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维普期刊数据库

*** Provenance-aware and Reproducible Workspaces

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Data Intelligence 2022年第2期4卷 243-258页

作者： alessandro spinuso Mats Veldhuizen Daniele Bailo Valerio Vinciarelli Tor Langeland Koninkljk Nederlands Meteorologisch Instituut De BiltUtrecht 3731GAThe Netherlands Istituto Nazionale Geofisica e Vulcanologia RomeLazio 00143Italy NORCE Norwegian Research Centre AS BergenHordaland NO-5838Norway

Modern interactive tools for data analysis and visualisation are designed to expose their functionalities as a service through the *** present in this paper a Web API(SWIRRL)that allows Virtual Research Environments(VREs)to easily integrate such tools in their websites and re-purpose them to their *** APl deals,on behalf of the clients,with the underlying complexity of allocating and managing resources within a target cloud *** combining storage and containerised services,offering analysis notebooks and other visualisation software,the APl creates dedicated working sessions on-demand,which can be accessed *** to the API's support for workflow execution,SWIRRL workspaces can be automatically populated with data of interest collected from external data *** system keeps track of updates and changes affecting the data and the tools by adopting versioning and standard provenance *** are provided with interactive controls enabling traceabilityand recovery actions,including the possibility of creating executable snapshots of their *** is built in cooperation with two research infrastructures in the field of solid earth science and climate data *** report on the particularadoptions and use cases.

关键词： Productivity Data-analysis Reproducibility Provenance Cloud-computing

维普期刊数据库

Editors' Note:Special Issue on Canonical Workflow FrameworksforResearch

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Data Intelligence 2022年第2期4卷 149-154页

作者： Peter Wittenburg Alex Hardisty Amirpasha Mozzafari Limor Peer Nikolay Skvortsov alessandro spinuso Zhiming Zhao Gemeindweg 55 47533 KleveGermany Cardiff University CardiffSouth GlamorganCF143UXWalesUK Forschungszentrum Julich GmbH 52425 JulichGermany Institution for Social and Policy Studies Yale UniversityNew HavenCT 06520USA Vavilov 44/2 121351 MoscowRussia Utrechtseweg 297 3731 GA De Biltthe Netherlands University of Amsterdam PO-Box 943231090 GH Amsterdamthe Netherlands

This special issue is on Canonical Workflow Frameworks for Research(CWFR).A workflow refers to a sequence of activities,which may be more or less computer-based,used with regularity in the research *** aim to identify common patterns in such scientifically motivated workflows and to offer libraries of components based on FAIR Digital Objects as the integrative standard.

关键词： Workflow computer process.

维普期刊数据库

Canonical Workflows to Make Data FAIR

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Data Intelligence 2022年第2期4卷 286-305页

作者： Peter Wittenburg Alex Hardisty Yann Le Franc Amirpasha Mozaffari Limor Peer Nikolay A.Skvortsov Zhiming Zhao alessandro spinuso FDO Forum Cardiff University e Science Factory Forschungszentrum Jülich GmbH Yale University Russian Academy of Sciences University of Amsterdam Royal Netherlands Meteorological Institute (KNMI)

The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices, yet the incentives for researchers to change their practices are presently weak. In addition, data-driven science has been slow to embrace workflow technology despite clear evidence of recurring practices. To overcome these challenges, the Canonical Workflow Frameworks for Research(CWFR) initiative suggests a large-scale introduction of self-documenting workflow scripts to automate recurring processes or fragments thereof. This standardised approach, with FAIR Digital Objects as anchors, will be a significant milestone in the transition to FAIR data without adding additional load onto the researchers who stand to benefit most from it. This paper describes the CWFR approach and the activities of the CWFR initiative over the course of the last year or so, highlights several projects that hold promise for the CWFR approaches, including Galaxy, Jupyter Notebook, and RO Crate, and concludes with an assessment of the state of the field and the challenges ahead.

关键词： Workflow Data management FAIR Principles Digital Objects

Canonical Workflows to Make Data FAlR

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Data Intelligence 2022年第2期4卷 286-305页

作者： Peter Wittenburg Alex Hardisty Yann Le Franc Amirpasha Mozaffari Limor Peer Nikolay A.Skvortsov Zhiming Zhao alessandro spinuso FDO Forum Gemeindweg 5547533 KleveGermany Cardiff University CardiffWales CF103ATUK eScience Factory 75570 Paris Cedex 12France Forschungszentrum Julich GmbH 52425 JulichGermany Yale University New HavenCT 06520USA Russian Academy of Sciences 121351 MoscowRussia University of Amsterdam PO-Box 943231090 GH AmsterdamThe Netherlands Royal Netherlands Meteorological Institute(KNMI) Utrechtseweg 2973731 GA De BiltThe Netherlands

The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices,yet the incentives for researchers to change their practices are presently *** addition,data-driven science has been slow to embrace workflow technology despite clear evidence of recurring *** overcome these challenges,the Canonical Workflow Frameworks for Research(CWFR)initiative suggests a large-scale introduction of self-documenting workflow scripts to automate recurring processes or fragments *** standardised approach,with FAIR Digital Objects as anchors,will be a significant milestone in the transition to FAIR data without adding additional load onto the researchers who stand to benefit most from *** paper describes the CWFR approach and the activities of the CWFR initiative over the course of the last year or so,highlights several projects that hold promise for the CWFR approaches,including Galaxy,Jupyter Notebook,and RO Crate,and concludes with an assessment of the state of the field and the challenges ahead.

关键词： Workflow Data management FAIR Principles Digital Objects