Within this collaborative project, various data collections were set up in the different use cases, and subsequently converted into Linked Open Data and made available in a joint ATM clairahPilot dataset on Druid (available here). As the datasets established in the various use cases were established and investigated through a shared spatial research approach, it is worthwhile to consider what the potential benefits are of combining these different datasets by means of an overarching data store. While the gathering of the separate datasets yielded specific research questions and results for each distinct discipline, linking these collections together now also offers researchers the opportunity to ask more complex questions that transcend disciplinary boundaries and provide more insights into the connections between the various datasets, and the historical subject matters that they collectively cover.
In our research, we used the dataset by Boudien de Vries (1986), containing a sample of persons from the Amsterdam electoral rolls of respectively 1854 and 1884, stored at the City Archives of Amsterdam (available here). We converted it into Linked Data and standardized three factors: the neighbourhood, the address and the occupation. Thanks to this standardization we were able to link three datasets already available on the web. Via the neighbourhood we related our observations to the population size per neighbourhood, created in the CEDAR project (Meroño Peñuela et al., 2016). Secondly, via the addresses we related both a geographical location on the map as well as a street name (available here). Finally, via the standardized occupation (in HISCO) we related a status value of the occupation (in HISCAM) (Zijdeman and Lambert, 2010). So our first result is a standardized version of De Vries’ dataset. Standing on the shoulders of other data-giants we: (a) visualised the distribution of the elite (b) compared the elite density of neighbourhoods in 1854 and 1884, (c) studied the relation between status and elite density per neighbourhood.
Meroño-Peñuela, A., Ashkpour, A., Guéret, C., & Schlobach, S. (2017). CEDAR: the Dutch historical censuses as linked open data. Semantic Web, 8(2), 297-310.
Boudien de Vries (1986) Electoraat en elite. Sociale structuur en sociale mobiliteit in Amsterdam 1850-1895. Amsterdam.
Zijdeman, R., & Lambert, P. (2010). Measuring social structure in the past: A comparison of historical class schemes and occupational stratification scales on Dutch 19th and early 20th century data. Journal of Belgian History/Belgisch Tijdschrift voor Nieuwste Geschiedenis/Revue Belge de Histoire Contemporaine, 40(1-2), 111-141.
In order to develop a better understanding of the consumption of film by Amsterdam’s historical cinema audiences, we have explored the potential of geospatial research and digital mapping practices in this use case (Horak, 2016), in order to make connections between cinema-related information and data on the socio-demographic composition of the historical city’s neighborhoods. An initial literature inventory of sources concerning Amsterdam’s past and present film venues (e.g. Van Bueren, 1996 & 1998), cinema entrepreneurs and historical practices of moviegoing laid the groundwork for this. On the basis of this, we started mapping Amsterdam’s historical cinema locations together with film-related variables for the period between the years 1907 and 1928, which respectively mark the establishment of the first permanent film theatres and the introduction of film screenings with sound in the Netherlands (Dibbets, 1993). Within the project framework, georeferenced and vectorized historical maps of Amsterdam were made available, on which we projected collected data concerning cinema locations and characteristics. By converting these data into Linked Open Data, the locations of film venues were connected to lists of historical Amsterdam neighborhoods and addresses, and the geographic coordinates contained in Cinema Context were replaced by more precise location points. By additionally combining this with data on the socio-economic composition of city neighborhoods (e.g. rental prices of houses, population development and density), we traced the correlations between cinemas’ locations and characteristics, and the socio-demographic profiles of the neighborhoods in which they were located.
There are many primary and secondary sources and recordings (of speakers born in the 19th century) of the language spoken in Amsterdam in the 19th century. Until now, our knowledge was fragmentary and incomplete, because sources were mostly studied in isolation. This use case was the first to link a large amount of data in order to make a reconstruction. In previous publications it was stated that there was an abundance of dialect variation in historical Amsterdam: according to the 19th-century linguist Johan Winkler and the historian Jan ter Gouw there was a whopping number of nineteen dialects spoken in 19th-century Amsterdam, distributed over various neighbourhoods. One of the questions we wanted to answer was whether it is possible to reconstruct this large amount of dialect variation. Alongside these neighbourhood-specific dialects there were also sociolects spoken throughout the city, roughly those of the lower, middle and higher social class. In the database we have collected 8,020 data points, and we categorized them into the following language domains: words, names, idiomatic expressions, speech sounds, word formation, syntax, songs, speech recordings. For each entry we recorded the information given in the original source, such as meaning, informant, district (dialect) or street were it was found, or sociolect. Furthermore, we enriched the entries with various information.
Below is a top 6 of the dialects from the Lexicon. Clearly, most entries are attributed to 'Bargoens', also known as the language of thieves. Sometimes words have just a different pronounciation, but below are examples of words and phrases that are uncommon to the Dutch language.
Top 6 dialects in Amsterdam Time Machine lexicon
The Lexicon data are modeled accoring to the Lemon model. Because this model is applied to various other lexicons, it is easy for developers, researchers and even machines to extract information from. In the beginning it might be a little hard to understand though. The query below shows the most important features that can be retrieved from the data. For more information see the lemon documentation.
A view on the available characteristics in the data
This data story was created by the CLARIAH Amsterdam Time Machine project.