Linked Data vs Curation Island

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You can now use nodegoat to query SPARQL endpoints like Wikidata, DBpedia, the Getty Vocabularies (AAT, ULAN, TGN), and the British Museum. Through the nodegoat graphic interface you query linked data resources and store their URIs within your dataset. This means that you can search all people in Wikidata using the string 'Rembrandt' and select the URI of your choice (e.g. 'https://www.wikidata.org/wiki/Q5598'). By doing so, you add external identifiers to your dataset and introduce a form of authority control in your data. This will help to disambiguate objects (like persons/artworks with similar names) and also enhances the interoperability of your dataset. Both these aspects make it easier to share and reuse datasets.

These two advantages (data disambiguation and data interoperability) are useful for researchers who work on small(-ish) but complex datasets. Researchers who feel that 'automated' research processes are unattainable for them as their data may be dispersed, heterogeneous, incomplete, or only available in an analogue format, are more likely to rely on something like the old fashioned card catalogue system in which all relevant objects and their varying attributes and relations are described. Luckily, we can also use digital tools to create and maintain card catalogues (databases). For a historian who is mapping the art market of a seventeenth century Dutch town, a database is a very powerful tool to store and analyse all objects (persons, artworks etc.) and the relations between these objects. Still, if no external identifiers are used, this dataset is nothing but a curated island (even if the data is published!).


Curation Island

Curation & Linked Data

The process we describe here aims to connect the craftsmanship of research in the humanities to the interconnected world of massive repositories, graph databases and authority files. Other useful purposes of linked data resources for the humanities have already been described extensively, like using aggregation queries to analyse large collections, thesaurus comparison/matching, or performing automated metadata reconciliation as described by the Free Your Metadata initiative.[....]

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nodegoat as an Interactive Museum Installation: 20.000 letters visualised through time and space

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The installation is located in the first section of the permanent exhibition. The wooden table has a cut-out (elevated) map of Europe as its surface. The visualisation is projected by a Barco F35 projector (WQXGA resolution). Visitors can interact with the installation by means of capacitive sensors.

We have developed an interactive installation for the new GRIMMWELT museum in Kassel, Germany. The installation visualises and lets visitors freely interact with the full correspondence network of Jacob and Wilhelm Grimm, involving a total of 20.000 letters and 1400 correspondence partners in a timespan of 80 years. The dataset of letters has been created by the Arbeitsstelle Grimm-Briefwechsel at the Institut für deutsche Literatur of the  Humboldt-Universität zu Berlin. We have developed the visualisation in cooperation with SPIN: Study Platform on Interlocking Nationalisms at the University of Amsterdam.

The installation implements a new geographical visualisation mode 'Movement' in nodegoat, in addition to the already available line-based 'Connection' mode. The Movement mode uses WebGL rendering (GPU) to animate large collections of objects smoothly. This mode also allows for a wide range of configuration parameters to finetune the visualisation to various scenarios. Due to the open and generic nature of nodegoat, we can now make use of the Movement mode for any other relevant dataset.

This short clip shows the new visualisation mode from within nodegoat:


A high resolution 1440p version of this clip is available here.[....]

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nodegoat Workshop at the Text Encoding Initiative Conference in Lyon 26-10-2015

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Cheveux © Marie-Jeanne Gauthé, via http://tei2015.huma-num.fr/en/.

During this year's Text Encoding Initiative Conference in Lyon, from 26 to 31 October 2015, we will host a nodegoat workshop. The workshop will last a full day and will take place on 26 October. Register here.

In this workshop we will support participants to employ explorative visualisations based on their own TEI data by means of nodegoat. A good example of how nodegoat can be used to create, manage, visualise, analyse and present structured data is the project on romantic nationalism by Joep Leerssen of the University of Amsterdam. The public interface of this collaborative research project can be consulted via http://romanticnationalism.net, or read more about it in the brochure (PDF).[....]

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

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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

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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

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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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Naming Objects: Plain vs Dynamic

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The accessibility and flexibility of nodegoat allows for a collaborative and ongoing data entry and data curation process. The experience learns that data consistency becomes a challenge as soon as data entry processes become collaborative or are executed over longer periods of time. Especially when the data structure is complex and data sources are ambiguous, consistency is an increasingly prominent factor. To ensure uniform identification of each object within the dataset, the name of an object should both be consistent and inclusive.

Within nodegoat the name of each object can be a plain text field, generated dynamically, or a combination of the two. When generated dynamically, the object name can be build from its definitions for consistency and include the definitions from other named objects for inclusiveness. A rather exhaustive naming scheme for a painting could look like this:

painting = title + material + artist (person = last name + first name + year of birth + location of birth (city = name + country code) + year of death + location of death (city = name + country code)) + year)

This produces:

'Man in oriental costume (oil on panel) - Rijn van, Rembrandt (1606 Leiden (NL) - 1669 Amsterdam (NL)) - 1632'

By generating object names dynamically, changes in named objects (such as artist and city in the example of the painting) are also reflected accordingly in the name of the objects.

Due to the unrestricted relational nature of the naming algorithm there is a potential problem for recursion. Recursion can be introduced directly (e.g. the name of a person includes the name of the person's parents) or further down the naming scheme. By limiting recursion to a single step it is possible to leverage this feat and include family ties within a person's name without running into an infinite loop.

In a future blog post we will discuss the possibility to complement the dynamic generation of object names with conditional formatting.

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Geographic visualisation of biographies of scholars. Tobias Winnerling (Heinrich-Heine-Universität Düsseldorf), project: "Wer Wissen schafft. Gelehrter Nachruhm und Vergessenheit 1700 – 2015".

Social Network Graph of the network around Dutch engineer Cornelis Meijer. Project: "Mapping Notes and Nodes in Networks".