The Jim Gray Award Lecture presented at the Microsoft Research Symposium on October 12, 2010.

1. The Reaming of Life Philip E. Bourne University of California San Diego pbourne@ucsd.edu Jim Gray eScience Award Lecture Oct. 12, 2010

2. Disclaimer I am a domain (life) scientist not a computer or information scientist I am fortunate enough to have a major biological resource (the Protein Data Bank) and a major biological journal (PLoS Computational Biology) as my playground I am part of the long tail I am naïve, but I am the majority Oct. 12, 2010 Jim Gray eScience Award Lecture

3. The Reaming of Life -What on Earth is He Talking About? A reamer is a tool for turning a roughly punched hole into an accurate and smooth one The digital data deluge has punched that rough hole For the life {other?} sciences to optimally advance we need an accurate and smooth conduit through which data can be distilled, analyzed, visualized, distributed and above all else comprehended Oct. 12, 2010 Jim Gray eScience Award Lecture

4. … and we need to accelerate the process by which this is donehere is why …. This is just another way of saying what Jim said and is embodied in the Fourth Paradigm Oct. 12, 2010 Jim Gray eScience Award Lecture

5. The Scientific Process is Too Slow to Respond to a Crisis – Either Global or Personal Oct. 12, 2010 Jim Gray eScience Award Lecture By the time the paper is published we could all be dead http://knol.google.com/k/plos-currents-influenza# Motivation

6. In a time of crisis the need for fast access to accurate data and any knowledge of that data are paramount Structure Summary page activity for H1N1 Influenza related structures Jan. 2008 Jan. 2009 Jan. 2010 Jul. 2009 Jul. 2008 Jul. 2010 3B7E: Neuraminidase of A/Brevig Mission/1/1918 H1N1 strain in complex with zanamivir 1RUZ: 1918 H1 Hemagglutinin * http://www.cdc.gov/h1n1flu/estimates/April_March_13.htm Motivation

7. If that is not enough…For some people the scientific process may be too slow to save their life Oct. 12, 2010 Jim Gray eScience Award Lecture Motivation

8. Josh Sommer – A Remarkable Young ManCo-founder & Executive Director the Chordoma Foundation Oct. 12, 2010 Jim Gray eScience Award Lecture http://sagecongress.org/Presentations/Sommer.pdf Motivation

9. Chordoma A rare form of brain cancer No known drugs Treatment – surgical resection followed by intense radiation therapy Oct. 12, 2010 Jim Gray eScience Award Lecture http://upload.wikimedia.org/wikipedia/commons/2/2b/Chordoma.JPG Motivation

10. Oct. 12, 2010 Jim Gray eScience Award Lecture http://sagecongress.org/Presentations/Sommer.pdf Motivation

11. Oct. 12, 2010 Jim Gray eScience Award Lecture http://sagecongress.org/Presentations/Sommer.pdf Motivation

12. Oct. 12, 2010 Jim Gray eScience Award Lecture http://sagecongress.org/Presentations/Sommer.pdf Motivation

13. Oct. 12, 2010 Jim Gray eScience Award Lecture If I have seen further it is only by standing on the shoulders of giants Isaac Isaac Newton From Josh’s point of view the climb up just takes too long > 15 years and > $850M to be more precise Adapted: http://sagecongress.org/Presentations/Sommer.pdf Motivation

14. Oct. 12, 2010 Jim Gray eScience Award Lecture http://sagecongress.org/Presentations/Sommer.pdf Motivation

15. Oct. 12, 2010 Jim Gray eScience Award Lecture http://sagecongress.org/Presentations/Sommer.pdf Motivation

16. Oct. 12, 2010 Jim Gray eScience Award Lecture http://fora.tv/2010/04/23/Sage_Commons_Josh_Sommer_Chordoma_Foundation Motivation

17. Now we are all hopefully motivated let us break this down to what actually needs to be done in my opinion Here are a few big things … Oct. 12, 2010 Jim Gray eScience Award Lecture

