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LHCb Open Data Ntupling Service: On-demand production and publishing of custom LHCb Open Data
/ Aidala, Christine (U. Michigan, Ann Arbor) ; Fitzgerald, Dillon (U. Michigan, Ann Arbor) ; Habermann, Kai (U. Bonn) ; Kramer, Ludwig (U. Bonn) ; Morris, Adam (CERN) ; Neubert, Sebastian (U. Bonn) ; Nogga, Piet (U. Bonn) ; Rodrigues, Eduardo (U. Liverpool) ; Donadoni, Marco (CERN) ; Rosendal, Daan (CERN) et al.
The LHCb Ntupling Service enables on-demand production and publishing of LHCb Run 2 Open Data and aims at publishing them through the CERN Open Data Portal. [...]
arXiv:2504.00610.
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Fulltext
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On the importance of computational reproducibility in fostering Open and FAIR Science
/ Simko, Tibor (speaker) (CERN) ; Gruenpeter, Morane (speaker) (Software Heritage)
In this talk we propose to survey the computational reproducibility practices, opportunities and challenges in view of fostering Open and FAIR Science in research communities.
We discuss several thinking models regarding computational reproducibility, focusing on the broader knowledge preservation and reuse aspects rather than on the raw computing evolution aspects.
Building upon several use cases from experimental particle physics and related scientific disciplines, we discuss the variety of sociological and technological challenges inherent in making the research innately reproducible and reusable.
From the researcher point of view, we argue how "preproducibility" should come early in the scientific process in order to ensure its future reusability.
From the data infrastructure point of view, we argue how the data repository services benefit from accompanying "analysis engines" to ensure the correctness of data curation procedures of the validity of data usage recipes.
The ultimate goal of the Open Science and Data Preservation efforts is to facilitate future reuse and reinterpretation of scientific data by new generation of researchers. A strong focus on the computational reproducibility of original data analyses provides a way to facilitate the reuse and reinterpretation of Open and FAIR data even many years after the original publication.
This talk is heavily inspired, but not limited to, the experiences and lessons learnt from the past ten years of running the CERN Open Data portal and the REANA reproducible analysis platform for the particle physics community..
2025 - 2027.
Conferences; Open Science Fair 2025
External links: Talk details; Event details
In : Open Science Fair 2025
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Recommendations for Best Practices for Data Preservation and Open Science in HEP
/ Campana, Simone (CERN) ; Chakaberia, Irakli (LBL, Berkeley) ; Chen, Gang (Beijing, Inst. High Energy Phys.) ; Diaconu, Cristinel (Marseille, CPPM) ; Doglioni, Caterina (U. Manchester (main)) ; Fitzgerald, Dillon S. (U. Michigan, Ann Arbor) ; Garonne, Vincent (Brookhaven) ; Gentil-Beccot, Anne (CERN) ; Heiniger, Fleur (CERN) ; Hildreth, Michael D. (Notre Dame U.) et al.
These recommendations are the result of reflections by scientists and experts who are, or have been, involved in the preservation of high-energy physics data [...]
FERMILAB-PUB-25-0611-PPD ; arXiv:2508.18892.
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146.
Fermilab Library Server - Fulltext - Fulltext
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Scalable ATLAS pMSSM computational workflows using containerised REANA reusable analysis platform
/ Donadoni, Marco (CERN) ; Feickert, Matthew (U. Wisconsin, Madison (main)) ; Heinrich, Lukas (Munich, Max Planck Inst.) ; Liu, Yang (SYSU, Guangzhou) ; Mečionis, Audrius (CERN) ; Moisieienkov, Vladyslav (CERN) ; Šimko, Tibor (CERN) ; Stark, Giordon (UC, Santa Cruz, Inst. Part. Phys.) ; García, Marco Vidal (CERN)
In this paper we describe the development of a streamlined framework for large-scale ATLAS pMSSM reinterpretations of LHC Run-2 analyses using containerised computational workflows. The project is looking to assess the global coverage of BSM physics and requires running O(5k) computational workflows representing pMSSM model points. [...]
arXiv:2403.03494.-
2024 - 8 p.
