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This paper describes the development of a new data acquisition standard for synthetic biology. This comprises the creation of a methodology that is designed to capture all the data, metadata, and protocol information associated with biopart characterization experiments. The new standard, called DICOM-SB, is based on the highly successful Digital Imaging and Communications in Medicine (DICOM) standard in medicine. A data model is described which has been specifically developed for synthetic biology. The model is a modular, extensible data model for the experimental process, which can optimize data storage for large amounts of data. DICOM-SB also includes services orientated toward the automatic exchange of data and information between modalities and repositories. DICOM-SB has been developed in the context of systematic design in synthetic biology, which is based on the engineering principles of modularity, standardization, and characterization. The systematic design approach utilizes the design, build, test, and learn design cycle paradigm. DICOM-SB has been designed to be compatible with and complementary to other standards in synthetic biology, including SBOL. In this regard, the software provides effective interoperability. The new standard has been tested by experiments and data exchange between Nanyang Technological University in Singapore and Imperial College London.

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This paper is a joint effort between five institutions that introduces several novel similarity measures and combines them to carry out a multimodal segmentation evaluation. The new similarity measures proposed are based on the location and the intensity values of the misclassified voxels as well as on the connectivity and the boundaries of the segmented data. We show experimentally that the combination of these measures improve the quality of the evaluation. The study that we show here has been carried out using four different segmentation methods from four different labs applied to a MRI simulated dataset of the brain. We claim that our new measures improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.

This study introduces a new data model, based on the DICOM-SB (see glossary of terms for definition of acronyms) standard for synthetic biology, that is capable of describing/incorporating the data, metadata and ancillary information from detailed characterisation experimentsto present DNA components (bioparts) in datasheets. The data model offers a standardised mechanism to associate bioparts with data and information about component performancein a particular biological context (or a range of contexts, e.g. chassis). The data model includes the raw, experimental data for each characterisation run, and the protocol details needed to reliably reproduce the experiment. In addition, it provides metrics (e.g. relative promoter units, synthesis/growth rates etc.) that constitute the main content of a biopart datasheet. The data model has been developed to directly link to DICOM-SB, but also to be compatible with existing data standards, e.g. SBOL and SBML. It has been implemented within the latest version of the API that enables access to the SynBIS information system. The work should contribute significantly to the current standardisation effort in synthetic biology. The standard data model for datasheets is seen as a necessary step towards effective interoperability between part repositories, and between repositories and BioCAD applications.