18. A Few Things to Accelerate the Rate of Scientific Discovery Better communication, data and knowledge access, and new modes of discovery, which means: We need data and knowledge about that data to interoperate i.e. we need new kinds of fast, versatile publications and data archives We need to be more open with both We need to think more about the tools that analyze, visualize and annotate data to maximize knowledge discovery Reward systems need to change We need scientist management tools We need to be less fixated on the big data problems We need to unleash the full power of the Internet Oct. 12, 2010 Jim Gray eScience Award Lecture Hard Easy

19. We Need Data and Knowledge About That Data to Interoperate The Knowledge and Data Cycle 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 User clicks on content Metadata and webservices to data provide an interactiveview that can be annotated Selecting features provides a data/knowledge mashup Analysis leads to new content I can share 4. 1. 3. A composite view of journal and database content results 1. A link brings up figures from the paper 3. 2. 2. Clicking the paper figure retrieves data from the PDB which is analyzed PLoS Comp. Biol. 2005 1(3) e34

20. We Need Data and Knowledge About That Data to Interoperate – What is Stopping US? Governance – publishers vs. database providers Reward Metadata standards for provenance, privacy etc. Exemplars …. Oct. 12, 2010 Jim Gray eScience Award Lecture Caveat: Each discipline is different – I speak very much from a biomedical sciences perspective

21. Certainly the Argument for Interoperability in the Biomedical Sciences is Strong 1078 databases reported in NAR 2008 MetaBase http://biodatabase.org reports 2,651 entries edited 12,587 times PubMed contains 18,792,257 entries ~100,000 papers indexed per month In Feb 2009: 67,406,898 interactive searches were done 92,216,786 entries were viewed Data as of April 14, 2009 We need data and knowledge about that data to interoperate PLoS Comp. Biol. 2005 1(3) e34

22. A Small Example - The World Wide Protein Data Bank The single worldwide repository for data on the structure of biological macromolecules Vital for drug discovery and the life sciences 39 years old Free to all http://www.wwpdb.org We need data and knowledge about that data to interoperate PLoS Comp. Biol. 2005 1(3) e34

23. The World Wide Protein Data Bank – The Best Case Scenario Paper not published unless data are deposited – strong data to literature correspondence Highly structured data conforming to an extensive ontology DOI’s assigned to every structure http://www.wwpdb.org We need data and knowledge about that data to interoperate PLoS Comp. Biol. 2005 1(3) e34

24. Example Interoperability: The Database View www.rcsb.org/pdb/explore/literature.do?structureId=1TIM We need data and knowledge about that data to interoperate BMC Bioinformatics 2010 11:220

25. Example Interoperability: The Literature Viewhttp://biolit.ucsd.edu Nucleic Acids Research 2008 36(S2) W385-389 We need data and knowledge about that data to interoperate

26. ICTP Trieste, December 10, 2007 We need data and knowledge about that data to interoperate

27. Semantic Tagging & Widgets are a Powerful Tool to Integrate Data and Knowledge of that Data, But as Yet Not Used Much Oct. 12, 2010 Jim Gray eScience Award Lecture Will Widgets and Semantic Tagging Change Computational Biology? PLoS Comp. Biol. 6(2) e1000673 We need data and knowledge about that data to interoperate

28. Semantic Tagging of Database Content in The Literature or Elsewhere http://www.rcsb.org/pdb/static.do?p=widgets/widgetShowcase.jsp PLoS Comp. Biol. 6(2) e1000673 Semantic Tagging

29. We need data and knowledge about that data to interoperate

30. The Publishers are Starting to Do It Oct. 12, 2010 Jim Gray eScience Award Lecture From Anita de Waard, Elsevier

31. This is Literature Post-processingBetter to Get the Authors Involved Authors are the absolute experts on the content More effective distribution of labor Add metadata before the article enters the publishing process We need data and knowledge about that data to interoperate

