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| Hauptseite > Publikationsdatenbank > Canonical Workflows to Make Data FAIR > print |
| 001 | 906811 | ||
| 005 | 20230712161210.0 | ||
| 024 | 7 | _ | |2 doi |a 10.1162/dint_a_00132 |
| 024 | 7 | _ | |2 ISSN |a 2096-7004 |
| 024 | 7 | _ | |2 ISSN |a 2641-435X |
| 024 | 7 | _ | |2 Handle |a 2128/31435 |
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| 037 | _ | _ | |a FZJ-2022-01708 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 020 |
| 100 | 1 | _ | |0 P:(DE-HGF)0 |a Wittenburg, Peter |b 0 |e Corresponding author |
| 245 | _ | _ | |a Canonical Workflows to Make Data FAIR |
| 260 | _ | _ | |a Cambridge, MA |b MIT Press |c 2022 |
| 336 | 7 | _ | |2 DRIVER |a article |
| 336 | 7 | _ | |2 DataCite |a Output Types/Journal article |
| 336 | 7 | _ | |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |a Journal Article |b journal |m journal |s 1672836893_22540 |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 ORCID |a JOURNAL_ARTICLE |
| 336 | 7 | _ | |0 0 |2 EndNote |a Journal Article |
| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Hardisty, Alex |b 1 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Franc, Yann Le |b 2 |
| 700 | 1 | _ | |0 P:(DE-Juel1)166264 |a Mozaffari, Amirpasha |b 3 |u fzj |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Peer, Limor |b 4 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Skvortsov, Nikolay A. |b 5 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Zhao, Zhiming |b 6 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Spinuso, Alessandro |b 7 |
| 773 | _ | _ | |0 PERI:(DE-600)2973844-1 |a 10.1162/dint_a_00132 |g p. 1 - 20 |n 2 |p 286–305 |t Data Intelligence |v 4 |x 2096-7004 |y 2022 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/906811/files/dint_a_00132%20%281%29.pdf |y OpenAccess |
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