Vu210610futurejournal

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Vu210610futurejournal

  1. 1. The Future of the Journal Anita de Waard , a.dewaard@elsevier.com Disruptive Technologies Director, Elsevier Labs June 21, 2010
  2. 2. Science is made of information...
  3. 3. Science is made of information... ...that gets created...
  4. 4. Science is made of information... ...that gets created... ... and destroyed.
  5. 5. What is the problem?
  6. 6. What is the problem? 1. Researchers can’t keep track of their data.
  7. 7. What is the problem? 1. Researchers can’t keep track of their data. 2. Data is not stored in a way that is easy for authors.
  8. 8. What is the problem? 1. Researchers can’t keep track of their data. 2. Data is not stored in a way that is easy for authors. 3. For readers, article text is not linked to the underlying data.
  9. 9. The Vision Work done with Ed Hovy, Phil Bourne, Gully Burns and Cartic Ramakrishnan
  10. 10. The Vision Work done with Ed Hovy, Phil Bourne, Gully Burns and Cartic Ramakrishnan 1. Research: Each item in the system has metadata metadata (including provenance) and relations to other data items metadata added to it. metadata metadata metadata
  11. 11. The Vision Work done with Ed Hovy, Phil Bourne, Gully Burns and Cartic Ramakrishnan 1. Research: Each item in the system has metadata metadata (including provenance) and relations to other data items metadata added to it. 2. Workflow: All data items created in the lab are added metadata to a (lab-owned) workflow system. metadata metadata
  12. 12. The Vision Work done with Ed Hovy, Phil Bourne, Gully Burns and Cartic Ramakrishnan 1. Research: Each item in the system has metadata metadata (including provenance) and relations to other data items metadata added to it. 2. Workflow: All data items created in the lab are added metadata to a (lab-owned) workflow system. 3. Authoring: A paper is written in an authoring tool which can pull data with provenance from the workflow tool in the appropriate representation into the document. metadata metadata Rats were subjected to two grueling tests (click on fig 2 to see underlying data). These results suggest that the neurological pain pro-
  13. 13. The Vision Work done with Ed Hovy, Phil Bourne, Gully Burns and Cartic Ramakrishnan 1. Research: Each item in the system has metadata metadata (including provenance) and relations to other data items metadata added to it. 2. Workflow: All data items created in the lab are added metadata to a (lab-owned) workflow system. 3. Authoring: A paper is written in an authoring tool which can pull data with provenance from the workflow tool in the appropriate representation into the document. metadata 4. Editing and review: Once the co-authors agree, the paper is ‘exposed’ to the editors, who in turn expose it to metadata reviewers. Reports are stored in the authoring/editing system, the paper gets updated, until it is validated. Rats were subjected to two grueling tests (click on fig 2 to see underlying data). These results suggest that the neurological pain pro- Review Revise Edit
  14. 14. The Vision Work done with Ed Hovy, Phil Bourne, Gully Burns and Cartic Ramakrishnan 1. Research: Each item in the system has metadata metadata (including provenance) and relations to other data items metadata added to it. 2. Workflow: All data items created in the lab are added metadata to a (lab-owned) workflow system. 3. Authoring: A paper is written in an authoring tool which can pull data with provenance from the workflow tool in the appropriate representation into the document. metadata 4. Editing and review: Once the co-authors agree, the paper is ‘exposed’ to the editors, who in turn expose it to metadata reviewers. Reports are stored in the authoring/editing system, the paper gets updated, until it is validated. 5. Publishing and distribution: When a paper is published, a collection of validated information is exposed to the world. It remains connected to its related Rats were subjected to two data item, and its heritage can be traced. grueling tests (click on fig 2 to see underlying data). These results suggest that the neurological pain pro- Review Revise Edit
  15. 15. The Vision Work done with Ed Hovy, Phil Bourne, Gully Burns and Cartic Ramakrishnan 1. Research: Each item in the system has metadata metadata (including provenance) and relations to other data items metadata added to it. 2. Workflow: All data items created in the lab are added metadata to a (lab-owned) workflow system. 3. Authoring: A paper is written in an authoring tool which can pull data with provenance from the workflow tool in the appropriate representation into the document. metadata 4. Editing and review: Once the co-authors agree, the paper is ‘exposed’ to the editors, who in turn expose it to metadata reviewers. Reports are stored in the authoring/editing system, the paper gets updated, until it is validated. 5. Publishing and distribution: When a paper is published, a collection of validated information is exposed to the world. It remains connected to its related Rats were subjected to two data item, and its heritage can be traced. grueling tests (click on fig 2 to see underlying 6. User applications: distributed applications run on this data). These results suggest that ‘exposed data’ universe. the neurological pain pro- Some other publisher Review Revise Edit
  16. 16. What is needed to get there?
