Start by downloading the .zip file located at: Refine
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First, create a project in Refine by uploading a dataset. 
Once Refine has finished verifying your data, it gives you an intermediary screen that allows you to name your project (1), select which worksheets get imported (2), and some data-handling options (3). Select "Create Project" when you are satisfied with dataset.
Refine, as a default, displays 10 rows. You can have it display up to 50 but not more (1). Refine is not a tool for modifying data within cells one at a time. It is best used for dealing with whole swaths of data. Refine does that by a tool called 'facet' (2), which is an option you find by clicking on the down-arrow on which ever column you wish to facet. Faceting data is like a filter for selecting data that meets a certain criteria- it can be a word, length of an entry, or just lumping data into how many times it occurs. You can also facet many rows at once, to get a very precise set of data which you can then act on. In the example below, the facet was set to 'text facet' (3). Faceting the column this way shows the data in the cells (4), and how many times that data is used . The column 'Type status' (4) will only have a handful of variety (4 choices in this case). Something like 'Collection Number' would have many- 411 choices (5). Please note that the Facets allow you to sort by name or count. 
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When preparing a spreadsheet for upload to Specify, there are many instances where you'll need to combine data from many columns into one, or transpose data from one column into another. Refine makes this very simple, using the 'Transform' option. For example, we are putting the previous storage location information into the Comments section, and renaming this column 'Inventory Remarks' (the field name in Specify). Renaming the columns can be done via the 'Edit Column' drop down, but taking the information from 3 fields and adding it to another field is a little trickier. For this, we will need to combine a few tricks we got from Refine Recipes. The recipe for Merging columns is:
cells["col1"].value + ", " + cells["col2"].value
cells = each cell in whatever is inside the [].
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The same 'recipe' is the foundation for how we take our old database numbering style and make it Specify compatible. But first, we have to use the recipe for padding with 0's.
The recipe is:
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"0000"[0,4-value.length()] + value |
The recipe says to take the value, add as many 0's as are needed to make the field have 4 values stored in it. This will add 4 0's to our value. If we need more, we increase the number of 0's to what we need, then change the number inside the brackets to match.
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To make the Collection have 3 digits, paste this expression into the Transform window for the Collection column-value
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value +"000"[0,3-value.length()] |
To make the Specimen number have 8 digits, paste this expression into the Transform window:
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"0000000" + value[ |
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0,8-value.length()] |
* Notice how the order has changed a little- the string of 0's is at the start, and the value is added to it. This gives us 0 padding in front of the number. For the Collection, we wanted the 0's to come at the end of the collection acronym so the expression structure was swapped around.
Changing the suffixes is a little more involved. First, facet the column. All the entries for '.' can be bulk edited to "000". Don't put a decimal in just yet. Create a text filter and type this into the box: [a-zA-Z] 
1) Select the 'regular expression' box. We are telling the text filter to show records that have a-z in them, capitol or lower case letters.
1a) Transform on the filtered column:
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value +"000"[0,3-value.length()] |
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Remember, for letters we want A00 B00 and so on.
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2) Now change the a-zA-Z to 0-9, leaving the brackets in place.
Transform 2a) Transform on the filtered column:
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"000"+ value[0,3-value.length()] |
You now have all suffixes transformed and properly formatted.
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