Data Models

EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles.

This is the preliminary data model of EXC2075 PN1-3 provided in Markdown. The main goal of this document is to define a data storage standard for Particle Image Velocimetry (PIV) recordings. The data model is still under developement. PIV is an optical, particle-based measurement technique used to measure fluid flow velocities. By illuminating small particles in the flow field with a laser sheet and analyzing their displacement between two consecutive images, PIV provides highly time and space resolved data on velocity profiles and flow structures. EXC2075 PN1-3 focuses on understanding the turbulent pumping mechanisms in different porous structures topologies with different characteristic porous scales. These fluid flow interactions between energy, mass and momentum transfer need to be further understood to improve engineering applications such as transpiration cooling, filtration processes and heat exchangers. To that aim time-resolved velocity measurements were performed at the interface between a turbulent free flow and various porous structures.

Data models are commonly written in generic formats like XML or JSON, which makes them machine-readable and -actable. However, it also means that specialized software is required to handle the format and allow for integration. Unfortunately, the development of software often lags behind the format, which can lead to compatibility issues. Additionally, data models are often exclusive to a single format, while applications require different formats. To solve these problems, Software-driven Research Data Management (sdRDM) offers a generic object model for the data model. This model allows for more flexibility and compatibility across different formats, which can simplify the management of research data. Software-driven RDM allows the user to build modular data models from existing standards and to link them. Data models can also be generated from Markdown documents or from XML Schema Definitions. Furthermore, sdRDM data models can be interchanged to other standards by developing interfaces.

ZnH5MD allows easy access to simulation results from H5MD trajectories. It provides a Python interface and can convert existing data to H5MD files as well as export to other formats.