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We present an overview of the recently funded Merging Science and Cyberinfrastructure Pathways: The Whole Tale project (NSF award #1541450). Our approach has two nested goals: 1) deliver an environment that enables researchers to create a complete narrative of the research process including exposure of the data-to-publication lifecycle, and 2) systematically and persistently link research publications to their associated digital scholarly objects such as the data, code, and workflows. To enable this, Whole Tale will create an environment where researchers can collaborate on data, workspaces, and workflows and then publish them for future adoption or modification. Published data and applications will be consumed either directly by users using the Whole Tale environment or can be integrated into existing or future domain Science Gateways.
Whole Tale http://wholetale.org is a web-based, open-source platform for reproducible research supporting the creation, sharing, execution, and verification of Tales for the scientific research community. Tales are executable research objects that ca
Recent reproducibility case studies have raised concerns showing that much of the deposited research has not been reproducible. One of their conclusions was that the way data repositories store research data and code cannot fully facilitate reproduci
Although a standard in natural science, reproducibility has been only episodically applied in experimental computer science. Scientific papers often present a large number of tables, plots and pictures that summarize the obtained results, but then lo
The drive for reproducibility in the computational sciences has provoked discussion and effort across a broad range of perspectives: technological, legislative/policy, education, and publishing. Discussion on these topics is not new, but the need to
We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary re