Here are some of the file formats supported by OpenRefine:

If you need other formats, you can add them by way of OpenRefine extensions.

Project creation with OpenRefine is straightforward and consists of three simple steps: selecting your file, previewing the import, and validating to let OpenRefine create your project. Let's create a new project by clicking on the Choose Files button from the This Computer tab, selecting your dataset (refer to the following information box), then clicking on Next.

On the next screen, you get an overview of your dataset as it will appear in OpenRefine. In the bottom-right corner, you can see the following parsing options as shown in the following screenshot:

By default, the first line will be parsed as column headers, which is a common practice and relevant in the case of the Powerhouse dataset. OpenRefine will also attempt a guess for each cell type in order to differentiate text strings from integers, dates, and URLs among others. This will prove useful later when sorting your data (if you choose to keep the cells in plain text format, 10 will come before 2, for instance).

Another option demanding attention is the Quotation marks are used to enclose cells containing column separators checkbox. If you leave it selected, be sure to verify that the cell values are indeed enclosed in quotes in the original file. Otherwise, deselect this box to ensure that the quotation marks are not misinterpreted by OpenRefine. In the case of the Powerhouse collection, quotes are used inside cells to indicate object titles and inscriptions, for instance, so they have no syntactic meaning: we need to deselect the checkbox before going further. The other options may come in handy in some cases; try to select and deselect them in order to see how they affect your data. Also, be sure to select the right encoding to avoid special characters to being mixed up. When everything seems right, click on Create Project to load your data into OpenRefine.