Map from the David Rumsey Map Collection, published as a IIIF image. The image has been georeferenced with the Allmaps editor, and the map tiles are generated by the Allmaps Tile Server. Click here to explore this interactive visualisation.

Thanks to the Allmaps project, it is now possible to use any map that has been published as a IIIF image as a background map in your geographic visualisations in nodegoat.

The International Image Interoperability Framework (IIIF) is a set of open standards for publishing digital objects, maintained by a consortium of cultural institutions. The list of institutions that publish their digitised maps as IIIF images is constantly growing. This overview provides a number of examples of available resources. The David Rumsey Map Collection also contains a large number of maps that have been published as IIIF images.

nodegoat Users have been able to use (historical) maps that are published as XYZ-tiles. We have now updated our Guide 'Use a Historical Map' to describe the steps you need to take to use IIIF images as a background map for your geographic visualisations in nodegoat. The Guide uses an example of a historical map published in the Digital Collections of Leiden University Libraries.

On Wednesday 11 October 2023 we will run a nodegoat Workshop at Stockholm University. The workshop will take place between 10.00 and 16.30 at Bergsmannen, Stockholm University. This is an in-person event and registration is required. Registration deadline is 27 September.

Click here for more information and registration.

During the conference 'Graphs and Networks in the fourth dimension – time and temporality as categories of connectedness', jointly organised by the Historical Network Research community and Graphs & Networks in the Humanities, we presented a new nodegoat feature: 'temporally-aware dynamic network analysis'. This new functionality extends the Scope and Chronology Statements.

The Scope functionality is used throughout nodegoat to traverse your data model and select elements to be included in a visualisation, analysis, or export. With the Scope, you can limit or expand your data selection. In a prosopographical analyses you might want to include all educational institutes related to one person, plus all the relations of these institutes, while omitting all other personal relations of a person. Follow this Guide to learn how to configure a Scope.

Chronology Statements that you make in nodegoat allow you to specify what you mean by a statement like 'circa'. Instead of using qualitative statements about vagueness, Chronology Statements provide you with a way of making quantitative statements about vagueness. Chronology Statements also allow you to make relational date statements: 'the date point is between the sending of letter X and the sending of letter Y'. Follow this Guide to learn how to store uncertain dates by using Chronology Statements and follow this Guide to learn how to store relational dates by using Chronology Statements.

The temporally-aware dynamic network analysis functionality makes the temporal options offered by the Chronology Statements available on any level of a Scope. This allows you to apply and pass temporality to time-bound connections in any of a Scope's paths. The dates from Chronology Statements can be sourced from every step in the traversal: ascendant or descendant nodes, and combinations. Selected configurations can be applied on any/all of the connections/edges: outbound or inbound directionality, and combinations.

Example: Academic Connections

With this functionality it is now possible to dynamically generate networks of people who attended the same educational institute at the same time, without specifying any dates in a filter. The temporally-aware dynamic network analysis functionality applies the initial date on every other relationship that appears on a specified path:

Two persons shown having an overlapping academic connection out of four persons.

The obvious benefit of this approach is the scalability of this functionality, as it allows you to quickly scrutinise complex networks based on time-bound connections:[....]

This year the Leipzig Research Centre Global Dynamics (ReCentGlobe) has set up a nodegoat Grow installation to service two multi-year research projects. The project 'Die Produktion von Weltwissen im Umbruch' uses nodegoat to analyse the globalisation of knowledge production by mapping the development of Area Studies and Global Studies in the German context over the past 15 years. The project 'African non-military conflict intervention practices' uses nodegoat to build a comprehensive database of non-military interventions since 2004 by the African Union and by Regional Economic Communities.

As a result of this collaboration, the ReCentGlobe initiative organises a public nodegoat workshop within the framework of the Digital Lab infrastructure. The workshop will take place at the ReCentGlobe institute on 25 July 2023. More information about the programme and registration can be found here.

The project 'GLOBO: Global Bones. Entangled Histories, Transfers and Translations in the Early Modern Age' led by Urte Krass of the Institute of Art History at the University of Bern is looking for a database administrator to collaborate in the creation and development of their database structure in nodegoat and to to maintain their nodegoat platform and data.

The position will start on August 1 2023. More information about the position and on how to apply can be found here.

The TIC-Collaborative project of Ghent University and Maastricht University has published a dataset on international social reform congresses and organisations (1846-1914). This dataset has been created and maintained in a nodegoat installation at Ghent University since early 2014.

The data publication is described in 'Social Reform International Congresses and Organizations (1846–1914): From Sources to Data' that has been published in the Journal of Open Humanities Data.

The data has been published as CSV files in the 'IISH Data Collection' repository and can be downloaded here. The dataset contains 1206 organisations, 1052 publications, 23247 people, 1690 conferences, and 35609 conferences attendance statements. All these statements have been enriched with spatial-temporal attributes which allows for the diachronic and geographic analysis and exploration of the relational data.[....]

nodegoat has been extended with four new features in the past months. These new features were commissioned by three research projects from Switzerland, Slovenia, and The Netherlands. All nodegoat users can now make use of these features.

Data Model Viewer

This feature has been commissioned by the Historical Institute of the University of Bern for the REPAC project.

Overview of the data model that has been enabled in one of the projects of the Repertorium Academicum Germanicum.

When the complexity of the data model that you have implemented in nodegoat grows, it might be challenging to maintain an overview of all the Object Types, Object Descriptions, Sub-Objects, Sub-Object Descriptions, and all the relationships in between these elements. You can now generate an overview of all the elements of your data model that have been enabled in a Project.

