deletions | additions       diff --git a/Data Quality.tex b/Data Quality.tex  index ad1b597..d0bdd7b 100644  --- a/Data Quality.tex  +++ b/Data Quality.tex 
...
\subsection{Data Quality}  A key concern in linking datasets to publications is the provision of quality referenced data. metrics.  Materials data can be provided as two basic types: experimental and computational; both types assume underlying models. In order for data and these associated models to be usable, their quality must be ascertained. In this context, it is useful to define the following for data and models: \begin{itemize}  \item Pedigree – Where did the information come from?  \item Provenance – How was the information generated (protocols and equipment)? This metadata should be sufficient to reproduce the provided data data.  \item Verification – How accurately does the computation solve the underlying equations of the model for the quantities of interest?   \item Validation – How accurately does the model represent reality for the quantities of interest?  \item Uncertainty – What is the quantitative level of confidence in our predictions?  \item Sensitivity – How sensitive are results to changes in inputs? inputs or upon assumed boundary conditions?  \end{itemize}