nodegoat is a web-based data management, analysis & visualisation environment.
Using nodegoat, you can define, create, update, query, and manage any number of datasets by use of a graphic user interface. Your custom data model autoconfigures the backbone of notegoat's core functionalities.
Within nodegoat you are able to instantly analyse and visualise datasets. nodegoat allows you to enrich data with relational, geographical and temporal attributes. Therefore, the modes of analysis are inherently diachronic and ready-to-use for interactive maps and extensive trailblazing.
In order to share the functionalities of nodegoat with the scholarly community, scholars and research institutes are invited to use nodegoat for their own research purposes. Send an email to email@example.com to discuss using nodegoat for your research projects.
nodegoat allows scholars to build datasets based on their own data model and offers relational modes of analysis with spatial and chronological forms of contextualisation. By combining these elements within one environment, scholars are able to instantly process, analyse and visualise complex datasets relationally, diachronically and spatially; trailblazing.
nodegoat follows an object-oriented approach throughout its core functionalities. Borrowing from actor-network theory this means that people, events, artefacts, and sources are treated as equal: objects, and hierarchy depends solely on the composition of the network: relations. This object-oriented approach advocates the self-identification of individual objects and maps the correlation of objects within the collective.
nodegoat can host multiple projects with different relational data models and manage users with various privileges and project affiliations. With the integration of version history and dynamic discussion fields, users can work on and discuss different aspects of the datasets without losing any data in the process.
Scholars define their own data models freely and dynamically with no limitations to relational structures or depths. This model allows for filtering and analysis of complex relations between objects in your datasets. Paths between different objects can be analysed to expose relational networks. As each object can be supplemented with geographical and temporal attributes, diachronic geographic and social visualisations of your datasets are directly available.
nodegoat is capable of processing complex queries. In nodegoat you query your data by means of filtering functionalities. These filters are based on the data model that has been set up and are as complex or as simple. Also, these filters will go as deep as the connections that can be made within the data model. Each filter can be stored and re-used by other users and can be used for various functionalities within a research project. For instance, once a filter has been stored a user can ‘follow’ a filter and be notified when an object matching this filter has been added or modified.
nodegoat allows scholars to define in-text references to any object in your dataset. Example: should you include a transcript of a meeting, you can 'tag' people or organisations mentioned in this transcript. The database will save this reference as a relation between the object of the person/organisation and the object of the meeting.
nodegoat is built to be fully platform independent. It is possible to import complex and relational datasets from file and to export clean relational datasets in standard JSON or XML formatting.