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Computers are central in processing scientific data. This data is typically expressed as numbers and strings. Appropriate annotation of bare data is required to allow people or machines to interpret it and to relate the data to real-world phenomena. In scientific practice however, annotations are often incomplete and ambiguous let alone machine interpretable. This holds for reports and papers, but also for spreadsheets and databases. Moreover, in practice its often unclear how the data has been created. This hampers interpretation, reproduction and reuse of results and thus leads to suboptimal science. In this paper we focus on annotation of scientific computations. For this purpose we propose the ontology OQR (the Ontology of Quantitative Research). It includes a way to represent generic scientific methods and their implementation in software packages, invocation of these methods and handling of tabular datasets. This ontology promotes annotation by humans, but also allows automatic, semantic processing of numerical data. It allows scientists to understand the selected settings of computational methods and to automatically reproduce data generated by others. A prototype application demonstrates this can be done, illustrated by a case in food research. We evaluate this case with a number of researchers in the considered domain
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