About on Bas Ernst

Graduation Open University - BSc in Computing and IT

Sun, 29 Mar 2026 11:32:33 +0200

On Saturday 28 March 2026 I attended the graduation ceremony of the Open University at the Barbican Centre in London, an absolutely brilliant event and a memorable way to celebrate with my biggest supporters the conclusion of an eight-year journey studying part-time towards a BSc in Computing and IT. I have always enjoyed learning, ever since I was a child, and that curiosity helped me through the many assignments, deadlines, and eventually a thesis on data analysis in diplomacy and gender. What I learned during these years is already proving useful in my work. And who knows, perhaps computational cultural diplomacy might one day become a discipline in its own right. For now, I am simply grateful for the journey… and already thinking about the next study objective for the coming years.

Evaluating Cultural Diplomacy projects with Neo4j

Sat, 21 Mar 2026 11:32:33 +0200

I will once again work with the small dataset I used for my previous post. However, this time I will not use Calc or Excel, but a graph database. Of course, for this tiny dataset, using this tool could be considered overkill. However, as I plan to add more projects and countries to the analysis in future iterations, the advantages of a graph-based database will become clearer. Since it is well suited for analysing networks, it will allow us, in future versions of this project, to analyse cultural relations by examining them as a network.

For now, I will partially repeat the same analysis as in my previous post, but I will also add some new analyses that are not easily possible with a tabular spreadsheet. For more info on the data, see evaluating_cultural_diplomacy_made_easy

Beyond that, I find graph databases more intuitive than relational databases. In cultural diplomacy, missions and their teams either manage cultural projects directly (engaging artists directly) or collaborate with external partners. This is far easier to visualise as a network than as a table of columns and rows.

Graph databases are built on the idea that things in real life are connected to each other. These things are called nodes, and they can have relationships. Graph databases are highly flexible and easy to work with, supporting a wide range of data types. For example, Wikipedia is based on a graph database. For my project, I’ll be using Neo4j, one of the market leaders in this space. Its query language is called Cypher, and Neo4j offers excellent learning resources on its website, making it quick to pick up. I use both their Community Server Edition, which runs locally on my system, and their cloud solution, Aura, which adds useful tools like Bloom for exploration and dashboards. Of course, if you’re working with sensitive data, it’s worth while considering other solutions than corporate clouds. Since I’m using fake data for this project, there’s no issue.

Let’s start by considering how to manage our data and constructing a simple data model. Conceptually, I find the Theory of Change useful (it has of course it limits, not in the last place it implies presumes causality between inputs and outputs which is not easy to demonstrate). It analyses how policy implementation can achieve meaningful impact. Using this framework, we can:

Identify a challenge to address,
Deploy specific instruments,
Generate results,
Analyse the outcomes, and
Measure how these contribute to broader policy goals.

The Theory of Change helps structure the thought process into a model that supports cultural diplomacy, focusing on instruments most likely to generate positive outcomes. You can read more about it on Wikipedia Theory of Change

For this use case, I opted for the following data model. In Cypher a datamodel can be called with the following query:

CALL db.schema.visualization()

Starting on the left, each team member is linked to one or more organizations they follow. Each organization manages one or more projects, which in turn contribute to specific goals. Additionally, each project is associated with a date, a region, and a discipline.

One feature I particularly appreciate is the ability to generate a graph visualisation of team members and their partners. While this is a small example, it effectively illustrates the team’s dynamics: who is working on what, and what the team is collectively focused on. For this example, I’ve included two team members, Eve and Mike, along with their local cultural partners. To visuelise their relations, I used the following query:

(t:TeamMember)-[f:FOLLOWS]->(p:Partner) RETURN t,f,p;

Which returns:

As in my previous post, Neo4j allows you to calculate aggregations such as the average, maximum, and minimum grant amounts. Using its built-in dashboard functionality, you can easily generate visualisations, such as a bar graph showing the total and average grants awarded to different cultural disciplines.

MATCH (p:Project)-[:HAS_DISCIPLINE]->(d:Discipline)
RETURN d.name AS discipline, COUNT(p.naam) AS projects, SUM(p.grant) AS totalGrants, SUM(p.grant)/COUNT(p.naam) AS `average grant`
ORDER BY projects DESC;

Neo4j Aura generates an interactive graph that allows the user to indicate what variables to include. The user can switch between properties. In this case, the totals and averages are displayed.

