Large and linked in scientific publishing

“Big-data” science has been growing by leaps and bounds over the last decade. While data availability has provided myriad new opportunities for research, full use of these data across all the life sciences requires more focused mechanisms to reach the promise of community resource projects. This is especially true for smaller labs that do not have the computational facilities to take full advantage of such resources, which are intended to speed work and provoke novel hypotheses for testing.Unique to GigaScience—and essential to achieving community-wide goals for taking full advantage of large, sharable datasets across the board—is the creation of a system that more easily links publications to their complete datasets, provides citable, countable credit for data producers, and makes data more accessible and useable to the entire life-science community. To address some of these issues, we have devised a new journal model that integrates manuscript publication with a database that houses and provides tools for the data used in these publications. The database, GigaDB, provides all included datasets with reference-section citable DOIs; GigaDB data have already been referenced in several top tier journals (for details, see [1]).Additionally, although the “omics” communities have well-established data sharing mechanisms and standards, there are many fields that produce equal if not larger data sets that are not readily sharable and that require more work for establishing standards and sharing. Thus, GigaScience and GigaDB are especially interested in supporting non-omics type research, as these typically have sharable data but no broadly accepted public repositories or completely established means to promote the widest free sharing of data.We do want to stress, as this has been an issue raised by many, that if there are permanent or community-agreed upon databases available (e.g. NCBI, EBI databases, and similar), we require that the data be submitted to those as well. Th