Our community now owns decades of in situ measurements from multiple spacecraft missions, stored in public international and national databases. Exploring databases and browsing data, searching for the signatures of a plasma process of interest, now represents a real bottleneck in our daily workflow ; not only because of the massive and rapidly increasing amount of data, but also because of its intrinsic complexity. Indeed, in situ measurements represent 1D temporal cuts through 3D complex and unsteady structures. Compiling lists of events, performing statistical analysis is therefore a daunting task.
SciQLOP is a new project aiming at developing an open-source software bringing together an efficient and ergonomic multi-mission data browsing interface with a smart signature learning and recognition core built on state-of-the-art machine learning techniques. It also embeds an ipython terminal for users to interact with data and offering virtually limitless analysis options.
This presentation will present the SciQLOP project, the current state of the development, objectives of the first year release and discuss future goals and possibilities.