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Viewing universities as landscapes of scholarship, VIVO keynote, 2017-08-04

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Viewing universities as landscapes of scholarship, VIVO keynote, 2017-08-04

  1. 1. Viewing universities as landscapes of scholarship Jodi Schneider @jschneider jschneider@pobox.com 2017-08-04 VIVO keynote New York, NY
  2. 2. 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"
  3. 3. A large general collection of information – say a research library – can be seen as a plateau, raised above the surrounding plain. Heaney 2000
  4. 4. A specialized collection of particular importance is like a sharp peak. Heaney 2000 flickr: pefectfutures/3299973538/
  5. 5. Upon a plateau there might be undulations representing strengths and weaknesses. Heaney 2000 Heaney 2000
  6. 6. 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/
  7. 7. 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"
  8. 8. 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
  9. 9. What might the information landscape look like?
  10. 10. VosViewer http://www.vosviewer.com/ University collaboration map
  11. 11. What might the information landscape of a university cover?
  12. 12. The information landscape of the university would have to consider • People & Organizations • Spaces & Places • Activities & Resources • Ideas • …Maybe More?
  13. 13. VosViewer http://www.vosviewer.com/ Cardiology 2006-2010
  14. 14. Slice into topical landscapes • The information landscape of a reearch group • … of a program • … of a department • … of a college
  15. 15. It would interlock with increasingly larger landscapes • The information landscape of a university • … of a region • … of a nation • … of the world
  16. 16. Also sliced into topical landscapes • The information landscape of a subfield • … of a field • … of a mega-field
  17. 17. VosViewer http://www.vosviewer.com/ Patient safety 2006-2010
  18. 18. How do fields differ? Patient safety 2006-2010 Cardiology 2006-2010
  19. 19. Where are the opportunities in the field? Patient safety 2006-2010 Cardiology 2006-2010
  20. 20. Where are the opportunities in the field? Patient safety 2006-2010 Cardiology 2006-2010 And who is at the pinch points?
  21. 21. What stakeholder questions could this map help answer?
  22. 22. Your systems have GREAT data!
  23. 23. Does it provide a pathfinder for your stakeholders?
  24. 24. Who are YOUR stakeholders? What do they want?
  25. 25. 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?
  26. 26. 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?
  27. 27. Collection & research management questions • What were the papers in top 10 journals published by our people last year? • What books have faculty published?
  28. 28. 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?
  29. 29. 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.
  30. 30. “the scholar is concerned at the initial survey to identify areas rather than specific features” “enable scholars to read the map”
  31. 31. http://scimaps.org/mapdetail/a_chart_illustrating_124
  32. 32. http://scimaps.org/mapdetail/being_a_map_of_physi_171
  33. 33. What questions do YOUR stakeholders want to “read off the map”?
  34. 34. Needs an ECOSYTEM of data • Not just native system data • Not just MY institution
  35. 35. Data you own vs. data you get from others • People & Organizations • Spaces & Places • Activities & Resources • Ideas • …Maybe More?
  36. 36. Needs to be user-centered • Linked Data Principles • The Del.ici.ous lesson
  37. 37. Linked Data Principles • Linked Data Principles https://www.w3.org/DesignIssues/LinkedData.html
  38. 38. 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
  39. 39. 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
  40. 40. Linked Data Principles How interlinked is your data? https://www.w3.org/DesignIssues/LinkedData.html
  41. 41. The Del.ici.ous lesson http://bokardo.com/archives/the-delicious-lesson/
  42. 42. 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/
  43. 43. 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/
  44. 44. Mapping knowledge claims & evidence
  45. 45. “[Y]ou can transform a fact into fiction or a fiction into fact just by adding or subtracting references” - Bruno Latour
  46. 46. ... 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
  47. 47. “The conversion of hypothesis to fact through citation alone.” - Stephen Greenberg
  48. 48. 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
  49. 49. “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
  50. 50. 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
  51. 51. Modeling arguments and evidence
  52. 52. https://dvcs.w3.org/hg/rdf/raw-file/default/rdf-primer/index.html
  53. 53. 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.”
  54. 54. 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.”
  55. 55. 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.”
  56. 56. Modeling arguments and evidence
  57. 57. 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.
  58. 58. 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.
  59. 59. Modeling arguments and evidence
  60. 60. 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
  61. 61. 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
  62. 62. Mapping knowledge claims & evidence
  63. 63. Where are the opportunities in the field? Patient safety 2006-2010 Cardiology 2006-2010 And who is at the pinch points?
  64. 64. Together we can have a fuller view of our information landscape • People & Organizations • Spaces & Places • Activities & Resources • Ideas • …Maybe More?
  65. 65. Heaney 2000
  66. 66. • What would a “Connected Graph of Scholarship” do, that we can’t do now?

Editor's Notes

  • Varied crowd – VIVO administrators, ontologists, librarians. Well-informed about SPARQL, Linked Data, concept of an ontology. But need to introduce ideas.

    ====
    Viewing universities as landscapes of scholarship

    The university can be seen as a collection of individuals, or as an administrative engine, but what sets a university apart is the production of knowledge and knowledgeable people, through teaching, learning, and scholarly inquiry. In 2000, Michael Heaney proposed that the information landscape could be viewed "as a contour map" with both peaks and troughs. We extend this analogy to take universities, and their faculty members, themselves as a part of this information landscape. This leads us to ask how we can apply linked data not just to a single university but to interconnect universities, and to survey the university itself as a landscape to support scholarly inquiry. In particular, we ask what would a “Connected Graph of Scholarship” do, that we can’t do now?
  • CC-BY Jodi Schneider

  • http://www.flickr.com/photos/pefectfutures/3299973538/


  • CC-BY Jodi Schneider
  • http://www.flickr.com/photos/pefectfutures/3299973538/


  • CC-BY Jodi Schneider

  • VosViewer http://www.vosviewer.com/
  • VosViewer http://www.vosviewer.com/
  • http://britishlibrary.typepad.co.uk/magnificentmaps/2010/07/magnificent-maps-that-didnt-make-the-exhibition-4.html
  • CC-BY Jodi Schneider
  • http://britishlibrary.typepad.co.uk/magnificentmaps/2010/07/magnificent-maps-that-didnt-make-the-exhibition-4.html
  • http://scimaps.org/mapdetail/a_chart_illustrating_124
    VII.2 A Chart Illustrating Some of the Relations between the Branches of Natural Science and Technology

    “Harold Johann Thomas (H.J.T.) Ellingham was a professor of chemistry at the Imperial College of Science, Technology and Medicine in London and a member of the Royal Institute of Chemistry. In 1948, he produced a hand-drawn map showing the relationships between the branches of natural science and technology. The work is premised on the distance-similarity metaphor, in which objects more similar to each other are more proximate in space. Additional relationships are indicated by the direction of the labels. Ellingham’s map is one of the earliest known examples of a visual frontend to a body of literature. Ellingham overlies the coverage of each of the available index and abstracting services in the United Kingdom onto the chart to indicate which areas of science the indexes covered. Overlay 1 features broad index and abstract services that cover large areas of science. Overlay 2 features more focused index and abstract services that cover specific areas of scientific research. Ellingham also intended that his two-dimensional map should be wrapped as if around a cylinder to show the continued relationships of topics on the extreme left side with those on the extreme right side. 
    References:
    Ellingham, H.J.T. 1948. “Divisions of Natural Science and Technology.” In Report and Papers Submitted to The Royal Society Scientific Information Conference. London: Burlington House. 
    Ellingham, H.J.T. 1948. A Chart Illustrating Some of the Relations Between the Branches of Natural Science and Technology. Courtesy of The Royal Society. In “7th Iteration (2011): Science Maps as Visual Interfaces to Digital Libraries,” Places & Spaces: Mapping Science, edited by Katy Börner and Michael J. Stamper. http://scimaps.org.
  • I.6 Ph.D. Thesis Map

