About the journal

Data at scale changed what a research question can be. It also split the work of asking one across several communities that do not always read each other: the people who prove what an algorithm can do, the people who build the systems that run it, the people who curate the data it consumes, the people who decide what may lawfully be done with that data, and the people who use all of it to answer a question inside a field.

This journal exists to hold those strands in one place. Its subject is the methods, systems, and analysis of data at scale, taken both as a research subject in its own right and as an instrument for research elsewhere. A contribution earns its place by what it operates on and what it adds, not by which of those communities produced it.

That breadth is deliberate and it is bounded. A manuscript is in scope when it makes a substantive contribution to big-data research; a topic label, a large file, or the use of analytical software does not settle the question on its own. The Aims and Scope page states the boundary in full, with the evidence each kind of contribution carries.

The intended readership is researchers and practitioners who work with data at scale and need to know what was done, on what material, and how far the result carries: computer scientists and statisticians, data engineers and systems researchers, analysts and visualization researchers, and researchers in the fields where these methods are applied.

The journal is also read by people who must decide what may be done with data — in research governance, in data protection, in institutional review. Work on privacy, disclosure control, fairness and the legal frameworks around data use is part of the subject, not an adjunct to it.

Because the audience is mixed, manuscripts are asked to state the data, the method, and the claim plainly enough that a reader from a neighboring strand can judge them.