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Register a new userAdvancing computational reproducibility in the Dataverse data repository platform
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Added by
Ana Trisovic
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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 reproducibility due to the absence of a runtime environment needed for the code execution. New specialized reproducibility tools provide cloud-based computational environments for code encapsulation, thus enabling research portability and reproducibility. However, they do not often enable research discoverability, standardized data citation, or long-term archival like data repositories do. This paper addresses the shortcomings of data repositories and reproducibility tools and how they could be overcome to improve the current lack of computational reproducibility in published and archived research outputs.
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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 loosely describe the steps taken to derive them. Not only can the methods and the implementation be complex, but also their configuration may require setting many parameters and/or depend on particular system configurations. While many researchers recognize the importance of reproducibility, the challenge of making it happen often outweigh the benefits. Fortunately, a plethora of reproducibility solutions have been recently designed and implemented by the community. In particular, packaging tools (e.g., ReproZip) and virtualization tools (e.g., Docker) are promising solutions towards facilitating reproducibility for both authors and reviewers. To address the incentive problem, we have implemented a new publication model for the Reproducibility Section of Information Systems Journal. In this section, authors submit a reproducibility paper that explains in detail the computational assets from a previous published manuscript in Information Systems.
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.
Without a doubt, the electronic learning makes education quite flexible. Nowadays, all organizations and institutions are trying to avoid Monotony and the delay and inertia. As well the universities should be improving their systems continually to achieve success. Whereas, the students need to access the dissertations in the library. In this paper we will present Dissertations Repository System Using Context Module to allow the students to benefit the dissertations which is in the library flexibly.
In the age of technology, the information communication technology becomes very important especially in education field. Students must be allowed to learn anytime, anywhere and at their own place. The facility of library in the university should be developed. In this paper we are going to present new Quantitative Study for Dissertations Repository System and also recommend future application of the approach.
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 capture the code, data, and environment along with narrative and workflow information needed to re-create computational results from scientific studies. Creating reproducible research objects that enable reproducibility, transparency, and re-execution for computational experiments requiring significant compute resources or utilizing massive data is an especially challenging open problem. We describe opportunities, challenges, and solutions to facilitating reproducibility for data- and compute-intensive research, that we call Tales at Scale, using the Whole Tale computing platform. We highlight challenges and solutions in frontend responsiveness needs, gaps in current middleware design and implementation, network restrictions, containerization, and data access. Finally, we discuss challenges in packaging computational experiment implementations for portable data-intensive Tales and outline future work.
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