Your SlideShare is downloading. ×

Where is Open Going?

3,761

Published on

Keynote presentation for the SPARC 2014 conference in Kansas City on March 3, 2014

Keynote presentation for the SPARC 2014 conference in Kansas City on March 3, 2014

Published in: Education
0 Comments
5 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
3,761
On Slideshare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
30
Comments
0
Likes
5
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Where is Open Going? Philip E. Bourne pbourne@ucsd.edu http://www.slideshare.net/pebourne/ 3/01/14 2014 SPARC Annual Meeting 1
  • 2. Where is Open Going? The answer depends on who you ask Here is my biased viewpoint 3/01/14 2014 SPARC Annual Meeting 2
  • 3. My Background/Bias • Mostly Biomedical • RCSB PDB/IEDB Database Developer – Views on community, quality, sustainability … • PLOS Journal Co-founder – Open Science Advocate • Associate Vice Chancellor for Innovation – Business models, interaction with the private sector,sustainability • Professor – Mentoring, reward system, value (or not) of research • NIH Strategist/Transformer - ?? 3/01/14 2014 SPARC Annual Meeting 3
  • 4. Perhaps the first question to ask is: What is the endpoint? 3/01/14 2014 SPARC Annual Meeting 4
  • 5. Where Is Open Going? 3/01/14 2014 SPARC Annual Meeting 5
  • 6. What Does The Democratization of Science Imply? • The obvious – participation by all • Not so obvious – More scrutiny – New types of rewards – More equal value placed on all participants – The removal of artificial boundaries that corral knowledge (through power and resources) within silos that do not make sense as complexity increases 3/01/14 2014 SPARC Annual Meeting 6
  • 7. Consider some personal examples that illustrate these implications 3/01/14 2014 SPARC Annual Meeting 7
  • 8. More Scrutiny – Highlights Lack of Reproducibility • I can’t immediately reproduce the research in my own laboratory: • It took an estimated 280 hours for an average user to approximately reproduce the paper • Workflows are maturing and becoming helpful • Data and software versions and accessibility prevent exact reproducibility Daniel Garijo et al. 2013 Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome PLOS ONE 8(11) e80278 . 3/01/14 2014 SPARC Annual Meeting 8
  • 9. Why New Types of Rewards? • I have a paper with 16,000 citations that no one has ever read • I have papers in PLOS ONE that have more citations than ones in PNAS • I have data sets I am proud of few places to put them • I edited a journal but it did not count for much 3/01/14 2014 SPARC Annual Meeting 9
  • 10. Equal Value Placed on Participants • The UC System has Research Scientists (RS) & Project Scientists (PS) as well as tenured faculty – RS/PS have no senate rights yet: – RS/PS frequently teach – RS/PS frequently have more grant money – RS/PS typically perform more service – RS/PS are most of the data scientists you know 3/01/14 2014 SPARC Annual Meeting 10
  • 11. Are Increasingly Found on the Google Bus 3/01/14 2014 SPARC Annual Meeting 11
  • 12. Institutional Boundaries • Academia – Departments of physics, math, biology, chemistry etc. persist but scholars rarely confine themselves to these disciplines • NIH – 27 institutes and centers, many dedicated to specific diseases & conditions – yet a specific gene may transcend ICs 3/01/14 2014 SPARC Annual Meeting 12
  • 13. I have argued that the democratization of science is compelling I have not argued for the value of open access to this picture because you know that already 3/01/14 2014 SPARC Annual Meeting 13
  • 14. I Would Also Argue That This Process is About to Accelerate • Others provide a more compelling argument: – – – – 3/01/14 2014 SPARC Annual Meeting Google car 3D printers Waze Robotics 14
  • 15. From the Second Machine Age From: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson & Andrew McAfee 3/01/14 2014 SPARC Annual Meeting 15
  • 16. So what will this look like for an institution? Institutions will become digital enterprises 3/01/14 2014 SPARC Annual Meeting 16
