Background: The OSCAR project aims (a) to research the current state of open science in the European aeronautics and air transport (AAT) research landscape and (b) to implement open science into the European AAT research landscape. To reach the second goal of OSCAR we strive (b.1) to develop an open science code of conduct for the European AAT research landscape and (b.2) to harmonise the main topics of this open science code of conduct with the common consortium agreement models (CAMs) in this field. Objective: The primary objective of the present analysis is to establish an information basis for the strategic alignment of the project. The main question of the analysis at hand is as follows: Is open science (already) relevant in the existing CAMs in European AAT research fields? Methods: We performed a qualitative and quantitative (multi variate) content analysis (Mayring 2014; Blasius and Baur 2014) of five representative CAMs used in the European AAT research landscape. Content analysis is a well-established, scientific method of empirical social science for objective information retrieval (Blasius and Baur 2014). The analysis is comprised of two main steps. The first step was to perform a theoretical background analysis in combination with an automatic topic modelling of open science to determine the important categories of open science. The second step was to analyse the content of the CAMs (a) qualitatively and (b) quantitatively. Results: We determined 18 important categories of open science in general. The inter rater reliability between coder 1 and coder 2 is α = 0.422 (Krippendorff’s α). Although we did not achieve the level of agreement of α ≥ .667, there is however, a systematic agreement between the two coders, as shown by the Kendall rank correlation coefficient τ with τ = 0.344626 (p > .001). The observed category frequencies by coder 1 are significant (p < .001) the ones observed by coder 2 are not significant (p > .6) (Fisher’s exact test). The category frequency is significantly higher than expected (p > .001) (Fisher’s exact test). The documents showed a high degree of inter-document similarity. Our analysis showed that the most relevant categories present in the CAMs are: (1) Intellectual Property, (2) Open Source Software, (3) Open Data, (4) Ethics and responsibility. However, it is interesting to note that digitalisation does not seem to be particularly relevant in the given CAMs. Conclusion: Our analysis of the five major CAMs that are widely used in the European AAT research landscape shows that open science and its underlying conceptual framework is indeed relevant in these CAMs. In the forthcoming course of the OSCAR project, we should focus on developing communication strategies that tie on the four identified categories. The OSCAR project should focus on already used EU standards and guidelines and existing best practices of the industry and scientific community. To raise awareness, we should address the policy makers as well as the main stakeholders. Policy makers should make clear statements, commitments and rules. The main stakeholders should be well informed. We should develop simple opt-in, opt-out models for open science that can be used in the projects with ease. Our opt-in, opt-out models should emphasis the integration of conventional intellectual property management and open science practices. Data: All data, statistics and media used and generated by this project can be found in the zip files: data.zip, stats.zip and media.zip