Fraunhofer Institute for Applied Information Technology FIT
Fraunhofer Institute for Applied Information Technology FIT

FAIR Data Training

About the course:

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 sharing sensitive or health data. You will have the opportunity to try out the process flows and standards which allow the data to be better organized and sharable, but yet stay protected. After the course you will be able to improve data quality and create FAIR data sets.

 

Learning objectives:

  • what FAIR means for the industries 
  • when FAIR is FAIR enough
  • why FAIR data is so essential for today's life science industry
  • how to transform or initiate a FAIRification process in the organisation

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. 

Learner journey

Program in detail

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

First phase

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

The second phase is a practical online workshop, which all fellows are expected to attend. During the workshop, participants work in small groups and have the opportunity to consult their project with FAIR Data experts.

Third phase

In the third phase, the fellows explore interoperability, hosting requirements, and data sharing solutions in different settings. The fellows first assess the interoperability and hosting requirements in their own organizations and then get personalized guidance from the experts of FAIR Data Project to proceed with their project. At the end, each participant will write up the observations and preliminary solutions which are to be presented in the phase four.

Fourth phase

Phase four is a group meeting where the participants are to present their FAIRification project results, demonstrate their understanding of FAIR data management and their capability of acting as FAIR data experts.

Certification

The participants that have successfully presented their projects and passed the test will receive certificates of completion.

Our experts

I am a Software Engineer with 24 years of experience in multinational vendors. I experienced most of the service delivery and management steps from hands-on technology consultancy to technical management and program management for different domains: DBMS, DW, SOA, SSO and Datalake. Since 2019 I have been working as a Technical Lead in distributed analytics infrastructure development (PHT), Proposal writing and Project Management.
Hello, I am Zeyd Boukhers, a computer scientist and AI specialist. My passion lies in the field of FAIR Data, Data Science, and Machine Learning. I am currently co-leading the FAIR Data and Distributed Analytics group at Fraunhofer FIT, working to advance the field with a team of talented individuals. I hold a PhD in Pattern Recognition and a Master's in AI, which have allowed me to gain a deep understanding of these fields and develop my expertise. I also share my expertise through research publications and engaging talks on the topics of FAIR Data and Data Science.”

More details:

When:       September 2024   

Where:      Online 

Duration:  14 weeks

 

 

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Thank you for your interest in our course. We regret to inform you that due to unexpected funding challenges, we might not be able to provide the fellowships for this course as initially planned.

However, we are in the process of creating a paid version of this course for the future. Rest assured, we will notify you as soon as this version is ready.

We appreciate your understanding and patience in this matter and hope to serve your learning needs soon. If you’d like to be informed about any updates regarding this course, please register below.

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."