    Computer scientist Keith V. Nesbitt’s hand-drawn map, which was inspired by the Sydney metro map, shows interconnecting ideas running through his Ph.D. thesis. Nesbitt’s thesis concerns the design of multisensory displays of abstract data with the motivation of mining this data. On the map, each separate “track of abstract thought” in the thesis is represented by a different color. Related ideas correspond to category stations along that track. Overlapping ideas are shown as connected stations. The familiarity of metro maps makes the diagram easy for readers to interpret. As the space in which the tracks are laid is invariant to rotation and mirroring, it is possible to read the map in any direction. However, there is a cultural bias for the tracks to be followed from left to right and top to bottom. 
    References:
    Nesbitt, Keith V. 2004. “Getting to More Abstract Places Using the Metro Map Metaphor.” In Proceedings of the 8th International Conference on Information Visualization, 488-493. Washington: IEEE Computer Society. 
    Nesbitt, Keith V. 2003. “Multi-Sensory Display of Abstract Data.” PhD diss., University of Sydney.
    Nesbitt, Keith V. 2004. PhD Thesis Map. Courtesy of IEEE and Keith V. Nesbitt, Charles Sturt University, Australia, ©2004 by IEEE. In “1st Iteration (2005): The Power of Maps,” Places & Spaces: Mapping Science, edited by Katy Börner and Deborah MacPherson. http://scimaps.org.
  • X.1 Being a Map of Physics

    This map is the culmination of a six-year-long labor of love by noted physicist, visual artist, poet, and peace activist Bernard H. Porter. Porter began compiling the historical data upon which the map is based in 1932 while a fellow in radioactive research at Brown University. He then took most of the summer of 1933, working out of his parent’s home in Houlton, Maine, to compose the map’s striking visuals. The following years were spent circulating the map among prominent physicists and historians of science to verify its accuracy. The end result is a rich geography of a scientific field, one that uses mapping conventions to make understandable the way ideas move and develop over time. Ambitious in scope, the map traces the history of physics from the 6th century B.C. to the present day. Key theoretical starting points such as ‘Mechanics,’ ‘Sound,’ ‘and Light’ appear as water sources from which streams of thought emerge. Located alongside these rivers are “villages” representing figures like Isaac Newton, Alessandro Volta, Werner Heisenberg, and other major contributors to the development of physics. Surrounding it all is the map’s border, which is decorated with 24 diagrams that frequently appear in the work of physicists. 
    References:
    Porter, Bernard. 1939. Being a Map of Physics. Courtesy of Maine State Library and Mark Melnicove. In "10th Iteration (2014): The Future of Science Mapping," Places & Spaces: Mapping Science, edited by Katy Börner and Samuel Mills. http://scimaps.org
  • http://britishlibrary.typepad.co.uk/magnificentmaps/2010/07/magnificent-maps-that-didnt-make-the-exhibition-4.html
  • Right now – disconnected, each university for itself
    Even OpenVIVO – limited questions -- INDIVIDUAL
    Admin needs, not SCHOLARS’ needs
    Need visualization, USE of the data, showing patterns, making it easy to query by example
    LINKED DATA – has a role
  • Some first-class objects in the system have gotten more attention than others.
    Which are the first-class objects in VIVO?
    Publication, author, department(?)
    Department, university, DISCIPLINE
  • CC-BY Jodi Schneider
  • 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
    Latour, Bruno. Science in action: How to follow scientists and engineers through society. Harvard University Press, 1987. p33
  • 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
    Latour, Bruno. Science in action: How to follow scientists and engineers through society. Harvard University Press, 1987. p33
  • “As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
  • “As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
  • “As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
  • “As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
  • “As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
  • “As is commonly the case, different evidence is used by each lab - either because certain data were not accessible, or some labs judged certain data to be unreliable or irrelevant to the claim, or some labs interpreted the same data in different ways. SEPIO translates this scenario into the following narrative and set of instances to be represented in its formal modeling of the data.”
  • “A model of the evidence for and against the assertion escitalopram does not inhibit CYP2D6. This is based on the Micropublications ontology, and reuses the ev- idence taxonomy (dikbEvidence), terms (dikb), and data from the DIKB. The Drug Ontology (DRON) and Protein Ontology (PRO) are reused in semantic qualifiers. A more detailed view of Method Me1 is shown in Figure 1. "
  • http://britishlibrary.typepad.co.uk/magnificentmaps/2010/07/magnificent-maps-that-didnt-make-the-exhibition-4.html
  • CC-BY Jodi Schneider
  • http://www.flickr.com/photos/pefectfutures/3299973538/
  • CC-BY Jodi Schneider
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