Data is becoming a valued scholarly product instead of a byproduct of the research process. Federal funding agencies and publishers are encouraging, and sometimes requiring, researchers to share data that have been created with public funds. There are many benefits to researchers to sharing your data. Sharing your data can increase the impact of your work, lead to new collaborations or projects, enable verification of your published results, provide credit to you as the creator, and provide great resources for education and training. Data sharing also benefits the greater scientific community, funders, the public by encouraging scientific inquiry and debate, increases transparency, reduces the cost of duplicating data, and enables informed public policy.
William H. Hannon Library has a solution for those looking for opportunities to sharing data. Digital Commons @ Loyola Marymount University and Loyola Law School can also platform to preserve and share data sets. Through Creative Commons, your data can get licensed, not copyright. We can place embargoes and other means for access control, such as making datasets available on-campus only.
- Share your data upon publication.
- Share your data in an open, accessible, and machine readable format (e.g., csv vs. xlsx, odf vs. docx, etc.)
- Deposit your data in a subject or institutional repository so your colleagues can find and use it, like Digital Commons. Check out these institutional repositories that house datasets: UMass Medical School, Utah State University, Chapman University.
- Deposit your data in your institution’s repository to enable long term preservation.
- License your data so people know what they can do with it. Read this guide to demystify the process.
- Tell people how to cite your data.
- When choosing a repository, ask about the support for tracking its use. Do they provide a handle? Can you see how many views and downloads? Is it indexed by Google, Google Scholar, the Data Citation Index?
Things to Avoid
- “Data available upon request” – This is NOT sharing the data.
- Sharing data in PDF files.
- Sharing raw data if the publication doesn’t provide sufficient detail to replicate your results.
Today’s post was written by Marie Kennedy, Serials and Electronic Resources Librarian, and Jessea Young, Digital Initiatives Librarian. Content inspired by Love Data Week. Image source: hellocatfood on flickr (CC BY-SA 2.0)