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Dernière mise à jour : Mai 2018

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ChemProject

2020-10-15 Training CheMoocs-ChemFlow

Training in multivariate data processing tools

Training in multivariate data processing tools on October 15-16, 2020 at INRAE Nantes, organized by the TRANSFORM department, ChemHouse and STAT -SC

Presentation

Following the INRAE ​​“Data mining” day on November 25, 2019, the TRANSFORM coordinators with experts from the ChemHouse and Statsc networks are offering you on October 15 and 16 in Nantes two days of work on your own data sets to find out how to use them. the best information with mathematical and statistical tools. Participation in these days is limited to 45 people and requires skills in data processing (possibility of following the MOOC CheMoocs before the days). You will find more details on the attached brochure.

Public

You are a researcher, engineer, doctoral student or post-doctoral student or a researcher / doctoral student
You generate multivariate data through your experiments (IR-NMR-Raman spectra, sensory data, spectra
mass, etc.), you want to better explore and describe these datasets, in particular by reducing their dimension
and better understanding the data structure.

  • Either you have already followed chemometrics MOOCs, in particular CheMOOCs, you carry out PCA or PLS but you want to deepen your skills in multivariate data processing, or work on spectral data sets. Come deepen your skills and discuss with experts from the CHEMHOUSE network and STATSC in particular.
  • Either you do not yet have this expertise but are ready to follow some grains of MOOCs by mid-October (estimated investment time of ~ 40 hours) because you want to improve your data processing, start apply these principles to your datasets and come and discuss these treatments during the in-depth days

What skills will you acquire?

Mathematical / statistical methods and tools that will allow you to acquire as much information as possible
from your data: from their principle to their application on your data sets using the Chemflow software.

Context/objectives

As part of the scientific animation of the TRANSFORM department, a
initial online training (content of 2 MOOCs) then two days of deepening skills in
multivariate data processing are proposed. These days will make it possible to pool the tools of
chemometrics / data analysis set up in particular by CHEMHOUSE chemometrics as well as
STATSC statisticians.

Registration

Registration by sending an email to the list: chemflow-formation@inrae.fr, specifying your name, function, unit of
connection, types of data to analyze and expectations. Deadline for registration September 1, 2020.
Please note the training is limited to 45 people including trainers !!!
The training is free and only the accommodation / transport costs for the deepening days are payable.
charge of the participant's Unit.

Prerequisites

  1. Follow or have followed the 2 CheMOOCs MOOCs available in open archives on the FUN platform (Chemometrics chapter 1/2 unsupervised methods https://www.fun-mooc.fr/courses/course-v1:Agreenium+66002+session04/ about then Chapter 2 supervised methods https://www.fun-mooc.fr/courses/course-v1:Agreenium+66002+session04_2/about - training to follow at your own pace and in priority grains 1-4, 8-9 , 11;
  2. Upload your data into Chemflow (https://vm-chemflow-francegrille.eu/) before the in-depth days - but we will get back to you with instructions until then! To access Chemflow, first send an email to chemflow@chemproject.org to open a Chemflow account.

Advanced days program

Day 1

  • Presentation of the Galaxy platform and getting started / Importing data
  • ACP + PLS under Chemflow

Day 2

Practical work on participant data

Team

Trainers / Organizing Committee

Belal Gaci, Virginie Rossard, Eric Latrille, Martin Ecarnot, JeanMichel Roger, Jean-Claude Boulet, Maxime Metz, Fabien Goge, Philippe Courcoux, Benoit Jaillais and Mohamed Hanafi.

TRANSFORM Organizing Committee

Claire Bourlieu and Gabriel Paës with the help of Cesar Aceves, Rallou Thomopoulos
and the support of Laurence Prévosto.