NFDI4DataScience registry for reproducible Data Science and Artificial Intelligence
FAIR registry
NFDI4DS
Reproducibility
DOI:
10.5281/zenodo.7129715
Publication Date:
2022-09-30
AUTHORS (6)
ABSTRACT
{'references': ['1. \\tWilkinson MD, Dumontier M, Aalbersberg IjJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3: 160018. doi:10.1038/sdata.2016.18', '2. \\tChue Hong NP, Katz DS, Barker M, Lamprecht A-L, Martinez C, Psomopoulos FE, et al. FAIR Principles for Research Software (FAIR4RS Principles). 2022. doi:10.15497/RDA00068', '3. \\tGoble C, Cohen-Boulakia S, Soiland-Reyes S, Garijo D, Gil Y, Crusoe MR, et al. FAIR Computational Workflows. Data Intelligence. 2020;2: 108–121. doi:10.1162/dint_a_00033', '4. \\tCastro LJ, Katz DS, Psomopoulos F. Working Towards Understanding the Role of FAIR for Machine Learning. PUBLISSO; 2021. doi:10.4126/FRL01-006429415', '5. \\tBaker M. 1,500 scientists lift the lid on reproducibility. Nature. 2016;533: 452–454. doi:10.1038/533452a', '6. \\tSchultes E, Wittenburg P. FAIR Principles and Digital Objects: Accelerating Convergence on a Data Infrastructure. In: Manolopoulos Y, Stupnikov S, editors. Data Analytics and Management in Data Intensive Domains. Cham: Springer International Publishing; 2019. pp. 3–16. doi:10.1007/978-3-030-23584-0_1', '7. \\tSoiland-Reyes S, Sefton P, Crosas M, Castro LJ, Coppens F, Fernández JM, et al. Packaging research artefacts with RO-Crate. Data Science. 2022; 1–42. doi:10.3233/DS-210053', '8. \\tWalsh I, Fishman D, Garcia-Gasulla D, Titma T, Pollastri G, Capriotti E, et al. DOME: recommendations for supervised machine learning validation in biology. Nature Methods. 2021. doi:10.1038/s41592-021-01205-4']}
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