As I was going through the OpenRefine tutorial, I didn’t quite understand exactly what the function of the program was until a few steps in. Then it clicked that this process of narrowing and reducing discrepancies between data entries would be invaluable for cleaning up the dataset as a whole. It would be much more manageable when creating data visualizations. I couldn’t help but think about when I tried to fit the whole dataset into RAW to create a data viz for the project \u2013 what came out was a garbled mess of text overlain on text \u2013 creating these black blobs of entries. By reducing the amount of entries, it would make sense that this would not only provide cleaner data visualizations, but also more accurate ones. I wonder how exactly these datasets are generated, perhaps they are programmed or input manually. I could see room for error and discrepancies resulting from both of these methods.<\/p>\n
For the Nixon Tapes dataset, I could see how splitting multi-valued sets would be integral to separating Nixon from who he participated in the conversation with. This would give a category specifically for who was interacting with Nixon in the moment of recording, and provide for better network analysis data.<\/p>\n","protected":false},"excerpt":{"rendered":"
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