Modtrove and the role of electronic notebooks

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Presentation slides for OSDD Malaria meet at Sydney University - 24/2/2012

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  • Clicking on this applet launches another applet that enables visualisation in 3d, property prediction, rotation and further editing.
  • Natural Language Processing and Chemical Tagging using the OSCAR4 library from the PMR group at University of Cambridge[Demo if online] http://103.1.186.131:9000/chemicaltaggerElse: next slides
  • Two actions are demonstrated here, finding all solvents in the experiment (molecules attr solvent) and finding all apparatus in the experiment.
  • Modtrove and the role of electronic notebooks

    1. 1. Modtrove and the future of electronic lab notebooks@mikeatusyd Mike Rmrob5648@uni.sydney.edu.au http://www.flickr.com/photos/karenchiang/504
    2. 2. Traditional Lab books http://www.flickr.com/photos/mbiskoping/6075387388/
    3. 3. Handwritten notes, diagramsRetrieve by date, section or index. Slow.Digital notes, interactive diagrams/tablesand generic templatesRetrieve by date, section, index, metadata,Keywords etc. Fast. http://www.flickr.com/photos/mbiskoping/6075387388/
    4. 4. What is ?•Derivative of Labtrove from the University of Southampton (since 2010)•An open source electronic notebook•Blog style functionality (as opposed to wiki style as in OpenWetWare)•Modtrove adds some improved user interface features and functionality changes whilemaintaining the core of a blog engine.•The source of Modtrove is available on Github (http://github.com/miike/modtrove)•A running demo will also be shown
    5. 5. •Fully WYSIWYG editing Integration•Sortable tables•Integration into existing frameworks – Chemspider API•It’s free•Anyone can use it and anyone is free to modify it Social networks•Easy to backup/archive•Searchable•Multi-platform (Windows, Mac, smartphones, tablets) >400 data sources Physical instruments Quick response codes Everybody (used as physical labels)
    6. 6. Near Future•Embeddable chemical structures that can be read allows for substructure search + easy visualistion
    7. 7. Text mining for chemical entitiesChemical entities appear as linksQuantities visualised in greenTemperature phrases visualised in pinkTime phrases visualised in red
    8. 8. Okay, but why should I care?• Plot the evolutionary tree of an organic synthesis project
    9. 9. A paradigm shift in the retrieval of recorded information “Identify the solvents used in this reaction” Parts of Speech tagging to identify Relevant components NLP+Chemical tagging “Identify the solvents used in this procedure” Tagged post (XML) <MOLECULE> <NAME>Ethanol</NAME> Call to appropriate function </MOLECULE> findSolvents(taggedpost)Result: “Identified ethanol as a solvent”
    10. 10. ThankyouAny questions?

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