Abstract
As the sciences have become increasingly data driven, it is clear that information integration is critical to their advancement. By integrating diverse data, we can allow biologists to discover big-picture patterns or behaviors, or to do comparative analyses among different organisms or systems. By enabling collaborative editing and annotation of integrated data — incorporating contributions from parties with different viewpoints — we can facilitate higher-quality, better-understood data. One of the open challenges, however, lies in developing the right architectures and models for supporting effective data integration and exchange in science.
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© 2009 Springer-Verlag Berlin Heidelberg
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Ives, Z.G. (2009). Data Integration and Exchange for Scientific Collaboration. In: Paton, N.W., Missier, P., Hedeler, C. (eds) Data Integration in the Life Sciences. DILS 2009. Lecture Notes in Computer Science(), vol 5647. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02879-3_1
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DOI: https://doi.org/10.1007/978-3-642-02879-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02878-6
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