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J. Cheney, Finkelstein, A., Ludäscher, B., and Vansummeren, S., Principles of Provenance (Dagstuhl Seminar 12091), Dagstuhl Reports, vol. 2, pp. 84-113, 2012.
P. V. Coveney and Atkinson, M. P., Preface. Crossing boundaries: computational science, e-Science and global e-Infrastructure, Philosophical Transactions of the Royal Society Series A, vol. 367, pp. 2425-2427, 2009.
M. Atkinson, Preface, in THE DATA BONANZA: Improving Knowledge Discovery for Science, Engineering and Business, M. Atkinson, Baxter, R., Brezany, P., Corcho, O., Galea, M., Parsons, M., Snelling, D., and van Hemert, J., Eds. John Wiley & Sons Ltd., 2013, p. xix-xxviii.
D. E. Croft, van Hemert, J., Wykoff, C. C., Clifton, D., Verhoek, M., Fleming, A., and Brown, D. M., Precise montaging and metric quantification of retinal surface area from ultra-widefield fundus photography and fluorescein angiography, Ophthalmic Surg Lasers Imaging Retina, vol. 45, pp. 312–317, 2014.
S. Gesing, Kohlbacher, O., and van Hemert, J. I., Portals for Life Sciences—a Brief Introduction, in Proceedings of the IWPLS09 International Workshop on Portals for Life Sciences, 2009.
A. E. Eiben, Elia, D., and van Hemert, J. I., Population dynamics and emerging features in AEGIS, in Proceedings of the Genetic and Evolutionary Computation Conference, 1999, p. 1257--1264.
D. Snelling, Platforms for Data-Intensive Analysis, in THE DATA BONANZA: Improving Knowledge Discovery for Science, Engineering and Business, M. Atkinson, Baxter, R. M., Brezany, P., Corcho, O., Galea, M., Parsons, M., Snelling, D., and van Hemert, J., Eds. John Wiley & Sons Ltd., 2013, pp. 197-201.
F. Zhu, (PhD Thesis) Brain Perfusion Imaging - Performance and Accuracy. 2012.
J. I. van Hemert and Urquhart, N. B., Phase transition properties of clustered travelling salesman problem instances generated with evolutionary computation, in LNCS, Birmingham, UK, 2004, vol. 3242, p. 150--159.
H. Evans, Atkinson, M. P., Brown, M., Cargill, J., Crease, M., Draper, S., Gray, P. D., and Thomas, R., The pervasiveness of evolution in GRUMPS software, Softw., Pract. Exper., vol. 33, pp. 99-120, 2003.
M. Atkinson, Persistence and Java — A Balancing Act , in Objects and Databases, vol. 1944, K. Dittrich, Guerrini, G., Merlo, I., Oliva, M., and Rodriguez, E. M., Eds. Springer, 2001, pp. 1-31.
M. P. Atkinson, Persistence and Java - A Balancing Act, in Objects and Databases, 2000, pp. 1-31.
L. Han, The performance model of dynamic Virtual Organization (VO) formations within grid computing context , Chaos, Solitons & Fractals , vol. 40, no. 4, pp. 1599-1608, 2009.
C. S. Liew, Atkinson, M. P., Ostrowski, R., Cole, M., van Hemert, J. I., and Han, L., Performance database: capturing data for optimizing distributed streaming workflows, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 369, no. 1949, pp. 3268-3284, 2011.
F. Zhu, Rodríguez, D., Carpenter, T., Atkinson, M., and Wardlaw, J., Parallel perfusion imaging processing using GPGPU, Computer Methods and Programs in Biomedicine, p. -, 2012.
F. Zhu, Rodríguez, D., Carpenter, T., Atkinson, M., and Wardlaw, J., A Parallel Deconvolution Algorithm in Perfusion Imaging, in Healthcare Informatics, Imaging, and Systems Biology (HISB), San Jose, California , 2011.
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J. Gomes, David, M., Martins, J., Bernardo, L., Garcia, A., Hardt, M., Kornmayer, H., Marco, R., Rodríguez, D., Diaz, I., Cano, D., Salt, J., Gonzalez, S., Sanchez, J., Fassi, F., Lara, V., Nyczyk, P., Lason, P., Ozieblo, A., Wolniewicz, P., and Bluj, M., Organization of the International Testbed of the CrossGrid Project, in Cracow Grid Workshop 2005, 2005.
A. Barker, Weissman, J. B., and van Hemert, J., Orchestrating Data-Centric Workflows, in The 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid), 2008, p. 210--217.
G. McGilvary, Atkinson, M., Barker, A., and Lloyd, A., Optimum Platform Selection and Configuration for Computational Jobs, in All Hands Meeting 2011, York, 2011.
R. Filgueira, Singh, D. E., Isaila, F., Carretero, J., and Loureiro, A. G., Optimization and evaluation of parallel I/O in BIPS3D parallel irregular application, in IPDPS, 2007.
C. S. Liew, Optimisation of the enactment of fine-grained distributed data-intensive workflows. Edinburgh: The University of Edinburgh, 2012.
D. Rodríguez, Carpenter, T. K., van Hemert, J. I., and Wardlaw, J. M., An open source toolkit for medical imaging de-identification, European Radiology, vol. 20, no. 8, p. 1896--1904, 2010.
A. Grimshaw, Morgan, M., Merrill, D., Kishimoto, H., Savva, A., Snelling, D., Smith, C., and Berry, D., An Open Grid Services Architecture Primer, Computer, vol. 42, p. 27--34, 2009.
C. Allan, Burel, J. - M., Moore, J., Blackburn, C., Linkert, M., Loynton, S., MacDonald, D., and et al., OMERO: flexible, model-driven data management for experimental biology, NATURE METHODS, vol. 9, no. 3, pp. 245-253, 2012.
M. Antonioletti, Atkinson, M., Baxter, R., Borley, A., Hong, N. C., Collins, B., Davies, J., Hardman, N., Hicken, G., Hume, A., Jackson, M., Krause, A., Laws, S., Magowan, J., Nowell, J., Paton, N. W., Pearson, D., and To, OGSA-DAI: Two Years On, in GGF10, Berlin, Germany, 2004.

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