This blog post is part of a series on storing uncertain data in nodegoat: 'How to store uncertain data in nodegoat', 'Incomplete source material', 'Conflicting information', 'Ambiguous identities'.

Most scholars think about their research material in terms of nuances, vagueness, and uniqueness, whereas data is perceived as binary, strict, and repetitive. However, working with a digital tool does not mean that you can only work with binary oppositions or uncontested timestamps. On the contrary: by creating a good data model, you are able to include nuances, irregularities, contradictions, and vagueness in your database. A good data model is capable of making these insights and observations explicit. Instead of smoothing out irregularities in the data by simplifying the data model, the model should be adjusted to reflect the existing vagueness, conflicts, and ambiguities.

Before you start to adjust your data model to accommodate uncertainty, you should first try to determine the causes for uncertainty in your data. Most forms of uncertainty in data can be grouped in three categories: incomplete source material, conflicting information, or ambiguous identities.

These types of uncertainty can be dealt with in different ways. The next three blog posts will walk you through a number of possible solutions. The described strategies are not the only possible solutions: each research question is unique and may call for a solution of its own.

Incomplete source material

When the information you need is not available, incomplete, or vague you have to decide if you want to leave the respective parts in your data empty or enter data based on inference or conjecture. Read the blog post 'How to store uncertain data in nodegoat: incomplete source material' to learn how to deal with incomplete source material.

Conflicting information

You might encounter conflicting source material. Two sources might differ about the name of a person, or the date of an event. To account for all possible perspectives, you can include the conflicting statements in your data. Read the blog post 'How to store uncertain data in nodegoat: conflicting information' to learn how to deal with conflicting information.[....]

Next week there will be a nodegoat workshop at the 'DARIAH-EU Annual Meeting' in Gent. This event will take place on 10-13 October. The nodegoat workshop will be on Tuesday 11 October from 14:00 to 15:30. You can find the full program here.

There will also be a nodegoat workshop at the conference 'Creating Spatial Historical Knowledge. New Approaches, Opportunities and Epistemological Implications of Mapping History Digitally'. This conference is organised by the German Historical Institute in Washington DC. The conference takes place on 20-22 October. The nodegoat workshop will be on Thursday 20 October from 14.15 to 16.00. This workshop requires individual registration. The full program of the conference can be found here.

We have proposed a session at the THATCamp Amsterdam on Linked Data challenges. Together with Ingeborg van Vugt we plan to discuss the benefits and difficulties of Linked Data in the humanities.

After a stimulating Virtual Heritage Network conference last year in Maynooth, we look forward to this year's conference in Cork. The conference will take place on 8-10 December.[....]

The following interactive visualisation explores the movements of 10.896 Representatives of the United States Congress, from Roger Sherman's birth in 1721 up until all its members in 2015. The Representatives move from their place of birth to their place of education and finally to their possible place of death. Click here to open the interactive visualisation.

Last April, we gave a talk at the tenth Historical Network Research workshop in Düsseldorf about the 'Reversed Classification' functionality in nodegoat. To illustrate what you can accomplish with this functionality, we queried Wikidata to get a dataset of all the members of the US House of Representatives, including their date and place of birth and death, their professions, and the institutes where they took their education. We used this data to perform a reversed classification process that groups the representatives into career politicians or politicians with a heterogeneous career. From there, you could start looking at geographical patterns or educational backgrounds of these groups. See a graph of this network with these two 'career' nodes included here (canvas).

The diachronic geographical visualisation of all this data in nodegoat turns out to be a nice bonus.

We have taken data available in Wikidata and DBpedia on 'Military Conflicts' to create this interactive visualisation in nodegoat:

Wikidata

From the outside, it can be a challenge to keep up with all the developments within the ever expanding universe of wiki*/*pedia. So it's good to be reminded now and then of all the structured data that has become available thanks to their efforts:

This looks pretty neat, especially since Wikidata currently has over 947 million triples in their data store. Since battles usually have a place and a date, it would be nice to import this data into a data design in nodegoat and visualise these battles through time and space (diachronic geospatiality ftw).[....]