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The Sociology of Ontologies in Neurosciences

The Sociology of Ontologies in Neurosciences. Phillip Lord, School of Computing Science, Newcastle University. Background to the CARMEN project The role that we see for ontologies. Why neurosciences is different. How we are planning to build them. Overview. Research Challenge.

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The Sociology of Ontologies in Neurosciences

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  1. The Sociology of Ontologies in Neurosciences Phillip Lord, School of Computing Science, Newcastle University

  2. Background to the CARMEN project The role that we see for ontologies. Why neurosciences is different. How we are planning to build them Overview

  3. Research Challenge • Worldwide >100,000 neuroscientists(~ 5,000 in UK) are generating vast amounts of data • Principal experimental data formats: •  molecular (genomic/proteomic) •  anatomical (spatial) •  behavioural • neurophysiological (time-series electrical measures of activity) • Neuroinformatics concerns how these data are handled and integrated, including the application of computational modelling Understanding the brain may be the greatest informatics challenge of the 21st century

  4. CARMEN – Focus on Neural Activity • raw voltage signal data collected by patch-clamp and single & multi- electrode array recording Understanding the brain may be the greatest informatics challenge of the 21st century  resolving the ‘neural code’ from the timing of action potential activity neurone 1 neurone 2 neurone 3

  5. Potential Barriers • Technical • Multiple proprietary data formats  Volume of the data to be analysed • Cultural •  Multiple communities each acting independently •  Concerns about the consequences of sharing data All of this will sound very familiar to biologists, and others

  6. The project was funded starting from this October – hence it’s about 3 weeks old. Therefore, this talk is based on my initial impressions I don’t actually know anything about sociology A disclaimer

  7. Neurosciences seems to have very similar problems to bioinformatics Bioinformatics is rich with metadata; this isn’t yet true with neuroinformatics What are the differences between bio and neuroinformatics Whats the difference?

  8. Age and Impact.

  9. DNA and Protein sequence form a core datatype for bioinformatics It’s simple to structure and to store, and it is of high-value Initially, there wasn’t much of it, and textual metadata was fine. Many people built tools over it, for transforming and manipulating. No sequences!

  10. Neurosciences data is hard • Most neurosciences data is relatively simple in structure • But often contextually complex • And sometimes associated with behavioural features • Without additional metadata, the raw data is relatively meaningless • In this, it shares much with microarray data.

  11. Data Sharing was an early tradition in biology. Gene patenting, NDAs and the like came as quite a surprise Many political battles were fought, culminating with Clinton/Blair statement Data Sharing in bioinformatics

  12. The data is easy to structure, but the metadata is not Is therefore much harder to share data usefully Many neuroscientists come from a medical background tends to be more of a hierarchical, secretive profession – all worried about getting sued. A lot of neuroscientists use invasive, live animal experiments security is more than a passing concern. Data Sharing in Neurosciences

  13. The achievements and processes of bioinformatics are familiar to neuroscience it seems to be easier to argue for the value of standardisation But less of a do-it-yourself attitude “But you can’t just make up a standard” “We’re just trying to build a list of terms, which we all understand. Then the experts can turn it into an ontology” A Following Wind

  14. Currently, we are term gathering ignorance is our key weapon! Many of the analysis steps are straight-forward maths/stats Much of the experimental metadata should be transferable from bioinformatics. Approach

  15. How to define the most essential metadata, for highest win. How to engage the community into providing the metadata Will we be able to adapt the knowledge from bio, or will it be too complex? Are we doomed to relieve our past? The issues

  16. We need to avoid “ontology for everything” Probably easier to avoid “reinventing the wheel” Simple to start with a migratory path Conclusions

  17. Frank Gibson Carmen Investigators Jim Austin, Colin Ingram, Paul Watson, Stuart Baker, Roman Borisyuk, Stephen Eglen, Jianfeng Feng, Kevin Gurney, Tom Jackson, Marcus Kaiser, Stefano Panzeri, Rodrigo Quian Quiroga, Simon Schultz, Evelyne Sernagor, V. Anne Smith, Tom Smulders, Miles Whittington Acknowledgements

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