The present dataset is the first one to provide detailed information on civil society organizations engaged in an emerging field of civil society activity and of scholarship: corporate accountability. The rationale behind generating this dataset lies on the fact that there was little data driven information on the activities of (global) civil society in holding corporations accountable. Available data focused, so far, on the big Western-based NGOs and was mainly oriented toward case studies. As such, the dataset is the first source providing quantitative data on the pluriverse of organizations active in the field of corporate accountability. It includes 42 variables, grouped into six categories. The data covers both structural/organizational information (such as organizational features, number of employees, etc.) and information pertaining to the more subjective dimensions of activities (such as aims, values, and blame attribution), and allows for systematic and cross-national analyses. Further, if provides detailed information on the activities and repertoires carried out and employed by organizations. The classifications are based on common practices in the use of AOE and PEA. It also includes a wide range of network variables (e.g. partner organizations) and information on funding (sources of funding, annual budget, etc.). It can, therefore, be used for a variety of analysis, including but not limited to studying the interaction between funding and activities, location and political leaning, repertoires of action and values, etc. The coding was based on information provided on the organizations’ websites (e.g., “What we do” sections) and in annual reports. Both current and past activities (if mentioned) were coded. The data was gathered between December 2022 and April 2023. So far, the dataset has been used to study the effect of donor dependency on civil society. We found that official funding impedes contentious repertoires and severely negatively impacts on the willingness of organizations to aim for a reform of the economic system.