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'.[....]

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.[....]