Currently, information of species recognition on parchments is provided either using proteomics or DNA analysis. Each technique presents with inherent difficulties, and sometimes, it is not possible to obtain an unambiguous result. In this work, the application of time‐of‐flight secondary ion mass spectrometry paired with chemometrics, namely principle component analysis, to the analysis of parchment specimens has been investigated in order to assess if any information may be gleaned and help in the process of recognition or even providing any further information to conservators to be used for preservation of historical parchment. A series of specimens of various ages and species were analysed on both skin and flesh sides. Results indicate that it is fairly straightforward to distinguish between goat and sheep while calf is more difficult to separate from other species. In addition, an unknown sample exhibits data that would classify it as a goat specimen. More work is needed to ascertain which side is being analysed for any unknown materials as the behaviour varies amongst the species examined in this work. Copyright © 2016 John Wiley & Sons, Ltd.

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Performance measurement in construction has been a topic of academic and industry inquiry in the UK since the 1990s. Despite the time elapsed, there is little evidence of a consistent industry-wide performance framework that drives decision-making and supports consistent measurement of performance on construction projects. A review of academic advancements and industry practices has been conducted to understand performance measurement in the construction industry, including the metrics assessed, processes for collecting and analysing data, and current limitations. The adoption of digital technologies on construction projects can support timely measurement of performance metrics, allowing for feedback and corrective action to improve performance. However, organisations struggle to connect the top-down measurement value with the bottom-up data capture technologies. The study of an exemplar commercial project was used to inductively develop a data-to-dashboard strategy that supports decision making in construction. The proposed strategy aligns performance metrics, digital tools and processes, and data analysis techniques in a consistent approach to interpret performance-related data and understand key issues. The development and review of the strategy on a live construction project highlights the challenges experienced with multi-source data integration and the translation of information into knowledge that drives decisions and deployment of timely corrective measures. The application of the strategy would ensure a consistent definition of metrics early in the project, and the continuous measurement of leading indicators. Future research will review the proposed strategy on further case study projects and develop an industry-wide multi-level performance measurement framework that uses the proposed strategy to improve performance.