Versioning Data Is About More than Revisions: A Conceptual Framework and Proposed Principles

« A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc. In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto different platforms. All these differences between versions of datasets need to be understood by researchers who want to cite the exact version of the dataset that was used to underpin their research. Failing to do so reduces the reproducibility of research results. Ambiguous identification of datasets also impacts researchers and data centres who are unable to gain recognition and credit for their contributions to the collection, creation, curation and publication of individual datasets. (…) »

source > datascience.codata.org, Klump, J., Wyborn, L., Wu, M., Martin, J., Downs, R.R. and Asmi, A., 2021. Versioning Data Is About More than Revisions: A Conceptual Framework and Proposed Principles. Data Science Journal, 20(1), p.12. DOI: http://doi.org/10.5334/dsj-2021-012

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