Two US federal government agencies recently published exciting, forward-looking guidance on data sharing for researchers receiving US federal funding. First, the National Institute of Health (NIH) – following an extended process of public comment and revisions – published its guidance on data sharing for grantees under the new NIH-wide Data Management and Sharing Policy.
Guest post by Marina Mohd Hamdan, West Virginia University
This is a guest post by Jessica Nina Lester (Indiana University), Noah Goodman (Education Development Center's Center for Children & Technology), & Michelle O’Reilly (University of Leicester)
Since QDR’s founding in 2014, we have gained an increasing appreciation for the power and complexity of the academic “ecosystem” comprising the various institutions that create and disseminate knowledge. Funders, disciplinary associations, journals and publishers, and units at colleges and universities such as Ethics Boards and Sponsored Research Offices, all shape social science research, and the sharing of the data that research produces.
The scientific response to the global pandemic has shown, among other things, the value of open science, collaboration, and data sharing. In that spirit, QDR will support efforts to share qualitative and multi-method social science data underlying COVID-19 related research.
Welcome to Love Data Week. Every year, research data professionals from libraries, data repositories, and other organizations celebrate great ways to use data and best practices in taking care of research data. You can find lots of us tweeting using #LoveData20 or #LoveDataWeek. At QDR, we’re celebrating this year by releasing our first software tool for researchers, the R package archivr (pronounced “archiver”).
In a recent “Dear Colleague Letter”, the National Science Foundation (NSF) encourages researchers to adopt best practices in managing research data. NSF frequently uses “DCLs” to make researchers aware of funding priorities and preferred practices, so if you are thinking of applying for NSF funding, you should pay close attention to such pronouncements.
When QDR adopted the Dataverse platform in early 2017, one of our goals was to improve the software, development primarily with quantitative data in mind, for qualitative data and the researchers using it. A little more than one year into using Dataverse software at QDR, we have made significant strides in this direction. Here is a quick overview of some of our biggest additions. I also talk about some of these in the video below.
