Speaking at the X-Tech conference in Paris earlier this year, Peter Murray-Rust of the University of Cambridge made a plea for ‘open data’ in science. Science is increasingly based on the re-use of existing published data. Traditionally this has been associated with primary journal articles, either within the text or attached as supplementary information. There is increasing community pressure to publish this in open, machine-accessible form, either into data centers or as supplemental data. This allows data and text mining to generate new areas of knowledge-driven science. Linking data from different disciplines is enabled by new web technologies that create syntheses from which new insights arise.
Unfortunately, most publishers make no effort to encourage the machine-readable publication of data and several actively oppose it by practices such as licenses, copyright and bans on robotic downloads. Many publisher requires authors to hand over copyright on data, even though it can be argued that these are facts. Murray-Rust advocates a policy of unrestricted access to scientific data in semantic, ‘machine-understandable’ form. This can be leveraged by text-mining and automated high-throughput spidering. More from http://en.wikipedia.org/wiki/Open_Data.
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