Finally, Cypher queries can be integrated into a Jupyter notebook. This combination is very powerful, merging the flexibility of a graph database with the robust data analysis and visualisation tools offered by Pandas. In a future update, I’ll share examples of how this approach can enhance the analysis of cultural diplomacy projects. Before that, I will write a post in which I will illustrate setting up the graph database combining this small dateset and Neo4j.

Setting up a graph database

Fri, 06 Mar 2026 11:32:33 +0200

In my previous post I mentioned using a graph database to store data from a small cultural diplomacy database. This would be most useful considering partners and projects as a network. The example data that I am using here is rather small, but eventually I can add more data and this type of database will become more usefull. Intuitively a graph database is easier to visualise mentally than a traditional relational database. Basically it boils down to ’things’ that are related to other ’things'.

In this post I will show how to set up the nodes and relationships that model the reality of my simple cultural diplomacy dataset. I will start with the datamodel that visualises how the data elements relate to each other. Using the data in my previous post, it consist of the nodes and relationships that I will build now.

Let’s start working from left to righ. I will first create the nodes of the two team members. Cypher can read csv files. In this case fields are separated by a semi-colon, which we should indicate. Cypher reads the csv file form row to row.

LOAD CSV WITH HEADERS
FROM 'file:///randomProjectList2.csv' AS row
FIELDTERMINATOR ';'
WITH row
MERGE (p:`TeamMember` {naam: row.`TeamMember`})

When I run this code, Neo4j will return a graphical representation of the team member nodes. As some of the projects are followed by more then one team member, we get three nodes of which one with the list containing both member. We can limit the representation to the only two staff members by separating the names that are present in a list of names.

LOAD CSV WITH HEADERS
FROM 'file:///randomProjectList2.csv' AS row
FIELDTERMINATOR ';'
WITH row,
 split(row.TeamMember, ',') AS members
UNWIND members AS staff
WITH row, trim(staff) AS TeamMember

MERGE (p:`TeamMember` {naam: row.`TeamMember`})

The creation of the other nodes is pretty much following a similar pattern. Except for projects, where I have added a various properties that describe projects.

FROM 'file:///randomProjectList.csv' AS row
FIELDTERMINATOR ';'
WITH row,

MERGE (p:Project {name: row.`Project Name`})
SET p.id = row.`Project ID`
SET p.grant = toFloat(row.Grant)
SET p.budget = toFloat(row.`Project budget`)
SET p.disciplines = row.Discipline
SET p.regios = row.Region
SET p.months = row.Month
SET p.participants = toInteger(row.`Number national participants`)

The nodes are related with each other through relationships. The relationships express how and in what way the nodes are connected. Team members follow cultural partner organisations. Cultural organisations organise cultural projects. In Cypher this can be define with the following expression. In this example the two nodes TeamMember and Partner are connected with the relationship FOLLOWS. The same procedure can be followd for a Partner that ORGANISES a Project In cypher these triplets are formed as follows.

(t:TeamMember)-[f:FOLLOWS]->(p:Partner)
(p:Partner)-[o:ORGANISES]->(a:Project)

And just as an example, this is how you can create a relationship:

LOAD CSV WITH HEADERS
FROM 'file:///randomProjectList.csv' AS row
FIELDTERMINATOR ';'
WITH row,
 split(row.Discipline, ',') AS disciplines
UNWIND disciplines AS sector
WITH row, trim(sector) AS discipline

MERGE (d:Discipline {name: discipline})
MERGE (p:Project {naam: row.`Project Name`})

MERGE (p)-[:HAS_DISCIPLINE]->(d)

These were some examples and code snippets how to set up the graph database. Neo4j’s website contains some great documentation as well as online course that will teach you effectively how to create cypher queries to set up up your database and return data from it. Neo4j Docs.

Why Evaluation and Monitoring Matter in Cultural Diplomacy

Sun, 22 Feb 2026 11:32:33 +0200

There are many definitions about cultural diplomacy. I profer to consider my work as a pracitce through which states and/or international organisations pursue foreign policy objectives. There exists many approaches how to implement cultural diplomacy. In this field measuring results and impacts is often considered problematic. There is still a common perception that cultural projects cannot be measured. Some argue that quantitative analysis fails to capture the complexity and nuance of artistic work. Others point out a more practical challenge: in cultural diplomacy there is often limited time, and evaluation activities are the first to be postponed or neglected. I understand the difficulties of measuring impact of artistic work, but in this field of work we are not merely artistic directors. Cultural diplomats collaborate with organisations and practitioners who create and deliver artistic content. Therefore, cultural diplomacy is less about making artistic choices and more about building networks, facilitating partnerships, and identifying strategic opportunities. Our role is often one of connection, coordination and positioning rather than curation. So, despite the difficulties, why is it important to try to measure results in cultural diplomacy?

