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Goal: Advertise the DCC on Insite

Audience: Frederick National Lab employees

Big picture to wrap in: We try to do things according to FAIR principles and metadata standards. We have interactions with the academic research community to use best practices. Our goals is to enable repositories for emerging data types, where the approach to data storage is not done in a standard way like it is with genomics.

Data repositories are important tools in cancer research, providing safe and sustainable locations to store data, providing access to input data for meta-analyses, and allowing researchers to collaborate and share information across a common resource. The Center for Strategic Scientific Initiatives (CSSI) sponsors a diverse array of projects that generate datasets that vary in content and format, yet are related across certain defining characteristics or metadata. Integrated management of the datasets across all sponsored projects make the data more accessible, easily accessed, and potentially reused by the cancer research community. 

The CSSI Data Coordinating Center (CSSI DCC) stores and manages access to data generated in support of cancer research funded or supported by the CSSI. This data is in the standard Investigation-Study-Assay tab-delimited format (ISA-TAB) format, which describes a scientific investigation, its study or studies, and each study's assay(s). For more information on the ISA-TAB format, refer to the following section, What is ISA-TAB?, as well as the ISA-TAB specification Image Added .

The CSSI DCC Portal is the repository for CSSI DCC data. It serves the following purposes:

• 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.Goal