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Contract number
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As of 15.02.2021

Number of staff members
scientific publications
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General information

Name of the project: Big data technologies for mega-science projects

Strategy for Scientific and Technological Development Priority Level: а

Goals and objectives

Research directions: Big data, mega-science

The practical value of the study

Project objective: Creation of a big data laboratory at the Kurchatov Institute for research and development in data processing and analysis of ultra high volumes of data

Development and support of PanDa workload management system for data analysis of the ATLAS experiment at the Large Hadron Collider (LHC). PanDa software was used to provision successful and efficient analysis of data collected in the first two runs of LHC. Also, software was developed for analysis of metadata of scientific publications and their automatic accounting.

The automated workflow processing platform was developed to process data in single particle imaging experiments at x-ray free electron lasers. The platform allows you to automatically reconstruct the structure of the object under study using raw experimental data. The structure of bacteriophage pr772 was reconstructed with a resolution better than 7 nm, which is an improvement over previously achieved results in single particle imaging experiments.

Implemented results of research:

PanDa software is used to manage computational tasks for data analysis in LHC experiments


Deutsches Elektronen-Synchrotron, Germany - Collaborative Research. As a result of the collaboration, two joint single particle imaging experiments were carried on the European XFEL (p2145 and p2146). Together with experiment amox34117 at LCLS (Stanford, USA, September 2018), they were analysed and the structure of bacteriophage pr772 was reconstructed. Results were published in IUCrJ. The automated workflow processing platform was jointly developed to analyse data in single particle imaging experiment at free electron lasers.

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Aad G. et al.
Combined Measurement of the Higgs Boson Mass in p p Collisions at s= 7 and 8 TeV with the ATLAS and CMS Experiments //Physical review letters. – 2015. – Т. 114. – №. 19. – С. 191803.
Aad G. et al.
Muon reconstruction performance of the ATLAS detector in proton-proton collision data at root s=13 TeV //The European Physical Journal C. – 2016. – Т. 76. – №. 5. – С. 292.
Golosova M. et al.
PanDA Workload Management System meta-data segmentation //Procedia Computer Science. – 2015. – Т. 66. – С. 448-457.
Borodin M. et al.
Scaling up ATLAS production system for the LHC Run 2 and beyond: project ProdSys2 //Journal of Physics: Conference Series. – IOP Publishing, 2015. – Т. 664. – №. 6. – С. 062005.
Klimentov A. et al.
BigData and computing challenges in high energy and nuclear physics //Journal of Instrumentation. – 2017. – Т. 12. – №. 06. – С. C06044.
Barreiro F. H. et al.
The ATLAS production system evolution: new data processing and analysis paradigm for the LHC Run2 and high-luminosity //Journal of Physics: Conference Series. – IOP Publishing, 2017. – Т. 898. – №. 5. – С. 052016.
Assalauova D. et al.
An advanced workflow for single-particle imaging with the limited data at an X-ray free-electron laser //IUCrJ. – 2020. – Т. 7. – №. 6.
Teslyuk A. et al.
Development of Experimental Data Processing Workflows Based on Kubernetes Infrastructure and REANA Workflow Management System //Russian Supercomputing Days. – Springer, Cham, 2020. – С. 563-573.
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