  • 17. Components of The Academic Digital Enterprise • Consists of digital assets – E.g. datasets, papers, software, lab notes • Each asset is uniquely identified and has provenance, including access control – E.g. publishing simply involves changing the access control • Digital assets are interoperable across the enterprise 3/01/14 2014 SPARC Annual Meeting 17
  • 18. Life in the Academic Digital Enterprise • Jane scores extremely well in parts of her graduate on-line neurology class. Neurology professors, whose research profiles are on-line and well described, are automatically notified of Jane’s potential based on a computer analysis of her scores against the background interests of the neuroscience professors. Consequently, professor Smith interviews Jane and offers her a research rotation. During the rotation she enters details of her experiments related to understanding a widespread neurodegenerative disease in an on-line laboratory notebook kept in a shared on-line research space – an institutional resource where stakeholders provide metadata, including access rights and provenance beyond that available in a commercial offering. According to Jane’s preferences, the underlying computer system may automatically bring to Jane’s attention Jack, a graduate student in the chemistry department whose notebook reveals he is working on using bacteria for purposes of toxic waste cleanup. Why the connection? They reference the same gene a number of times in their notes, which is of interest to two very different disciplines – neurology and environmental sciences. In the analog academic health center they would never have discovered each other, but thanks to the Digital Enterprise, pooled knowledge can lead to a distinct advantage. The collaboration results in the discovery of a homologous human gene product as a putative target in treating the neurodegenerative disorder. A new chemical entity is developed and patented. Accordingly, by automatically matching details of the innovation with biotech companies worldwide that might have potential interest, a licensee is found. The licensee hires Jack to continue working on the project. Jane joins Joe’s laboratory, and he hires another student using the revenue from the license. The research continues and leads to a federal grant award. The students are employed, further research is supported and in time societal benefit arises from the technology. From What Big Data Means to Me JAMIA 2014 21:194 3/01/14 2014 SPARC Annual Meeting 18
  • 19. Let us now turn to the biomedical sciences and look at what might happen if the NIH were to become a digital enterprise 3/01/14 2014 SPARC Annual Meeting 19
  • 20. As of Today • Assumed the role of Associate Director for Data Science (ADDS):     NIH Data Science Point Person Reports to NIH Director Lead the BD2K initiative Trans-NIH responsibilities for data  Eric Green, Acting [Modified slide from Eric Green] 3/01/14 2014 SPARC Annual Meeting 20
  • 21. The focus is on data, but I do not think that can be separated from the research life cycle as you will see… 3/01/14 2014 SPARC Annual Meeting 21
  • 22. I Want To Engage With This Community To: • Help me understand the most pressing problems • Begin a dialog • Inform you of what I am currently thinking • Inform you of relevant NIH initiatives that are underway or planned • Have you change my thinking appropriately 3/01/14 2014 SPARC Annual Meeting 22
  • 23. The NIH process thus far … An external advisory group provided a valuable blueprint for what should be done acd.od.nih.gov/diwg.htm 3/01/14 2014 SPARC Annual Meeting 23
  • 24. Blueprint Recommendations • Promote central and federated catalogs – Establish minimal metadata framework – Tools to facilitate data sharing – Elaborate on existing data sharing policies • Support methods and applications – Fund all phases of software development – Leverage lessons from National Centers • Training – More funding – Enhance review of training apps – Quantitative component to all awards • On campus IT strategic plan – Catalog of existing tools – Informatics laboratory – Ditto big data • Sustainable funding commitment 3/01/14 2014 SPARC Annual Meeting acd.od.nih.gov/diwg.htm 24
  • 25. Let me outline in general terms where I see my effort being spent going forward http://pebourne.wordpress.com/2013/12/ 3/01/14 2014 SPARC Annual Meeting 25
