This study aimed to determine to what extent do CS project descriptions actually contain the kinds of information relevant to prospective participants and whether this information is conveyed in a comprehensible and attractive manner. To this end, we conducted a qualitative content analysis of a random sample of 120 English-language project descriptions stored in the CS Track database. The coding rubric used for this study is based on the ten-step template for writing engaging project descriptions we recently designed and published. The sample was produced as follows: After creating a dataset containing only English-language project descriptions, we excluded all descriptions which consist of less than 100 or more than 500 words. Texts of less than 100 words cannot be expected to contain a significant amount of information. Project descriptions of more than 500 words are less likely to be read in their entirety than shorter texts and thus ill-suited to the task of capturing the readers’ interest and prompting them to join the project in question. Finally, we applied the ‘random’ function of RStudio to randomly select 120 texts from the resulting dataset of 1283 descriptions. The qualitative content analysis was performed in two consecutive steps. First, in order to ensure that the coding rubric is fit for purpose and all categories within it well-defined and demarcated, all three members of the research team independently coded 40 project descriptions. After discussing the results and making slight modifications to the coding rubric, each team member coded roughly one third of the remaining 80 descriptions. Preliminary results suggest that the majority of project descriptions in our sample fail to mention how citizen scientists will benefit from participating, what kind of training they will receive, how their contributions will be acknowledged, and whether they will have access to project results. Furthermore, the project’s goals, its target audience, and the tasks volunteers will be expected to complete are very often not described explicitly and clearly enough. For instance, very few project descriptions contain concrete information on required skills and equipment or on the time commitment associated with participation. This dataset contains the coding rubric used to analyse project descriptions and a visualisation of preliminary results.