Session Themes for SciDataCon 2021
As well as the overarching theme of SciDataCon 2021 and its high-level subthemes, session proposals for SciDataCon may address any of its core, persistent themes give below with examples of topics.
Please note that the simple presentation of research results is not in scope. The primary focus should always be on the data issues in different fields of research.
SciDataCon Themes & Example Topics
Data and Research
Addresses major research questions from the perspective of data issues. The discussion of such data issues should relate concretely and directly to specific research questions. Topic areas include:
- Transformations in research as a result of the digital revolution.
- Achievements in data-driven science across all research areas.
- The implications of Big Data and broad ‘diverse’ data in research projects, including the shared challenges of Big Data management and analysis across the scientific and commercial sectors.
- Data issues and strategies for research projects and programmes in any area of research; in particular, those of international, interdisciplinary, and/or transdisciplinary scope). For example, the collection, analysis and integration of data in relation to study of the Earth’s system, disaster risk research; data-driven and sustainable cities; and biodiversity, ecology, and human health.
- Data availability and data quality for research.
- Disciplinary and interdisciplinary case studies on data issues, data (non-)availability, and barriers and solutions.
- Data requirements for monitoring; for example, in relation to the Sustainable Development Goals or the Sendai Framework.
Data Science and Data Analysis
Addresses the frontier scientific, technical, and epistemological challenges associated with data in research. Topic areas include:
- Interoperability standards in data and metadata.
- Reproducibility, statistical, and technical challenges in data-intensive research.
- Analysis, mining, visualization, exploration, and/or representation issues for data that are complex or of large scale.
- Data science and infrastructures for Big Data.
- Linked Open Data and semantic enrichment.
- Data integration and analysis of diverse datasets.
- Research software, data systems architecture, and so on.
Addresses advances in sustainable, long-term data stewardship. Topic areas include:
- Data management and curation systems and practices.
- Increasing trustworthiness in data stewardship.
- Development and sustainability of institutional/national/international data repositories/services/infrastructure.
- Long-term digital preservation.
- Data stewardship in the research lifecycle and in research infrastructures.
- Rescue of research data at risk.
Policy and Practice of Data in Research
Addresses data policies and practice, as well as the role of data in scholarly communications. Topic areas include:
- Data policy development and harmonization; in particular for science/research.
- Legal interoperability, and issues around the harmonization of rights waivers and licenses for research data.
- Assessment of the impact and of the economic and societal value of data.
- Costs, value proposition, business models, and economics of data infrastructure.
- Mapping the limits of Open Data.
- Data publication and citation.
- Research data and scholarly communications.
- Motivations, recognitions, and rewards in research practice.
Data and Education
Addresses educational and training responses to the digital revolution. Topic areas include:
- Capacity building and education in data science, data management, and data handling.
- Workforce requirements for data science and data curation.
- Curricula and competency frameworks for data in research.
Data, Society, Ethics, and Politics
Addresses the broader dimensions of data and data-driven research in relation to society. Topic areas include:
- Open research data and Open public data.
- Citizen science and crowdsourcing.
- Ethical and legal issues associated with data and research.
Open Data, Innovation, Industry and Development
Addresses the interactions among industry, innovation, and data. Topic areas include:
- Private sector roles and public–private partnerships.
- Examples of innovation and development based on Open Data.
- Research and data-driven innovation.
- Collaborations between the research sector and commercial organizations; particularly, in relation to data stewardship, data analysis and data science, and Open Data.
- Data for development and innovation.