Immigration has been a major trend in the last decades in Europe. However, immigrant access to the social security systems has remained a contentious issue having gained additional salience in light of the recent asylum-seeking developments. We focus on welfare chauvinism, the idea that immigrants should not participate in welfare resources, as an attitudinal dimension. We seek to answer two primary questions: To what extent are different types of objective and subjective material deprivation related to welfare chauvinism? What is the role of the recent asylum seeker influx? Using European Social Survey data and employing binary and generalized ordered logit models with country fixed effects, we find perceptions of deprivation to be more meaningful than objective factors related to potential job loss, and some relationships depend on the specific type of deprivation. On the country level, in line with the deservingness of asylum seekers as a group, higher levels of asylum seeking are related to lower levels of welfare chauvinism, while GDP per capita is not associated with welfare chauvinism. Finally, the generalized ordered logit model shows that some relationships vary according to the strictness of welfare chauvinism, which would not be visible in a conventional ordered logit model.

PurposeBudgeting data curation tasks in research projects is difficult. In this paper, we investigate the time spent on data curation, more specifically on cleaning and documenting quantitative data for data sharing. We develop recommendations on cost factors in research data management.Design/methodology/approachWe make use of a pilot study conducted at the GESIS Data Archive for the Social Sciences in Germany between December 2016 and September 2017. During this period, data curators at GESIS - Leibniz Institute for the Social Sciences documented their working hours while cleaning and documenting data from ten quantitative survey studies. We analyse recorded times and discuss with the data curators involved in this work to identify and examine important cost factors in data curation, that is aspects that increase hours spent and factors that lead to a reduction of their work.FindingsWe identify two major drivers of time spent on data curation: The size of the data and personal information contained in the data. Learning effects can occur when data are similar, that is when they contain same variables. Important interdependencies exist between individual tasks in data curation and in connection with certain data characteristics.Originality/valueThe different tasks of data curation, time spent on them and interdependencies between individual steps in curation have so far not been analysed.

Modern microbial and ecosystem sciences require diverse interdisciplinary teams that are often challenged in “speaking” to one another due to different languages and data product types. Here we introduce the IsoGenie Database (IsoGenieDB; https://isogenie-db.asc.ohio-state.edu/), a de novo developed data management and exploration platform, as a solution to this challenge of accurately representing and integrating heterogenous environmental and microbial data across ecosystem scales. The IsoGenieDB is a public and private data infrastructure designed to store and query data generated by the IsoGenie Project, a ~10 year DOE-funded project focused on discovering ecosystem climate feedbacks in a thawing permafrost landscape. The IsoGenieDB provides (i) a platform for IsoGenie Project members to explore the project’s interdisciplinary datasets across scales through the inherent relationships among data entities, (ii) a framework to consolidate and harmonize the datasets needed by the team’s modelers, and (iii) a public venue that leverages the same spatially explicit, disciplinarily integrated data structure to share published datasets. The IsoGenieDB is also being expanded to cover the NASA-funded Archaea to Atmosphere (A2A) project, which scales the findings of IsoGenie to a broader suite of Arctic peatlands, via the umbrella A2A Database (A2A-DB). The IsoGenieDB’s expandability and flexible architecture allow it to serve as an example ecosystems database.

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