Data Harmonization for a Molecularly Driven Health System
Data commons have emerged as the best current method for enabling data aggregation across multiple projects and multiple data sources. Good data harmonization techniques are critical to maintain quality of data within a data commons, as well as to allow future meta-analysis across different data com...
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Veröffentlicht in: | Cell 2018-08, Vol.174 (5), p.1045-1048 |
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Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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Zusammenfassung: | Data commons have emerged as the best current method for enabling data aggregation across multiple projects and multiple data sources. Good data harmonization techniques are critical to maintain quality of data within a data commons, as well as to allow future meta-analysis across different data commons. We present some of the current best practices for data harmonization.
Data commons have emerged as the best current method for enabling data aggregation across multiple projects and multiple data sources. Good data harmonization techniques are critical to maintain quality of data within a data commons, as well as to allow future meta-analysis across different data commons. We present some of the current best practices for data harmonization. |
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ISSN: | 0092-8674 1097-4172 |
DOI: | 10.1016/j.cell.2018.08.012 |