Maelstrom Research fosters the development of methods and software to support data harmonization and integration. Our international team provides open access to tools and metadata describing studies and harmonization programs as well as expertise to research projects making use of harmonized data.

Resources

Data harmonization methods, open-source software, federated analysis solutions, and publications.

Partnerships

Partnerships to develop new tools and provide infrastructure support to networks or individual studies.

Metadata

Repository of information facilitating the conception and achievement of data harmonization programs.

News

  • New publication: DataSHIELD: taking the analysis to the data, not the data to the analysis http://t.co/4iRrbX5zFB — 5 months 8 hours ago
  • Opal 2.0 is now available. Release notes here: http://t.co/jOrYXGBjoh — 11 months 2 weeks ago
  • New publication: Data harmonization and federated analysis of population-based studies: the BioSHaRE project http://t.co/rofTgEqCIy — 1 year 2 months ago
  • The longitudinal study catalogue includes new features: description of study subpopulations and data collection events. — 1 year 3 months ago
  • Mica 8.2 is now available. Release notes here: http://t.co/tpjk9FJKHD — 1 year 3 months ago

Data Harmonization Research and Tools

Created in 2012, Maelstrom Research emerged from the activities of the Public Population Project in Genomics and Society, (P³G), the Biobank Standardization and Harmonization for Research Excellence in the European Union (BioSHaRE-EU), and the Canadian Longitudinal Study on Aging (CLSA).

Our goals are to foster a better usage of individual study data and encourage collaborative research through improved documentation, interoperability and integration of datasets. We conduct applied and methodological research, and develop methods and open-source software, principally to support retrospective (i.e. pre-existing) data harmonization and integration. These methods and software allow studies and harmonization projects to more efficiently:

  • Collect and document data
  • Evaluate the potential to harmonize and share existing data
  • Process data from multiple studies under a common (i.e. harmonized) format
  • Share harmonized data through secure central pooling or federated database systems