Welcome to the Amyloid workshop: User-friendly analysis of spectroscopy data with Quasar - multivariate statistics and machine learning
LINXS, in collaboration with the SMIS beamline at SOLEIL and the Biolab from the University of Ljubljana, is organising a 3-day hands-on workshop to introduce the QUASAR software, to address the infrared user community's need for a user-friendly and open-source software for data analysis

This 3-day hands-on training will take place at LINXS, Lund during January 13-15, 2021. During this workshop, we will focus on spectroscopic data analysis. The workshop is targeted at hyper-spectral imaging users (current, future or potential) working on biomedical applications, material-engineering, physical-chemical sciences, and more.
The purpose of this training is to provide a practical introduction to the QUASAR software, using tutorials and examples on synchrotron data sets as well as real-life Raman and IR imaging datasets.

What is QUASAR?

QUASAR is an open-source software for hyper-spectral imaging techniques (based on the Orange machine learning and data visualization suite).
QUASAR allows user-friendly analysis using visual programming. Routine tools like baseline correction, normalization, different versions of EMSC, differentiation and smoothing can be combined with multivariate statistical and machine learning methods, such as principal component analysis or various clustering methods. Savable and shareable workflows ensure consistent analysis across different projects, or the development of different analysis to same large dataset. Visualization tools enable quick inspection of the data and the results of the analyses.
The goal of the workshop is to teach the basic operations of chemical imaging to prepare the student to generate and interpret such images using QUASAR, new free software.

Why should you attend?

The workshop will bring together curious students and young researchers and introduce them to essential data mining and machine learning concepts in spectroscopic data analysis. Participant will learn about data visualization and machine learning with Quasar. Upon completion, participants will be able to analyze your own data and use them to develop predictive models. The workshop will be hands-on, with examples or own data

 Workshop content

·Data exploration and visualization.
·Clustering, uncovering of groups in data.
·Classification and predictive modeling.


·3-day theory/hands-on course on key approaches of data science
·Free software and data sets used during the course
·Certificate of attendance


Applications are now open, and will be closed 20th of November 2020. Registration should include short motivation letter (max half a page) on why attending the meeting (please enclose in the registration form). Please specify your science case/methodology. All applicants will be informed of their application outcome shortly after.
The workshop is limited to 20 people on-site (on first come, first served basis) but can of course have more people participating digitally, selected on a fair distribution basis across the research groups, giving priority to early career scientists.


Workshop fee for the on-site participant is 500 SEK for PhD students and 800 SEK for Postdocs and senior researchers. Fee includes meals and refreshments during the workshop. Applicants are required to cover the cost of their accommodation and the cost of their transport to Lund, Sweden.

There is no fee for digital participation.


Dr. Ferenc Borondics, SOLEIL, France
Dr. Christophe Sandt, SOLEIL, France
Dr. Marko Toplak, University of Ljubljana,  Slovenia


The Amyloid working group at LINXS and AI Lund (website)

Workshop agenda

Day 1
Getting started with Quasar (installation, basic Orange and Quasar functionality)
Basics of IR and Raman spectroscopy (lecture and presenting the need for statistical analysis)
Spectral Preprocessing
Advanced visualization
Statistical inspection of data
PCA, PCA imaging
Hands-on work with participants' data

 Day 2
Supervised learning
Introduction to supervised learning
Classification of spectra and hyperspectral datasets using various methods
Model inspection and cross-validation
Common errors
Hands-on work with participants' data

Day 3

Unsupervised learning
Introduction to unsupervised learning
Clustering of spectra and hyperspectral datasets using various methods
Common errors
Partial Least Squares regression
Hands-on work with participants' data

Workshop room, 5th floor
Ideon Delta 5 Scheelevägen 19 Lund

Digital participation: No Fee
On-site participation: 500 SEK for PhD students and 800 SEK for Postdocs and senior researchers.

Registration for this event is currently open.
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