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Data sharing is important in the biological sciences to prevent duplication of effort, to promote scientific integrity, and to facilitate and disseminate scientific discovery. Sharing requires centralized repositories, and submission to and utility of these resources require common data formats. This is particularly challenging for multidimensional(More)
We present a novel unsupervised artificial neural network for the extraction of common features in multiple data sources. This algorithm, which we name Exploratory Correlation Analysis (ECA), is a multi-stream extension of a neural implementation of Exploratory Projection Pursuit (EPP) and has a close relationship with Canonical Correlation Analysis (CCA).(More)
We investigate the use of artificial neural networks in classifying hyperspectral data. Such data when collected from remote sensors provides extremely detailed coverage of e.g. the mineralogical composition of planetary surfaces, however the volume of data supplied often overwhelms traditional classifiers. When we wish to investigate such data sets in an(More)
Data-intensive research depends on tools that manage multi-dimensional, heterogeneous data sets. We have built OME Remote Objects (OMERO), a software platform that enables access to and use of a wide range of biological data. OMERO uses a server-based middleware application to provide a unified interface for images, matrices, and tables. OMERO's design and(More)