32. Word 2007 Add-in for authors Allows authors to add metadata as they write, before they submit the manuscript Authors are assisted by automated term recognition OBO ontologies Database IDs Metadata are embedded directly into the manuscript document via XML tags, OOXML format Open Machine-readable Open source, Microsoft Public License http://www.codeplex.com/ucsdbiolit We need data and knowledge about that data to interoperate

33. Challenges Authors Carrot IF one or more publishers fast tracked a paper that had semantic markup it might catch on Publishers Carrot Competitive advantage We need data and knowledge about that data to interoperate

34. The Promise – A Hypothetical Example Cardiac Disease Literature Immunology Literature Shared Function We need data and knowledge about that data to interoperate

35. A Few Things to Accelerate the Rate of Scientific Discovery Better communication, data and knowledge access, and new modes of discovery, which means: We need data and knowledge about that data to interoperate i.e. we need new kinds of fast, versatile publications and data archives We need to be more open with both We need to think more about the tools that analyze, visualize and annotate data to maximize knowledge discovery Reward systems need to change We need scientist management tools We need to be less fixated on the big data problems We need to unleash the full power of the Internet Oct. 12, 2010 Jim Gray eScience Award Lecture Hard Easy

36. One Small Example – The Molecular Biology Toolkit (MBT) jMol, VMD … are de facto standard important tools for rendering biological molecules .. but They are not versatile ie do not for example: Respond to the data they are reading Offer views that match the users interests Allow the user to annotate the data Allow those annotations to be shared (published?) Oct. 12, 2010 Jim Gray eScience Award Lecture Think More About the Tools

37. MBT Featureshttp://mbt.sdsc.edu Offer a framework not an end user application Responds to the data type Support read write access Encourages others to write end user applications Discourages feature creep Oct. 12, 2010 Jim Gray eScience Award Lecture Immunologists Immunome Research, 2007 3(1):3 Medicinal Chemists BMC Bioinformatics 2005, 6:21. Think More About the Tools

38. A Few Things to Accelerate the Rate of Scientific Discovery Better communication, data and knowledge access, and new modes of discovery, which means: We need data and knowledge about that data to interoperate i.e. we need new kinds of fast, versatile publications and data archives We need to be more open with both We need to think more about the tools that analyze, visualize and annotate data to maximize knowledge discovery Reward systems need to change We need scientist management tools We need to be less fixated on the big data problems We need to unleash the full power of the Internet Oct. 12, 2010 Jim Gray eScience Award Lecture Hard Easy

39. Reward Systems Need to ChangeWhat is Needed? Author disambiguation Auditing (identification and metrics) of all scholarship - means new tools Seniors need to promote alternative forms of scholarship Juniors need to respond Oct. 12, 2010 Jim Gray eScience Award Lecture Ten Simple Rules for Getting Promoted as a Computational Biologist in Academia PLoS Comp Biol to appear Reward Systems Need to Change

40. Example Tools Oct. 12, 2010 Jim Gray eScience Award Lecture http://www.researcherid.com/ http://pubnet.gersteinlab.org/ http://www.biomedexperts.com

41. What Are these Alternative Forms of Scholarship? Reviews Curation Research [Grants] Journal Article Poster Session Conference Paper Blogs Community Service/Data Reward Systems Need to Change

42. Reward Systems Need to Change

43. A Unique Identifier is Going to Happen It is DOIs for people Some scientists will resist The winner is ORCHID? Reward Systems Need to Change

44. Ideally the ID will be Tagged to Every Piece of Scholarly Communication I an Not a Scientist I am a Number PLoS Comp. Biol. 2008 4(12) e1000247 Reward Systems Need to Change

45. A Few Things to Accelerate the Rate of Scientific Discovery Better communication, data and knowledge access, and new modes of discovery, which means: We need data and knowledge about that data to interoperate i.e. we need new kinds of fast, versatile publications and data archives We need to be more open with both We need to think more about the tools that analyze, visualize and annotate data to maximize knowledge discovery Reward systems need to change We need scientist management tools We need to be less fixated on the big data problems We need to unleash the full power of the Internet Oct. 12, 2010 Jim Gray eScience Award Lecture Hard Easy

