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Data education is gaining traction across disciplines and degree levels in higher education. Teaching data skills in the Social Sciences in today's data-driven world is vital for preparing the next generation of dataliterate and critical social scientists. The ability to identify, assess, analyze, and communicate well and responsibly with data is key for scholars and professionals to navigate dynamic and expansive information ecosystems. This paradigm shift demands instructors to adapt their curricula and pedagogy to advance students’ computational and statistical knowledge. This paper presents some of the findings from a local report of a larger national project which explored pedagogical techniques and instructional support needs for teaching undergraduates with quantitative data in the Social Sciences. Results revealed that the core learning goal of instructors is to develop students' critical thinking skills with data, including the conceptual understanding of the research methods employed in the field; the ability to critically evaluate research methodologies, findings, and data sets; and prowess using quantitative and computational tools and technologies. A recurring theme across interviews was students’ fear of math and technology and the challenges these fears pose to data-related instruction. Instructors value participation in a community of practice and are eager for more institutional support to advance their computational skills. Based on these findings, we suggest avenues for academic libraries to further develop services, activities, and partnerships to aid data instruction efforts in the Social Sciences. [ABSTRACT FROM AUTHOR]

As data-sharing becomes more prevalent throughout the natural and social sciences, the research community is working to meet the demands of managing and publishing data in ways that facilitate sharing. Despite the availability of repositories and research data management plans, fundamental concerns remain about how to best manage and curate data for long-term usability. The value of shared data is very much linked to its usability, and a big question remains: What tools support the preparation and review of research materials for replication, reproducibility, repurposing, and reuse? This paper describes key curation tasks and new data curation software designed specifically for reviewing and enhancing research data. It is being developed by two research groups, the Institution for Social and Policy Studies at Yale University and Innovations for Poverty Action, in collaboration with Colectica. The software includes curation steps designed to improve the research materials and thus to enable users to derive greater value from the data: Checking variable-level and study-level metadata, verifying that code can reproduce published results, and ensuring that PII is removed. The tool is based upon the best practices of data archives and fits into repository and research workflows. It is open-source, extensible, and will help ensure that shared data can be used. [ABSTRACT FROM AUTHOR]

In recent years, Semantic Web technologies have matured and have made their way into various domains - for example, bioinformatics and eGovernment - where they are used in different applications to provide value-added services for users. In this paper, we present an overview of several representative applications that use Semantic Web technologies and show the potential of these technologies in the Linked Data world. We then present existing Semantic Web applications specifically for the social sciences and highlight their impact on this domain. Integrating the Semantic Web vision into the social sciences results in clear benefits, which we identify and discuss. [ABSTRACT FROM AUTHOR]