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@@ -7,4 +7,35 @@ This is a guide for anyone who needs to share data with a statistician. The targ
 * Students or postdocs in scientific disciplines looking for consulting advice
 * Junior statistics students whose job it is to collate/clean data sets
 
-The goal of this guide is to ensure the most reproducible and the most 
+The goals of this guide are to provide some instruction on the best way to share data to avoid the most common pitfalls
+and sources of delay in the transition from data collection to data analysis. The Leek group works with a large
+number of collaborators and the number one source of variation in the speed to results is the status of the data
+when they arrive at the Leek group. Based on my conversations with other statisticians this is true nearly universally.
+
+My strong feeling is that statisticians should be able to handle the data in whatever state they arrive. It is important
+to see the raw data, understand the steps in the processing pipeline, and be able to incorporate hidden sources of
+variability in one's data analysis. On the other hand, for many data types, the processing steps are well documented
+and standardized. So the work of converting the data from raw form to directly analyzable form can be performed 
+before calling on a statistician. This can dramatically speed the turnaround time, since the statistician doesn't
+have to work through all the pre-processing steps first. 
+
+
+What you should deliver to the statistician
+====================
+
+For maximum speed in the analysis this is the information you should pass to a statistician:
+
+1. The raw data.
+2. A [tidy data set](http://vita.had.co.nz/papers/tidy-data.pdf) 
+3. An explicit and exact recipe you used to go from 1 -> 2 
+
+Let's look at each part of the data package you will transfer. 
+
+
+
+What you should expect from a statistician
+====================
+
+
+
+