46. The Truth About My Laboratory I have ?? mail folders! The intellectual memory of my laboratory is in those folders This is an unhealthy hub and spoke mentality We Need Scientist Management Tools

47. The Truth About My Laboratory I generate way more negative that positive data, but where is it? Content management is a mess Slides, posters….. Data, lab notebooks …. Collaborations, Journal clubs … Software is open but where is it? Farewell is for the data too http://artbyvida.com/portfolio.php Computational Biology Resources Lack Persistence and Usability. PLoS Comp. Biol. 2008 4(7): e1000136 We Need Scientist Management Tools

48. Many Great Tools Out There Oct. 12, 2010 Jim Gray eScience Award Lecture Taverna We Need Scientist Management Tools

49. Where I See the Problems The long tail is confused Lack of interoperability between the options The reward (publishing) is still removed from the available tools Oct. 12, 2010 Jim Gray eScience Award Lecture We Need Scientist Management Tools

50. A Few Things to Accelerate the Rate of Scientific Discovery Better communication, data and knowledge access, and new modes of discovery, which means: We need data and knowledge about that data to interoperate i.e. we need new kinds of fast, versatile publications and data archives We need to be more open with both We need to think more about the tools that analyze, visualize and annotate data to maximize knowledge discovery Reward systems need to change We need scientist management tools We need to be less fixated on the big data problems We need to unleash the full power of the Internet Oct. 12, 2010 Jim Gray eScience Award Lecture Hard Easy

51. Yes YouTube Can Increase the Rate of Discovery Unleash the full power of the Internet

52. The Lab ExperimentPaper+Rich Media My students enjoyed the experience The shyest student was actually the most bold in front of the camera “We will become a generation of “science castors” They liked the exposure for the most part – rather than the PI it puts them out in front Unleash the full power of the Internet

53. Organic Growth 3 Years Later www.scivee.tv Some of their work viewed 20,000+ times Global audience of researchers, educators and academic/research institutions 60,000 unique visitors & 2M pageviews/month 16,000 registered users & 600 communities 5,000 uploads of video content (about journal articles, conferences, research news and classes) Growing 4-5% monthly Sustainability - evolving a business model supporting journals and conferences Unleash the full power of the Internet

54. What Emerged: SciveeCasts Products ApplicationProductPrimary Customers Journals PubCastJournals, publishers, societies Meetings PosterCast Societies, conference orgs. SlideCast Comm. PaperCast Societies, journals Podcast SlideCast Education PosterCast Societies, universities SlideCast BooksBookCast Publishers, book sellers Unleash the full power of the Internet

55. Summarizing the Reaming of Life By “Life” I mean experiences in the Life Sciences By “Reaming” I mean the the making of something smooth, fast and accurate The Monty Python parody is on conversation cards for getting a dialog going .. The rest is just a few examples of the small ways we are trying to address big problems in the hope they will inspire us all to think more deeply about the problem Oct. 12, 2010 Jim Gray eScience Award Lecture

56. Acknowledgements BioLit Team Lynn Fink Parker Williams Marco Martinez RahulChandran Greg Quinn MBT John Moreland John Beaver Microsoft Scholarly Communications Pablo Fernicola Lee Dirks SavasParastitidas Alex Wade Tony Hey wwPDB team Andreas Prilc DimitrisDimitropoulos SciVee Team Apryl Bailey Leo Chalupa Lynn Fink Marc Friedman (CEO) Ken Liu Alex Ramos Willy Suwanto Ben Yukich http://www.scivee.tv http://biolit.ucsd.edu http//www.pdb.org http://www.codeplex.com/ucsdbiolit

57. pbourne@ucsd.edu Questions?