Main objectives of sparse2big

(c) Comma Soft AG 2018, fastgenomics.org

Large data sets with many variables frequently contain unobserved, missing or noisy entries. Dealing with these missing values is crucial for ay later step of analysis. Solutions in various fields have been developed, from general sample imputation to modeling observation processes or making downstream anlyses robust against missing values. Only when properly dealing with these sparse data sets, including the combination of multiple sparse observations of the same entity, we can hope to achieve meaningful big data and in-depth insightful analyses. Hence developing, evaluating and sharing methods for data imputation and integration will be an enabler for many research areas, with potential use cases ranging from patient data in medicine to remote sensing in geography or sample noise in imaging.

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Sparse2big consortium partners

In the sparse2big consortium eight Helmholtz Centers work together and several groups collaborate on different smaller projects to achieve the main goal. The following Helmholtz Centers are involved:

  • Helmholtz Zentrum Munich (HMGU)
  • German Center for Neurodegenerative Diseases (DZNE)
  • German Cancer Research Center (DKFZ)
  • Forschungszentrum Jülich (FZJ)
  • German Research Centre for Geosciences and Hasso Plattner Institute at the University of Potsdam (GFZ/HPI)
  • Helmholtz Centre for Infection Research (HZI)
  • Max-Delbrück Center for Molecular Medicine (MDC)
  • Helmholtz Centre for Environmental Research (UFZ)
  • and companies, such as Comma Soft AG, HP, and IBM.

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