How I failed to do Open Notebook Cheminformatics

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I always believed that with Open Data, Open Source, and Open Standards I was doing the right thing; that it was enough for a better science. However, I have come to the realization that these features …

I always believed that with Open Data, Open Source, and Open Standards I was doing the right thing; that it was enough for a better science. However, I have come to the realization that these features are not enough. Surely, they aid Open collaborations, though not even sufficient there, but they fail horribly in the "scientific method." Because while ODOSOS makes work reproducible, it lacks the context needed by scholars to understand what it solved. That is, it details out in much detail how some scientific question is answered, but not what question that was. As such, it fails to follow the established practices in scholarly research. In this presentation I will show how I should have done some of my research, and ponder on reasons why I had not done so.

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  • 1. Department of Bioinformatics - BiGCaT 1 How I failed to do Open Notebook Cheminformatics Egon Willighagen (@egonwillighagen) 14 July 2014, Jean-Claude Bradley Memorial Symposium #jcbms
  • 2. Department of Bioinformatics - BiGCaT 2 Jean-Claude Bradley
  • 3. Department of Bioinformatics - BiGCaT 3 “Open Notebook Science” First response: jealousy
  • 4. Department of Bioinformatics - BiGCaT 4 How I failed • I did Open Science – Strong focus on reproducibility – Open Source, Open Data, Open Standards (ODOSOS) • I did share notes... • I wrote up the stories (in a blog)...
  • 5. Department of Bioinformatics - BiGCaT 5 Realization • Scholars need notebooks – They need exact instructions – Just giving the outcome and tools is not enough • This applies to cheminformatics too
  • 6. Department of Bioinformatics - BiGCaT 6 First notes where during education
  • 7. Department of Bioinformatics - BiGCaT 7 ODOSOS • Software: – The Chemistry Development Kit • based on CompChem, Jmol and JchemPaint – Bioclipse, Jmol, ... • Data – Blue Obelisk Data Repository – RDF translations of knowledge bases • Standards – eNanoMapper ...
  • 8. Department of Bioinformatics - BiGCaT 8 Scribbling...
  • 9. Department of Bioinformatics - BiGCaT 9 Scribbline...
  • 10. Department of Bioinformatics - BiGCaT 10 Scribbline...
  • 11. Department of Bioinformatics - BiGCaT 11 Why important? • Going back to the original (raw data). • Pedagogical effect • Education (howto's) • Machines care about negative data • If we want to progress, we need to understand not just global patterns, but the fine details too
  • 12. Department of Bioinformatics - BiGCaT 12 Why cheminformatics too? • Where is the latest RDF of solubility data? Of the melting point data? • The trust problem applies to algorithms as much as data • What if...
  • 13. Department of Bioinformatics - BiGCaT 13 What I have in mind... WikiPedia, CC-BY-SA, http://en.wikipedia.org/wiki/Curtin %E2%80%93Hammett_principle
  • 14. Department of Bioinformatics - BiGCaT 14 Possible ONS of cheminformatics • Is this set of atom types covering ChEBI? • Can we map this metabolomics data to pathways? • How many CAS registry numbers can I resolve for this data set?
  • 15. Department of Bioinformatics - BiGCaT 15 Conclusion Some patience is needed, but I will start Open Notebook Science. (And I will push this concept with my Bart and Cristian too.)
  • 16. Department of Bioinformatics - BiGCaT 16 Benchmarking / metrics