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dc.contributor.authorBeg, Marijan
dc.contributor.authorBelin, Juliette
dc.contributor.authorKluyver, Thomas
dc.contributor.authorKonovalov, Alexander
dc.contributor.authorRagan-Kelley, Min
dc.contributor.authorThiery, Nicolas
dc.contributor.authorFangohr, Hans
dc.date.accessioned2021-03-03T11:30:02Z
dc.date.available2021-03-03T11:30:02Z
dc.date.issued2021-01-15
dc.identifier.citationBeg , M , Belin , J , Kluyver , T , Konovalov , A , Ragan-Kelley , M , Thiery , N & Fangohr , H 2021 , ' Using Jupyter for reproducible scientific workflows ' , Computing in Science and Engineering , vol. Early Access . https://doi.org/10.1109/MCSE.2021.3052101en
dc.identifier.issn1521-9615
dc.identifier.otherPURE: 272713944
dc.identifier.otherPURE UUID: 68fa4ba8-e35c-4f62-a1fa-8db302f3661b
dc.identifier.otherScopus: 85099730291
dc.identifier.otherWOS: 000638203500003
dc.identifier.urihttp://hdl.handle.net/10023/21544
dc.descriptionFunding: This work was financially supported by the OpenDreamKit Horizon 2020 European Research Infrastructure project (676541) and the EPSRC Programme grant on Skyrmionics (EP/N032128/1).en
dc.description.abstractLiterate computing has emerged as an important tool for computational studies and open science, with growing folklore of best practices. In this work, we report two case studies - one in computational magnetism and another in computational mathematics - where a dedicated software was exposed into the Jupyter environment. This enabled interactive and batch computational exploration of data, simulations, data analysis, and workflow documentation and outcome in Jupyter notebooks. In the first study, Ubermag drives existing computational micromagnetics software through a domain-specific language embedded in Python. In the second study, a dedicated Jupyter kernel interfaces with the GAP system for computational discrete algebra and its dedicated programming language. In light of these case studies, we discuss the benefits of this approach, including progress towards more reproducible and re-usable research results and outputs, notably through the use of infrastructure such as JupyterHub and Binder.
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofComputing in Science and Engineeringen
dc.rightsCopyright © IEEE 2020. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/MCSE.2021.3052101.en
dc.subjectJupyteren
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectComputer Science(all)en
dc.subjectEngineering(all)en
dc.subject3rd-DASen
dc.subject.lccQA75en
dc.titleUsing Jupyter for reproducible scientific workflowsen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.Centre for Interdisciplinary Research in Computational Algebraen
dc.contributor.institutionUniversity of St Andrews.St Andrews GAP Centreen
dc.identifier.doihttps://doi.org/10.1109/MCSE.2021.3052101
dc.description.statusPeer revieweden


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