{"id":458,"date":"2013-09-19T13:41:03","date_gmt":"2013-09-19T13:41:03","guid":{"rendered":"https:\/\/blogs.nature.com\/scientificdata\/?p=458"},"modified":"2015-12-17T14:34:20","modified_gmt":"2015-12-17T14:34:20","slug":"the-data-descriptor-making-your-data-reusable","status":"publish","type":"post","link":"https:\/\/blogs.nature.com\/scientificdata\/2013\/09\/19\/the-data-descriptor-making-your-data-reusable\/","title":{"rendered":"The Data Descriptor \u2013 making your data reusable"},"content":{"rendered":"

Scientific<\/i> Data<\/i>\u2019s call for submissions is fast approaching, and accepted manuscripts will be featured at our platform\u2019s launch in Spring 2014. We hope you\u2019ll want to take part!<\/p>\n

If you\u2019re interested in publishing your datasets\u2014and getting credit for them\u2014all the information you need to draft and submit a manuscript is now available on our website <\/a>(See Box 1, \u201cGetting ready to submit\u201d).<\/p>\n

<\/p>\n

But if you\u2019re still deciding whether\u00a0Scientific Data<\/i>\u00a0is right for you, let\u2019s begin with an explanation of why we need quality data publications, and the role that\u00a0Scientific Data<\/i>\u2019s\u00a0content will play in promoting data reuse.<\/i><\/p>\n

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Box 1. Getting ready to submit<\/h3>\n

Simply follow these three steps:<\/p>\n

    \n
  1. Deposit your data to an appropriate repository.<\/a><\/li>\n
  2. Draft the Data Descriptor manuscript. See our manuscript templates<\/a> and sample Data Descriptors.<\/a><\/li>\n
  3. Draft some tables describing your samples, assays, and data outputs. See our guidelines<\/a> and templates<\/a>.<\/li>\n<\/ol>\n<\/div>\n

    Have you come across interesting datasets that you couldn\u2019t reuse, or where it was difficult even to judge the data\u2019s technical quality?<\/b><\/p>\n

    Incomplete and fragmented descriptions are often the culprit; information about the experimental context and data processing steps may be limited or inaccurate. Even in the best scenario, when datasets are in public databases and supported by published journal articles, it can be challenging to identify and bring together all the information needed to reuse the data.\u00a0The level of annotation provided for each dataset record can also vary from database to database. Moreover, research articles associated with a given dataset focus primarily on the interpretation of the data in light of a specific hypothesis, and cross-referencing between related database records and journal articles is still performed in an\u00a0ad hoc<\/i>\u00a0fashion\u2014meaning that the true reuse potential of even publicly deposited datasets may actually be limited.<\/p>\n

    How does\u00a0Scientific Data<\/i>\u00a0help you maximize discovery, citations and reusability of your data?<\/b><\/p>\n

    Our new content type, the\u00a0Data Descriptor<\/b>, is designed to provide detailed descriptions of life sciences, biomedical and environmental datasets: focusing exclusively on how these have been produced, by whom, and how they could be reused by independent investigators. Data Descriptors combine traditional\u00a0narrative\u00a0<\/i>content (the manuscript) with\u00a0structured\u00a0<\/i>information (the experimental metadata; see Box 2, \u201cThe Data Descriptor\u201d, for more information). Published Data Descriptors will link out to both related journal articles and data files stored at data repositories. The\u00a0structured<\/i>\u00a0information will be harmonized by our in-house curators for consistency and to aid searches. In the end, Data Descriptors will be an important bridge in the existing scientific information ecosystem between results-based research and data outputs, and will include value-added elements that serve both human and machine consumption.<\/p>\n

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    Box 2. The Data Descriptor<\/h3>\n

    Each published Data Descriptor will contain:<\/p>\n