Out of the 32 datasets, 14 contain data on the variable occupation. Most of these datasets are either prison or military related, yet also two slave-datasets stand out. The query also revealed that the completeness of the variable differs per dataset. For nine datasets, a value for occupation was nearly always available, yet for others this was only 7 to 60 percent.

Individual datasets were then loaded into RStudio to trace the spelling variants of the five occupations (see R-script). There were actually 15 datasets that registered occupations, but two of them were unusable. The ‘Cuba Army Recruits’-dataset differentiates between occupational types (unskilled, professional,...) that are not registered as occupation in microHeights, which explains why they do not show up in the above script (remove filter # on line 19). Accordingly, in ‘UK: Prisoners […] at Wandsworth’ codes from 2-6 were used referring to the Armstrong classification (Horrell et. al (2009)). For the five selected occupations, we manually sifted through these individual datasets to trace variations on spelling, capitalization, and abbreviation (see R-script).

Initially, we intended on using the occupations-URI in the microHeights dataset as UID to link our cleaned file with. Hypothetically, each of the different spellings found in an individual dataset had their exact twin as URI in microheights, like: /occupation/#spelling_variant. But this was not the case. For example, ‘CORDONNIER’ appeared all-capitalized in the individual dataset, but in microHeights only '/occupation/cordonnier' existed. Nonetheless, different URI’s existed for uncapitalized '/occupation/trabalhador' and capitalized '/occupation/Trabalhador'. Another telling example was ‘dienstmagd’ from the dataset ‘Czech Prison Repy’. Applying the filter on line 20 in the query above, SPARQL refers to 114 observations for ‘dienstmagd’ in this dataset. However, when we explored the raw data in R (see R-script) only 10 were counted. Variations in spelling include ‘d’, ‘D’, ‘dienstm’, ‘Dienstm’ and ‘DienstmÃ¤dchen’, but in total these sum up to 124, implying that 10 observations are lost in microHeights. This suggests that the conversion of raw occupational data to microHeights’ URI’s did not happen in a systematic manner, or in one that we are not aware of. Consequently, we only standardized URI’s of occupations that we did find in microHeights

Ruben Schalk kindly linked microHeights with our cleaned occupation 'keys' using URI_MH as UID. The query below provides an overview on how the observations in the standardized data and used filters compare to the original datasets per birth decade. We filtered out all females, and adulthood is (broadly) defined being from ages 20-55 years.