Better ESS Research with Reproducibility Practices, Quality Information, and Provenance Metadata
Convener:
Christin HenzenECSECS
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Co-conveners:
Joan Masó,
Ivana Ivanova,
Tomasz MiksaECSECS,
Daniel NüstECSECS
First, one can argue that open and reproducible research (ORR) provides better quality information through transparency and sharing. The quality information from ORR allows going beyond limited research metrics and can help to advance research. For example, openness & transparency (1) may make creators more careful in sharing research artefacts, (2) enables inspection by others, and (3) facilitates re-use of methods/tools with multiple use cases. Is there a straight line that can be drawn from quality information from ORR to the quality of research and the impact of a piece of research?
Second, a crucial aspect to document results in research workflows is provenance and one can argue that provenance is a precursor to ORR and for providing accurate quality information. However, capturing provenance metadata is (1) not an attractive topic, and (2) not supported by many tools. Can ORR be achieved without specific handling of provenance information?
In this session, the triangle of reproducibility, quality information, and provenance shall be explored to answer questions such as: Is achieving a high reproducibility a synonym for high quality? Is provenance documentation a precursor for reproducibility? Can all three corners of the triangle of reproducibility, quality information, and provenance be achieved in combination?
Topics of submissions may include but are not limited to:
• Perspectives on the challenges for geoscience research reproducibility, provenance and quality management
• Summaries and reviews of studies on reproducibility vs. provenance and reproducibility vs. quality
• Quality measures for ORR artefacts and for provenance of research outputs
• Reports on (un)successful reproductions of published research due to lack of provenance information
• Methods and tools for sharing reproducible geoscience workflows, documenting provenance or dealing with the evolution of data and databases underlying geoscience research
• Implementation of standards for data provenance and quality in data management
• Methods and tools to audit data analysis processes and workflows