The Arab Council for the Social Sciences (ACSS) recently launched the Arab Social Science Data Archive, an initiative that aims, among other things, to address the limited access to quantitative datasets in the Arab region and show how challenges associated with sharing data with the broader community can be overcome with careful data management and planning.
Through this new initiative, the ACSS and the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill organized a three-day workshop on “Data Sharing for the Public Good” from November 30, 2018 to December 2, 2018 in Beirut. The workshop was funded by the Carnegie Corporation of New York.
Through this new initiative, the ACSS and the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill organized a three-day workshop on “Data Sharing for the Public Good” from November 30, 2018 to December 2, 2018 in Beirut. The workshop was funded by the Carnegie Corporation of New York.
Designed for researchers, administrators, librarians, and archivists, the workshop gathered 15 participants from Algeria, Egypt, Lebanon, Morocco and Sudan, who work on data in academic settings and in NGOs. The participants worked in different fields including Biological Sciences, Economics, Human Rights, Library and Archival Sciences, Media and Communications, and Political Sciences.
The workshop examined the opportunities and challenges of sharing quantitative data resources and the mechanisms available to help researchers produce and share high-quality datasets. More specifically, the workshop focused on the following topics:
The workshop examined the opportunities and challenges of sharing quantitative data resources and the mechanisms available to help researchers produce and share high-quality datasets. More specifically, the workshop focused on the following topics:
- The benefits of quantitative data sharing to both the individual researcher and to the scientific enterprise as a whole.
- The complexities of research data based on disciplinary context, format, technical requirements, and applicable policies.
- The implementation of data management standards and best practices across the research data lifecycle.
- The application of the Data Quality Review framework for ensuring computational reproducibility of reported research findings.
- Mechanisms for archiving research data for long-term preservation, discovery, access, and reuse.