The dynobench repository contains a benchmark for system identification methods. Currently includes models of 10 dynamical systems: Bacterial respiration, Bar magnets, Glider, Lotka-Volterra, Predator-Prey, Shearflow and Van der Pol from the Strogatz dataset, as well as Lorenz, Coupled phase oscillators and Stuart-Landau models for dynamical systems that often appear in the research community. They also add variety to the benchmark as the Lorenz oscillator model introduces a larger set of state variables (three compared to two), and the coupled phase oscillators model is non-autonomous, which is reflected in the explicit incorporation of time in its equations. The repository contains the 'data' folder, where the simulations of ten dynamical systems are stored, simulated under 6 different configurations of data quality. The first dimention modifies the data length and coarseness, where a 'small' dataset includes simulations of 10 seconds with a 0.1 sampling step, and a 'large' dataset includes simulations of 20 seconds with a 0.01 sampling step. The second dimention of data quality modifies the amount of noise in the data, where there are three levels of noise (no noise, moderate levels with 30 dB signal-to-noise ratio and high levels of noise with 13 dB signal-to-noise ratio). The data can be used by itself, without the need to look at the python code. The repository also contains the main.py script by which the data can be generated. The 'src' folder contains additional python scripts that are needed to generate the data. The data were created by first randomly setting the initial values for one category, in particular a configuration of 'small', 'noise-free' and 'train' data (using inits_type = "random"). Then, all the other configurations were generated by using the same initial values. Inside the script main.py there is more information about the settings and how to run the script. The benchmark was created as a part of the research described in the paper titled Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data, written by Omejc et al. (in submission).