*** Presentation of AG Smartlabs within our IVV Kolloquium on 7.9., see https://sso.uni-muenster.de/LearnWeb/learnweb2/course/view.php?id=67509#coursecontentcollapse24 ***
Digitization continues to make great strides in research as well as in university teaching. With its eScience initiative, WWU is proactively addressing this issue with offerings such as the Service Center Digital Humanities (SCDH), the Service Center Data Management (SCDM), the InterKIWWU teaching program in the area of artificial intelligence (AI), and WWU IT offerings in the areas of high-performance computing and research data infrastructure. Current trends such as machine learning and research data management are addressed here, but in our opinion the tasks that arise in the course of the digitization of processes around research laboratories in the natural sciences are not yet sufficiently in view. This page serves to collect information on this topic.
!!! This page is still under construction. !!!
Technological solutions
1. research data management: generation, processing and storage of instrument data without recourse to the manufacturer's software
- example with the electronic lab book Chemotion: https://www.chemotion.net/chemotionsaurus/docs/eln/devices
2. (remote) control: Instrument control via remote desktop or programming interfaces
- example with pyVISA: https://pyvisa.readthedocs.io/en/latest/
see also: https://www.uni-muenster.de/NWZ/en/eScience-at-NWZ/deviceintegration.html
3. data visualization and processing:
- Matplotlib: a plotting library for creating static, animated, and interactive visualizations. https://matplotlib.org/stable/index.html
- OpenCV: a computer vision library that provides image processing and computer vision algorithms. https://opencv.org/
- Pillow: a library for image processing that is a fork of the Python Imaging Library (PIL). https://pillow.readthedocs.io/en/stable/handbook/overview.html
- scikit-image: an image processing library that implements algorithms for image analysis, filtering, segmentation, and more. https://scikit-image.org/
- Seaborn: a library based on Matplotlib that provides a high-level interface for visualizing statistical models. https://seaborn.pydata.org/tutorial.html
- Plotly: a library for creating interactive, web-based visualizations. https://plotly.com/python/
- Pyvista: a library for 3D plotting and mesh analysis and more. https://docs.pyvista.org/
4. sample management:
- example with sampleDB: https://scientific-it-systems.iffgit.fz-juelich.de/SampleDB/
5. inventory of experimental equipment and materials:
6. integration of the above solutions via electronic lab books:
- example with Chemotion see above
7. integration of the above solutions via project management software:
- example with openproject: https://www.openproject.org/
Information and training events:
- Resources on device integration: https://imperia.uni-muenster.de/NWZ/en/eScience-at-NWZ/deviceintegration.html
- Information events on research data management and scientific software development: see https://www.uni-muenster.de/NWZ/eScience-at-NWZ/index.html
- Workshops and hackathons on smart lab topics?
- The Experts List as a starting point for 'inhouse consulting', i.e. consulting by colleagues with expert knowledge built up over many years: https://imperia.uni-muenster.de/NWZ/en/eScience-at-NWZ/expertslist.html
On our own account:
Your contributions to this page are welcome via email to dgd@uni-muenster.de!
The 'AG smart labs' meets once a semester to discuss how the topic can be further developed in the sense of the above points. If you are interested, please contact us (Hossein Ostovar, h.ostovar@uni-muenster.de and Martin Korth, dgd@uni-muenster.de).