Exponential growth rates in data generation combined with non-negligible error rates in the scientific literature [1] have conspired to make critical evaluations impracticable in many scenarios for the average scientist. Data volumes have grown to such a degree that many traditional data collection and interpretation approaches cannot scale sufficiently to remain comprehensive and current, or to effectively track shifting interests within research and industrial communities. It is thus necessary to strongly rely on a substantially increased role for digital archives, automated analysis and machine learning approaches. The approach adopted at the Thermodynamics Research Center (TRC) at the National Institute of Standards and Technology (NIST) is dynamic data evaluation, whereby a reliable and comprehensive data archive is used in conjunction with an algorithmically-encoded expert analysis in order to generate up-to-date property recommendations. In developing an infrastructure to support this high-throughput critical analysis for thermophysical properties, TRC staff have created technological solutions for data collection, curation and communication to quickly meet the challenges encountered under real-world conditions. These include user experience-driven data entry tools, and international data standards and ad hoc derivatives of them. The particulars of these tools will be discussed as well the importance of being both proactive and reactive in the information technology development process. [1] Chirico, R.D.; Frenkel, M.; Magee, J.W.; Diky, V.; Muzny, C.D.; Kazakov, A.; Kroenlein, K.; Abdulagatov, I.; Hardin, G.; Acree, W.E.; Brenneke, J.F.; Brown, P.L.; Cummings, P.T.; de Loos, T.W.; Friend, D.G.; Goodwin, A.R.H.; Hansen, L.D.; Haynes, W.M.; Koga, N.; Mandelis, A.; Marsh, K.N.; Mathias, P.M.; McCabe, C.; O’Connell, J.P.; Pádua, A.; Rives, V.; Schick, C.; Trusler, J.P.M.; Vyazovkin, S.; Weir, R.D.; Wu, J. “Improvement of Quality in Publication of Experimental Thermophysical Property Data: Challenges, Assessment Tools, Global Implementation, and Online Support” J. Chem. Eng. Data 2013, 58, 2699-2716.

Data infrastructural design for informing critical evaluation

251st National Meeting (2016)

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