Without data, it becomes difficult to tell the story of what has been achieved. Careful analysis can reveal patterns and insights that would otherwise remain hidden. Numbers do not replace narratives, but they support them.
Cultural teams in diplomatic missions should also see themselves as learning teams. Reflection requires input. Structured monitoring helps teams understand what worked well, what did not, and why. This is not about control; it is about continuous improvement.
There is also an important dimension of accountability. In diplomacy, we work with taxpayers’ money. The public has a right to know how cultural budgets are used and what impact they generate.
Finally, from an internal perspective, having reliable data strengthens a team’s position. Evidence-based reporting allows you to defend your work and demonstrate performance using your own data.

Another common obstacle is the perceived lack of technical knowledge. How should projects be tracked? How should data be structured? In reality, one can start very simply. Tools such as LibreOffice Calc, Excel, or Google Sheets are entirely sufficient to build a basic monitoring system. If you are interested, you can find the tutorial here: evaluating_cultural_diplomacy_made_easy

Evaluation of cultural projects made easy

Sun, 15 Feb 2026 11:32:33 +0200

Making evaluations of cultural projects easy

In general, those working in cultural diplomacy tend to focus on making projects happen. Collecting and analysing data for monitoring and evaluation purposes is often seen as a challenge. A lack of time, limited expertise, or simply a lack of priority can all stand in the way. However, with relatively little effort, it is possible to gather basic project data, carry out straightforward analysis, calculate key indicators and produce meaningful visualisations. Even setting up a simple document in Excel, Calc, or any other readily available spreadsheet programme can be an effective first step towards visualising the results of your work.

This tutorial provides some easy steps to evaluate projects. For this purpose I made a simple Excel sheet that can be a starting point. Click here to download.

This will enable us to answer questions such as:

How many projects did we support?
In which regions did we support projects?
How many projects took place per month?
How many projects were there per discipline?
What is the average, minimum and maximum grant amount?
How many euros did our partners invest for each euro from our side?
Which projects scored best against our policy goals?

What do you need?

A list of projects
Grants per project
Project budget (Dutch part of the budget)
Starting date
Month (enter the month as a number; Excel will convert this to the correct month).
Number of our national participants (artists, makers, …) per project
Disciplines per project

A score per policy goal, depending on how you measure a project’s contribution to policy goals.

Step 1: Define the column names. For example

Project ID
Project Name
Subsidy
Project budget
Partner organisation
Region
Start date
Month
Number of national participant
Goal 1
Goal 2
Goal 3
Weighted average
Discipline

Then comes the more tedious part: populating your Excel spreadsheet with data. Ensure that numbers are recognised as numbers and dates as dates. You can do this by selecting the relevant cells and checking ‘Format cells’. Numbers should not be treated as text; otherwise, calculations will not be possible.

All the data in this example has been generated randomly. The scores for the three imaginary policy goals could be the scores that you and your team gave to projects, or the results of a questionnaire. It depends on what is available.

The weighted average of the scores for the policy goals is calculated so that a final score between 0 and 1 is returned.

Goal 1 contains scores from 1 to 5; therefore, the results are divided by 5.

Goal 2 contains scores from 0 to 2; therefore, the results are divided by 3.

Since there are three policy goals in this example, the sum of the three scores is divided by 3.

The whole table is ordered so that the weighted average column goes from highest to lowest score. This will be needed for a visualisation later.

Step 2 Aggregate results

In order to create visualisations, we need to prepare the data by grouping the required variables. Excel wants us to calculate totals by discipline, month, and so on before we can use the data for charts.

We are going to count how many times a project takes place in a certain region. I created a new list of regions in column T and used Excel’s built-in COUNTIF function to count how many times a region appears in that column. If you click on one of the cells, you can see the formula: =COUNTIF($G6:$G23;T6). This counts how many times the word in cell T6 (which refers to ‘Region 1’) is present in the column from G6 to G23. You can use this format for other aggregations, of course.

