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

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

3. Slide 3 Research Challenge Understanding the brain may be the greatest informatics challenge of the 21 st century 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

4. Slide 4 Need for Cooperation Understanding the brain may be the greatest informatics challenge of the 21 st century OECD Neuroinformatics Working Group identified the need to work cooperatively in order to achieve major advances Cooperation will permit: development of common processes best value from data, including long term curation ‘mega-analysis’ of large data sets integration of data sets across different scales and different approaches interdisciplinary research

5. Slide 5 CARMEN – Focus on Neural Activity resolving the ‘neural code’ from the timing of action potential activity Understanding the brain may be the greatest informatics challenge of the 21 st century neurone 1 neurone 2 neurone 3 raw voltage signal data collected by patch-clamp and single & multi- electrode array recording

6. Slide 6 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 Potential Barriers

7. Slide 7 A disclaimer 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

8. Slide 8 Whats the difference? 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

9. Slide 9 Age and Impact.

10. Slide 10 No sequences! 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.

11. Slide 11 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.

12. Slide 12 Data Sharing in bioinformatics 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

13. Slide 13 Data Sharing in Neurosciences 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.

14. Slide 14 A Following Wind 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”

15. Slide 15 Approach 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.

16. Slide 16 The issues 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?

17. Slide 17 Conclusions We need to avoid “ontology for everything” Probably easier to avoid “reinventing the wheel” Simple to start with a migratory path

18. Slide 18 Acknowledgements 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