Digital Humanities Are a Two-Way Street.

n The History Manifesto, Jo Guldi and David Armitage seek a panacea for what they call short-termism—the shrinking chronological focus of historical studies that leads to the overproduction of micro-histories—in “new” longue durée histories that will rely on big data and on digital humanities.24 We agree that big data and digital humanities offer new and valuable ways of making history, including the history of science, and their use should be encouraged. Nevertheless, we would like to point out some of the pitfalls of using big data and digital humanities in history writing, especially in history of science. We would like to stress that digital humanities do not only entail big data. The History Manifesto blurs the fact that there are many different kinds of historians. Some historians resort to digital humanities because they wish to tackle the lack of data or the data’s disparities, uneven distribution, or low quality. For example, the parchment analysis project at the University of York attempts to identify the species and breed of the animals used in parchment making by means of mass spectrometry; this information will contribute invaluable data about the economy and agriculture of medieval England.25 The physicist Vito Mocella used new x-ray technology to read the charred papyri from Herculaneum buried by the eruption of Vesuvius.26 Big data cannot replace micro-histories, but only complement them. It does not provide a synthesis of the overabundant micro-histories but, rather, a distinct perspective on the historical record that stems from a different methodological basis. Therein also lies its special value for historians. Digital humanities offer new possibilities to historians of science. However, historians are not the only ones to gain from using digital tools, as Guldi and Armitage note when they compare historians with social scientists. The History Manifesto (p. 94) supports Michael Friendly’s proposal that historians should become technical experts in time-series analysis in virtually all