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We strive to make marine images FAIR. We maintain data profiles to establish terms for marine imagery and we develop standard operating procedures for handling marine images and software tools to apply the vocabulary and procedures to marine imagery.

Analysis of Microcalorimetric Metabolic Data Using Monod's Equation

A Django app to manage members, topics and more in an academic community

This project is subject to active development. We apologize for the lack in documentation and hope to improve on this in the near future.

The Linear Meta-Model optimization (LiMMo) is a Python-tool designed for the objective tuning of regional climate models (RCM) and numerical weather prediction models (NWPM) to gridded observational datasets.

In this approach, the surface 2D output of the RCM/NWPM is approximated using regression method (linear, piecewise-linear, quadratic). A user-defined error norm, which quantifies the difference between the regression approximation and the observations, is then minimized using a gradient-based optimization method.

The generated documentation can be found here: https://hifis.net/doc

Materials of the workshop Introduction to Git and GitLab. You can find the reference website here: https://hifis.net/workshop-materials/git-intro/

Store elements to be displayed on unity (login.helmholtz.de and login-dev.helmholtz.de), for example: info banner.

The source code of the HIFIS homepage. --> https://hifis.net

Generic Library for Asynchronous Data Operations and Streaming