CARMEN: Code analysis, repository, and modelling for e-Neuroscience

神经生理学的研究包括分析来自神经系统（脑细胞网络;活的和培养的）的数据，以及开发模型来解释形成数据特征的过程，以及这些表达的高级功能;即行为和思想。从神经元系统捕获和分析数据是耗时、困难和昂贵的：存在许多技术，一些使用多通道电记录，一些使用离子敏感荧光染料。不同的技术具有不同的优势：有些具有高时间分辨率，而另一些具有高空间分辨率。从这些数据衍生的模型也存在于许多层面，从膜嵌入离子通道和神经递质的详细建模到隔室神经模型，通过小型神经网络模型，到数千个神经元的更大模型。所有模型和算法都需要数据来确定它们的许多参数和特性。目前，这项活动主要是一个实验室的科学：数据集在实验室内共享，并与一些计算建模。这项研究也没有组织起来，以确保小型专业研究社区产生的数据和模型可以很容易地整合起来，为更大的图景做出贡献。数据集在实验者完成他们的实验报告后被丢弃，或者以不能广泛访问的格式存档。该项目旨在使用GRID来改变这一点：它将使实验者能够将他们的数据集存档在一个结构中，使建模人员和算法开发人员能够广泛访问它们。如果没有对实验条件的准确描述，实验数据集是无用的，因此将开发一组适当的元数据来增强数据，使项目研究人员能够通过以合理的参考形式共享数据来更广泛和持久地合作。此外，该项目将为神经科学数据提供综合和协调的服务，实现神经元信号检测、分类和分析，以及可视化和建模。数据安全对实验者来说至关重要：他们不希望只是匿名的数据贡献者，而是直接参与对其数据集的进一步分析，这将得到支持。此外，我们将实现对流式实验数据的直接近实时分析，为分布式专家团队提供信息，从而优化困难的实验。这些干预措施将促进神经科学这一领域的研究实践发生重大变化，这将使为了了解大脑而进行的重大研究投资产生最佳价值。

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.