We propose a data analysis management platform that allows free and full interaction with the data handling, modelling, simulation, and visualization resources of the PNI centres supported by core routines on the users own computer. This will be achieved through well-defined API which will allow legacy and external high performance resources to be linked in an effective and timely manner.

Levels Involved in Data Analysis Work Flow
Level 1: Data treatment
The first program block comprises fast, efficient, common, generic libraries, implemented at server or stand-alone level providing the essential initial data treatment steps, e.g. transformation from angles, detector pixel positions, etc. into physical space like (Qx,Qy,Qz,E) including normalization, background and sensitivity corrections.

Level 2: Data visualization
This allows for the interactive presentation of n-dimensional data as projections, cuts or iso-surfaces. For the challenges presented here, recognition algorithms for correlations in the data and automated search strategies for clustering and classifying are of particular significance. This enables the identification of relevant sections of parameter space. This involves constructing common libraries, but the final aim is for user tools tailored for the physical problems.
Level 3: Simulations

This offers access to expert software and CPU-intensive applications. The aim is to perform on-demand and quasi-real time simulations to both obtain first information within constraints on physical parameters as well as input for further more sophisticated analysis. Envisaged capabilities include access to Monte Carlo instrument simulations, DFT and analytical expressions, but also the combination of these to simulate the total scattering response.

Level 4: Modelling/Analysis
Here, the connection with the physical parameters is made. Comparison is made between model and data by the use of statistical analysis, e.g. regression or Bayesian analysis. Tailored plug-in modules from the user community are extremely important. The challenge is to address comprehensive data sets, so simultaneous fitting of multi-technique/-multi-probe data is central. Open source and usercontributed software have successfully been implemented recently, for example in McSTAS/VITESS by the instrument simulation community, and we will follow the best practice here.

Level 5: Real space visualization
Especially for the non-expert user, a visualization of the model in easiest terms is of highest importance, which usually means real space. This step is always an important one in making the connection between structure and functionality. What is required here is to translate the determined system parameters into a readily viewable representation, such as crystal structure or set of vibrational modes. Again, the most convenient and meaningful representation goes hand in hand with the type of system being studied, and we take the same approach as before in writing core libraries linked with tailored tools.