Latest Blog Posts

Dataverse 4.0, Next Week!

At the beginning of next week, April 13th, Harvard Dataverse will be upgraded to Dataverse 4.0! The current version of the Harvard Dataverse ( 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.  

Dataverse 4.0: Permissions

Dataverse 4.0 has an entirely new way to grant access to a dataverse, dataset, and restricted files. Each dataverse and dataset has their own permissions page.

Dataverse Permissions

To access the permissions for a dataverse, click on the Edit button then select Permissions.

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Latest Presentations

Towards a Common Deposit API (Dataverse Example), at Dataverse Community Meeting Pre-Meeting Workshop, Harvard University, Cambridge, MA 02138, Tuesday, June 9, 2015

Presented by: Elizabeth Quigley and Phil Durbin.

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.


Latest Publications

Honaker J. Efficient Use of Differentially Private Binary Trees, in TPDP15: First Workshop on the Theory and Practice of Differential Privacy, London, UK.; 2015.Abstract
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.

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