Data integration and building a profile for yourself as an online scientist
•Download as PPT, PDF•
2 likes•4,367 views
This document discusses building an online profile as a scientist in the era of big data and open science. It begins with an overview of the speaker's background working in academia, industry, and as an entrepreneur. The speaker then discusses various online tools and platforms that scientists can use to share their work and expertise, such as ORCID, LinkedIn, Google Scholar, SlideShare, and ResearchGate. He emphasizes the importance of making contributions openly available online in order to increase visibility and measure impact through alternative metrics. The speaker also provides examples of using these tools to showcase his own career and publications.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Data integration and building a profile for yourself as an online scientist
Similar to Data integration and building a profile for yourself as an online scientist (20)
Recently uploaded
Recently uploaded (20)
Data integration and building a profile for yourself as an online scientist
- 1. Chemical Information in the Big Data Era: Data Quality, Data Integration and Building a Profile for Yourself as an Online Scientist Antony Williams ORCID ID:0000-0002-2668-4821
- 2. My background… • From 1985-present day • PhD’ed in the UK • Canadian Government lab as postdoc • Academia as NMR Facility Manager • Fortune 500 Company as Technology Leader • Start-up – product manager and CSO • Consultant – chemistry informatics industry • Entrepreneur – Created “ChemSpider” • Publisher - Royal Society of Chemistry • EPA-NCCT as cheminformatics expert
- 3. Of interest to faculty?
- 4. CASE Systems – Natural Products CH3 14.40(fb) CH3 16.80(fb) CH2 19.30(fb) CH2 21.60(fb) CH3 21.70(fb) CH2 24.40(fb) CH3 26.40 C 33.50(fb) CH3 33.50(fb) CH2 38.30(fb) CH2 38.30(fb) CH2 39.10(fb) C 39.60 CH2 40.20 CH2 42.10(fb) CH 55.50(fb) CH 56.20(fb) C 106.10 CH2 106.20(fb) CH 120.80 C 141.90 C 146.00 C 148.40 C 148.50 CH 151.30 C 153.00 NH2 N N N N O CH3 CH3 CH3 CH3 CH3 CH2 NH2 N N N N O d A ( 1 3 C ) : 1 . 7 1 9 d N ( 1 3 C ) : 2 . 0 1 6 d I ( 1 3 C ) : 2 . 3 1 3 m a x _ d A ( 1 3 C ) : 8 . 5 8 0 1 CH3 CH3 CH3 CH3 CH3CH2 NH2 N N N N O d A ( 1 3 C ) : 3 . 5 3 4 d N ( 1 3 C ) : 4 . 8 1 2 d I ( 1 3 C ) : 3 . 6 8 4 m a x _ d A ( 1 3 C ) : 1 3 . 2 8 0 2 CH3 CH3 CH3 CH3 CH3 CH2 NH2 N N N N O d A ( 1 3 C ) : 4 . 0 1 0 d N ( 1 3 C ) : 4 . 6 6 2 d I ( 1 3 C ) : 3 . 6 1 0 m a x _ d A ( 1 3 C ) : 1 2 . 2 3 0 3
- 5. Maybe you know this???
- 6. Computational Analysis at NCCT
- 7. Public Access and Systems
- 8. My Hopes for Today • Encourage you in the “era of participation” • Provide an overview of tools available • Share some stories, statistics and strategies • Encourage you to “share for the sake of science” OUTCOMES • You will claim an ORCiD • You take responsibility for your online profile • You will invest >1 hour per week
- 9. I would tell a chemistry joke… But all of the good ones…
- 10. An ambitious idea…. • Let’s map together all online chemistry data and build systems to integrate it • Heck, let’s integrate chemistry and biology data and add in disease data too if we can • Let’s extract property data and model it and see if we can extract new relationships – quantitative and qualitative • Let’s make it all available on the web…for free
- 12. What about this…. • We’re going to map the world • We’re going to take photos of as many places as we can and link them together • We’ll let people annotate and curate the map • Then let’s make it available free on the web • We’ll make it available for decision making • Put it on Mobile Devices, give it away…
- 13. Where is chemistry online? • Encyclopedic articles (Wikipedia) • Chemical vendor databases • Metabolic pathway databases • Property databases • Patents with chemical structures • Drug Discovery data • Scientific publications • Compound aggregators • Blogs/Wikis and Open Notebook Science
- 14. • ~35 million chemicals and growing • Data sourced from >500 different sources • Crowd sourced curation and annotation • Ongoing deposition of data from our journals and our collaborators • Structure centric hub for web-searching • …and a really big dictionary!!!
