A Multidimensional Reconstruction of the Order of Composition of Historical Manuscripts from Textual and Material Evidence

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This project aims to use Isaac Newton’s very large manuscript Nachlass (collection of unpublished manuscripts left behind after his death) as a platform for exploring and assessing the combined use of multiple techniques for dating those manuscripts. The project focuses on his alchemical writings, which includes laboratory notebooks, indices of alchemical substances, and transcriptions from other sources. The research team will develop an integrative approach to assessing the data generated in using these techniques on the manuscripts. The project will use network graph analysis to determine a relative chronology of the parts within the large body of manuscripts. A solution to the chronology of Newton's alchemical manuscripts will serve to promote a better understanding of his place in the field at particular periods, since his involvement in this research extended over four decades and involved numerous contacts and collaborators, some of whom were important in their own right. A solution to the problem of dating large manuscript collections will benefit researchers in many areas of scholarship in addition to history of science, not to mention librarians and museum workers. The research team will develop a method for integrating the data generated from the use of multiple techniques to generate a chronology of Newton’s Nachlass. Those techniques include computational methods for text analysis (e.g. Latent Semantic Analysis and Topic Modeling), spectrometric analysis of inks and papers by XRF spectrosopy, comparison of watermarks by means of the latest imaging technologies, tracking Newton’s handwriting practices and evolving use of alchemical symbols, and the systematic study of his citations. In addition to applying these multiple techniques, the project will follow an integrative approach to assessing the data generated by them. In order to achieve this result, the project will use network graph analysis with tensors to visualize and to identify clusters of manuscripts and passages whose respective chronological markers should correlate and distinguish one another in time and place of use across several dimensions of evidence based on different standards, for example, watermarks, inks, and citations. Even where a clean overlay of graphs does not result, the nature of the poor 'fit' would be clearly outlined in more than one dimension where solutions may be found. Metrical techniques will also be developed for determining the degree of agreement that constitutes good versus poor fit. The result will be a model for determining relative chronology of the parts within large textual corpora that will be transportable to other projects.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.