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Novel proposals for FAIR, automated, recommendable, and robust workflows

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dc.contributor.authorAbhinit, Ishan
dc.contributor.authorAdams, Emily K.
dc.contributor.authorAlam, Khairul
dc.contributor.authorChase, Brian
dc.contributor.authorDeelman, Ewa
dc.contributor.authorGorenstein, Lev
dc.contributor.authorHudson, Stephen
dc.contributor.authorIslam, Tanzima
dc.contributor.authorLarson, Jeffrey
dc.contributor.authorLentner, Geoffrey
dc.contributor.authorMandal, Anirban
dc.contributor.authorNavarro, John-Luke
dc.contributor.authorNicolae, Bogdan
dc.contributor.authorPouchard, Line
dc.contributor.authorRoss, Rob
dc.contributor.authorRoy, Banani
dc.contributor.authorRynge, Mats
dc.contributor.authorSerebrenik, Alexander
dc.contributor.authorVahi, Karan
dc.contributor.authorWild, Stefan
dc.contributor.authorXin, Yufeng
dc.contributor.authorFerreira da Silva, Rafael
dc.contributor.authorFilgueira, Rosa
dc.date.accessioned2023-02-17T15:30:05Z
dc.date.available2023-02-17T15:30:05Z
dc.date.issued2022-11-13
dc.identifier281632700
dc.identifier3cf33f73-2786-4352-9a1e-638b57483edf
dc.identifier85147541427
dc.identifier.citationAbhinit , I , Adams , E K , Alam , K , Chase , B , Deelman , E , Gorenstein , L , Hudson , S , Islam , T , Larson , J , Lentner , G , Mandal , A , Navarro , J-L , Nicolae , B , Pouchard , L , Ross , R , Roy , B , Rynge , M , Serebrenik , A , Vahi , K , Wild , S , Xin , Y , Ferreira da Silva , R & Filgueira , R 2022 , Novel proposals for FAIR, automated, recommendable, and robust workflows . in 2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS) . , 10023942 , IEEE , pp. 84-92 , 17th Workshop on Workflows in Support of Large-Scale Science , Dallas , Texas , United States , 14/11/22 . https://doi.org/10.1109/works56498.2022.00016en
dc.identifier.citationworkshopen
dc.identifier.isbn9781665451925
dc.identifier.isbn9781665451918
dc.identifier.urihttps://hdl.handle.net/10023/27007
dc.descriptionFunding: This work is partly funded by NSF award OAC-1839900. This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. libEnsemble was developed as part of the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This research used resources of the OLCF at ORNL, which is supported by the Office of Science of the U.S. DOE under Contract No. DE-AC05-00OR22725.en
dc.description.abstractLightning talks of the Workflows in Support of Large-Scale Science (WORKS) workshop are a venue where the workflow community (researchers, developers, and users) can discuss work in progress, emerging technologies and frameworks, and training and education materials. This paper summarizes the WORKS 2022 lightning talks, which cover five broad topics: data integrity of scientific workflows; a machine learning-based recommendation system; a Python toolkit for running dynamic ensembles of simulations; a cross-platform, high-performance computing utility for processing shell commands; and a meta(data) framework for reproducing hybrid workflows.
dc.format.extent9
dc.format.extent1696333
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartof2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS)en
dc.subjectScientific workflowsen
dc.subjectFAIR principlesen
dc.subjectHigh performance computingen
dc.subjectData integrityen
dc.subjectEnsemblesen
dc.subjectMachine learningen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectComputer Science(all)en
dc.subject3rd-DASen
dc.subjectNISen
dc.subjectMCCen
dc.subject.lccQA75en
dc.titleNovel proposals for FAIR, automated, recommendable, and robust workflowsen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doi10.1109/works56498.2022.00016
dc.date.embargoedUntil2022-11-13
dc.identifier.urlhttps://doi.org/10.1109/WORKS56498.2022en


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