In this article, we present documentation of the georeferenced social science survey data that are spatially linked to geospatial data attributes. We introduce the challenges of documentation, as different metadata standards are used for both data sources: social science survey data and geospatial data. In particular, we analyze the extent to which the social sciences metadata standard DDI Lifecycle is capable of incorporating the geosciences metadata standard ISO 19115. We find that the most challenging attributes to describe are those concerning the geographic structure of the geospatial data, especially if they stem from different sources. To navigate these issues, we developed and evaluated four workaround approaches which we demonstrate in a case study on the georeferenced German General Social Survey. Because not all of the approaches apply equally to every research project and institution, we provide a scheme to assist in making informed and weighted decisions.
Social media data (SMD) have become an important data source in the social sciences. The purpose of this paper is to investigate the experiences and practices of researchers working with SMD in their research and gain insights into researchers' sharing behavior and influencing factors for their decisions. To achieve these aims, we conducted a survey study among researchers working with SMD. The questionnaire covered different topics related to accessing, (re)using, and sharing SMD. To examine attitudes toward data sharing, perceived subjective norms, and perceived behavioral control, we used questions based on the Theory of Planned Behavior (TPB). We employed a combination of qualitative and quantitative analyses. The results of the qualitative analysis show that the main reasons for not sharing SMD were that sharing was not considered or needed, as well as legal and ethical challenges. The quantitative analyses reveal that there are differences in the relative importance of past sharing and reuse experiences, experienced challenges, attitudes, subjective norms, and perceived behavioral control as predictors of future SMD sharing intentions, depending on the way the data should be shared (publicly, with restricted access, or upon personal request). Importantly, the TPB variables have predictive power for all types of SMD sharing.
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