  • 26. ADDS Initial Thrusts • • • • • • • • How data are currently being used Lightweight metadata standards Data & software registries Expanded policies on data sharing, open source software Training programs & reward systems Institutional incentives Private sector incentives Data centers serving community needs 3/01/14 2014 SPARC Annual Meeting 26
  • 27. ADDS Initial Thrusts • • • • • • • • How data are currently being used Lightweight metadata standards Data & software registries Expanded policies on data sharing, open source software Training programs & reward systems Institutional incentives Private sector incentives Data centers serving community needs 3/01/14 2014 SPARC Annual Meeting 27
  • 28. We need to start by asking, how are we using the data now? Only then can we make rational decisions about data – large or small 3/01/14 2014 SPARC Annual Meeting 28
  • 29. How Data Are Used Structure Summary page activity for H1N1 Influenza related structures Jan. 2008 Jul. 2008 * http://www.cdc.gov/h1n1flu/estimates/April_March_13.htm Jan. 2009 Jul. 2009 Jan. 2010 Jul. 2010 3B7E: Neuraminidase of A/Brevig Mission/1/1918 H1N1 strain in complex with zanamivir 1RUZ: 1918 H1 Hemagglutinin 3/01/14 29 2014 SPARC Annual Meeting [Andreas Prlic]
  • 30. We Need to Learn from Industries Whose Livelihood Addresses the Question of Use 3/01/14 2014 SPARC Annual Meeting 30
  • 31. ADDS Initial Thrusts – More Detail • Now: – – – – – Data centers (under review) Data science training grants (call out) Pilot data catalog consortium (call out) Genomic Data Sharing Policy (being finalized) Piloting “NIH-drive” • What Is Planned: – Extended public-private programs specifically for data science activities – Interagency activities – International exchange programs – Cold Spring Harbor-like training facilities – by-coastal? – Programs for better data descriptions – Reward institutions/communities – Policies to get clinical trial data into the public domain 3/01/14 2014 SPARC Annual Meeting 31
  • 32. ADDS Initial Thrusts – More Detail • Now: – – – – – Data centers (under review) Data science training grants (call out) Pilot data catalog consortium (call out) Genomic Data Sharing Policy (being finalized) Piloting “NIH-drive” • What Is Planned: – Extended public-private programs specifically for data science activities – Interagency activities – International exchange programs – Cold Spring Harbor-like training facilities – by-coastal? – Programs for better data descriptions – Reward institutions/communities – Policies to get clinical trial data into the public domain 3/01/14 2014 SPARC Annual Meeting 32
  • 33. Pilot NIH-Drive • Investigator A from the NCI makes frequent reference to the over expression of genes x and y. • Investigator B from the NHLBI makes frequent reference to the under expression of genes x and y • Automatic notification of a potential common interest before publication or database deposition 3/01/14 2014 SPARC Annual Meeting 33
  • 34. Let me come back to the big picture.. 3/01/14 2014 SPARC Annual Meeting 34
  • 35. First consider what we do (or wish we could do) every day: We take actions on digital data increasingly across boundaries 3/01/14 2014 SPARC Annual Meeting 35
  • 36. Actions on Biomedical Data Implies: • • • • • • • • • Insuring data quality and hence trust Making data sustainable Making data open and accessible Making data findable Providing suitable metadata and annotation Making data queryable Making data analyzable Presenting data as to maximize its value Rewarding good data practices 3/01/14 2014 SPARC Annual Meeting 36
  • 37. Actions on Biomedical Data Implies: • • • • • • • • • Insuring data quality and hence trust Making data sustainable Making data open and accessible Making data findable Providing suitable metadata and annotation Making data queryable Making data analyzable Presenting data as to maximize its value Rewarding good data practices 3/01/14 2014 SPARC Annual Meeting 37
  • 38. Boundaries on Biomedical Data Implies: • Working across biological scales • Working across biomedical disciplines • Working across basic and clinical research and practice • Working across institutional boundaries • Working across public and private sectors • Working across national and international borders • Working across funding agencies 3/01/14 2014 SPARC Annual Meeting 38
