In an editorial titled, “Large and linked in scientific publishing” the editors of GigaScience provide an introduction to new publication and discuss what they plan to offer readers.
We are delighted to announce the launch of GigaScience, an online open-access journal that focuses on research using or producing large datasets in all areas of biological and biomedical sciences. GigaScience is a new type of journal that provides standard scientific publishing linked directly to a database that hosts all the relevant data.
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.
In addition to trying to make the availability and use of data associated with our papers more transparent, we are also focusing on doing the same with our peer-review model. We are using an opt-out open peer review system, a system that is becoming increasingly accepted in the medical community. Reviewers’ names are included with their reviews unless a reviewer has reasons not to be named and opts out. The reviews will be available in the pre-publication history section of our papers so that the entire set of comments and history can be seen by anyone interested in the additional insight that may come from the behind-the-scenes discussions surrounding the review.
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Reinforcing and upholding GigaScience’s goals to promote open-data and reproducibility of research, GigaDB also aims to provide a home, when a suitable public repository does not exist, for the supporting data or tools featured in the journal and beyond.