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Mapping platforms into a new open science model for machine learning

dc.contributor.authorWeißgerber, Thomas
dc.contributor.authorGranitzer, Michael
dc.date.accessioned2021-06-21T12:16:42Z
dc.date.available2021-06-21T12:16:42Z
dc.date.issued2019
dc.description.abstractData-centric disciplines like machine learning and data science have become major research areas within computer science and beyond. However, the development of research processes and tools did not keep pace with the rapid advancement of the disciplines, resulting in several insufficiently tackled challenges to attain reproducibility, replicability, and comparability of achieved results. In this discussion paper, we review existing tools, platforms and standardization efforts for addressing these challenges. As a common ground for our analysis, we develop an open science centred process model for machine learning research, which combines openness and transparency with the core processes of machine learning and data science. Based on the features of over 40 tools, platforms and standards, we list the, in our opinion, 11 most central platforms for the research process in this paper. We conclude that most platforms cover only parts of the requirements for overcoming the identified challenges.en
dc.identifier.doi10.1515/itit-2018-0022
dc.identifier.pissn2196-7032
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/36660
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofit - Information Technology: Vol. 61, No. 4
dc.subjectMachine Learning Research
dc.subjectOpen Science
dc.subjectReproducibility
dc.subjectReplicability
dc.subjectUnderstandability
dc.subjectPlatforms
dc.subjectResearch Processes
dc.titleMapping platforms into a new open science model for machine learningen
dc.typeText/Journal Article
gi.citation.endPage208
gi.citation.publisherPlaceBerlin
gi.citation.startPage197

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