  17. 17. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly
  18. 18. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements
  19. 19. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights
  20. 20. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights D. Social change: Scientists who store, track and annotate their work
  21. 21. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights D. Social change: Scientists who store, track and annotate their work E. Semantic/Linked Data XML repositories.
  22. 22. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights D. Social change: Scientists who store, track and annotate their work E. Semantic/Linked Data XML repositories. F. Publishing systems that run application servers.
  23. 23. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly tool builders B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights D. Social change: Scientists who store, track and annotate their work E. Semantic/Linked Data XML repositories. F. Publishing systems that run application servers.
  24. 24. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly tool builders B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements tool builders C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights D. Social change: Scientists who store, track and annotate their work E. Semantic/Linked Data XML repositories. F. Publishing systems that run application servers.
  25. 25. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly tool builders B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements tool builders C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights standards bodies D. Social change: Scientists who store, track and annotate their work E. Semantic/Linked Data XML repositories. F. Publishing systems that run application servers.
  26. 26. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly tool builders B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements tool builders C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights standards bodies D. Social change: Scientists who store, track and annotate their work institutes, funding bodies, individuals E. Semantic/Linked Data XML repositories. F. Publishing systems that run application servers.
  27. 27. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly tool builders B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements tool builders C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights standards bodies D. Social change: Scientists who store, track and annotate their work institutes, funding bodies, individuals E. Semantic/Linked Data XML repositories. publishers F. Publishing systems that run application servers.
  28. 28. What is needed to get there? A. Workflow tools: Linked-data-based workflow tools for all sciences: scalable, safe, and user-friendly tool builders B. Authoring and reviewing tools: that enable use of rich and provenance-tracked elements tool builders C. Metadata standards: Standards that allow exchange of information on any knowledge item created in a lab, including provenance/privacy/IPR rights standards bodies D. Social change: Scientists who store, track and annotate their work institutes, funding bodies, individuals E. Semantic/Linked Data XML repositories. publishers F. Publishing systems that run application servers. publishers
  29. 29. A. Workflow tools are emerging
  30. 30. A. Workflow tools are emerging http://MyExperiment.org
  31. 31. A. Workflow tools are emerging http://VisTrails.org http://MyExperiment.org
  32. 32. A. Workflow tools are emerging http://VisTrails.org http://MyExperiment.org http://wings.isi.edu/
  33. 33. B. Authoring ‘ecosystems: SWAN person SWAN Semantic Relationships comment concept Claim publication hypothesis gene Claim publication group publication Public Excel file PDFs Private comment publication person Claim publication MSWORD file Slide by Tim Clark
  34. 34. B. Authoring ‘ecosystems: SWAN person SWAN Semantic Relationships annotates comment authoredBy makes hasEvidence concept annotates Claim publication shareWith hypothesis makes hasEvidence gene Claim publication hasEvidence discussedIn group publication Public Excel file describes describes PDFs Private makes hasEvidence annotates comment publication person Claim hasEvidence authoredBy authorOf publication shareWith describes MSWORD file Slide by Tim Clark
  35. 35. C. Metadata: HCLS SiG Scientific Discourse http://esw.w3.org/HCLSIG/SWANSIOC: Project Description Provide a Semantic Web platform for biomedical discourse which can be evolved over time into a more general facility for many types of scientific discourse, and which is linked to key biological categories specified by ontologies. Discourse categories should include research questions, scientific assertions or claims, hypotheses, comments and discussion, experiments, data, publications, citations, and evidence. Our primary scientific use cases will be derived from problems in digital scientific communications and web-based research collaboratories supporting research in neurological disorders and therapies. The scientific use cases will motivate a series of informatics use cases which can later be generalized across wider areas of biology and medicine.
