Adoption of data standards during the early planning stages of a clinical study will ensure uniformity of the data that will be later acquired and reduce errors that can stem from post-hoc efforts to harmonize and convert data.
Data quality is a concern; errors in the data can stem from inaccurate or missing information in the patient record, from the failure to abstract relevant data, or from incorrect coding of the abstracted data.
Error related to the reference standard can stem from sample preparation (in the form of pipetting) as well as spectra processing and analysis.
A uniform probability of σ (default: 5%) is assumed for any mismatch between a read and a genome, which can stem from genome variability and sequencing errors.
Failure of model predictions to fit the empirical data can stem from the collective effect of small errors in the rate constants used.
Genomic instability can stem from either internal defects (i.e., cell-cycle errors) or external factors, when genotoxic compounds are present in the environment of the cell (oxidants, chemicals, high-energy rays, and others) (Hanawalt 1998).
Erroneous results can stem from inadequate or overfixation, antigen retrieval, antibody sensitivity, or interpretation error.
Sometimes conflicts can stem from simple misunderstandings.
Data on innovation outcomes can stem from multiple sources.
Bargaining power can stem from being legally married.
