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The problem is that repositories are often not flexible enough to store data that do not conform to known standards. Genomics, for example, benefits from community genomics standards groups and open industry standards. Many other fields of study are without such standards, yet generate significant amounts of data, without a standard way to store it that would make the data more accessible, easily accessedretrieved, and potentially reused by the research community.

The FNLCR supports the Center for Strategic Scientific Initiatives Data Coordinating Center (CSSI DCC) stores and manages access to data generated in support of cancer research funded or supported by the CSSI. The Frederick National Lab team, led by Andrew Quong (see team members below), developed the ), an innovations center within NCI, by managing science and technology initiatives to support development programs critical to nearly all cancer and biomedical research programs in new higher risk areas, including data management, high definition single cell analysis, immuno-mass cytometry, clinical proteomics, and antibody characterization that may question existing paradigms and lead to hypothesis testing. A knowledgeable team of software developers in DSITP, led by Uma Mudunuri and managed by Andrew Quong, have developed the CSSI DCC Portal, a repository for CSSI DCC data and also emerging data types without a standard approach to data storage. The data currently in the DCC conform to the standard Investigationstandard Investigation-Study-Assay tab-delimited formatformat (ISA-TAB) format, which describes a scientific investigation, its study or studies, and each study's assay(s).

The DCC's goal is to use the CSSI DCC Portal to store emerging data types in addition to ISA-TAB. primary goals of the FNLCR CSSI Data Coordination Center (DCC) are to facilitate access to research data for the greater cancer research community.  Facilitating this is the DCC's design approach, which follows follows FAIR (Findable, Accessible, Interoperable, and Reusable) principles for scientific data management and stewardship, applied metadata standards, and involved interactions with the research community to seek out and apply their best practices.

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• Provides a common location and web access to data from disparate data types including gene expression results from Next Generation Sequencing, microarray experiments, histopathological images, metabolomics data and proteomics data, allowing for easy access by multiple collaborators and researchers located at different geographic locations. Is flexible enough to handle new and unspecified data types.
• Stores the data in one common location so that you can make biological insights that would otherwise be missed by having data in multiple locations.
• Applies the information gained from one study to multiple studies and projects.
• Allows you to search the metadata from each study to identify datasets of interest.
• Develops data storage and data mining modules that can be applied across studies, avoiding duplication of effort and saving costs.
• Develops and/or adopts common vocabularies, data standards, and ontologies for data representation, storage, and comparison.

See Neo4j  Neo4j - Rapidly Prototyping a Semantic Graph for  for more information about technologies used in CSSI DCC.

The  The CSSI DCC Portal project team includes:

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