  36. 36. C. Metadata: SWAN The Knowledge Ecosystem: Interlocking Cycles of Research Draw conclusions Draw conclusions Communicate Collect data Collect data Perform Perform experiment Gather info experiment Synthesize Create/modify Create/modify hypothesis hypothesis SWAN Slide by Tim Clark
  37. 37. C. Metadata: Annotation Ontology foaf:person rdf:Type http://www.ht.org/ foaf.rdf#me June 1, 2010 pav:createdBy pav:createdOn ann:annotates http://anyurl.com/sf_pat01.html hasTag rdf:Type hasTopic Tag Atomic tag FMA:skull ann:context onDocument Linear skull fracture rdf:Type Other annotations on the same document: 1. Atomic annotation on image (tag: “hematoma”) 2. General annotation (tag: “injury”) InitEndCornerSelector init Other annotations on similar documents: (304, 507) 1. General annotation (tag: “skull fracture”) rdfs:SubClassOf end (380, 618) ImageSelector Slide by Tim Clark
  38. 38. D. Linked Data: E.g. for Elsevier
  39. 39. D. Linked Data: E.g. for Elsevier <ce:section id=#123>
  40. 40. D. Linked Data: E.g. for Elsevier this says <ce:section id=#123> mice like cheese
  41. 41. D. Linked Data: E.g. for Elsevier said @anita on May 31 2010 this says <ce:section id=#123> mice like cheese
  42. 42. D. Linked Data: E.g. for Elsevier but we all know she was jetlagged then said @anita on May 31 2010 this says <ce:section id=#123> mice like cheese
  43. 43. D. Linked Data: E.g. for Elsevier immutable, $$, proprietary but we all know she was jetlagged then said @anita on May 31 2010 this says <ce:section id=#123> mice like cheese
  44. 44. D. Linked Data: E.g. for Elsevier immutable, $$, proprietary dynamic, personal, task-driven, - open? but we all know she was jetlagged then said @anita on May 31 2010 this says <ce:section id=#123> mice like cheese
  45. 45. D. What to link? Semantic annotation grid
  46. 46. D. What to link? Semantic annotation grid
  47. 47. D. What to link? Semantic annotation grid Granularity collection document claim triple entity
  48. 48. D. What to link? Semantic annotation grid Granularity collection document claim triple entity Moment measure author/editor typesetter/production reader/data minin
  49. 49. D. What to link? Semantic annotation grid Granularity collection document claim triple entity Moment measure author/editor typesetter/production reader/data minin Meansmanual semi-automated automated
  50. 50. D. What to link? Semantic annotation grid Granularity collection document claim Automated Copy Editing triple entity Moment measure author/editor typesetter/production reader/data minin Meansmanual semi-automated automated
  51. 51. D. What to link? Semantic annotation grid Granularity collection document claim Automated Copy Editing triple entity Moment measure author/editor typesetter/production reader/data minin Reflect Meansmanual semi-automated automated
  52. 52. D. A start: .XMP RDF in all our PDFs: DC + PRISM
  53. 53. E. Publishing on an Application server
  54. 54. E. SD as application server: an example
  55. 55. Next Steps: • Fall 2010: ‘Beyond the PDF’: Workshop organized by Phil Bourne @UCSD: –Take one paper from his group –And all data that went into making that paper –Including all correspondence, raw data, etc. –Challenge: how better to represent that? • 2010 - 2011: Try to gather resources, current efforts, etc. on virtual platform • August 2011: FoRC: Future of Research Communications –Dagstuhl Workshop –Involve key people (include funding bodies, libraries, institutions) to see where bottlenecks are • Start using these tools and writing this way!

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