Strategic Goals, Roadmap, and Releases

Strategic Goals

The Strategic Goals of the Dataverse Project are our highest-level guide.  These goals are to:

  1. increase adoption (users, dataverses, datasets, installations, journals)
  2. finish Dataverse 4 migration features
  3. develop capability to handle sensitive, large scale, and streaming data
  4. expand data and metadata features for existing and new disciplines
  5. expand archival and preservation features
  6. increase interoperability through implementation of standards
  7. increase contributions from the open-source development community
  8. improve UX and UI
  9. continue to increase the quality of the software

Throughout the year, we'll identify big steps that we can take to focus on one or more of these goals. These big steps are represented on our Roadmap. The Roadmap items that we're about to work on will be well defined, but those Roadmap items that are further out may just be big problems we know we need to solve in some way. Although we are committed to Roadmap items below, the timeframe of the items further out might vary slightly as critical issues, other priorities or dependencies rise.

Once we know what features and enhancements we'll add in order to honor the steps on the roadmap, we'll plan a Release. If the release text is hyperlinked, you can click on it to be taken to our task board to see the status of the release's tasks.


Q4 2016

  • File Landing Page

  • Increased Interoperability


4.6 - File Page, Harvesting Enhancements, File Upload Enhancements (released December 13th)

A new File Landing Page provides a static URL for files and will serve as a base for expanded file options, such as versioning, provenance, and metadata explorations. Files are now easier to upload to Dataverse after a behind-the-scenes update to the file upload process. 

Updated OAI-PMH validation will make metadata sharing easier. Sharing research across multiple platforms will increase Dataverse adoption for both researchers and organizations. 


Q1 2017

  • File Replace

  • ORCID Authentication

4.6.1 File Replace and ORCID (released March 14th)

File Replace will allow users to replace an existing dataset file with a new version. Users will also be able to view and download previous versions of the file. This expansion of the data and metadata features of Dataverse increases reproducibility. 

ORCID support adds an additional single sign on option and makes it easier to sign in to Dataverse and to share research across multiple platforms.

Q2 2017

  • Tabular Mapping

  • Administrative Dashboard

  • Customization

  • Support for Large Scale Data

4.6.2 Tabular Mapping

Tabular files containing geospatial information may be tagged and mapped via the publicly available WorldMap platform maintained by Harvard University's Center for Geographic Analysis.


4.7 Dashboard and Customization

The Dashboard will allow administrators to manage their Dataverse installation's users and permissions. Restoring administrative functionality will help complete Dataverse 4. 

A new, customizable homepage will improve the user experience for researchers visiting and using Dataverse. 


4.8 Large Data Upload Integration

Dataverse integration with the Data Capture Module, an optional component for deposition of large datasets (both large number of files and large files).  Specific support for large datasets includes client-side checksums, non-http uploads (currently supporting rsync over ssh) and preservation of in-place directory hierarchy. This will expand Dataverse to other disciplines and will allow the project to handle large scale data.

Building on the ORCID Authentication made available in a previous release, further integration will allow users to publish Dataverse datasets to their ORCID profile and will pre-populate ORCID IDs on dataset create. 





Q3 2017

  • Support for Streaming Data
  • Support for Sensitive Data

4.9 Support for Streaming Data

Integration with the Billion Object Platform (BOP) will allow users to explore and cite streaming data. This will expand Dataverse to other disciplines and will allow the project to handle new types of data.



4.9.1 Data Provenance

Integrating with a data provenance system will allow users to track of where data files and datasets came from and how they were modified. This expansion of the data and metadata features of Dataverse increases reproducibility. 



5.0 DataTags Integration

By implementing DataTags file-level security and access requirements, integration with DataTags interview tool, and the PSI differential privacy tool, Dataverse will be able to support sensitive data.