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. develop capability to handle sensitive, large scale, and streaming data
  3. expand data and metadata features for existing and new disciplines
  4. expand archival and preservation features
  5. increase interoperability through implementation of standards
  6. increase contributions from the open-source development community
  7. improve UX and UI
  8. 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.

Q3 2018

  • Ingest Upgrades
  • Infrastructure Upgrades
  • Dataset Linking

4.9.2 Ingest Upgrades, New Import APIs, Infrastructure Upgrades (Released August 2nd)

Stata 14, Stata 15, and .tsv files will now be ingested by Dataverse. New APIs will allow datasets with existing DOIs to be imported into Dataverse. Bootstrap and Primefaces, which power the Dataverse front end, have been updated.  

 

4.9.3 Optional File PIDs, Initial Internationalization Work, Dataset Linking (Released September 29th)

Installations can choose whether or not to mint PIDs for individual files. Initial work towards internationalization, including a language toggle. 

Q4 2018

  • Code Deposit
  • Additional Data Transfer Options
  • Local Access and Multiple Storage Locations
  • GDPR Compliance
  • Preservation Export APIs

4.10 Additional Data Transfer Options, Local Access, and Multiple Storage Locations

All installations will be able to use Dataverse's integration with the Data Capture Module, an optional component for deposition of large datasets (both large number of files and large filesize).  Specific support for large datasets includes client-side checksums, non-http uploads (currently supporting rsync via 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.

Administrators will be able to configure a Dataverse installation to allow datasets to be mirrored to multiple locations, allowing faster data transfers from closer locations, access to more efficient or cost effective computation, and other benefits.

 

4.x OAI-ORE and BagIt Exports

The Qualitative Data Repository team is adding support for data and metadata exports from Dataverse in packaged archival form

 

4.x GDPR Compliance

New Features, Enhancements, Guidance, and Documentation for the Dataverse Community about how to comply with GDPR.

 

4.x Code Deposit

Researchers will be able to mint a DOI for and archive a version of their code in Dataverse.

2019

  • Sensitive Data Support Through DataTags
  • Dataset and File Page Redesign
  • Embargo
  • Preserve File Hierarchy
  • Make Data Count Integration

5.0 Sensitive Data Support through DataTags, Dataset and File Page Redesign

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

To facilitate the changes needed to support sensitive data deposit and access, the Dataset and File Page will also be redesigned, with special attention paid to their usability.

5.x Embargo

Researchers will be able to set an availability schedule for their data.

5.x Preserve File Hierarchy

Researchers can preserve a dataset's files' directory structure, for easy import, computation, and navigation.

5.x Make Data Count Integration

Dataverse will integrate with Make Data Count and report standardized usage metrics.