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Lost In Translation when machines meet STM content

Oct 30, 2014

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This slidedeck outlines the Resource Identification Initiative and how partners within the group are working to improve reproducibility in science by making experimental methods more machine readable.

  • 1. Lost In Translation when machines meet STM content This presentation was designed to be delivered live. To help you understand the content we have added these notes

2. 3 Questions Behind the shared vision held by the partners of the Resource Identification Initiative lies a number of questions. 3. Which one?REPRODUCIBILITY: In the scientific community it is difficult to find objective qualitative information about research materials. Choosing the wrong products often means failed experiments 4. Where is it?EFFICIENCY: Poor resource visibility means that labs around the world waste thousands of man hours duplicating eachothers work. Greater visibility of produced research materials would dramatically improve efficiency in science and reduce waste. .. 5. Who has used it?CONNECTIVITY: By its nature, science is a collaborative endevour. Efficiently identifying knowledge and expertise when required is key to progressing discovery. .. 6. The Role of Content.. Research content has evovled over time as a means of communicating conclusions. However, the real untapped value in content is the information about the journey 7. Who has used it? Where is it? Every article contains valuable information about experimental procedures and materials. When cross referenced with location, author and time data, powerful new experimental and research insights are Which one? revealed 8. Challenges.. Todays research articles are designed to be read one at a time by humans. To cross reference we rely on our notetaking, memory and prior knowledge. Machines have the potential to dramatically improve the efficiency of how we glean insight from content. But. 9. 1 2 3 XMLAmbiguityCulture1) Every publisher has slightly different XML standards. 2) The vocabularly for describing research entities is ambiguous. 3) There is a poor culture of facilitating data mining and enforcing best annotation practice in the publishing industry. 10. XMLThe XML produced by different publishers can be significantly different. This makes indexing and analysing content at scale challenging 11. AmbiguityInsufficient annotation and naming in content makes it difficult to disambiguate material entities. Take this glass beads example. 12. Sigma produces at least 5 variations of glass beads, which version is being referred to in the article? 13. CultureVs Publishers have traditionally made money by attracting great content and selling access to as many people as possible . Advances in technology have largely been viewed by publishers as a means to do more of the same at a lower cost. Publishers have been slow to adopt practices that make their content machine accessible 14. Who is involved.. The RII is backed a wide group of interests working together to change how experimental resources are documented in new research content 15. The group includes publishers, academic groups, funding agenicies, resource repositories and commercial companies 16. Shared Goals.. The group has a number of shared goals with the aim of improving the machine accessiblity of STM content in a practical and sustainable way 17. 1. Unique IdentifierAB_1234578 1) By agreeing and assigning standard unique identifiers for all known research materials (commercial and non-commercial) 18. 2. Editor Awareness Drive adoption Better XML standards Content machine friendly 2) By working with publishers and other community members to encourage the inclusion of unique indentifiers at the authoring stage and devising strategies for XML standardisation... 19. 3. Distribution3) By developing technology and APIs to diseminate research material information in a standarised form 20. 4. Annotation4) While NIF is focussing on research material annotation at the prepublication stage, scrazzl is working on a seperate initiative to drive retrospective annotation of published research- Pre-publication - Prospective- Post-Publication - Retrospective 21. 5. Interoperability5) One of the main aims of the RII is to support the adoption of a standardised public research material onthology and vocabulary that is interoperable with other exsisting biological onthologies 22. Our Destination So what does success look like? 23. ConnectivityEvery new article published will contain unique identifiers either in the visible text or in the underlying metadata. This will improve machine readability and will dramatically improve the semantic connectivity of articles 24. ReproducibilityData driven qualitative metrics of material entities will be available, improving reproducibility and driving efficiency.. 25. VisibilityImproved Geo and time dependent resource availability visualisation will be possible. Finding where resources are and identifying key technical experts will be more efficient 26. Questions? E: david.kavanagh@scrazzl.com T: +353 (0) 863-867-990 Twitter: @dkavanagh www.scrazzl.com