organising_data/Organising-data.md

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-You are probably used to organsing data by storing it in nested folders or directories.  A heirachical system like this is a very natural  to organise your data in terms of the explanatory variables in your experiment.
-For example, an experiment can be  organised 
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-` organism > treatment > replicate`
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-` date     > organism > treatment `
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-There are some downsides to this approach:
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-#####  Sometimes we don't know the relative importance of variables:
-The final organisation of the experiment may be different from what was planned.  
-We the data is analysed is makes sense to organise the explanatory variables by their relative effect size. 
-If it turns out that the within organism variation is larger than the treatment effect size the analysis will require the data is organised by replicate rather than treatment. 
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-##### We may no not know which variables are important:
-Although an experiment was planed to examine a set of explanatory variables, it turn out that one of the extraneous variables we thought shouldn't affect the results turns out be important.  
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-##### Exploratory analysis and meta-analysis
-You may want to explore the relationships in a dataset that was not collected with that purpose in mind.  For example, it may a 'opportunistic' dataset that was not collected as part of a controlled experiment, or you may be performing a meta-analysis that involves multiple datasets that were collected for different purposes.    
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-# Organising in OMERO 
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 OMERO takes a rather different approach to organising data from the nest directories approach that allows more flexibility in how data is organised.   
 There are two types of containers (folders) in OMERO which are structured as