How to create a taxonomy for management buy-in

Library Director & Taxonomist, Cambridge Healthtech at Pharmaceutical Division, SLA
Jan. 5, 2020
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
How to create a taxonomy for management buy-in
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How to create a taxonomy for management buy-in

Editor's Notes

  1. Making a business case for taxonomies
  2. I’ve been working with taxonomies for decades, with support from the company President , though it took awhile to call what we had a taxonomy. Still working on making data optimal for data scientists. Recently realized we have multiple taxonomies, with varying degrees of documentation.
  3. Life Science specific applications include Drug discovery, Drug repurposing, Pharmacovigilance/adverse effects monitoring, Real world Evidence, Mapping complex disparate relationships (such as model organisms' data). I’m particularly interested in predictive analytics and trend analysis, with my projects still in very early stages.
  4. I’m focusing on similarities between taxonomies and ontologies today, not the differences. I’m not going to talk about whether you should be using taxonomies or ontologies, or linked data or knowledge graphs. These are important discussions and other talked will consider them.. I’m also looking forward to hearing talks on machine learning, automatic tagging and governance as well.
  5. Questions I most want to ask? Ones where the answer will surprise me.
  6. The more we learn the more we realize we still have to understand. New insights don’t always invalidate old knowledge – but understanding becomes more and more granular. Taxonomies alone won’t solve all of these challenges, but they are an important part of the process.
  7. I Some of my examples are life science specific, but a fair amount of my taxonomy work is more general as well.
  8. A study of 500 plus biology papers published in a 20-year span suggested that up to 80% of raw data collected for studies in the early 1990s is lost, “mostly because no one knows where to find it.”  Current Biology 2014 Digital data are ephemeral ... “’homeless’ data quickly become no data at all.” Berman, Science 2019 Monya Baker, Nature 2016 survey of  researchers.  https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970 Current Biology Jan 6 2014, described in Wiener-Bronner 2013 Atlantic article https://www.theatlantic.com/national/archive/2013/12/scientific-data-lost-forever/356422/  Who Will Pay for Public Access to Research Data?  Francine Berman1,  Vint Cerf2  Science  09 Aug 2013: Vol. 341, Issue 6146, pp. 616-617  DOI: 10.1126/science.1241625 https://science.sciencemag.org/content/341/6146/616.summary Keep in mind the dangers of “overfitting” data.
  9. “we are confident that the true cost is much higher than the estimated” PWC Study looked at time spent, cost of storage, license costs, research retraction, double funding, interdisciplinarity and potential economic growth. Return on Investment of Natural Language Processing, Linguamatics paper cites time savings of 10x to 1000x or 1 FTE a year for every 10-12 drugs monitored, and costs savings of $40,000 by not investing in a project that would have had a negative outcome, or an improvement of $50-100K in risk adjusted revenue per disease area , productivity gains, discovery of 33 novel drug targets, applications for clinical trials and pharmacovigilance, automation of manual curation of publications and clinical trial endpoints, and re-use of existing data from clinical trials to speed up drug development. https://www.linguamatics.com/products/return-investment
  10. And it’s even harder than I realized when I wrote the proposal for this conference. Learned at BioIT May 2019 just how difficult it is to engage the C-Suite.
  11. Am using some of these sentences as scripts for in-house conversations now. 
  12. Some people want to rely only on algorithms and automation.  I’m still advocating a hybrid approach, with varying success.   
  13. Consider collecting data on extent of existing problems with finding or reusing data.
  14. People use an amazing variety of terms to describe terminology functions.
  15. See the Go Fair website for more information on this set of principles with guidelines on how to make data FAIRer.
  16. Many of the concepts I’m working with are cutting- to bleeding- edge and terminology is evolving organically. Lots of uncertainty  Think of the image of shooting at a moving target –  or as hockey great Wayne Gretzky said. “I just try to skate to where the puck is going to be.”
  17. Do your homework. Know your audience. Think strategically. Are there detractors or skeptics? How can you address them?
  18. Right now people seem willing to throw millions into machine learning or artificial intelligence – but are reluctant to invest in data readiness and data quality efforts. We’ve got to collaborate! We’ve got to share!
  19. “Expect things to take even longer than you anticipate. We know less today than we will tomorrow (which means we know the least when we start” [Thanks to Terrell Russell of the iRODS Consortium Understand the workflows of the people whose problems you are trying to solve. Drastic workflow changes often means change will never happen. Users are much better at telling you what they don’t like than knowing what they really want, but may not be able to envision. Thank people for this feedback. Focus on 80/20 - actually 20/80. Definitely don’t aim for 100%.
  20. Nature 2016 survey of  researchers.  https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970 Current Biology Jan 6 2014, described in Atlantic article https://www.theatlantic.com/national/archive/2013/12/scientific-data-lost-forever/356422/  Who Will Pay for Public Access to Research Data?  Francine Berman1,  Vint Cerf2  Science  09 Aug 2013: Vol. 341, Issue 6146, pp. 616-617  DOI: 10.1126/science.1241625 https://science.sciencemag.org/content/341/6146/616.summary Life Science Leader 2019 March 1, “AI In Life Sciences: Seeing past the Hype”  Francois Nicolas and comment by Christy Wilson Pistoia Alliance Ontologies Mapping https://www.pistoiaalliance.org/projects/current-projects/ontologies-mapping/ Ontology mapping for semantically enabled applications Summary of recent progress from thesauri, taxonomies to ontologies  “when biomedical research is under a deluge of an increasing amount and variety of data…  Semantic alignment and data standardization are vital to solve if we are going to harness modern technologies such as machine learning"  Drug Discovery Today May 2019