JEDI trainings are themed workshops centered on scientific tools devised for doctoral students, engineers and all the partners of the Institute of Complex Systems of Paris. The registration is free and open ( you can fill in the form available on the relevant page upon selecting the training you would like to attend). These trainings are delivered by engineers who are pursuing research at ISC-PIF or other partner labs. These trainings are held thanks to the support of the DIM grants («Domaine d’intérêt Majeur»; «Major Research Area») on cross-cutting issues of complex systems.

Calendar
Previous trainings

04 mai 2017 : Gargantext

27 Avril 2017: QGIS I

23 Mars 2017: Multivac

30 Mars 2017: Apprendre Netlogo

30 Mars 2017: Modéliser avec Netlogo

23 février 2017 : Gargantext

2 février 2017 : NetLogo/OpenMole

17 Novembre 2016 : R

24 Novembre 2016 : OpenMOLE

19 Mai 2016: QGIS – partie 2

2 Juin 2016: Text Mining – Introduction

9 Juin 2016: Gargantext : faites un état de l\’art, une carte des sciences, analysez une base de documents, en moins d\’une heure.

23 Juin 2016: OpenMOLE – partie 1

12 Mai 2016: QGIS – partie 1

21 Avril 2016: GraphStream

14 Avril 2016: QGIS – partie 2

31 Mars 2016: Gargantext : faites un état de l\’art, une carte des sciences, analysez une base de documents, en moins d\’une heure.

17 Mars 2016: Chiffrer et signer ses données avec GPG

10 Mars 2016: QGIS – partie 1

25 Février 2016: Tout faire avec R !

4 Février 2016: OpenMOLE – partie 1

28 Janvier 2016: Gargantext : faites un état de l\’art, une carte des sciences, analysez une base de documents, en moins d\’une heure.

14 Janvier 2016: OpenMOLE avancé

10 Décembre 2015: LinkRBrain

26 Novembre 2015: Initiation à OpenMOLE

12 Novembre 2015: Tout faire avec R !

29 Octobre: Calibration, analyse de sensibilité et validation de modèles en simulation avec des algorithmes évolutifs

15 Octobre: Initiation à OpenMOLEe 1

1er juin 2017: QGIS II

8 juin 2017 : Gargantext

29 juin 2017: QGIS III

6 juillet 2017 : OpenMOL

formations at iscpif dot fr

Hey! Get in touch if you have any queries, want to suggest a training or if you wish to become a JEDI workshop facilitator…

formations at iscpif dot frMathieu Leclaire

Header GargantextGargantext is a web application that merges advanced tools for text-mining, network analysis and interactive visualisation; allowing new types of interaction with a given corpus. Gargantext enables its users to produce their own knowledge map or even to pinpoint the latest state of the art findings in a few minutes.


Header Text MiningText-mining refers to an in-depth scrutiny of texts in natural language. It processes sequences of character strings in view of obtaining a well structured set of information. It is a hybrid field that saw the light of day in the aftermath of an encounter between an array of several engineering issues awaiting to be addressed ; to cite a few of them: terms identification, themed classification of knowledge and opinions, extraction of phenomena from the news, etc.


Header OpenMoleNowadays, modellers are building calculation codes and methods that grow more complex by the day. Understanding the behaviour of models has become an arduous and daunting task for the very researchers who have devised them, not to mention that data processing on personal computers and machines has become impossible to achieve, resulting in a catch-22 situation. OpenMOLE, a digital platform putting forward high level methods of large-scale distribution, has been particularly devised to carry on these tasks and to meet this double challenge.


Header R«R» has been gaining prominence since its inception, 10 years ago. This (coding) language had about 30 packages ( libraries of functions) by the beginning of the 21st century (namely the years 2000s) before reaching 6000 thanks to the expansion it has undergone. Today, R allows its users to carry on statistical analyses, data manipulation, graph analysis, text analysis, spatial analysis, etc.


3MULTIVAC Platform aims at helping the scientific community to overcome large-scale datasets challenges. Our existing datasets are available in a variety ranging from structured, semi-structured to unstructured. Our infrastructure streams millions of data every day ; therefore, big data architectures are designed and implemented to not only help our internal projects and team members but also to allow other scientists and researchers to take advantage of our amazing services in scale of billions of data.


Netlogo is a language that is generally used to quickly build multi-agent models. However, the Netlogo platform proposes a small number of tools to execute stochastic models simultaneously . Throughout this training, we will see how the OpenMOLE platform meets this challenge through putting forward a large set of experiments and devolving them to distributed environments without any strain.