  • 39. Boundaries on Biomedical Data Implies: • Working across biological scales • Working across biomedical disciplines • Working across basic and clinical research and practice • Working across institutional boundaries • Working across public and private sectors • Working across national and international borders • Working across funding agencies 3/01/14 2014 SPARC Annual Meeting 39
  • 40. These issues have been around a long time The good news is that “Big Data” has bought more attention to the problem 3/01/14 2014 SPARC Annual Meeting 40
  • 41. What Are Big Data? • Large datasets from high throughput experiments • Large numbers of small datasets • Data which are “ill-formed” • The why (causality) is replaced by the what • A signal that a fundamental change is taking place – a tipping point? 3/01/14 2014 SPARC Annual Meeting 41
  • 42. The NIH is Starting to Think About the Digital Enterprise, Witness… bd2k.nih.gov 3/01/14 2014 SPARC Annual Meeting 42
  • 43. What Will Define the NIH Digital Enterprise? • • • • • • • • • NCBI/NLM Trans-NIH collaboration – a culture change Long-term NIH strategic planning The BD2K Initiative A “hub” of data science activities International cooperation Interagency cooperation Data sharing policies External forces…. 3/01/14 2014 SPARC Annual Meeting 43
  • 44. This is great, but what will it look like to the end user and to those interested in scholarly communication? 3/01/14 2014 SPARC Annual Meeting 44
  • 45. One Possible End Point 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 4. 1. 1. A link brings up figures from the paper 2. 3/01/14 3. A composite view of journal and database content results 3. 2. Clicking the paper figure retrieves data from the PDB which is analyzed 1. User clicks on thumbnail 2. Metadata and a webservices call provide a renderable image that can be annotated 3. Selecting a features provides a database/literature mashup 4. That leads to new papers PLoS Comp. Biol. 2005 1(3) e34 45
  • 46. To get to that end point we have to consider the complete research lifecycle 3/01/14 2014 SPARC Annual Meeting 46
  • 47. The Research Life Cycle will Persist IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION 3/01/14 2014 SPARC Annual Meeting 47
  • 48. Tools and Resources Will Continue To Be Developed Authoring Tools Lab Notebooks Data Capture Analysis Tools Software Scholarly Communication Visualization IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION 3/01/14 2014 SPARC Annual Meeting 48
  • 49. Those Elements of the Research Life Cycle will Become More Interconnected Authoring Around a Common Framework Tools Lab Notebooks Data Capture Software Analysis Tools Scholarly Communication Visualization IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION 3/01/14 2014 SPARC Annual Meeting 49
  • 50. New/Extended Support Structures Will Emerge Authoring Tools Data Capture Lab Notebooks Analysis Tools Scholarly Communication Software Visualization IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Commercial & Public Tools DisciplineBased Metadata Standards Community Portals Git-like Resources By Discipline Data Journals New Reward Systems Training Institutional Repositories 3/01/14 2014 SPARC Repositories CommercialAnnual Meeting 50
  • 51. We Have a Ways to Go Authoring Tools Data Capture Lab Notebooks Software Analysis Tools Scholarly Communication Visualization IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Commercial & Public Tools DisciplineBased Metadata Standards Community Portals Git-like Resources By Discipline Data Journals New Reward Systems Training Institutional Repositories 3/01/14 2014 SPARC Repositories CommercialAnnual Meeting 51
  • 52. Where is Open Going? • Slowly towards the democratization of science • Which changes how institutions think and operate – they become digital enterprises • This in turn impacts the scholarly research lifecycle and hence scholarly communication • I will be working to help the NIH be a leading institution in this change 3/01/14 2014 SPARC Annual Meeting 52
  • 53. pbourne@ucsd.edu Thank You! Questions?

×