Health data faker is a Python-based package which allows the user to use a declarative approach to create fake or test data sets. using either Python code, or a YAML or JSON-based template file. The package can be embedded directly into Python scripts, or it can be used through a command-line script. It has been used to generate data in both the EASUL and SLAIDER projects.
Headfake takes ideas from other declarative packages (e.g. pydbgen), but also adds support for a number of additional features including statistically distributed random values; dependent fields and custom fields as well as approaches for transforming generated fields before/after the generation process.Data management in clinical metabolomics studies is often an ad-hoc manual process. Existing freely available software tools do not provide an effective solution to manage the data collection process in these studies. LabPipe was created to streamline collection and management of both metadata and metabolomics data. It is driven through a local client which guides the sample collection process using customisable study-specific meta-data entry. Management and transfer of data is handled by a server through access controlled web services. The platform has been positively received by users and now is an essential study management component for clinical metabolomics locally.
Headfake is freely available to download under an MIT open-source license.
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