Do you ever wonder whether archiving your data is worth the effort?\u00a0 Archiving your data for the first time might seem like a big task.\u00a0 But organising and preserving your data in a purpose-built archive can save headaches later.<\/p>\n
<\/p>\n
Data archives perform two functions. \u00a0They keep\u00a0your data safe for the\u00a0long term\u00a0and provide a platform to\u00a0share your data.<\/p>\n
During a project, data are usually stored in a reliable place, where everyone can find it. \u00a0But at the end of a project, researchers leave and new projects take over. \u00a0Data archives give you a place to put your data where you can find them in the future. \u00a0Adding documentation to the dataset ensures you will be able to understand them.<\/p>\n
Many funders now expect you to share your data where possible.\u00a0 It is good\u00a0practice to provide data to support a publication so readers can verify your results.\u00a0 You can either cite the dataset, or provide a data access statement.\u00a0 In both cases, the reference should identify the dataset and explain where to access it. \u00a0Archiving the data in a dedicated system gives you a unique identifier.\u00a0 This is usually a Digital Object Identifier (DOI) which also acts as a link to the data.<\/p>\n
For many researchers, getting a DOI is the reason they archive their data. \u00a0Planning to archive your data can save a rush to make it available\u00a0publishing deadlines.<\/p>\n
There are two main options for archiving your data:<\/p>\n
Some research areas have well-established subject archives for data. \u00a0It is best to check whether an archive exists for your type of data. \u00a0These archives will usually collect detailed information about the data. \u00a0This makes it easier to find and combine similar data.<\/p>\n
Universities and other organisations run generic archives to enable researchers to share their results. \u00a0The Library\u00a0runs the Research Data Archive<\/a> for\u00a0University of Bath researchers. You can read more\u00a0about how the Archive is being used in a sister post to this post<\/a>.<\/p>\n Read more: Selecting an archive<\/a><\/p>\n To use the Research Data Archive, create a dataset record in Pure.<\/p>\n This can be basic - just title, creators and year. \u00a0Funding information is also helpful at this stage.<\/p>\n We then import your record into the Research Data Archive. \u00a0At this point, we tell you what the DOI will be so you can include it in your paper.<\/p>\n When you prepare your data, you will need to consider two things.<\/p>\n Format<\/strong><\/p>\n Save your data in open, standard formats if possible. \u00a0This makes files accessible to\u00a0more people and can ensure the data remain usable in the long term.<\/p>\n Structure<\/strong><\/p>\n If you have many\u00a0files it's best to arrange these in a folder structure on your computer. \u00a0Either create a .zip or .tar.gz file of the data and upload this to the archive. \u00a0This will make it easier to download.<\/p>\n Read more: Organising your data<\/a><\/p>\n To make your dataset useful, you need to describe it. \u00a0Some description helps users find your\u00a0data. \u00a0The rest helps them understand your data.<\/p>\n Title<\/strong><\/p>\n Give your dataset a clear, descriptive title. \u00a0If it supports a specific paper, using \"Dataset for [title of paper]\" is effective. \u00a0Otherwise try to describe the data in a few words,\u00a0e.g.\u00a0ENLITEN household dynamic study datasets.<\/p>\n Abstract and Lay Summary<\/strong><\/p>\n You should provide an abstract to provide a description of the data. \u00a0This should cover the main aspects of the data so a visitor\u00a0can decide whether it is relevant. \u00a0You can write this for an expert audience, but make sure to explain acronyms that are not standard.<\/p>\n If you expect your data to be interesting to the general public, consider writing a lay summary. \u00a0This should explain your data in plain language, and help a user to decide whether to explore the data.<\/p>\n Documentation<\/strong><\/p>\n You need to write documentation explaining what the data are and how you created them. \u00a0This doesn't have to be long, but should cover topics such as:<\/p>\n Read more: Describing your data<\/a><\/p>\n Detailed metadata<\/strong><\/p>\n For some data, it is useful to include details such as geolocation or dates of collection. \u00a0Do\u00a0include this information\u00a0if it is\u00a0important to your dataset.<\/p>\n You'll also want to include information about accessing the data. \u00a0This is important if they are not open access. \u00a0Describe access conditions if users need to make special arrangements to get the data. \u00a0Add\u00a0a licence to\u00a0help\u00a0end users make decisions about whether to use your data in their study.<\/p>\n You should let us know by email if there are restrictions on your data. \u00a0We can record this so these will be clear for future decisions.<\/p>\nUsing the Research Data Archive<\/h1>\n
Preparing\u00a0your data<\/h2>\n
Describing your data<\/h2>\n
\n