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

There are many entities that share a name. This is often the case for cities (e.g. Springfield), or people (e.g. Francis Bacon). When you encounter such a name in a source, the context usually provides you with enough clues to know which of the entities is meant. However, in some cases the context is too vague or the entities too similar to be certain. In these cases you need to resort to interpretation and disambiguation. This is genuine scholarly work, since you always have to interpret your sources.

This blog post will describe a case in which disambiguation is needed. We will use the example of a research process that aims to reconstruct scholarly networks in the 17th and 18th century. In a research process that deals with scholarly networks, the source material will largely consist of citations and mentions in documents.

The disambiguation process will be described by means of a snippet taken from a publication by an anonymous author in 1714 with the title 'An account of the Samaritans; in a letter to J---- M------, Esq;' (ESTC Citation No. N16222).

This blog post uses the data model that was created in the nodegoat guide 'Create your first Type', and will use elements from the guide 'Add External Identifiers', and from the guide 'Add Source References'.

To store 'mentioned' statements, you can use the Type that was created in the guide 'Add Source References' and add a new Sub-Object in which mentions can be saved. To change the model, go to Model and edit the Type 'Publication'. Switch to the tab 'Sub-Object' and create a new Sub-Object with the name 'Mention'. Set the Date to 'None' and Location to 'None'. In the tab 'Description', click the green 'add' button twice to create three Sub-Object Descriptions. Name the first 'Person', the second 'Page Number', and the third 'Notes'. Set the value type for 'Person' to 'Reference: Type' and select the Type 'Person'. Set the value type for 'Page Number' to 'Integer' and set the value type for 'Notes' to 'Text'.

These settings are not set in stone. Adjust them so that they work for your project.[....]

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'.

When you work your way through your source material, you might encounter two sources that deal with the same subject but contain contradictory data. In these cases, you usually have two options: you either choose one of the sources based on its reliability, or you make an interpretation that combines the data from both sources. To account for disagreement in your sources, a third option is to include both statements in your dataset. This blog post will show you how to include conflicting information in your nodegoat project.

This blog post uses the data model that was created in the nodegoat guide 'Create your first Type'. If you haven't set up a data model in your nodegoat environment yet, you can follow this guide to do so. If you already have a data model, you can apply the steps discussed below in your own data model.

The multiple births of John Chamberlayne

The Dictionary of National Biography of 1887 writes that John Chamberlayne was born 'about 1666'.

Leslie Stephen, Dictionary of National Biography, v. 10 (Elder Smith & Co., 1887), page 9. Available at wikisource.

In a more recent lemma, in the Oxford Dictionary of National Biography (2009 version), he is said to be born in '1668/9'.

The first source, published in 1887, does not give any details on the way in which the statement 'about 1666' was formulated. The second source, first published in 2004, states that he matriculated from Trinity College, Oxford, on 7 April 1685, aged sixteen. This fact has allowed the author to conclude that he must have been born in either 1668 or 1669. This assumption rests on two premises: that the matriculation record lists an exact age (and not an estimate) and that John Chamberlayne knew the date of his birthday precisely (which in the 17th century was not necessarily the case).[....]

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'.

You are often confronted with omissions or with inconclusive statements when you deal with historical source material. To let your dataset reflect the nature of your sources, it is important that you include these vague or uncertain statements in your data. This blog post will go over a number of strategies that will help you to deal with these cases in your nodegoat project.

A common scenario is a case where you lack information. When this happens, you can decide to leave a given description, date, or location empty. This gives you the ability to create a filter that finds the objects that have empty descriptions, dates, or locations.

In another situation you might encounter a source that is partially lacking. The source does provide you with some information, but is inconclusive about the certainty of the information. An example of this is the entry on John Chamberlayne in the Dictionary of National Biography:

Leslie Stephen, Dictionary of National Biography, v. 10 (Elder Smith & Co., 1887), page 9. Available at wikisource.

The first sentence of his entry reads: "CHAMBERLAYNE, JOHN (1666–1723), miscellaneous writer, a younger son of Edward Chamberlayne [q. v.], was born about 1666, probably in or near London."

We will discuss four strategies for accommodating this uncertain source: a true/false statement on certainty, a scale on the level of certainty, entering chronology statements, and entering geometries.[....]

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.[....]

In the past years, we have given various nodegoat workshops to groups of scholars and students. Even though the entry level of the participants varied from workshop to workshop there were similar challenges that emerged every time. These challenges can be grouped into the following three questions:

1. What is a relational database?
2. My material is very vague/ambiguous/uncertain/contradictory/unique/special, how can I use this in a database?
3. How do I use the nodegoat interface?

nodegoat Workshop at the University of Luxembourg.

Since most of the workshops we give are nodegoat-specific, we aim to teach participants how to do data modelling from within the nodegoat interface. Because of this, and as a result of the usual time constraints (often half a day), we have to leave the first two fundamental questions largely untouched. To remedy this, we have written two blog posts in which we aim to cover the first two questions. The third question is being addressed in the nodegoat video tutorials, the FAQ & forum, and in the near future the documentation.[....]

Photograph of the staff of the International Institute of Bibliography, writing and classifying records

One of the most obvious questions to start with when working with structured data in the humanities is: what is data? Miriam Posner has captured this challenge in the title of her talk on this topic: 'Humanities Data: A Necessary Contradiction'. Oftentimes, scholars think about their research material in terms of nuances, vagueness, uniqueness, whereas data is perceived as binary, strict and repetitive. The realisation that nuances, vagueness, and uniqueness can also be captured by data in a database is something that has to grow over time.

As soon as we start talk about 'data' it is important to keep two things in mind. First, we should be ready to reflect on the fact that data oriented processes can dehumanise data. This process has been described by Scott Weingart in his essay on digitising and storing holocaust survivor stories. Even though we can efficiently organise large collections of data, the implications of this process have to be taken into account.[....]

Manuscripts and Archives Division, The New York Public Library. Room 100, including card catalogs

At a certain moment in your research process, you might decide that you need to order your material in a structured format. A reason could be that there are too many different people in your body of research and it's becoming hard to keep track of them, let alone their different attributes. Another reason could be that you have repetitive sources, like letters or books, that you want to store and include in your analysis.

In the old days, you would get yourself a card catalogue and start reworking your notes onto these little handy cards.[....]

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.[....]