- 15. ChemSpider
- 16. ChemSpider
- 20. RSC Books
- 21. Google Books
- 22. Organic Chemistry is hard…
- 23. …it has alkynes of trouble
- 25. Molfiles 10 9 0 0 1 0 0 0 0 0 1 V2000 31.2937 -9.0366 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 26.6526 -9.0366 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 31.2937 -7.7066 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 30.1161 -9.6877 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 25.5096 -9.6877 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 28.9731 -9.0366 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 27.8163 -9.7016 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 26.6664 -7.7066 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 32.4367 -9.6877 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 30.1161 -11.0177 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 3 1 2 0 0 0 0 4 1 1 0 0 0 0 9 1 1 0 0 0 0 7 2 1 0 0 0 0 5 2 2 0 0 0 0 8 2 1 0 0 0 0 6 4 1 0 0 0 0 4 10 1 6 0 0 0 7 6 1 0 0 0 0 M END
- 26. Molfiles • Molfiles are the primary exchange format between structure drawing packages • Can be different between different drawing packages • Most commonly carry X,Y coordinates for layout • Can support polymers, organometallics, etc. • Can carry 3D coordinates
- 27. Stereo
- 28. Tautomeric forms
- 31. InChI • SINGLE code base managed by IUPAC – integrated into drawing packages. No variability as with SMILES • InChI Strings can be reversed to structures – same problem as with SMILES – no layout • Adopted by the community (databases, blogs, Wikipedia) – good for searching the internet
- 32. Multiple Layers
- 33. Tautomers
- 34. Stereo
- 35. InChIStrings Hash to InChIKeys
- 36. Structure search the web
- 37. Exact Search
- 38. Skeleton Search
- 39. Data Quality/Standardization • MANY structures meant to be something online are MISREPRESENTED. • Commonly you will have better success finding information by name searches than structure – with many caveats of course… • Validating chemical structure representations is laborious work – and it’s shocking to review data…
- 40. Data Quality Issues Williams and Ekins, DDT, 16: 747-750 (2011)Science Translational Medicine 2011
- 41. Data quality is a known issue
- 42. Data quality is a known issue
- 43. Patent data in public databases
- 44. Patent data in public databases
- 45. You just can’t trust atoms!
- 48. What is the Structure of Vitamin K1?
- 49. Date Quality Issues and $$$$
- 50. Many Names, One Structure
- 51. But big and often noisy
- 52. Text Mining on IUPAC Names The N-(β-hydroxyethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4- thiadiazol-5-yl)urea prepared in Example 6 , thionyl chloride ( 5 ml ) and benzene ( 50 ml ) were charged into a glass reaction vessel equipped with a mechanical stirrer , thermometer and reflux condenser . The reaction mixture was heated at reflux with stirring , for a period of about one-half hour . After this time the benzene and unreacted thionyl chloride were stripped from the reaction mixture under reduced pressure to yield the desired product N-(β-chloroethyl)-N-methyl-N'-(2- trifluoromethyl-1,3,4-thiaidazol-5-yl)urea as a solid residue
- 53. Text Mining on IUPAC Names The N-(β-hydroxyethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4- thiadiazol-5-yl)urea prepared in Example 6 , thionyl chloride ( 5 ml ) and benzene ( 50 ml ) were charged into a glass reaction vessel equipped with a mechanical stirrer , thermometer and reflux condenser . The reaction mixture was heated at reflux with stirring , for a period of about one-half hour . After this time the benzene and unreacted thionyl chloride were stripped from the reaction mixture under reduced pressure to yield the desired product N-(β-chloroethyl)-N-methyl-N'-(2- trifluoromethyl-1,3,4-thiaidazol-5-yl)urea as a solid residue
- 54. Name to Structure Conversion
- 55. Name to Structure Conversion
- 56. What could we get?
- 57. PhysChem first: Melting Points • Melting/sublimation/decomposition points extracted for 287,635 distinct compounds from 1976-2014 USPTO patent applications/grants • Sanity checks used to flag dubious values – probably 130-4°C • Non-melting outcomes recorded e.g. mp 147- 150°C. (subl.) • What models could be built?
- 58. Modeling “BIG data” • Melting point models developed with ca. 300k compounds • Required 34Gb memory and about 400MB disk space (zipped) • Matrix with 2*1011 entries (300k molecules x 700k descriptors) • >12k core-hours (>600 CPU-days) for parameter optimization • Parallelized on > 600 cores with up to 24 cores per one task • Consensus model as average of individual models • Accuracy of consensus model is ~33.6 °C for drug-like region compounds • Models publicly available at http://ochem.eu
- 60. ESI – Text Spectra
- 61. ChemSpider ID 24528095 H1 NMR
- 62. We want to find text spectra? • We can find and index text spectra:13C NMR (CDCl3, 100 MHz): δ = 14.12 (CH3), 30.11 (CH, benzylic methane), 30.77 (CH, benzylic methane), 66.12 (CH2), 68.49 (CH2), 117.72, 118.19, 120.29, 122.67, 123.37, 125.69, 125.84, 129.03, 130.00, 130.53 (ArCH), 99.42, 123.60, 134.69, 139.23, 147.21, 147.61, 149.41, 152.62, 154.88 (ArC) • What would be better are spectral figures – and include assignments where possible!
