As green, renewable energy is increasing by the installation of more and more wind turbines, the assessment of their impact on protected species has to be improved by more automatized, data-driven risk analyses. We have developed as set of two workflows to extract simple parameters for collision risk models from GPS tracks of sensitive bird species during nesting. The workflows have been integrated into the free MoveApps platform and are available there. The analysis code of all components of the workflows is openly available on GitHub, and improvement and adaption to other, similar requirements is encouraged. With three example data sets of white storks (WS), red kites (RK) and marsh harriers (MH), we illustrate how the workflows are used. The first workflow identifies nesting sites and time of nesting from the GPS tracks, the second workflow calculates flight speeds, flight duration, flight height and distance from the nest. Estimated flight speeds show low within species variability, with averages of 4.1 m/s (MH), 6.9 m/s (RK) and 10.9 m/s (WS). Extracted times of nesting are widely spread through the season and flight height and distance to the nest when in flight show large differences between individuals and years. Similar to the central evaluation distances around the nest required by national legislation, the 50% in-flight usage thresholds are 700 m (MH), 1100 m (RK) and 1400 m (WS). Flight height during nesting is rather low, on average 16 m (MH), 75 m (RK) and 193 m (WS) above the ground. These values can help to estimate collision risk with wind turbines for large birds in Central Europe during their nesting period. Finally, the developed MoveApps workflows (an possible adaptions) can be used to extract required parameters from tracking studies of any other vulnerable species or populations in an unbiased, automated manner to improve wind turbine placement in relation to nesting sites.