nodegoat Tutorials

The tutorials about the virtual research environment (VRE) nodegoat are intended for the users of nodegoat Go, which is available for students and research at the University of Bern. But the tutorials also work with other nodegoat installations.

For those in a hurry, skip tutorials no. 1-9 and start right away with tutorial no. 10: In this tutorial a short video (no sound, no comments) shows from scratch how to create a simple project with a data model and how to import and visualize data. For the tutorial you need a nodegoat account and the test data set I provide on this website. If you don’t have an account yet, ask your friend where to get one. Or your institution (university), if they provide nodegoat as a digital tool. If you are studying or working at the Faculty of Philosophy and History at the University of Bern, you can get a nodegoat account here: Or get a student account directly at nodegoat:


1. Getting started: create your first project in ‘Management’ /  add Object Types in ‘Model’ / activate the Object Types in ‘Management’ / work with the Object Types in ‘Data’

See further information about ‘Types’:


2. Your first visualization: Locations must be stored in the Sub-Object of an Object Type

See further information about Locations:   (Working with Spatial Data)


3. Entering dates: Dates must be stored in the Sub-Object of an Object Type

See further information about dates: (Working with temporal Data)


4. Entering vague dates: Vague dates must be stored in Chronology

See further information about vague dates: (Working with temporal Data)


5. Importing Locations with CSV data: Geo coordinates must be stored in Sub-Objects

Sample CSV data: Locations (40k) with and Geo Coordinates:


Hint: In nodegoat are already about 130k locations from preinstalled (Type: City). These locations can be used and extended by all users as a collaborative work.

See further information about importing data: (Import Data)


6. Create your first relation: Relations (Categories) are created in the Model to be used in Data

See further information about relations: (Add a Related Type)


7. Create your first Classification (Category): Classifications are created in the Model to be used in Data.

See further information about Classifications: (Add a Classification)


8. Expand your Data Model: Person with ‘Event Birth’.

There are many ways in nodegoat to model your data. See a different Data Model: (Create your first Type, Correspondence Networks)


9. Change the background map

In nodegoat you can integrate background maps as you like, if the maps are available on a tile server via link. So you need only this link to the map. But where can you find such links? Google is your friend. Or this short tutorial. Go in nodegoat to the Visualisation Settings.

Then go to the Visual Settings tab. You will automatically get to the Geographical Settings.

Standard map is the Google Map with its copyright. Remove the Google Map link in the Map field and insert your new link for your map. Change the copyright as desired.

Save your Map settings:

I have compiled some links here. Don’t be afraid of the length of the links, they are just like this, sometimes longer, sometimes shorter:

Google Map


Grey map without places


Digital Atlas of the Roman Empire{z}/{x}/{y}.png

See for this cool project:

Mercator map from 1607{z}/{x}/{y}.png?key=mpcE7jAf5llCJV0hoUfk

The example of the Mercator map refers to the Georeferencer, a service for online maps, where you can find a lot of links to historical maps. Many institutions have their own account at Georeferencer like the David Rumsey Collections with a useful overview of the referenced maps (world map):

Create an account at Georeferencer to get the link for a map provided (for example by the David Rumsey collection). Log in at Georeferencer, choose a map here:

Then go to: ‘This map’ and to ‘get Links’. Copy the link into the Map field of nodegoat.

As another example, the British Library also has an account at Georeferencer. You can find their maps here, on the interactive map:


10. Import your Excel data (CSV data), which have geo coordinates (longitude and latitude) into nodegoat and visualize the data on a map

In this tutorial I provide a set of test data with geocoordinates that you can easily import and visualize, like the map below. It shows positions of ships calculated from logbooks (18th – 19th century). The data are especially interesting for historical climate research.

Prerequisite is that you already have a nodegoat account. If you don’t have an account yet, ask your friend where to get one. Or your institution (university), if they provide nodegoat as a digital tool. If you are studying or working at the Faculty of Philosophy and History at the University of Bern, you can get a nodegoat account here: Alternatively you can get a student account directly at nodegoat:

The following video (no sound, no comments) shows a step by step guide from scratch. The video starts with the login into your nodegoat account. The next steps are: Create a project, create an object type, download data sample from this website, import and visualize the data:

If you prefer written instructions, you can continue here. These instructions are identical to the video, but contain some background information.

Login into your nodegoat account. Import the CSV data into your already existing project or create a new one: ‘climate project’. We will import data (sample) from a cool project about logbooks of ships which are important for weather observations. Here is the website of the project where the data is available: Database for the World’s Oceans (CLIWOC)

“The database consists of 287,114 logbooks written aboard Dutch, English, French, and Spanish sailing ships. The vast majority of these logbooks date from between 1750 and 1850, yet four ship logbooks were incorporated that predate 1750. These were centuries of European imperial expansion, and so the logbooks record the activities of sailors – both civilian and military – in oceans that span the entire globe.”

I have downloaded the following data: ‘Download as an Open Office Spreadsheet’

I opened the spreadsheet in Excel and first added a column on the far left to give the records (rows) a unique identifier, because they don’t have any. I’ve added for this the following into field A2 in Excel which contains the first record: =TEXT(ZEILE(A1);”000-000″). Then double-click on the icon to the right of the cell and it fills the whole column with identifiers. The identifiers are very important. With these identifiers you can later update your data records in nodegoat (‘Update Existing Objects’). I always import the identifiers into nodegoat first and then update the records with additional information based on the identifiers. In the nodegoat Import web interface you can choose whether you want to create new records or update existing ones.

In nodegoat you can import 50k of data records (rows) at once. So if you want to import all the more than 200k data rows, you have to split them up. I’ve already done this by providing a test data sample here with 20k records that you can use for your import. I have prepared this data and just selected a few columns to get started: Identifier, ship name, year, longitude and latitude. You can download the test data sample here:

climate project test data (CSV)

Your Data Model in your project for this data sample should look like this:

Just add one Sub-Object with ‘year + coordinates’

We will import the data into nodegoat via web interface (you can also import data via JSON interface, we will cover this in another tutorial).

Go to Model > Import > CSV Files, there you upload the downloaded CSV file (climate project test data).

Background: To import your data into nodegoat, the data must be available in a text file in UTF8 format. In Excel for example you can save your data as CSV data. Go to ‘Save as’ in your Excel sheet and choose CSV-UTF8 as data format. CSV means comma separated values (data). Open your CSV file with a text editor and you will see the many separators in the data.

Go to the Import Template. Map the fields of your CSV data to the fields in your data model. For the year (YR), select the Date Start field in your data model.

Now you can run your import template. You can first check a selection of the records to see if you have mapped the fields correctly. Click on Next to import the 20k data records.

Have a coffee now, you have already achieved a lot today.

After the import go to ‘Data’ and click on the Geografical Visualisation.

This is how your result should look like. Zoom in and don’t forget to play with the time slider. You will also discover ships in the desert of Africa, these are errors in the data that do not include either the longitude or latitude. So the visualization also helps you to detect such errors.

Reduce the points in the Visual Settings, if you like.


You can update now your data records based on the identifiers. Create a CSV file with the data you want to continue importing. When importing, select ‘Update Existing Objects’ and choose your identifier to map the CSV data to the corresponding record in your nodegoat database.