{"id":29453,"date":"2016-02-10T09:30:22","date_gmt":"2016-02-10T13:30:22","guid":{"rendered":"http:\/\/libapps.libraries.uc.edu\/liblog\/?p=29453"},"modified":"2016-02-10T09:30:22","modified_gmt":"2016-02-10T13:30:22","slug":"love-your-data-day-3-data-documentation","status":"publish","type":"post","link":"https:\/\/libapps.libraries.uc.edu\/liblog\/2016\/02\/love-your-data-day-3-data-documentation\/","title":{"rendered":"LOVE YOUR DATA Day 3 – Data Documentation"},"content":{"rendered":"

I say to you\u2026.slow down, smell the agar plates, take the time to document your data. Your future self will thank you, profusely.<\/p>\n

Proper documentation provides the context that your data needs to persist through time, to integrate into new systems and to give you credit for your contributions in the form of data citations. Where possible, you should consider contributing the following information along with your dataset.<\/p>\n

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  1. Complete Metadata<\/strong> for description and reuse\u2013 Descriptive metadata is information such as title of work, creator name, data submitted and description. This information can be searched to increase discoverability of your content. If you are following a suggested schema for your discipline, indicate that in the description or in the next document \u2013 the README file.<\/li>\n<\/ol>\n
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    1. README.txt File<\/strong> \u2013 This is a text document that provides relevant information such as purpose of the project and the organizational structure or relationship of the files. It explains terms that are unique to the dataset, keywords, omissions and errors. If you are using a file naming convention, you can explain it in the readme file. It is also the place to put additional details that may not be included in the descriptive metadata, such as additional information about external storage of the data, metadata schema followed or researcher contact information.<\/li>\n<\/ol>\n

      A good example can be found in the Data ab Initio blog post README.txt by Kristin Briney (<\/strong>http:\/\/dataabinitio.com\/?p=378<\/strong><\/a>) <\/strong><\/p>\n

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      1. Data Dictionary\/Codebook<\/strong> \u2013 This document explains all the variables and abbreviations associated with the dataset.<\/li>\n<\/ol>\n
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        1. Methodology\/Protocol <\/strong>\u2013 This document is the details steps taken to collect the data. If you are submitted modified data instead of a raw data set, you can explain those steps in this document.<\/li>\n<\/ol>\n

          More tips at<\/strong> the Love Your Data website <\/a> to help keep you write that love note to the future. Follow the event on Twitter at #LYD16.<\/p>\n

          Amy Koshoffer is the science informationist for UC Libraries based in the Geology Math and Physics library .<\/p>\n

          Resources:<\/p>\n