TY - JOUR
T1 - Data Harmonization for a Molecularly Driven Health System
AU - Lee, Jerry Ssu Hsien
AU - Kibbe, Warren Alden
AU - Grossman, Robert Lee
N1 - Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/8/23
Y1 - 2018/8/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85051648056&partnerID=8YFLogxK
U2 - 10.1016/j.cell.2018.08.012
DO - 10.1016/j.cell.2018.08.012
M3 - Comment/debate
C2 - 30142341
AN - SCOPUS:85051648056
SN - 0092-8674
VL - 174
SP - 1045
EP - 1048
JO - Cell
JF - Cell
IS - 5
ER -