Neuroscientists routinely throw away their raw experimental data after publishing a paper, locking valuable recordings of brain-cell activity in formats no one else can use. This project builds a shared digital infrastructure—a GRID-based repository with standardised metadata—so that data from multichannel electrical recordings and fluorescent dye imaging can be archived, searched, and re-analysed by modellers and algorithm developers anywhere. Currently, neurophysiology is a one-lab science: datasets are isolated, incompatible, and often lost. Without a common framework, models of everything from single ion channels to networks of thousands of neurons cannot be properly parameterised or validated against real recordings. If CARMEN succeeds, it will transform how experimentalists and computational neuroscientists collaborate. A modeller in one institution could test a new spike-sorting algorithm against a curated dataset from a lab in another country, while the original experimenter retains control and credit. The system will also support near real-time streaming of experimental data, allowing distributed teams to optimise difficult live recordings as they happen. This is primarily an infrastructure project for fundamental neuroscience—it does not directly treat disease or build a device—but by making expensive experimental data reusable, it could accelerate the discovery of how neural activity gives rise to behaviour and thought.
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Research in neurophysiology includes both analysis of data from neuronal systems (networks of brain cells; both live and cultured), and development of models to explain both the processes that form the character of data, and the high level function that these express; i.e. behaviour and thought. Capturing and analysing data from neuronal systems is time-consuming, difficult and expensive: many techniques exist, some using multichannel electrical recording, and some using ion-sensitive fluorescent dyes. Different techniques have different advantages: some have high time resolution, whereas others have high space resolution. The models that derive from this data also exist at many levels, from the detailed modelling of membrane-embedded ion channels and neurotransmitters to compartmental neural models, through models of small neural networks, to larger models of many thousands of neurons. All models and algorithms are hungry for data to determine their many parameters and characteristics. Currently this activity is largely a one-lab science: datasets are shared within a lab, and with some computational modellers. The research is also not organised to ensure that data and models produced by small communities of specialist researchs can easily be integrated to contribute to the bigger picture. Datasets are discarded after the experimentor has completed their experimental report, or are archived in a format that is not widely accessible. This project aims to use the GRID to change that: it will enable experimenters to archive their datasets in a structure, making them widely accessible for modellers and algorithm developers to exploit. Experimental datasets are useless without accurate descriptions of the experimental conditions, and hence an appropriate set of metadata will developed to augment the data, allowing the project researchers to collaborate more widely and persistently by sharing data in a sensible, referenced form. Further, the project will provide integrated and co-ordinated services for the neuroscience data, enabling neuronal signal detection, sorting and analysis, as well as visualisation and modelling. Data security is critically important to experimentors: they do not wish to be simply anonymous contributors of data, but to be directly involved in further analysis of their datasets, and this will be supported. Further we will enable direct near real-time analysis of streamed experimental data, providing information to distributed teams of specialists that will allow difficult experiments to be optimised. These interventions will catalyse a step change in research practice in this area of neuroscience, which will allow best value to be derived from the significant research investment that is made in order to understand the brain.
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