CINF 31
ThermoML: New IUPAC standard for thermodynamic data storage and exchange

Robert D. Chirico1, Michael Frenkel1, Vladimir V. Diky1, Qian Dong1, Kenneth N Marsh2, John H. Dymond3, William A. Wakeham4, Stephen E. Stein5, Erich Koenigsberger6, and Anthony R. H. Goodwin7. (1) Physical and Chemical Properties Division, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305-3328, (2) Department of Chemical and Process Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand, (3) Chemistry Department, University of Glasgow, Glasgow, G12 8QQ, United Kingdom, (4) School of Engineering Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom, (5) Physical and Chemical Properties Division, NIST, Gaithersburg, MD 20899, (6) Division of Science and Engineering, School of Mathematical and Physical Sciences, Murdoch University, Murdoch, WA 6150, Australia, (7) Schlumberger Technology Corporation, 125 Industrial Blvd., Sugar Land, TX 77478

ThermoML is an XML-based emerging IUPAC standard for storage and exchange of experimental, predicted, and critically-evaluated thermophysical and thermochemical property data. The basic principles, scope, and description of the structural elements of ThermoML will be discussed. ThermoML covers essentially all thermodynamic and transport property data for pure compounds, mixtures, and chemical reactions. Representations of uncertainties in ThermoML conform to the Guide to the Expression of Uncertainty in Measurement (GUM). Representation of fitted equations with ThermoML will also be described. The role of ThermoML in global data communication processes will be discussed with emphasis on a collaborative project with major journals (the Journal of Chemical and Engineering Data, The Journal of Chemical Thermodynamics, Fluid Phase Equilibria, Thermochimica Acta, and the International Journal of Thermophysics) for distribution of property data with benefit to authors, journal publishers, and data users. The project model described is readily applicable to other disciplines and data types.