Towards a 'Big Data Browser' for standardised datasets in neuroscience, and its application in ion channel and single cell modelling.

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Big data, and with it complex, multifaceted, publicly available data sets, has arrived in modern neuroscience research over the last decade. Unlike other fields however, the datasets in neuroscience are very heterogeneous and often require heavy post-processing and contextual annotation. This enrichment of metadata presents a challenge in itself because any data needs to be filtered and visualised with changing situational requirements, and their high dimensionality often make these data sets unruly and difficult for traditional static visualisation techniques. In preliminary work (submitted), we have manually annotated, enriched and standardised a dataset of over 2000 ion channel models according to their published context and their stereotypical response to predefined test protocol. We then developed a ``big data browser'' to allow users to quickly browse all ion channel models and visualise their functional characteristics with interactive filtering. These ion channel models have become the standard in the field for creating experimentally-constrained neuron and network models. Our method provides all necessary information for users searching for specific ion channel types to narrow down the circle of candidate models and can save an individual researcher as much as 4 weeks of preliminary literature research and test simulations.In this proposal, we aim to expand our work. Divided into three work packages, we aim to maintain and expand this resource in the context of ion channel models, as well as to make it a general case browser for neuroscience data. In WPI, we plan to expand the database to be simulator-independent and to include experimental data, making it more comprehensive and allow direct comparison of experiments to models. This will make the resource a useful tool for theorists as well as experimentalists. Next, we plan to integrate our database with other neuroscience resources, and maintain this all in one comprehensive and up-to-date platform. In this way, we can take advantage of existing frameworks and act as a complement to them.In WPII, we aim to expand the current functionality of the database to include multi-channel dynamics, multi-compartmental models, and finally to combine these two together to allow for analysis of complex dynamics. We will use the existing framework developed in our pilot work and WPI. Our ultimate goal is to produce a tool for the complete creation and testing of neuron models with complex morphology and channel composition. This will be of great benefit to experimentally-constrained modelling, making the creation of models easier and more intuitive. Testing a hypothesis of functional deficits based on proposed ion channel disfunction or morphological aberration will only take a matter of hours instead of several weeks.In WPIII, we aim to expand our data browser for ion channel models to other areas in neuroscience by making it a general 'Big Data Browser'. The main issue with big data currently is to create graphical interfaces that allow the exploration of complex datasets without requiring expert knowledge. We hope to create a unified interface for the storage, organisation and visualisation of datasets such as these, in order to make data more accessible and reusable. The end result will be an out-of-the-box data visualiser that can be adapted to any new dataset, and even to allow the comparison between datasets in an easy way.In summary, our proposal will provide the field with a resource for building detailed neuron models with complex ion channel dynamics and comparing them directly to experiments. In a second step, it will also provide the field with a much-needed data interface, adaptable to any dataset, solving a field-wide problem of how to efficiently share and compare heavily annotated big data sets.