Successfully reported this slideshow.
Your SlideShare is downloading. ×

infrastructure for communicating data-intensive science

Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Loading in …3
×

Check these out next

1 of 29 Ad

More Related Content

Slideshows for you (20)

Viewers also liked (18)

Advertisement

Similar to infrastructure for communicating data-intensive science (20)

More from Brian Bot (15)

Advertisement

Recently uploaded (20)

infrastructure for communicating data-intensive science

  1. 1. infrastructure for communicating data-intensive science brian m. bot | senior scientist | community manager | sage bionetworks clearScience
  2. 2. a non-profit organization which pilots a variety of components that are necessary to build a scientific research “commons” why? Sage Bionetworks
  3. 3. “We Must Guard Against the acquisition of unwarranted influence, whether sought or unsought, by the Military Industrial Complex” - Dwight D. Eisenhower 1961Medical
  4. 4. not conducive for a ‘commons’
  5. 5. institutional incrementalism individual tenure proprietary shortsighted solutions not conducive for a ‘commons’
  6. 6. commonsenabling a open data accessible platform clear communication
  7. 7. “The problem is that right now, it’s not easy to donate your data to health research.” “The goal of Consent to Research is to play a part in the transformation of health from something we experience passively to something we experience actively.” http://weconsent.usJohn Wilbanks, Chief Commons Officer open data
  8. 8. open data accessible platform clear communication commonsenabling a
  9. 9. accessible platform a collaborative compute space that allows scientists to share and analyze data together
  10. 10. open data accessible platform clear communication commonsenabling a
  11. 11. clear communication
  12. 12. Deception at Duke
  13. 13. research scandals represent merely the extreme of a continuum in the culture of academic research
  14. 14. the status quo tolerates poor communication of findings 6% 21% 8% 11% 54% cannot reproduce can reproduce in principle can reproduce w/discrepancies can reproduce from processed data w/ discrepancies can reproduce partially Ioannidis A. et al. Repeatability of published microarray gene expression analyses. Nature Genetics 41, 149-155 (2009) | doi:10.1038/ng.295
  15. 15. 208,294,724 datapoints 124 pages supplemental material ?? lines unobtainable source code ?? version or architecture of statistical analysis program (R) enumerable R packages and package dependencies key R package “ClaNC” no longer available 442 citations often what is in principle reproducible, is not practically reproducible unidentified publication ‣ from journal with 5 year impact factor of 28 ‣ article freely available for download ‣ data freely available for download
  16. 16. how are we to move science forward if we cannot understand what was done previously?
  17. 17. let’s go back to basics
  18. 18. 4. test hypothesis experimentally 5. analyze experimental data 7. publish results 6. draw conclusions based on data scientific method 1. define a question 2. gather information and resources (background research) 3. form a hypothesis 8. retest (frequently done by other scientists) 4. test hypothesis experimentally 5. analyze experimental data 7. publish results 6. draw conclusions based on data
  19. 19. 7. publish results
  20. 20. finitein ∞ ...
  21. 21. submit to journal analyze on local machine write a document sent to reviewers as pdf printed on paper static html representation experimentally generate data accepted & digitally typeset static pdf representation store on local server
  22. 22. are being artificially uncoupled from scientific claims science itself
  23. 23. clearScience re-imagining scientific communication allow consumption of content at a variety of levels of complexity and abstraction leverage Synapse RESTful APIs
  24. 24. clearScience allow consumption of content at a variety of levels of complexity and abstraction “hand the keys over” to the reviewers
  25. 25. scientific communication needs to evolve
  26. 26. along with science needs to evolve
  27. 27. “Scientists often study the past as obsessively as historians because few other professions depend so acutely on it. Every experiment is a conversation with a prior experiment, every new theory a refutation of the old” -Siddhartha Mukherjee, The Emperor of All Maladies
  28. 28. Acknowledgements Sage Bionetworks David Burdick - Senior Software Engineer Stephen Friend - President and CEO Erich S. Huang - Director of Cancer Research Michael Kellen - Director of Technology External Partners Myles Axton - Nature Genetics Phil Bourne - PLoS Computational Biology Josh Greenberg - Alfred P. Sloan Foundation Kelly LaMarco - Science Translational Medicine Eric Schadt - Mount Sinai School of Medicine

×