In our dataset, we are looking at records of what people had in their houses in the 1700s. With 44 content types and over 30,000 records, analyzing our dataset seemed like a daunting task. However, after exploring OpenRefine and its operations, I feel reassured and excited to tackle on this project! It was so cool to see how OpenRefine simplifies the process of cleaning one’s data. With the help of Professor Posner’s tutorial, I was able to navigate the program and figure out how to clean up errors like misspelling and extra whitespace. Our research questions focus on gender, such as asking if the amount or type of items owned varied based on gender as well as location. We can get this information by splitting multi-valued columns such as the description content type. Some descriptions list more than one item, however this may generate many blank boxes as some descriptions only list one item. This is definitely something I think would be help for our group’s research but it looks like I’ll have to continue playing around with OpenRefine!