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2020-06-15 Ethics and best practices for Data Science (09:00-17:00)

Free and mandatory for enrolled CAS Applied Data Science participants.


Ethics and best practices for scientific computing and data science

Computing and software is at the core of many science and business projects. Consequently reproducible code is a basic criterium for high quality science and products. Scientific code should be Findable, Accessible, Interoperable and Reproducible (FAIR). Producing FAIR code is a societal obligation for researchers working with software.

Working with data may come with high responsibility and require high moral integrity. Misuse of data may not only hurt individuals, but entire political and democratic systems. 

In this CAS Applied Data Science Module we reflect on ethics and best practices for sciences working with software and data. We learn about basic security issues and meassures, why and how to licence Free Open Source Software and how to use version control software like Git and produce good documentation with ReadTheDocs.  

The modules consists of 5 courses (see schedule) which can be visited independently.

Learning objectives
  • Criteria for good science - ethics in science
  • Ethics and best practises for scientific computing and data science
  • Collaborative distributed version control, code review (Git)
  • Cyber security fundamentals
  • Free Open Source Software and Licences  
  • Documentation
Target group
  • CAS Applied Data Science participants
  • Researchers
  • You should be able to navigate within the file tree on the command line and edit text files
  • Basics in at least one programming language. The training is as language independent as possible, but examples and practical work is in Python.
  • You will need to bring a laptop.
  • You need to install some software durin.g the course
  • Theoretical sessions with accompanying practical work. Discussion. Project work. Project presentation.
  • A certificate yielding 2 ECTS points will be delivered to participants who have attended the whole training and successfully presented their project work.
  • The coaches are local and external experts
Practical information (time, location ...)
Time : 2020-06-15 from 09:00 to 17:00 
Location : Online and Room 124, Mittelstrasse 43, University of Bern (max 10 people)
Online Room: Only visible for registered participants 

On-Site Registration:

Only 10 people are allowed to be in the room at Mittelstrasse. If you would like to be there in person, please sign up here (mandatory):

Please also read the university COVID measures information (german only) linked below.

The first 10 will be allowed. All other participants must attend remotely. The lecturer will sit in the room, however, giving the course just the same way as from home office. So there shouldn't be a big difference between remote and on-site participation.  

Training language: English
Participants : Max 24
Registraion : Mandatory
Coaches : Prof. Dr. Dr. Claus Beisbart, PD Dr. Sigve Haug (responsible, University of Bern)
Certificate: Certificate.
Schedule and content
09:00 - 09:30 Introduction (PD Dr Sigve Haug, ScITS University of Bern)
09:30 - 10:30 Best practices - introduction and distribution of tasks - team work
10:30 - 11:00 Break
11:00 - 12:30 Team work, discussion and presentations of best practices (all)
12:30 - 13:30 Lunch
13:30 - 14:00 Team work, discussion and presentations of best practices (all)
14:00 - 15:00 Standards of scientific computing and data science - perspectives from ethics and philosphy of science (Prof. Dr. Dr. Claus Beissbart)
15:00 - 15:30 Break
15:30 - 16:00 Software Disasters 
16:00 - 16:30 Planninng of the module projects and CAS exams 
About the coaches
Sigve studied physics in Germany, Spain and Norway. He has been involved in neutrino physics experiments and high energy frontier experiments, often with main focus on the computing challenges related to the large and distributed data from these experiments. Today he is working for the Albert Einstein Center for fundamental Physics and the Mathematical Institute of the University of Bern where he is leading the Science IT Support unit.