Reference number : GC-2017-Q1-03396, amendment number : , agreement type : G, recipient legal name : Glatard, Tristan (Concordia University)|Glatard, Tristan (Université Concordia), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The objective of my research program is to automate Big Data analyses from data processing to knowledge publication. In the next years, I will focus on the following three objectives of this immense challenge. (1) Interoperability among Big Data platforms: Big Data platforms used in science currently operate in silos, which hinders open science, reduces the overall quality of platforms by limiting competition, and creates technological dependence on particular software projects. I will design the building blocks of a decentralized network connecting platforms so that data, processing pipelines and analyses can be uniformly found, accessed and reused in various platforms. (2) Reproducibility of Big Data analyses over time and space: science is going through a severe reproducibility crisis, which, if not properly addressed, will prevent various disciplines from leveraging the tremendous wealth of data that is acquired: Big Data has to be converted into trusted knowledge. I will develop methods to identify, quantify and correct reproducibility issues, focusing on the challenges that originate in the computing infrastructure. (3) Performance optimization of Big Data computations: while they have been transformational for industry, Big Data technologies remain underused in science, due to the lack of appropriate benchmarks and performance optimization studies. I will design and create an optimized, easy-to-use Big Data processing environment for large scientific datasets. These three objectives echo the notorious V&rsquo;s of Big Data: interoperability addresses data Variety, reproducibility targets Veracity, and performance provides Velocity and manages data Volume. 
 
Overall, this program will speed up and improve the quality of knowledge production. It will foster cyberinfrastructure automation, by allowing scientific objects to seamlessly move across platforms, by ensuring the consistency of results computed in different platforms and by accelerating Big Data analyses. While potential applications of this research span the whole spectrum of scientific disciplines engaged in data science, I will focus on neuroinformatics, exploiting my long-standing top-level collaborations in this field and leveraging the tremendous experience acquired with the development and operation of the VIP and CBRAIN platforms. Technology transfer to the Canadian industry is also expected, in the IT (Big Data and cloud) and pharmaceutical sectors., program purpose french : The objective of my research program is to automate Big Data analyses from data processing to knowledge publication. In the next years, I will focus on the following three objectives of this immense challenge. (1) Interoperability among Big Data platforms: Big Data platforms used in science currently operate in silos, which hinders open science, reduces the overall quality of platforms by limiting competition, and creates technological dependence on particular software projects. I will design the building blocks of a decentralized network connecting platforms so that data, processing pipelines and analyses can be uniformly found, accessed and reused in various platforms. (2) Reproducibility of Big Data analyses over time and space: science is going through a severe reproducibility crisis, which, if not properly addressed, will prevent various disciplines from leveraging the tremendous wealth of data that is acquired: Big Data has to be converted into trusted knowledge. I will develop methods to identify, quantify and correct reproducibility issues, focusing on the challenges that originate in the computing infrastructure. (3) Performance optimization of Big Data computations: while they have been transformational for industry, Big Data technologies remain underused in science, due to the lack of appropriate benchmarks and performance optimization studies. I will design and create an optimized, easy-to-use Big Data processing environment for large scientific datasets. These three objectives echo the notorious V&rsquo;s of Big Data: interoperability addresses data Variety, reproducibility targets Veracity, and performance provides Velocity and manages data Volume. 
 
Overall, this program will speed up and improve the quality of knowledge production. It will foster cyberinfrastructure automation, by allowing scientific objects to seamlessly move across platforms, by ensuring the consistency of results computed in different platforms and by accelerating Big Data analyses. While potential applications of this research span the whole spectrum of scientific disciplines engaged in data science, I will focus on neuroinformatics, exploiting my long-standing top-level collaborations in this field and leveraging the tremendous experience acquired with the development and operation of the VIP and CBRAIN platforms. Technology transfer to the Canadian industry is also expected, in the IT (Big Data and cloud) and pharmaceutical sectors., agreement title english : Big Data platforms for science automation, agreement title french : Big Data platforms for science automation, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada