The Open Science of Deep Learning: Three Case Studies

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dc.contributor.authorMiller, Chrestonen
dc.contributor.authorHamilton, Leahen
dc.contributor.authorLahne, Jacoben
dc.date.accessioned2023-05-17T14:49:44Zen
dc.date.available2023-05-17T14:49:44Zen
dc.date.issued2023-02-15en
dc.date.updated2023-05-17T14:46:29Zen
dc.description.abstractObjective: An area of research in which open science may have particularly high impact is in deep learning (DL), where researchers have developed many algorithms to solve challenging problems, but others may have difficulty in replicating results and applying these algorithms. In response, some researchers have begun to open up DL research by making their resources available (e.g., code, datasets and/or pre-trained models) to the research community. This article describes three case studies in DL where openly available resources are used and we investigate the impact on the projects, the outcomes, and make recommendations for what to focus on when making DL resources available. Methods: Each case study represents a single project using openly available DL resources for a research project. The process and progress of each case study is recorded along with aspects such as approaches taken, documentation of openly available resources, and researchers’ experience with the openly available resources. The case studies are in multiple-document text summarization, optical character recognition (OCR) of thousands of text documents, and identifying unique language descriptors for sensory science. Results: Each case study was a success but had its own hurdles. Some takeaways are well-structured and clear documentation, code examples and demos, and pre-trained models were at the core to the success of these case studies. Conclusions: Openly available DL resources were the core of the success of our case studies. The authors encourage DL researchers to continue to make their data, code, and pre-trained models openly available where appropriate.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.7191/jeslib.626en
dc.identifier.issn2161-3974en
dc.identifier.issue1en
dc.identifier.orcidMiller, Chreston [0000-0003-4276-0537]en
dc.identifier.orcidLahne, Jacob [0000-0002-2344-1816]en
dc.identifier.urihttp://hdl.handle.net/10919/115089en
dc.identifier.volume12en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleThe Open Science of Deep Learning: Three Case Studiesen
dc.title.serialJournal of eScience Librarianshipen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciencesen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/Food Science and Technologyen
pubs.organisational-group/Virginia Tech/Libraryen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/CALS T&R Facultyen
pubs.organisational-group/Virginia Tech/Library/Research, Learning, and Informaticsen
pubs.organisational-group/Virginia Tech/Library/Research, Learning, and Informatics/Data Servicesen
pubs.organisational-group/Virginia Tech/Library/Research, Learning, and Informatics/Data Services/Informatics Lab & Data Services Projectsen

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