The first annual Dataverse Community Meeting June 9-11th was a success thanks to you, the Dataverse Community! Thank you to all who attended the meeting, presented, and participated in breakout sessions each afternoon. Read more about We are now a community!
At the beginning of next week, April 13th, Harvard Dataverse will be upgraded to Dataverse 4.0! The current version of the Harvard Dataverse (thedata.harvard.edu) will still be available for the next month for you to view. However, you will be able to edit, upload, or download in the new release 4.0 and can benefit from the new functionalities there. Read more about Dataverse 4.0, Next Week!
For the past few years Dataverse has been using the SWORD protocol as the standard for a Data Deposit API, but is this the standard all repositories should use for Data Deposit APIs? We will discuss the good parts and the challenges of this approach. Additionally this presentation will lead into the Panel Discussion consisting of various stakeholders from publishers, domain and general repositories, funding agencies, researchers, and industry.Read more about Towards a Common Deposit API (Dataverse Example)
Binary trees can be made differentially private by adding noise to every node and leaf. In such form they allow multifaceted exploration of a variable without revealing any individual information. While a differentially private binary tree can be used and read just like its conventional exact-valued analog, realizing that different combinations of nodes contain overlapping answers to the same information allows us to bring the statistical properties of multiple measurements under measurement error to noisy binary trees to create statistically efficient node estimates. We construct estimators that correctly use all available information in the tree, thus decreasing the error of nodes by up to eighty percent for the same level of privacy protection.