A General Primer for Data Harmonization
| dc.contributor.author | Cindy Cheng | |
| dc.contributor.author | Luca Messerschmidt | |
| dc.contributor.author | Isaac Bravo | |
| dc.contributor.author | Marco Waldbauer | |
| dc.contributor.author | Rohan Bhavikatti | |
| dc.contributor.author | Caress Schenk | |
| dc.contributor.author | Vanja Grujić | |
| dc.contributor.author | Timothy Model | |
| dc.contributor.author | Robert Kubinec | |
| dc.contributor.author | Joan Barceló | |
| dc.date.accessioned | 2025-08-26T08:36:11Z | |
| dc.date.available | 2025-08-26T08:36:11Z | |
| dc.date.issued | 2024-01-31 | |
| dc.description.abstract | Data harmonization is an important method for combining or transforming data. To date however, articles about data harmonization are field-specific and highly technical, making it difficult for researchers to derive general principles for how to engage in and contextualize data harmonization efforts. This commentary provides a primer on the tradeoffs inherent in data harmonization for researchers who are considering undertaking such efforts or seek to evaluate the quality of existing ones. We derive this guidance from the extant literature and our own experience in harmonizing data for the emergent and important new field of COVID-19 public health and safety measures (PHSM). | en |
| dc.identifier.citation | Cheng Cindy, Messerschmidt Luca, Bravo Isaac, Waldbauer Marco, Bhavikatti Rohan, Schenk Caress, Grujic Vanja, Model Tim, Kubinec Robert, Barceló Joan. (2024). A General Primer for Data Harmonization. Scientific Data. https://doi.org/https://doi.org/10.1038/s41597-024-02956-3 | en |
| dc.identifier.doi | 10.1038/s41597-024-02956-3 | |
| dc.identifier.uri | https://doi.org/10.1038/s41597-024-02956-3 | |
| dc.identifier.uri | https://nur.nu.edu.kz/handle/123456789/10036 | |
| dc.language.iso | en | |
| dc.publisher | Springer Science and Business Media LLC | |
| dc.relation.ispartof | Scientific Data | en |
| dc.rights | All rights reserved | en |
| dc.source | Scientific Data, (2024) | en |
| dc.subject | Harmonization | en |
| dc.subject | Data science | en |
| dc.subject | Field (mathematics) | en |
| dc.subject | Data quality | en |
| dc.subject | Extant taxon | en |
| dc.subject | Computer science | en |
| dc.subject | Quality (philosophy) | en |
| dc.subject | Management science | en |
| dc.subject | Business | en |
| dc.subject | Engineering | en |
| dc.subject | Biology | en |
| dc.subject | Marketing | en |
| dc.subject | Philosophy | en |
| dc.subject | Metric (unit) | en |
| dc.subject | Physics | en |
| dc.subject | Mathematics | en |
| dc.subject | Epistemology | en |
| dc.subject | Evolutionary biology | en |
| dc.subject | Acoustics | en |
| dc.subject | Pure mathematics | en |
| dc.subject | type of access: open access | en |
| dc.title | A General Primer for Data Harmonization | en |
| dc.type | article | en |
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