Preliminary Programme

Our understanding of ill-health in the United Kingdom in the nineteenth century tends to rely on sources concerned with mortality rather than morbidity. However the connection between the two cannot be taken for granted. The ultimate cause of death was not always, or perhaps not often, the reason for incapacity at work. The analyses that focus on non-fatal conditions are frequently limited by being specific to dangerous or noxious trades, such as mining; by a lack of geographical specificity; or by using data on sick-leave duration rather than on the health conditions themselves. This is a problem as the period in question saw an ‘epidemiological transition’ in which mortality rates declined while episodes of ill health increased.

One way of understanding the significance of morbidity is to identify the frequency of incapacity in the workforce. To address this question, this paper examines the health of workers in the Metropolitan Police c.1870-1900. It primarily uses data on the frequency and timing of ill health, causes of sick leave and early retirements recorded by its chief surgeon. The police force was organised into local divisions which each provided health data, allowing both a temporal and a spatial analysis of employees’ health conditions throughout the period using geographic information systems techniques. Comparisons with other local sources, such as other employers and public health data from the reports of London’s medical officers of health, are discussed. This research adds considerably to our understanding of health in late nineteenth-century London. (Show less)

In pre-modern times marriage was a pre-condition for procreation for the vast majority of the population. Ages at marriage differed between societies, social groups, and economic sectors within Europe. Ages at the birth of the first child differed accordingly, but was the time interval between marriage and first birth constant? This paper intends to test the hypothesis that there was a negative correlation between the female age at marriage and the first birth interval: the later a woman married, the shorter the time interval to the first birth was.
Possible reasons for this relationship are: missing fecundity for very early marriages, pre-nuptial conceptions in late-marriage societies, time pressure in late marriages (if you want more children, you cannot wait), negative effects of arranged marriages in young couples, and fertility differences between societies were not as large as differences in ages at marriage.
The data of the Mosaic and NAPP projects allows to compare hundreds of European regions in this respect. This data is only cross-sectional, so event-history analyses are not possible. The analysis will use the difference between the singulate mean age at marriage (SMAM) and the singulate mean age at first birth (SMAC), which is calculated according to the SMAM procedure.
First analyses confirm this hypothesis, not only on a European level, but also on the level of European macro-regions.

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This research aims at developing and utilizing a knowledge graph in the field of Dutch maritime data. Such data currently can be scattered over several datasets, have different semantics over the variables, and can have limited availability. However, these problems are not inherently solved by creating a linked data knowledge graph. The linked data still has to be accessible and usable by domain researchers.

The Vereenigde Oostindische Compagnie (VOC) was a large trading company founded in 1602 in the Netherlands. The VOC created and curated several written logbooks. These logbooks covered, for instance, the sailors who ventured on the journey to the East Indies and who might never return [1]. The logbooks also cover the cargo shipped by these journeys. Some rather innocent, such as spices, while others less so [2]. These logbooks were eventually converted to a digital format by efforts of archives and research institutes. By diving into these datasets, researchers can compose ‘stories’ about the people who lived then or connect the dots and discover some greater coherent theory. Publishing and managing these datasets in an open and structured method boosts research projects’ feasibility and value. Sharing data in standard formats such as XML and CSV is possible but comes with downsides. One is that the interpretation of datasets and files is not always straightforward; variables and values can be interpreted in different ways.

A knowledge graph can provide a solution to these problems. It can drastically reduce the time a researcher has to spend browsing and preparing different datasets. Instead, queries over multiple datasets linked in a knowledge graph can be answered almost instantly, even with the most complex questions. An additional benefit of a knowledge graph is the notion of a common vocabulary. In a common vocabulary, concepts, classes and properties are well defined and structured. Sharing information in a knowledge graph is easier for machines as well. With the use of the standardised SPARQL query language, multiple graphs can be queried simultaneously.

The research paper and the knowledge graph are validated by satisfying the competency questions of domain experts. The utility of the knowledge graph can be shown if these competency questions can be answered by querying the graph. A domain expert provided the first draft of competency questions:
- Which VOC Chamber was accountable for the highest number of slaves transported?
- How were the shipping routes in Asia divided between the various VOC chambers (for example, did ships from a specific chamber only sailed on certain routes)?
- What was the average value of cargo on VOC return voyages per crew member/ship's ton, and how did this evolve over time?
- To what extent was the value per ships' ton on return voyages correlated with the skipper's track record (how much experience, i.e., how many previous voyages / how quickly did a ship get to Asia on an outbound voyage)?
Other competency questions related to the development of the knowledge graph can also be verified, such as: “What is a sustainable method of developing a knowledge graph related to Dutch maritime data?” and “How can a useful knowledge graph be developed for Dutch maritime history?”.

The Dutch maritime data was provided, curated, and described by the researchers of the Huygens ING and by the Dutch National Archives. The common vocabulary used thus far is an adjusted version of the CIDOC CRM.

[1] https://www.nationaalarchief.nl/onderzoeken/zoekhulpen/voc-opvarenden
[2] http://resources.huygens.knaw.nl/boekhoudergeneraalbatavia

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