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.[....]