From archives to answers - Quantitative digitization of natural history collections

Navn på bevillingshaver

Henrik Lauridsen


Aarhus University


DKK 4,975,798




Semper Ardens: Accelerate


Developing tools for digitizing biological specimens in a way in which maximum quantitative information can be extracted. An example: One can CT scan a fish to produce stunning images of the skeleton which can be appreciated for their beauty and used for traditional purposes like systematics. However, if performed carefully this data can also provide deeper information about the distribution of different tissue components, which allows for a range of physiology relevant measurements like specific buoyancy adaptations to a habitat, as well as fisheries relevant measurements like fat content. This project is about refining the modern imaging techniques like CT and MRI originally developed for medical use so that maximum quantitative information can be extracted from biological specimens.


The contents of a natural history museum's collection of specimens not only represents an immense scientific value to present day researches, but rather, through the propagation of a lasting collection, a value for researchers in the womb of time, compared to which those now alive form but an insignificant fraction. This is the focal point of several national and international initiatives to digitize natural history museum collections - an immense task. This project is important because it does not focus on digitizing vast amounts of specimens just for the sake of digitization, but rather it is hypothesis driven and seeks to answer specific questions by digitizing selected collections in the most informed manner producing tools that can be used in large scale digitization efforts.


Achieving the expected outcome requires a multi step procedure. First an optimal procedure to non-invasively image a wide range of biological specimens must be developed and validated against destructive methods to measure chemical content. This relies on refinement of methods such as CT and MRI to provide quantitative measurements in preserved samples. Crucial to this task is the acquisition of a specialized CT system in order to image small specimens on a dedicated system. Secondly, any harmful effects of non-invasive procedures must be carefully tested. Thirdly, versatility of digitization methods must be evaluated - what parameters of biological relevance can be extracted, can quantitative measurements be extracted retrospectively, and how can machine learning aid automation processes?

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