JDDMA-DA: Data Applications – this sub-journal will be in some sense an interface to the raw, data dictated edge. Scientific domain experts will populate the board. This may in fact be the biggest sub-journal. The main goal of this sub-journal is the dissemination and application of the methods to important scientific data challenges. We envision this sub-journal having a different more promotional segment as well as a more traditional journal segment. A news blog based on the sub-journal is likely.

JDDMA-DC: Data Challenges – The idea of this sub-journal is to have a place for the domain experts to explain their data and data challenges. Understanding the viewpoint of data domain experts, who feel the pulse, so to speak, of the data challenges, is absolutely critical for high quality solutions.

JDDMA-LN: Lectures and Notes – This sub-journal will publish high quality lectures and lecture notes. We will include lecture slides that stand alone, as well lectures in combined slide-and-video form. Lecture notes will be published as well.

JDDMA-GA: Geometric Analysis – Geometric measure theory, and significant pieces of differential geometry, harmonic analysis, variational analysis, PDE, and nonlinear functional analysis. Both research with a direct connection to data problems and supporting theory with a more distant connection will be encouraged.

JDDMA-SS: Statistics and Stochastic Methods – Stochastic image models, SPDE models, random shapes, quantification of uncertainty, statistical learning the- ory, to name a few.

JDDMA-GN: Graphs and Networks – Network tomography, robust sensor net- works, inference on dynamic networks, and much more.

JDDMA-PDE: PDE methods – PDE with a view to data of any kind. PDE tools are being used to model and analyze data of all sorts. (Some of the PDE research will of course be published in JDDMA-GA.)

JDDMA-DSC: Dynamical Systems and Control – Dynamical systems meth- ods for data analysis are quite successful in many instances. The plethora of results concerning times series analysis comes to mind, for example.

JDDMA-ISPS: Images, Signals, Patterns and Shape – This is where the arti- cles closest to the data will be published – specific problems and specific results; solutions to industrial problems – the articles here might often have companion articles in one of the other sub-journals.

JDDMA-HDGM: High Dimensional Geometry and Modeling – Dimension reduction, compressed sensing, learning from data, etc. Learning from huge volumes of data requires us have efficient representations. Conversely, efficient models and representations must be learned from the data.

JDDMA-ACM: Algorithms and Computational Methods – Making the meth- ods work on huge data sets in real time is a very big deal – many customers of the data sciences will need the speed/volume bottleneck dealt with. And even though we expect that the exploitation of insights from many areas will be critical, final resolution of the challenges will most certainly involve clever algorithmic work.

JDDMA-O: Optimization – Most practical implementations of methods to deal with data require optimization of something.