Hello Ms Grün! Thank you for taking the time to talk about the Advanced Neural Data Analysis Course (ANDA). Which requirements apply and what do the participants take home in the end?
A good basic knowledge and understanding of mathematics is an essential requirement. Ideally, the participants study neuro-sciences or computational neuroscience. We ask for programming skills in Matlab, Python or C. In any case, it is an advantage if people come with specific questions they would like to approach in terms of data analysis.
Course credits can be counted to the students’ regular study programs. ANDA offers a pool of data analysis methods in neuro-sciences. Participants get to know tools in which analysis methods are already implemented so that they can be used more easily in day-to-day lab routines. They also learn to assess the determining factors for the use of these methods, to look at data more closely and to eventually select the appropriate method for the task.
Here you address the more practical phases of the course?
Yes. The course lasts about three weeks and is divided into two parts. The first part consists of lectures and exercises. In the second part, the students work in groups and have to tackle different data sets. The challenge is to find out which data sets are real and which are simulated. This practical part requires competent tutors. We are grateful for the support of tutors from different locations, who put in a high workload of their own. Without their efforts, the course would not be possible.
Offering a three-week course beyond university teaching is quite special. What is the story behind it?
In my opinion, data analytics is still a neglected field. In my early days as a lecturer in Freiburg, I already offered introductory courses on this topic. It quickly turned out that the really complex analytical procedures could not be taught in this way. Yet, I continued searching for a suitable format for teaching, especially with regard to digitization and reproducible data analysis because the data, in particular experimental data, became much more complex – the data sets comprise many sources,especially when it comes to experiments with animals performing behavioral tasks.
Where do you see the greatest challenge of the ANDA course?
The participants come from very diverse fields. Some have a background in informatics, computer science or physics. Others have a more experimental background. Therefore, the first part of the course deals with a lot of basics and scientific background. It is enhanced by evening lectures focusing on the bridge between experimental data and data-analytical approaches by discussing examples. Another advantage of the first part of the course is the presence of international speakers who are approachable beyond lecture times, which greatly facilitates the development of a professional dialogue.
You have just mentioned the enormous amount of data. Today, there are new possibilities with regard to infrastructure, especially with supercomputing, for which the Forschungszentrum Jülich is well-known. How much of an issue is this?
Our course dedicates a whole day to the management of large amounts of data. Thomas Wachtler is particularly involved here as the researchers of G-Node are continuously developing methods for metadata acquisition and processing. At Jülich, we develop the data analysis tool Elephant and work towards reproducible workflows using also the tools of Thomas Wachtler and his team. We also contribute this knowledge to the Human Brain Project, in which centrally accessible workflows are developed. These workflows facilitate access to platforms such as supercomputing, modeling or data analytics. Naturally, we also provide tools for parallelized data analysis on supercomputers. In short, ANDA offers a large package of possibilities.
You just mentioned Thomas Wachtler, head of the Bernstein Facility for Data Technology, G-Node, in Munich. He is co-organizer of the ANDA course. How can we picture the organization from the coordinators’ perspective?
We are already working towards the next course. In our weekly skype or video conferences, we discuss tasks regarding the content and the venue of the course. The participants live and learn at the same place in the secluded idyll at the monastery Overbach; this gives the course its special character. Financing as well as the recruitment of international speakers is an important factor. The logistics and final preparations do keep entire working groups busy in the final phase of such a course.
Martin Nawrot from Cologne is also one of the co-organizers. Between the three of you, you share a big workload, since the international lecturers in particular are one of the major success factors of the course.
Yes. I would also like to add that with Yifat Prut from the Hebrew University in Jerusalem we were able to win a fourth organizer. She knows ANDA as a keynote speaker and has always been very committed. The choice of speakers is based on the method they have developed or are developing further. Last year we were able to win Byron Yu (Carnegie Mellon University, Pittsburgh). Moshe Abeles (Hebrew University, Jerusalem) has been with us for the last two years. This year, we also had Jonathan Victor from the USA and Christian Machens from Portugal. In terms of research, these are the best people in the world in this field.
How do the students react to this? Can you give us a little insight into the atmosphere of the course?
To begin with, we always ask the students about their motivation. It turns out that many participants have very specific concerns. Due to the lively participation, the professional discussions with the faculty and the positive feedback at the end of the course, I am happy to say that the course was very well received.
The ANDA course is a highlight in the schedule of events of the Bernstein Network. Can you briefly outline its future?
The course is financed until 2019 through third-party funding of G-Node. We are already working towards further funding because the course is something special, for us as much as for the participants.
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