nodegoat is conceptualised and built by LAB1100. 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. Click here to request a hosted nodegoat account. It is also possible to run nodegoat on your (institute's) server. Send an email to firstname.lastname@example.org to inquire about setting up an institutional partnership.
The nodegoat user forum is hosted on the Historical Network Research website: nodegoat user forum. On this user forum you will find a nodegoat FAQ that helps you get familiar with the basic nodegoat functionalities: nodegoat FAQ.
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.
In nodegoat users 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 relational networks between objects in your datasets. Each object can be supplemented with geographical and temporal attributes, making diachronic geographic and social visualisations of your datasets directly available.
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, every change made to the data is stored and documented; users can work on different aspects of the datasets together.
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 and can be complex or simple. Each filter can be stored and re-used by other users and can be used for various functionalities and administrative tasks within a research project.
nodegoat guides the analysis of complexity in datasets by mapping relational paths formulated by the data model. nodegoat guides the exploration of datasets by testing conditions formulated by the user.
nodegoat allows users to define in-text references to any object in your dataset. nodegoat will save this reference as a relation between text and the tagged object.
nodegoat fully integrates source documentation and annotation (e.g. management of bibliographical data) in order to properly reference datasets. It is possible to save an extensive list of sources for each object and each object description. This functionality will build a bibliographical file in nodegoat and link books, journal articles and other sources to entered data. Facilitating cases of complex or conflicting sources, users are able to reference sources per object description.
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 CSV and JSON. nodegoat's API provides access to all of nodegoat's core functionalities, such as: project-based access to data and data model, user authentication, filters, conditions, relational path-based output, and URI management.
nodegoat integrates and assists linked data connectivity. Users can configure various SPARQL endpoint and API resources for easy and consistent usability. Linked data resources can be dynamically queried and filtered to store data or establish URI-based links.
nodegoat allows users to share data and research outcomes with a large audience by means of interactive public user interfaces. Public user interfaces can be configured to provide access to published data (specific datasets or analytical configurations and visualisations), or be of more experimental nature and provide open access to a project's research data.
Bree, P. van, Kessels, G., (2013). nodegoat: a web-based data management, network analysis & visualisation environment, http://nodegoat.net from LAB1100, http://lab1100.com