nodegoat Workshop at the Historical Network Conference in Lisbon on 16-9-2015

nodegoat workshop at the eighth HNR workshop Vom Text zum Netzwerk und zurück. Über die Wechselwirkungen im historischen Forschungsprozess 5/6 April 2014.

During this year's Historical Network Research conference in Lisbon 15-18 September, we will host a nodegoat workshop. The title of this workshop is: Conceptualise and Set Up a Historical Network Research Workflow. We will focus on conceptualising a data model for your own research question and explore the possibilities of storing your data structurually and creating interactive space/time visualisations. The workshop will last a full day and will take place on 16 September.

As nodegoat is a web-based data modeling and management tool that is equiped with functionalities to produce time-aware network analytics and visualisations, it is well suited for historical network analysis.[....]

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Mapping Memory Landscapes in nodegoat, the Indonesian killings of 1965-66

nodegoat is developed as a collaborative research environment that supports participatory research projects. To test its ability to combine various participatory roles with its ability to digest complex and heterogeneous data, we spent two weeks in Semarang, Indonesia working with a group of students to reveal an infrastructure of violence. These students interviewed survivors of state-sanctioned violence and entered the information they gathered directly into nodegoat. Based on these interviews, the students visited a number of sites and interviewed people who lived or worked on these sites. As the data came from personal accounts only, the visualisations that are produced in nodegoat can be characterised as memory landscapes. In this blog post we will describe both the process and the methodology of this project.

The Dutch Institute for War, Holocaust and Genocide Studies (NIOD) has set up a cooperation with the Universitas Katolik Soegijapranata (UNIKA) in Semarang, Indonesia that aims to address the anti-communist/leftist violence of 1965-66 in Semarang and the following years. The project that has emerged from this cooperation, ‘Memory Landscapes and the Regime Change of 1965-66 in Semarang’, is led by dr. Martijn Eickhoff (NIOD) and has resulted in two workshops at the UNIKA University in Semarang organised by Donny Danardono. The first workshop took place in January 2013, the second workshop was held in June 2014. During these two workshops students from UNIKA collected data on anti-communist/leftist violence by combining oral history and anthropological site research. The data includes relations between people as well as locations connected to the events of 1965 and the following years (e.g. places of mob violence, temporary detention, interrogation, torture, murder and mass burial).  [....]

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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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