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

Discovering + Publishing Data with Dataverse, at AMIGOS Open Source Virtual Conference, Thursday, September 17, 2015

This presentation will provide some background information on the Dataverse's history, offer examples of how libraries and archives are using Dataverse to share data, and showcase the new features developed in version 4.0 for researchers.


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. 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.
Altman M, Castro E, Crosas M, Durbin P, Garnett A, Whitney J. Open Journal Systems and Dataverse Integration– Helping Journals to Upgrade Data Publication for Reusable Research. The Code4Lib Journal [Internet]. 2015;30. Publisher's VersionAbstract
This article describes the novel open source tools for open data publication in open access journal workflows. This comprises a plugin for Open Journal Systems that supports a data submission, citation, review, and publication workflow; and an extension to the Dataverse system that provides a standard deposit API. We describe the function and design of these tools, provide examples of their use, and summarize their initial reception. We conclude by discussing future plans and potential impact.

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