles
How interlinked is your data?
https://www.w3.org/DesignIssues/LinkedData.html

43. The Del.ici.ous lesson
http://bokardo.com/archives/the-delicious-lesson/

44. The Del.ici.ous lesson
“The one major idea behind the Del.icio.us
Lesson is that personal value precedes
network value. What this means is that if we
are to build networks of value, then each
person on the network needs to find value for
themselves before they can contribute value to
the network. In the case of Del.icio.us, people
find value saving their personal bookmarks first
and foremost. All other usage is secondary.” –
Joshua Porter
http://bokardo.com/archives/the-delicious-lesson/

45. The Del.ici.ous lesson
What does your data do for the individual?
… the research group?
… the department?
… the field?
http://bokardo.com/archives/the-delicious-lesson/

46. Mapping knowledge claims & evidence

47. “[Y]ou can transform a fact into
fiction or a fiction into fact just by
adding or subtracting references”
- Bruno Latour

48. ... two miRNAs, miRNA-372 and-373, function as potential novel oncogenes in
testicular germ cell tumors by inhibition of LATS2 expression, which suggests that
Lats2 is an important tumor suppressor (Voorhoeve et al., 2006).
Raver-Shapira et.al, JMolCell 2007
miR-372 and miR-373 target the Lats2 tumor suppressor (Voorhoeve et al., 2006)
Yabuta, JBioChem 2007:
As claims get cited, they become facts:
To investigate the possibility that miR-372 and miR-373 suppress the
expression of LATS2, we...
Therefore, these results point to LATS2 as a mediator of the miR-372 and miR-373
effects on cell proliferation and tumorigenicity,
Voorhoeve et al, Cell, 2006:
Hypothesis
Implication
Cited Implication
Fact
Slide credit: Anita DeWaard: 'Stories that persuade with data' - talk at CENDI meeting January 9 2014
https://www.slideshare.net/anitawaard/stories-that-persuade-with-data-talk-at-cendi-meeting-january-
9-2014/6

49. “The conversion of hypothesis to
fact through citation alone.”
- Stephen Greenberg

50. Greenberg, Steven A.
"Understanding belief using
citation networks." Journal of
evaluation in clinical
practice 17.2 (2011): 389-393.
http://dx.doi.org/
10.1111/j.1365-
2753.2011.01646.x

51. “The conversion of hypothesis to fact
through citation alone.”
- Stephen Greenberg
Greenberg, Steven A. "How citation distortions create unfounded
authority: analysis of a citation network." BMJ 339 (2009): b2680.
https://doi.org/10.1136/bmj.b2680

52. Funded grants with citation bias &
citation distortion.
Greenberg, Steven A. "How citation distortions create unfounded
authority: analysis of a citation network." BMJ 339 (2009): b2680.
https://doi.org/10.1136/bmj.b2680

53. Modeling arguments and
evidence

54. https://dvcs.w3.org/hg/rdf/raw-file/default/rdf-primer/index.html

55. SEPIO – evidence lines
Brush, Matthew, Kent Shefchek, and Melissa Haendel. "SEPIO: a
semantic model for the integration and analysis of scientific
evidence." International Conference on Biomedical Ontology and
BioCreative. 2016. http://ceur-ws.org/Vol-1747/IT605_ICBO2016.pdf
“A proposition has_evidence
one or more evidence lines, which have_supporting_data
one or more data items used in evaluation of the
proposition’s truth.”

56. SEPIO – evidence lines example
Brush, Matthew, Kent Shefchek, and Melissa Haendel. "SEPIO: a
semantic model for the integration and analysis of scientific
evidence." International Conference on Biomedical Ontology and
BioCreative. 2016. http://ceur-ws.org/Vol-1747/IT605_ICBO2016.pdf
“A simplified account of existing evidence related to this proposition is presented below,
presenting summaries of five evidence lines (E1-E5) from five studies relevant to the
classification of the variant for Fabry Disease:
E1. Six affected individuals with the variant were found to have reduced GLA enzyme
activity.
E2. The variant was absent from 528 unaffected controls.
E3. The variant is predicted to cause abnormal splicing that inserts additional sequence.
E4. Pedigree analyses showed Fabry Disease phenotypes segregating with the variant.
E5. Population databases show high frequency of individuals homozygous for the variant.”

57. SEPIO – evidence lines example
Brush, Matthew, Kent Shefchek, and Melissa Haendel. "SEPIO: a
semantic model for the integration and analysis of scientific
evidence." International Conference on Biomedical Ontology and
BioCreative. 2016. http://ceur-ws.org/Vol-1747/IT605_ICBO2016.pdf
“A simplified account of existing evidence related to this proposition is presented below,
presenting summaries of five evidence lines (E1-E5) from five studies relevant to the
classification of the variant for Fabry Disease:
E1. Six affected individuals with the variant were found to have reduced GLA enzyme
activity.
E2. The variant was absent from 528 unaffected controls.
E3. The variant is predicted to cause abnormal splicing that inserts additional sequence.
E4. Pedigree analyses showed Fabry Disease phenotypes segregating with the variant.
E5. Population databases show high frequency of individuals homozygous for the variant.”

58. Modeling arguments and
evidence

59. SEE
Bö̈ lling, Christian, Michael Weidlich, and Hermann-Georg Holzhütter.
"SEE: structured representation of scientific evidence in the biomedical
domain using Semantic Web techniques." Journal of Biomedical
Semantics 5.1 (2014): 1.

60. SEE
Bö̈ lling, Christian, Michael Weidlich, and Hermann-Georg Holzhütter.
"SEE: structured representation of scientific evidence in the biomedical
domain using Semantic Web techniques." Journal of Biomedical
Semantics 5.1 (2014): 1.

61. Modeling arguments and
evidence

62. Micropublications
Clark, Tim, Paolo N. Ciccarese, and Carole A. Goble.
"Micropublications: a semantic model for claims, evidence, arguments
and annotations in biomedical communications." Journal of Biomedical
Semantics 5.28 (2014). http://dx.doi.org/10.1186/2041-1480-5-28

63. Jodi Schneider, Paolo Ciccarese, Tim Clark, Richard D. Boyce. “Using the Micropublications ontology and the
Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.” Linked
Science at ISWC 2014 http://ceur-ws.org/Vol-1282/lisc2014_submission_8.pdf

64. Mapping knowledge claims & evidence

65. Where are the opportunities in the
field?
Patient safety
2006-2010
Cardiology
2006-2010
And who is at the pinch points?

66. Together we can have a fuller view
of our information landscape
• People & Organizations
• Spaces & Places
• Activities & Resources
• Ideas
• …Maybe More?

68. Heaney 2000

72. • What would a “Connected Graph of
Scholarship” do, that we can’t do now?