OPEN SOFTWARE INITIATIVE (OSI)
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1 OPEN SOFTWARE INITIATIVE (OSI) AT OCT
2 DATA SCIENCE IN THE WORLD 2
3 UNIVERSITÉ PARIS-SACLAY 19 founding partners 3
4 UNIVERSITÉ PARIS-SACLAY + horizontal multi-disciplinary and multipartner initiatives to create cohesion 4
5 machine learning information retrieval signal processing data visualization databases Tool building Domain science A multi-disciplinary initiative to define, structure, and manage the data science ecosystem at the Université Domain scientist software engineering clouds/grids high-performance computing optimization human society life brain earth universe Software engineer researchers in 35 laboratories Biology & bioinformatics IBISC/UEvry LRI/UPSud Hepatinov CESP/UPSud-UVSQ-Inserm IGM-I2BC/UPSud MIA/Agro MIAj-MIG/INRA LMAS/Centrale Chemistry EA4041/UPSud Earth sciences LATMOS/UVSQ GEOPS/UPSud IPSL/UVSQ LSCE/UVSQ LMD/Polytechnique Economy LM/ENSAE RITM/UPSud LFA/ENSAE Machine learning LRI/UPSud LTCI/Telecom CMLA/Cachan LS/ENSAE LIX/Polytechnique MIA/Agro CMA/Polytechnique LSS/Supélec CVN/Centrale LMAS/Centrale DTIM/ONERA IBISC/UEvry Neuroscience UNICOG/Inserm U1000/Inserm NeuroSpin/CEA Particle physics astrophysics & cosmology LPP/Polytechnique DMPH/ONERA CosmoStat/CEA IAS/UPSud AIM/CEA LAL/UPSud datascience-paris-saclay.fr LIST/CEA Visualization INRIA LIMSI 5 Signal processing LTCI/Telecom CMA/Polytechnique CVN/Centrale LSS/Supélec CMLA/Cachan LIMSI DTIM/ONERA Statistics LMO/UPSud LS/ENSAE LSS/Supélec CMA/Polytechnique LMAS/Centrale MIA/AgroParisTech
6 WHAT IS THE CHALLENGE? As the flow of data increases, it is increasingly processed, analyzed, and acted upon by machines, not humans. NYU-CDS manifesto 6
7 TOOLS: LANDSCAPE TO ECOSYSTEM Data scientist Data engineer coding sprints Open Software Initiative code consolidator and engineering projects Software engineer Tool building software engineering clouds/grids high-performance computing optimization Data science statistics machine learning information retrieval signal processing data visualization databases Data domains Applied scientist interdisciplinary projects matchmaking tool design and innovation strategy workshops energy and physical sciences health and life sciences Earth and environment economy and society brain data science RAMPs and TSs IT platform for linked data annotation tools SaaS data science platform Domain expert Data trainer 7
8 Open Software Initiative (OSI) 8
9 CODING SPRINTS 9
10 PHD STUDENTS PROJECTS Objective: Strengthen students skills in software engineering by contributing to scientific open source software ENGINEERING PROJECTS Objective: Consolidate academic software, foster good engineering practice between labs, improve the tools for research 10
11 Data Challenges 11
12 DATA CHALLENGES 12 17
13 Beyond Data Challenges 13
14 RAPID ANALYTICS AND MODEL PROTOTYPING (RAMP) Prototyping Training Collaboration building 14
15 RAMPS Single-day coding sessions participants preparation is similar to challenges Goals focusing and motivating top talents promoting collaboration, speed, and efficiency solving (prototyping) real problems 15
16 ANALYTICS TOOLS TO PROMOTE COLLABORATION AND CODE REUSE 16
17 ANALYTICS TOOL TO PROMOTE COLLABORATION AND CODE REUSE 17
18 RAPID ANALYTICS AND MODEL PROTOTYPING 2015 Apr 10 Classifying variable stars 18
19 RAPID ANALYTICS AND MODEL PROTOTYPING 2015 June 16 and Sept 26 Predicting El Nino 19
20 RAPID ANALYTICS AND MODEL PROTOTYPING 2015 October 8 Insect classification 20
21 RAPID ANALYTICS AND MODEL PROTOTYPING 2015 Fall Drug identification from spectra 21
22 Giving access to data 22
23 IT PLATFORM FOR LINKED DATA A window to open data at We are not storing or handling existing large data sets Rather indexing, linking, and mapping, embedding in the worldwide linked data (RDF) ecosystem Storing small data sets of small teams is possible Subsets of large sets for prototyping Or simply store metadata plus pointer 23
24 IT PLATFORM FOR LINKED DATA 24
25 TODAY S KEYNOTES Fernando Perez Univ. California Berkeley Andreas Mueller New York University 25
26 TODAY S PROGRAM 9h00-9h15 Alexandre Gramfort, Welcome talk 9h15-10h15 Fernando Perez, UC Berkeley 10h15-10h30 Oscar Najera, Sphinx-Gallery: Package documentation made easy 10h30-11h00 Coffee Break 11h00-11h15 Mehdi Cherti: Py-Earth: Multivariate Adaptive Regression Splines in Python 11h15-11h30 Lorenzo Desantis, MNE from shell scripts and Unix commands to Python 11H30-12h00 Sarah Cohen Boulakia, Reproducible research in Bioinformatics 12h00-12h15 Karin Dassas, LoOPS Network for developers at 12h15-12h30 Cécile Germain, The io.datascience DaaS platform 12h30-14h00 Lunch break at Proto h00-14h20 Miguel Colom, A service-oriented platform for online physiological data analysis 14h20-14h40 Diem BUI THI, Automation for chemical data analysis techniques 14h40-15h00 Estelle Chaix, Information Extraction Challenge for Gene Regulation Network in plant 15h00-15h30 Coffee Break 15h30-15h50 Loic Estève, Mining brain imaging data: lessons learned from nilearn and joblib 15h50-16h10 J. Duperrier & D. Guarino, A customizable framework for neurophysiology data management and provenance tracking 16h10-16h25 Nicolas Goix, Anomaly detection algorithms in Scikit-Learn 16h25-16h40 Romain Brault, Structured Prediction with Opera-Lib 26 16h40-17h30 Andreas Mueller, Software and engineering efforts at NYU Center for Datascience
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