For disciplines, I changed the formula slightly to ensure that cells containing multiple disciplines separated by commas are counted individually. This formula is similar to that used to count the number of regions, except that it separates words divided by commas: =COUNTIF($P$6:$P$23;"\*" & W6 & "\*"). Here, “W6” refers to “Design”, and Excel looks in the P6–P23 column. Enclosing the W6 cell between stars and ampersands lets us count occurrences in cells with multiple results.

The procedure for the months is the same as for the regions. Again, make sure that the months in column I are stored as dates (you can do this by changing the cells’ format).

Step 3: Perform some basic calculations

Excel has all the basic functionality required to calculate averages, minimums and maximums. We will use this for grants. For example, the average grant is calculated using the formula =AVERAGE(D6:D23) in the “Subsidy” column.

Step 4: Visualise the results

Visualisations such as bar and pie charts are powerful tools for revealing the stories hidden in data. The Insert pane in Excel offers plenty of charting solutions. I have included some examples. The procedure is essentially the same for all of them.

First, select the data you want to use; for example, the disciplines and their scores.
Then click on the type of chart that you want in the ‘Insert’ pane, for example ‘bar chart’.
Excel will then generate the visualisation.

There are many options for fine-tuning the presentation of a chart. You can change colours, headings, and so on, depending on your use case. Excel also has a built-in feature “Recommended Charts” that assists the user selecting the most suitable chart.

Step 5: Conclusions

Even this short data sheet demonstrates that meaningful visualisations can be generated to show the results of the work carried out. By examining the responses gathered through the sheet, we were able to address all of the questions.

How many projects did we support?

18 Projects

In which regions did we support projects?

How many projects took place per month?

How many projects per discipline?

What is the average grant amount, the minimum, and maximum

Average grant per project € 5.650,00
Min grant € 1.400,00
Max grant € 9.200,00

How many euros did our partners invest for each euro from our side?

€ 6.11

Which projects scored best against our policy goals?

I hope this worksheet has served as a helpful example of how, with minimal effort and only a basic understanding of data analysis, you can begin collecting meaningful information about a cultural diplomacy programme. The data itself is not an end in its own right; rather, it provides a starting point from which you can tell the story of your work and demonstrate how your particular programme of projects has contributed to your wider objectives.

Gender and Diplomacy

Tue, 30 Dec 2025 21:32:33 +0200

Analysing Gender and Diplomacy using Neo4j and Pandas

In December 2025, I completed my BSc in Computing and IT at the Open University in the UK. For my final project, I deliberately chose a topic that connects my interest in data analysis with my professional field, diplomacy.

My final research project focused on gender and diplomacy. In summer 2024, I discovered the GenDip dataset developed by the University of Gothenburg. This dataset maps all diplomatic relations by gender for the period 1968–2021 and was published with an explicit invitation for scholars to analyse the data and enrich it with additional sources. One of the central questions posed by the project was whether states use the appointment of female ambassadors as an instrument of diplomacy in its own right.

I used a range of data analysis tools to address this and related questions, with particular attention to how the structure of international relations networks influences the behaviour of the diplomatic network. My research is based on graph analysis and offers alternative perspectives on explaining the persistent under-representation of women in diplomacy.

This report is an elaboration of the final research project. It presents the data analysis on gender and diplomacy and documents both the research process and its results. Much of the required reflections by the Open University on the learning process, references to previous OU modules and explanations of how the research builds upon them have been omitted here.

Despite some statistical limitations, my research confirms the GenDip findings on the systematic under-representation of women in diplomacy. By integrating additional datasets, I also propose alternative explanations. Here are some of the main findings:

The project demonstrates the potential of graph data science for research on diplomacy and international relations.
Women are under-represented in senior diplomatic positions, and states appear to make strategic decisions about where to post female ambassadors.
Countries with women holding influential positions in key foreign policy areas tend to attract a relatively higher share of female ambassadors.
Living conditions and levels of gender inequality in host countries are significant indicators: women are more frequently posted to countries with better overall conditions.
Despite its limitations, the additional religion dataset suggests that Buddhist and Muslim countries send a higher proportion of their female ambassadors to countries that promote gender equality, indicating that religion may be a factor worthy of further investigation.
The project started with the question of whether countries use female ambassadorial appointments as a political signal. By combining relevant variables with network centrality measures, I identified countries, beyond the frequently cited case of Saudi Arabia, that deviate from the dominant patterns within the diplomatic network. In this way, the analysis aims to provide useful input for future research.

To read the study, click Open PDF