- Published in : EPJ Web Conf. 295 (2024) 04035
Fulltext: document - PDF; 2403.03494 - PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.04035
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7.
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Second Analysis Ecosystem Workshop Report
/ Aly, Mohamed (Manchester U.) ; Burzynski, Jackson (Simon Fraser U.) ; Cardwell, Bryan (Virginia U.) ; Craik, Daniel C. (Zurich U.) ; van Daalen, Tal (Washington U., Seattle) ; Dado, Tomas (Dortmund U.) ; Das, Ayanabha (Prague, Tech. U.) ; Delgado Peris, Antonio (Madrid, CIEMAT) ; Doglioni, Caterina (Manchester U.) ; Elmer, Peter (Princeton U.) et al.
The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the needs of HL-LHC and DUNE, as well as the very pressing needs of LHC Run 3 analysis. [...]
arXiv:2212.04889 ; HSF-DOC-2022-02 ; FERMILAB-CONF-22-955-PPD.
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2022. - 27 p.
Fermilab Library Server - Fulltext - Fulltext
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Scalable Declarative HEP Analysis Workflows for Containerised Compute Clouds
/ Šimko, Tibor (CERN) ; Heinrich, Lukas Alexander (CERN) ; Lange, Clemens (CERN) ; Lintuluoto, Adelina Eleonora (CERN ; Helsinki U.) ; MacDonell, Danika Marina (Victoria U.) ; Mečionis, Audrius (CERN) ; Rodríguez Rodríguez, Diego (CERN) ; Shandilya, Parth (CERN ; LNM Inst. Info. Tech.) ; Vidal García, Marco (CERN)
We describe a novel approach for experimental High-Energy Physics (HEP) data analyses that is centred around the declarative rather than imperative paradigm when describing analysis computational tasks. The analysis process can be structured in the form of a Directed Acyclic Graph (DAG), where each graph vertex represents a unit of computation with its inputs and outputs, and the graph edges describe the interconnection of various computational steps. [...]
2021 - 12 p.
- Published in : 10.3389/fdata.2021.661501
Fulltext: PDF;
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Large-scale HPC deployment of Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN)
/ Hildreth, Michael (Notre Dame U.) ; Hurtado Anampa, Kenyi Paolo (Notre Dame U.) ; Kankel, Cody (Notre Dame U.) ; Hampton, Scott (Notre Dame U.) ; Brenner, Paul (Notre Dame U.) ; Johnson, Irena (Notre Dame U.) ; Simko, Tibor (CERN)
The NSF-funded Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN) project aims to develop and deploy artificial intelligence (AI) and likelihood-free inference (LFI) techniques and software using scalable cyberinfrastructure (CI) built on top of existing CI elements. Specifically, the project has extended the CERN-based REANA framework, a cloud-based data analysis platform deployed on top of Kubernetes clusters that was originally designed to enable analysis reusability and reproducibility. [...]
2020 - 6 p.
- Published in : EPJ Web Conf. 245 (2020) 09011
Fulltext by publisher: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.09011
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Open data provenance and reproducibility: a case study from publishing CMS open data
/ Šimko, Tibor (CERN) ; de Bittencourt, Heitor Pascoal (Helsinki Inst. of Phys.) ; Carrera, Edgar (San Francisco de Quito U.) ; Lopez, Diyaselis Delgado (Unlisted, PR) ; Lange, Clemens (CERN) ; Lassila-Perini, Kati (Helsinki Inst. of Phys.) ; Lintuluoto, Adelina (CERN ; Helsinki Inst. of Phys.) ; Iglesias, Lara Lloret (Cantabria Inst. of Phys.) ; McCauley, Thomas (Notre Dame U.) ; Okraska, Jan (CERN) et al.
In this paper we present the latest CMS open data release published on the CERN Oopen Data portal. Samples of collision and simulated datasets were released together with detailed information about the data provenance. [...]
2020 - 8 p.
- Published in : EPJ Web Conf. 245 (2020) 08014
Fulltext from publisher: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08014
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