Dataverse’s latest update includes support for large data transfers, a simplified upgrade process, and internationalization. It also includes over 100 other changes and new features, like API endpoints and bug fixes. To see the full release notes, check out the release notes on GitHub.
With its focus on qualitative and multi-method research, the Qualitative Data Repository (QDR) at Syracuse University curates, archives, and enables the sharing of qualitative research data. The Dataverse Project plays an integral part in propelling QDR’s mission to make the sharing of qualitative data a customary practice in the social sciences.
The Henry A. Murray Research Archive (MRA) at Harvard University’s Institute for Quantitative Social Science (IQSS) is a permanent repository for qualitative and quantitative research data. Founded in 1976, tucked away in Radcliffe Yard, the MRA was fondly known as The Murray Center, emphasizing the study of lives over time, with a particular focus on the lives and concerns of women. Even from these early years, the MRA was defined by its commitment...
We are extremely excited to announce the latest progression for our Dataverse community with the forming of a new Global Dataverse Community Consortium.
The vision is that the Global Dataverse Community Consortium (GDCC) will provide international organization to existing community efforts and will provide a collaborative venue for institutions to leverage economies of scale in support of Dataverse repositories around the world. It is our intention that the consortium be community driven and ultimately the goals to be defined by the participants. But recent developments...
This release introduces a modular Explore feature to support external tools. It includes performance enhancements for S3 storage, provides an API endpoint to move datasets and other improvements, includes documentation improvements and fixes a number of bugs.
For a full list of new features, enhancements, and bug fixes, check out the release notes.
Dataverse’s latest update adds more metadata to dataset landing pages, using a community-driven vocabulary supported by major search engines to make it even easier to find open data online.
Search results account for a large portion of traffic to datasets published online. For example, since Dataverse 4 was released in June 2015, at least a fifth of the traffic to dataset pages in the largest Dataverse installation, Harvard Dataverse, has come from search engines, mostly Google. Giving search engines and other systems richer metadata to index datasets will help people...
In this release we introduce support for AWS S3 file storage, providing Dataverse installations with a cloud option. We also include support for Large Data upload via rsync and integration with an external application, the Data Capture Module (DCM). Other enhancements include improved Swift object storage, csv file ingest improvements, support for increased password complexity, downloading large guestbooks, removal of a user's roles, improved documentation, and various bug fixes.