FAIR Data Training: Build Knowledge Graphs for LLMs

In today’s digital age, delivering AI-ready datasets is crucial for harnessing the full potential of AI in business and research. This requires creating a common understanding of data assets throughout the organization and a continuous adaption towards a common sense of knowledge. By understanding how to make datasets findable, accessible, interoperable and reusable (FAIR), this program positions you at the forefront of driving constant change and innovation in data management.

Mastering Data Literacy to Build AI Ready Datasets

The course is aimed to widen the idea of making the data FAIR: Findable, Accessible, Interoperable and Reusable. During this journey we will identify and work on common difficulties in making data ready for AI. You will have the opportunity to try out the process flows and standards which allow the data to be better organized in knowledge graphs. After the course you will be able to create a curated knowledge base for your organization and query it with large language models.

Learning Objectives

You will learn about the following topics:

building AI ready datasets following FAIRification guidelines and processes
standardizing knowledge by using vocabularies and ontologies
converting data to RDF and building knowledge graphs
using ChatGPT to interact with your data and knowledge graphs
driving change in data management culture

Watch part of the interview with Herman van Vlijmen on how to adopt the right mindset and become an ambassador for FAIR data.

Herman van Vlijmen is a professor of computational drug discovery at the university of Leiden, Head of Computational Chemistry at Janssen and project lead of the FAIRplus project. Read his bio here for more information.

Target Group

The course suits researchers and practitioners who want to make data FAIRer and thereby become FAIR data ambassadors within their own organizations and beyond. During the course we will provide you with the extraordinary opportunity to work with branch leaders on the real-life examples in order to provide FAIR data solutions.

Program in Detail

The modules of the FAIRification process are grouped into two phases:

First phase: foundations of FAIR data literacy and knowledge graphs

The first phase consists of theoretical online modules that answer the essential questions of what FAIR data means and what FAIR initiatives are doing, how to assess your data and set FAIRification goals, introduction to FAIR Cookbook, using and writing recipes.

Second phase: knowledge graph and Gen AI workshop

The second phase is a practical online workshop. During the workshop, participants work in small groups on different concepts they learned in the previous phase. You will experience what fully ai ready data means and get ready to transfer the knowledge into your own organization. The workshop will be accompanied by our FAIR Data experts.

Registration Form

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The participation fee is €2,900 per person and is exempt from VAT. For registrations made up to 6 weeks before the course start date, we grant an early bird discount of €200.

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Voices of our previous participants

What are your expectations of the program?

»I would like to know about FAIR but more importantly I would like to be able to apply a FAIRification process to any kind of project and data.

For this program I will have a use-case but I would like to be able to use this knowledge for any kind of future projects.«

»I didn't know before that the data we generate is extremely useful for people in the future as well. It is important for our project, for people outside of our project and people in the future.

I didn't know also that data scientists spend a lot of time (almost 80% of their time) in preparing the data for the analysis. The time that they spend preparing data do not spend analyzing them (only 20-40 %).

I have learned that FAIR datasets also can be part of the Machine learning algorithms by generating accurate predictions (Neuro-symbolic AI).

My objectives are to learn the principles of FAIR and some practical examples in order to apply them in the projects of my company and in the futre projects.«

»A holistic introduction to FAIR is a key takeaway.

I'm hearing on FAIR principles and criterion for quite sometime, but industry use cases and need for AI is something new to me.

You will learn about FAIR, its importance, use cases and involvement in advance analytics through these modules.

Understand about FAIR in 360 degree from concepts to implementation, will be my key learning for this program. Would like to take this learning and support effective FAIR implementation into my organization as well as to contribute to outside world.«