Digital Support for Heritage & Research: Slot 2
Het tweede timeslot bestond uit presentaties van Thomas Vermaut, Richard Zijdeman, Merel Geerlings, Enno Meijers en Ingeborg van Vugt. Beschikbare video's, abstracts en slides hiervan zijn op deze pagina te vinden.
General introduction about the digital tools and datasets developed by CLARIAH for research & Data Stories
Data Stories facilitate the creation of stories (scientific output) based on the analysis and often visualisation of events (data points) in data sets. Scholars that create data stories, can use queries on datasets and visualisations of the results or use low-level access methods such as APIs via Jupyter notebooks to do their analysis, sometimes referred to as “programming with data”. The Data Stories can be published in a way that an end-user can run and adapt the queries (requiring a run-time environment) or in written form (e.g. as a blog) that summarises and visualises results from a notebook (and provides the original Jupyter notebook as a provenance reference).
Disclosing Domestic Servants Registers as Linked Open Data
Domestic Servant Registers (DSR) have been an important source to study women's work. It is one of few sources, that specifically address the occupational activities of women and gives insight in patterns of migration. Moreover, DSRs are fine grained sources providing near realtime information on the house address level and are available for multiple countries and decades making them excellent sources for research (datasets). Historians and sociale science history researchers have used DSRs abundantly, but often only for a specific town or for a sample of individuals from a particular region. The reason for this, is that uncovering civil servant registries is labour intensive and registries have different formats making it harder to link regionally dispersed registers. Focussing on the domestic servant registries of 1887-1909 in the Dutch town Harderwijk, we show how a national or even international network of civil registries may be build using Linked Open Data. Borrowing from Schema.org and other vocabularies we show how multiple registries can be spatially and temporally connected. Additionally, we illustrate how observations from DSRs can be connected to other sources, such as civil registers, enhancing life course analysis on for example social inequality, marriage patterns and migration.
Welcome Records in Contexts! A new approach to archival accessibility
Records in Contexts is a new description standard for archives. It uses Linked Data techniques to offer the archivist better ways of describing archival material, both analogue and digital born. Because of that users of archival sources benefit even more: the chance to navigate seamlessly between different sources, and perhaps finding information they did not know they were looking for.
The Terminology Network is a search engine for terms. You enter a search query there - such as "Rembrandt" - and the Terminology Network then searches directly, in real time, terminology sources for matching terms. From the terms this returns, you can make a selection. For example, 'Rembrandt' in RKDartists or 'Rembrandt Harmensz. van Rijn' in the Dutch Thesaurus of Authors' Names. The Terminology Network is designed to be easily integrated into existing collection registration systems and new sources can be easily added.
Ingeborg van Vugt
Digital approaches to the Republic of Letters: network analysis, transparency and replicability in historical research. When computation is an intrinsic part of your research, it is important to publish a scholarly argument in a way that makes the code as accessible, transparent and readable as possible. This presentation aims to show a set of Jupyter notebooks that were created in the context of the ERC consolidator project ‘Sharing Knowledge in Learned and Literary Networks – the Republic of Letters as a Pan-European Knowledge Society’ (SKILLNET). The Jupyter notebooks show how specific historical research questions can be explored by analysing data from early modern letter collections, providing step-by-step instructions to replicate the research – starting from the data processing up to the final network visualisations and analyses. This does not only give greater validity to the findings but also allow other researchers to build on the analysis with their own data, and compare results and methodologies.