The United States spends billions of dollars every year to publicly support research that has resulted in critical innovations and new technologies. Unfortunately, the outcome of this work, published articles, only provides the story of the research and not the actual research itself. This often results in the publication of irreproducible studies or even falsified findings, and it requires significant resources to discern the good research from the bad. There is way to improve this process, however, and that is to publish both the article and the data supporting the research. Shared data helps researchers identify irreproducible results. Additionally, shared data can be reused in new ways to generate new innovations and technologies. We need researchers to “React Differently” with respect to their data to make the research process more efficient, transparent, and accountable to the public that funds them.
Kristin Briney is a Data Services Librarian at the University of Wisconsin-Milwaukee. She has a PhD in physical chemistry, a Masters in library and information studies, and currently works to help researchers manage their data better. She is the author of “Data Management for Researchers” and regular blogs about data best practices at dataabinitio.com.
Typically researchers share data via email, posting it to personal or via Google Drive or Amazon. However, these methods make it challenging to discover research data. Depositing data in repositories helps to discover, manage, cite and preserve data for the long-term. The options below by no means comprise a comprehensive list of repositories.
If you would like to suggest repositories to include in this list or need assistance with depositing your data to one of these repositories, please email digitalcommons@lmu.edu. Also, you can share sharing data with multiple repositories which will help increase visibility and preservation of your research, so carefully consider which repositories will help you achieve this.
Like any content that is used in the publication process, it is important to cite your sources, and this applies to using research data as well. Research data citation standards are still being developed, so the following elements are important when citing or providing citations for your own research data:
All research requires the sharing of information and data. The general philosophy is that data are freely and openly shared. However, funding organizations and institutions may require that their investigators cite the impact of their work, including shared data. By creating a usage rights statement and including it in data documentation, users of your data will be clear what the conditions of use are, and how to acknowledge the data source.
Include a statement describing the "usage rights" management, or reference a service that provides the information. Rights information encompasses Intellectual Property Rights (IPR), copyright, cost, or various Property Rights. For data, rights might include requirements for use, requirements for attribution, or other requirements the owner would like to impose. If there are no requirements for re-use, this should be stated.
Usage rights statements should include what are appropriate data uses, how to contact the data creators, and acknowledge the data source. Researchers should be aware of legal and policy considerations that affect the use and reuse of their data. It is important to provide the most comprehensive access possible with the fewest barriers or restrictions.
There are three primary areas that need to be addressed when producing sharable data:
If your data falls under any of the categories below there are additional considerations regarding sharing:
If you use data from other sources, you should review your rights to use the data and be sure you have the appropriate licenses and permissions.
When sharing data, or using data shared by others, researchers should be aware of any policies that might affect the use of the data. Including a usage rights statement makes clear to data repository users what the conditions of use are, and how to acknowledge the data source.
Source: DataONE