- 63. 1H NMR (CDCl3, 400 MHz): δ = 2.57 (m, 4H, Me, C(5a)H), 4.24 (d, 1H, J = 4.8 Hz, C(11b)H), 4.35 (t, 1H, Jb = 10.8 Hz, C(6)H), 4.47 (m, 2H, C(5)H), 4.57 (dd, 1H, J = 2.8 Hz, C(6)H), 6.95 (d, 1H, J = 8.4 Hz, ArH), 7.18–7.94 (m, 11H, ArH)
- 64. NMR Spectra • 2,316,005 distinct spectra in 2001-2015 USPTO Nucleus Count H 1993384 C 173970 Unknown 107439 F 22158 P 16333 B 980 Si 715 Pt 275 N 170 V 101
- 65. ESI Data also contains figures
- 66. “Where is the real data please?” FIGURE DATA
- 67. Data added to ChemSpider
- 68. Visibility Means Discoverability • Q: Does a Social Profile as a scientist matter? • You are visible, when you share your skills, experience and research activities by: • Establishing a public profile • Getting on the record • Collaborative Science • Demonstrating a skill set • Measured using “alternative metrics” • Contributing to the public peer review process • There are many ways to become “visible”
- 69. Scientists measured by Impact
- 70. How to Measure Impact
- 71. Your Research Outputs? • Research datasets • Scientific software • Publications – peer-reviewed and many others • Posters and presentations at conferences • Electronic theses and dissertations • Performances in film and audio • Lectures, online classes and teaching activities • What else??? • The possibilities to share are endless
- 72. Open Researcher & Contributor ID
- 73. Here’s why they are useful…
- 75. CONTRIBUTE to the community • Share your expertise in the new world of open • Share your Figures, share your data • Contribute to Wikis – Wikipedia and others • Participate in Open Notebook Science • Build tools and platforms to support chemists • Curate, use and comment on data • Get engaged on blogs and discussions
- 76. Oxidation by Sodium Hydride?
- 79. The new world of micropublishing
- 81. Micropublishing with Peer Review (a chemical synthesis blog?)
- 83. Interactive Data
- 84. You should be LinkedIn • LinkedIn for “professionals” • Expose work history, skills, your professional interests, your memberships – your profile WILL be watched! • Who you are linked to says a lot about who you are. Get Linked to people in your domain. • Professional relationships rather than just friendships. FaceBook-it for friends
- 89. Manage Articles Here Too.
- 91. My Google Scholar Profile http://scholar.google.com/citations?user=O2L8nh4AAAAJ
- 92. “I don’t have any publications” • This is YOUR choice! Conference Abstracts.. • You produce reports, presentations and posters during your studies – share them !
- 93. Slideshare – Highly Accessed
- 94. Slideshare – EXPANDED Audience
- 96. Slideshare – NOT Just Slides
- 98. ResearchGate
- 99. ResearchGate
- 100. I have a set of statistics & profiles • My Blog: www.chemconnector.com • Twitter: http://twitter.com/ChemConnector • ORCID: http://orcid.org/0000-0002-2668-4821 • Amazon Author Page: Follow Link to Author Page • My Klout: http://www.klout.com/#/ChemConnector • LinkedIn: http://www.linkedin.com/in/antonywilliams • SlideShare: http://www.slideshare.net/AntonyWilliams • Google Scholar Citations Profile: Antony Williams Citations • Wikipedia : http://en.wikipedia.org/wiki/Antony_John_Williams
- 101. The Power of Social Media
- 102. I recommend… • Register for an ORCID ID – then use it • Develop your LinkedIn profile • Publish to Slideshare • Track Google Scholar Citations (for now) • Choose: ResearchGate or Academia.edu • Set up an About.ME page to link everything • Participate in building your profile
- 103. Thank you Email: tony27587@gmail.com ORCID: 0000-0002-2668-4821 Twitter: @ChemConnector Personal Blog: www.chemconnector.com SLIDES: www.slideshare.net/AntonyWilliams
Editor's Notes
- Toxcast can help investigate particular endpoints for a chemical – an abundance of relevant data to model.
- US20140329929A1, The melting point and both NMR spectra are associated with the compound. Other physical quantities e.g. volumes, pressures etc. are also detected
- Mostly melting points (as opposed to sublimation/decomposition). Dubious values usually mistakes in the original document e.g. in this case probably a missing hyphen.
- Unknown spectra are almost always hydrogen. As carbon shifts are so different to hydrogen a very crude check could partition the unknowns into proton and carbon NMR. Small numbers of other obscure spectra also found (but also false positives due to really bizarre “OCR” errors of hydrogen or the likei.e. 1 in a million errors :-p)