05thOct2016

Network Visualisations of 38.000 Letters of 19th Century Intellectuals

Every bit of information that is entered into nodegoat can immediately be published through a public user interface. This allows the Encyclopedia of Romantic Nationalism in Europe to instantly publish articles and a wide range of research data. This data also includes a set of over 38.000 letters that can be queried through the public user interface. In this blogpost we discuss the steps we took to allow visitors to dynamically explore this dataset.

The Study Platform on Interlocking Nationalisms (SPIN) at the University of Amsterdam has created a dataset of metadata of over 38.000 letters of nineteenth century intellectuals. This data has been manually entered and imported semi-automatically (geo-referencing and disambiguating people was largely done by hand). Sources include a range of publications of letters, like Breve fra og til Carl Christian Rafn, med en biographi, plus two existing datasets: (1) the metadata of over 18.000 letters of Jacob and Wilhelm Grimm were provided by the Arbeitsstelle Grimm-Briefwechsel Berlin, and (2) the metadata of over 14.000 letters of Sir Walter Scott were provided by the Millgate Union Catalogue of W. Scott Correspondence; courtesy prof. Millgate & National Library of Scotland. The remaining 6.000 letters were entered by hand by SPIN, based on publications of letters of various other intellectuals throughout Europe. This means that the dataset is a combination of a number of personal networks and that we have an overrepresentation of letters sent by the people at the center of these personal networks.

This dataset is part of the Encyclopedia of Romantic Nationalism in Europe (ERNiE). ERNiE will include over 1.500 articles on topics and people associated with the era of romantic nationalism (e.g. Dress, design : Romanian, Karadžić, Vuk Stefanović, Felicia Hemans). ERNiE also includes other materials like monuments, architecture, art, and currency. ERNiE is coordinated by SPIN. The editor of ERNiE is Joep Leerssen.[....]

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06thJun2016

Members of the US House of Representatives - Wikidata

The following interactive visualisation explores the movements of 10.896 Representatives of the United States Congress, from Roger Sherman's birth in 1721 up until all its members in 2015. The Representatives move from their place of birth to their place of education and finally to their possible place of death. Click here to open the interactive visualisation.

Last April, we gave a talk at the tenth Historical Network Research workshop in Düsseldorf about the 'Reversed Classification' functionality in nodegoat. To illustrate what you can accomplish with this functionality, we queried Wikidata to get a dataset of all the members of the US House of Representatives, including their date and place of birth and death, their professions, and the institutes where they took their education. We used this data to perform a reversed classification process that groups the representatives into career politicians or politicians with a heterogeneous career. From there, you could start looking at geographical patterns or educational backgrounds of these groups. See a graph of this network with these two 'career' nodes included here (canvas).

The diachronic geographical visualisation of all this data in nodegoat turns out to be a nice bonus.

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09thJun2014

Reversed Classification

Working with data in the humanities, we’ve noticed that the debate on classifications is often focused on the definition of the classification and not so much on what it identifies. A well known example is of course ‘nationality’, but also a (historical) occupation/capacity and even seemingly unproblematic classifications like ‘the nineteenth century’ pose several problems.

Looking at data from an object-oriented perspective, using predefined classifications seems counterintuitive. Objects should define themselves by means of their varying attributes. Nodes and clusters emerge on the basis of correlation between objects.

Nevertheless, we understand the need to be able to identify these clusters in a structured manner without the need to perform sequences of filters. These ‘structured clusters’ should be able to be ordered, analysed and explored. For this reason, we have taken up the challenge to equip nodegoat with a functionality that allows for the definition of these clustered by means of fuzzy filtering settings. We have defined this process as ‘reversed classification’. Although we have merely conceptualised the challenge, and have yet to implement this, we want to share our ideas behind this.

In general, classifications emphasise a convention of value and vocabulary. The direction of a classification is outward, relating to the convention unidirectionally. In effect, the classification is unable to communicate/negotiate with the network it classifies. The reversal of classification opens up the convention by disclosing its parameters. Reversal allows the classification to be scrutinised, reconfigured and re-evaluate the objects it classifies.

Simply put: instead of identifying classifications and assigning these to objects in a dataset (like ‘sculptor’ or ‘German’), a user defines a multi-faceted filter spanning multiple datasets in which they define any number of parameters that are associated with a classification. This will reverse the classifying process as the definition of the classification is identified by the exchange between parameters of the classification and attributes of the object. [....]

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