Data modelling in nodegoat

Data modelling in nodegoat is object-oriented. A data model consists of objects (which can contain sub-objects for spatial and temporal information) and categories (classifications). Due to the open data structure of nodegoat, it is possible to either adapt a data model to a reference model (e.g. CIDOC) or to design a specific data model for more complex research questions. Check out the following example on data modelling in nodegoat for historians:


1) Objects

E.g. persons, events, institutions, texts, manuscripts, ideas, pictures, maps, etc.

Objects are used to basically organize the data. In addition, the objects can be described with object descriptions. For this purpose, different field types of Object descriptions are available:

2) Sub-objects in which you can enter dates (point, period, vague dates) and places.

The Object descriptions can also be used for the Sub-Objects.

The main purpose of the sub-objects is to contextualise the objects in time and space.

3) Categories

Difference between Objects and Categories? Objects are classified  with Categories. Example: An Object can be a person, and a Category the person’s profession. So easy? Yes. Objects and Sub-Objects can further have descriptions (Text, Images, Links, Relations). Sounds a bit like Excel to you? Yes. Objects are similar to columns in Excel, descriptions similar to rows. And Sub-Objects? This is special in Nodegoat, like you store in Excel Locations and Dates for a column. So if you have data in Excel or in a smiliar format, it must be quite easy to import it into Nodegoat? Yes, if your data has a clean structure and is consistent.

Are there any mandatory requirements or restrictions for data modelling in Nodegoat?

No, you can define your model individually, adapted to the research questions.

Should you adapt my model to existing standards, such as the CIDOC reference model?

That is possible, yes. Nodegoat can generate and provide standardised data and thus improve the interoperability of research data. However, it is advisable to first adapt the data model to the sources and to the research questions and to consider what new insights can be gained with this data model (and the data collected and visualised in it)? So you should ask yourself first, whether your data model is consistent and uniformly structured and, above all, whether outsiders understand what you want to represent and say with your data model and the data. A secret tip: Think from the end. What new insights do you want to present with your data and how do you want to visualise them? What should a map, a network or a time series with your data look like so that outsiders can understand it?

In summary, nodegoat is equipped to handle Linked Open Data, making it a suitable platform for researchers and academics who want to integrate external linked datasets into their projects and conduct comprehensive analyses across interconnected data sources.

But where should you start? Which objects and categories should be defined and how named?

That is the key question. Objects and Categories are on the same level at the beginning. Now, by creating relationships between the two (or between Objects), we form a structure (hierarchy) that represents our data model. Secret tip: First find about 5 Objects that are important for your data model. These are the archetypes of your model, the central types. If you are interested in people, you will create an Object ‘Person’. If you are researching books and their contents, then the object ‘Book’ is central to you. Pro tip: don’t make the Objects too small. For example, if you are interested in artists, it is better to create a ‘Person’ Object and a ‘Profession’ category where you can enter ‘Artist’. This way you can enter not only artists in the ‘Person’ Object, but also patrons or people related to the artists. This gives you an overview of all the people in your project in the ‘Person’ Object. And it prevents redundancies, for example if an artist is also a patron. Pro-tip: You can of course also use ‘Object’ as a name for the type Object and use it to record a wide variety of things (archaeological finds, manuscripts, books, pictures, maps, etc.).

A question that is often asked: Can I export all the data I have entered in nodegoat completely at any time?

Yes, either as CSV data, as ODT (Open Document Text) or via interface in JSON format (JSON export depends on the type of nodegoat installation). You can also export your data model and share it with the community. See the Tutorial on this blog how to do it.