Go to 'Management' and click the name of a Project that is listed in the overview of Projects. Set the 'Mode' to 'References Only' to hide all non-relational elements. Set the 'Size' to 'Full Height' to expand the height beyond the size of the window. You can specify a DPI value and download a 'png' version of the generated overview. To enhance the legibility of the graph you can reposition elements by means of dragging and dropping.[....]

The nodegoat Guides have been extended with a new section on 'Ingestion Processes'. An Ingestion Process allows you to query an external resource and ingest the returned data in your nodegoat environment. Once the data is stored in nodegoat, it can be used for tagging, referencing, filtering, analysis, and visualisation purposes.

You can ingest data in order to gather a set of people or places that you intend to use in your research process. You can also ingest data that enriches your own research data. Any collection of primary sources or secondary sources that have been published to the web can be ingested as well. This means that you can ingest transcription data from Transkribus, or your complete (or filtered) Zotero library.

The development of the Ingestion Process was part of the project 'Dynamic Data Ingestion (DDI)' (presented in this workshop series) and builds upon the Linked Data Resource feature (initially commissioned by the TIC-project in 2015 and extended in collaboration with ADVN in 2019).

Every nodegoat user is able to make use of these features. Next to the examples listed below, every endpoint that outputs JSON or XML can be queried. nodegoat data can be exported in CSV and ODT formats, or published via the nodegoat API as JSON and JSON-LD.

Wikidata

The first two guides deal with setting up a data model for places and people, and ingesting geographical and biographical data from Wikidata: 'Ingest Geographical Data', 'Ingest Biographical Data'. A number of SPARQL-queries are needed to gather the selected data. As writing these queries can be challenging, we have added two commented queries (here and here) that explain the rationale behind the queries.

These first two guides illustrate a common point in working with relational data (e.g. coming from graph databases, or relational databases): you need to first ingest the referenced Objects (in this case universities) before you can make references to these Objects (in this case people attending the universities).

A Chronological Visualisation that allows you to explore the distribution in time of the ingested data.

The third guide covers the importance of external identifiers. External identifiers can be added manually, as described in the guide 'Add External Identifiers', or ingested from a resource like Wikidata, as described in the newly added guide 'Ingest External Identifiers'.[....]

We have added various new sections to the nodegoat documentation and have published this via a new publication platform on nodegoat.net: nodegoat.net/documentation. Next to a revision of the existing content, this update also brings documentation on new features such as Ingestion Processes and Reconciliation Processes.

We have also republished the Guides using the same publication platform: nodegoat.net/guides. This makes publishing new Guides much easier, so expect to see new content there as well. We have added one new Guide already: after feedback on the lack of a general introduction to the basic principles of nodegoat we have published the Guide 'Basic Principles'.

The new and existing content can now also be searched via nodegoat.net/search. Use this to find Blog Posts, Use Cases, Documantion Sections, or Guides that mention things like tags or apis.[....]

Social visualisation of a subset of people in the COURAGE registry (in green) enriched with data from Wikidata: publications (in red) and publishing houses (in purple). The size of the nodes of the publishing houses is determined by their PageRank value.

The workshop series ‘Linking your Historical Sources to Open Data’ organised by the COST Action NEP4DISSENT aims to help researchers to connect their research data to existing Linked Open Data resources. These connections will ensure that research data remains interoperable and allow for the ingestion of various relevant Linked Open Data resources.

In two workshop sessions we will discuss the basic principles of Linked Open Data and show you how your project can benefit from this. We will do this by setting up a nodegoat environment and connect this to Linked Open Data resources. Data that has been collected in the COURAGE registry will be used to demonstrate how these connections can be set up. The COURAGE registry can be explored here, the data is available for download here. If you already have a configured nodegoat environment, you can use this during the workshop.[....]

Geographic visualisation of a dataset collected as part of the SNSF SPARK project 'Dynamic Data Ingestion': geographical origins of medieval scholars stored in the university history databases Projet Studium Parisiense, ASFE Bologna, Repertorium Academicum Germanicum, and Ottocentenario Universita di Padova.

nodegoat has been extended with new features that allow you to ingest data from external resources. You can use this to enrich your dataset with contextual data from sources like Wikidata, or load in publications via a library API or SPARQL endpoint. This extension of nodegoat has been developed as part of the SNFS SPARK project 'Dynamic Data Ingestion (DDI): Server-side data harmonization in historical research. A centralized approach to networking and providing interoperable research data to answer specific scientific questions'. This project has been initiated and led by Kaspar Gubler of the University of Bern.

Because this feature is developed in nodegoat, it can be used by any nodegoat user. And because the Ingestion processes can be fully customised, they can be used to query any endpoint that publishes JSON data. This new feature allows you to use nodegoat as a graphical user interface to query, explore, and store Linked Open Data (LOD) from your own environment.

These newly developed functionalities built upon the Linked Data Resource feature that was added to nodegoat in 2015. This initial development was commissioned by the TIC-project at the Ghent University and Maastricht University. This feature was further extended in 2019 during a project of the ADVN.

Workshop Series

We will organise a series of four virtual workshops to share the results of the project and explore nodegoat's data ingestion capabilities. These workshops will take place on 28-04-2021, 05-05-2021, 12-05-2021, and 26-05-2021. All sessions take place between 14:00 and 17:00 CEST. The workshops will take place using Zoom and are recorded so you can watch a session to catch up.

The first two sessions will provide you with a general introduction to nodegoat: in the first session you will learn how to configure your nodegoat environment, while the second session will be devoted to importing a dataset. In the third session you will learn how to run ingestion processes in order to enrich any dataset by using external data sources. The fourth session will be used to query other data sources to ingest additional data.[....]

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.[....]