NeXML: rich, extensible, and verifiable representation of comparative data and metadata.
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2012-07
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In scientific research, integration and synthesis require a common understanding of where data come from, how much they can be trusted, and what they may be used for. To make such an understanding computer-accessible requires standards for exchanging richly annotated data. The challenges of conveying reusable data are particularly acute in regard to evolutionary comparative analysis, which comprises an ever-expanding list of data types, methods, research aims, and subdisciplines. To facilitate interoperability in evolutionary comparative analysis, we present NeXML, an XML standard (inspired by the current standard, NEXUS) that supports exchange of richly annotated comparative data. NeXML defines syntax for operational taxonomic units, character-state matrices, and phylogenetic trees and networks. Documents can be validated unambiguously. Importantly, any data element can be annotated, to an arbitrary degree of richness, using a system that is both flexible and rigorous. We describe how the use of NeXML by the TreeBASE and Phenoscape projects satisfies user needs that cannot be satisfied with other available file formats. By relying on XML Schema Definition, the design of NeXML facilitates the development and deployment of software for processing, transforming, and querying documents. The adoption of NeXML for practical use is facilitated by the availability of (1) an online manual with code samples and a reference to all defined elements and attributes, (2) programming toolkits in most of the languages used commonly in evolutionary informatics, and (3) input-output support in several widely used software applications. An active, open, community-based development process enables future revision and expansion of NeXML.
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Vos, Rutger A, James P Balhoff, Jason A Caravas, Mark T Holder, Hilmar Lapp, Wayne P Maddison, Peter E Midford, Anurag Priyam, et al. (2012). NeXML: rich, extensible, and verifiable representation of comparative data and metadata. Systematic biology, 61(4). pp. 675–689. 10.1093/sysbio/sys025 Retrieved from https://hdl.handle.net/10161/26583.
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Hilmar Lapp
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