Replication data and code for: How impact factors shape actual open data practices in sociology and political science journals

The included survey dataset contains the variables from the original project with updates and the code to replicate some of the analyses of the paper. Some variables were anonymized (documented in the code and variable labels), and some additional variables from the questionnaire have not been used and are not part of this dataset. A large number of research articles have looked into data sharing and other ways of participating in open science practices. These articles have in common that willingness or past open science practice are usually measured as self-reports in a survey. We use a unique dataset of social science and political science researchers which allows us to identify whether open data practices have been actually carried out, rather than self-reported. Open data practices is defined as either sharing self-collected data or referencing reused data in a published paper. With this dataset we are also able to distinguish between the influence of journal characteristics and of personal characteristics on actual open science practices. In doing so, we apply multilevel regression to account for the nested structure of the data and find that the journal impact factor is a crucial factor for researchers to overcome the intention-behavior gap. With this paper we contribute to the literature on data sharing, specifically to research on the reward structure in academia. Contrary to previous findings, we can base our findings mainly on actually observed information, i.e. the journal’s impact factor and researchers’ actual open data practice. In addition, we are able to analyze the interaction between researchers’ personal characteristics and those of the journal. The included survey dataset contains the variables from the original project with updates and the code to replicate some of the analyses of the paper. Some variables were anonymized (documented in the code and variable labels), and some additional variables from the questionnaire have not been used and are not part of this dataset. A large number of research articles have looked into data sharing and other ways of participating in open science practices. These articles have in common that willingness or past open science practice are usually measured as self-reports in a survey. We use a unique dataset of social science and political science researchers which allows us to identify whether open data practices have been actually carried out, rather than self-reported. Open data practices is defined as either sharing self-collected data or referencing reused data in a published paper. With this dataset we are also able to distinguish between the influence of journal characteristics and of personal characteristics on actual open science practices. In doing so, we apply multilevel regression to account for the nested structure of the data and find that the journal impact factor is a crucial factor for researchers to overcome the intention-behavior gap. With this paper we contribute to the literature on data sharing, specifically to research on the reward structure in academia. Contrary to previous findings, we can base our findings mainly on actually observed information, i.e. the journal’s impact factor and researchers’ actual open data practice. In addition, we are able to analyze the interaction between researchers’ personal characteristics and those of the journal.