Special Methods Courses

Participating in a "Special Methods Course" is an obligatory part of the PC internship (M.Che.1321). In general, you should participate in a methods course before doing a PC internship.

Besides merely participating in a course, please remember to also register for the methods course in FlexNow (M.Che.1321.Mk), otherwise the module M.Che.1321 cannot formally be completed. Note that your participation can only be acknowledged when you do all homework that may be part of the course.

The following courses are offered on an irregular basis (at least one per semester):

Signing up for a course is organized via the "Methodenkurs" StudIP group of the current semester. Note that a course needs a minimum of three participants to take place.

The following courses have been offered in the previous semesters:

  • Summer Term 2024: Scientific Presentation Slides (R. Mata)
  • Winter Term 2023/2024: Software Development Tools (N. Lüttschwager)
  • Summer Term 2023: Scientific Presentation Slides (R. Mata)
  • Winter Term 2022/2023: Data evaluation with Python (N. Lüttschwager)
  • Summer Term 2022: Scientific Presentation Slides (R. Mata)
  • Winter Term 2021/2022: Scientific Writing (A. Wodtke)
  • Summer Term 2021: Scientific Presentation Slides (R. Mata)
  • Winter Term 2020/2021: Data evaluation with Python (N. Lüttschwager)
  • Summer Term 2020: Scientific Presentation Slides (R. Mata)
  • Winter Term 2019/2020: Data evaluation with Python (N. Lüttschwager)
  • Winter Term 2019/2020: Bayesian Statistics (J. Proppe)
  • Summer Term 2019: Scientific Presentation Slides (R. Mata)
  • Winter Term 2018/2019: Electronics (A. Knorr)
  • Summer Term 2018: Scientific Writing (A. Wodtke)

In the following, you find a short summary of the topics tought in special methods courses.

Data Evaluation with Python: Tools and Good Practice

In this course we will learn about some intermediate level topics regarding data evaluation and software development in Python. The course will help you to write code that is better organized, better readable, and less error prone.

Here are the main topics we will look at:

  • How to organize data, source code and notebooks
  • How to find the software packages we need
  • How to make our own code reproducible by working with environments and documenting dependencies
  • How to use the source control software Git to track changes of our code and synchronize code across several devices or cloud computing services
  • How to verify our code using code analysis and unit tests
  • How to use Jupyter Lab capabilities and interactivity to analyze and present data in an engaging way

Basic knowledge of data processing and plotting in Python is assumed.

The course material is available on https://gitlab.gwdg.de/jupyter-course/jupyter-course and my-binder.org (interactive notebooks with code examples).

Electronics (I)

  • Electronics and safety
  • Soft-soldering
  • Electrical measurements
  • Oscilliscopes
  • Characteristic impedance of cables
  • Control technology
  • Temperature measurements
  • Sensor technologies
  • Digital interfaces
  • Resistor color codes
  • Construction kit: LED signal light

Electronics (II)

In many laboratories, measuring and control devices equipped with modern technology are used to enable the most precise measurements. In order to better understand the methods behind this, various "hands-on" experiments are planned using selected examples to explain the concept of PID control, phase locked loop and the use of lock-in amplifier technology in more detail. The mathematical background will be explained in a theoretical part. The course will be held in German.

Scientific Presentation Slides

Although many of us spend hours attending or even holding presentations each year, it is hard to come by with a set of good practices. Each field has its own peculiarities and it is hard to transpose the rules on presentations from one community to another. What might work at a company, a sales pitch, might not be well received in a scientific setting. What might even be appropriate in geology or sociology, should perhaps be avoided in the specific case of chemistry.

This course offers a tailored overview on how to prepare your slides for a presentation, providing best and worst practice examples and extracting a set of rules one can apply to improve future talks. The intention is to make the attendants aware of their persistent errors, and make clear how one can address an audience through the use of slides. One will cover basic techniques in:

  • structuring of a scientific presentation
  • preparing documentation and data for a presentation
  • main elements of a scientific slide, work with graphic elements and text, common composition mistakes and advices

What the course will not cover is the presentation technique itself (e.g., posture, speech). One will primarily focus on presentation slides. This course is primarily intended for Ph.D. students but will also be open to Master students (up to a given maximum number of participants).

Scientific Writing

  • Different types of papers (reviews, full papers, letters)
  • Focus: Letter paper using the MPU approach (MPU = minimal publishable unit)
  • Drafting workflow
  • Structure and section contents
  • Revising with “adjustable self-criticism”
  • Making excellent figures
  • Improving writing style and being concise
  • Practical examples and exercises
  • Potentially ending with a letter paper ready for publication

Download Scientific Writing course outline (PDF)

Software Development Tools and Reproducible Data Evaluation

In this course we will learn about some intermediate level topics regarding tools for software development and reproducible data evaluation:

  • Using Jupyter to combine source code, output, and documentation into a single document
  • Using the source control software Git and GitLab to track changes in source code and collaborate
  • Using a Language Server to help us with autocompletion, code corrections, code navigation, and more
  • Using virtual environments (Python), projects (Julia), or Docker (all languages) to build software environments for reproducible data evaluation

Equipped with this knowledge, we will build a small project and make it runable on Binder, a service that lets anybody with an internet connection and a recent browers inspect, modify and run our code (example and its git repository).

For this course, you should have some prior programming experience with Python and not be afraid of using the command line.

Technical Drawing

  • Why technical drawing?
  • Axonometric representation
  • Projection
  • Basics of dimensioning
  • Fitting tolerance
  • Sectional view
  • Screws and threads
  • Sealing rings and notches
  • Vacuum flanges
  • High pressure chambers