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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
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Learning from open source software projects to improve scientific review.

Satrajit S Ghosh1, Arno Klein, Brian Avants

  • 1McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge MA, USA.

Frontiers in Computational Neuroscience
|April 25, 2012
PubMed
Summary
This summary is machine-generated.

The current scientific peer-review process has many flaws. We propose an open, interactive system using technology to improve review quality, transparency, and scientific credibility.

Keywords:
code review systemsdistributed peer reviewopen source software developmentpost-publication peer reviewreputation assessmentreview quality

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Area of Science:

  • Scientific publishing
  • Scholarly communication
  • Research integrity

Background:

  • Peer-reviewed publications are crucial for science dissemination.
  • The existing peer-review system faces challenges including expertise demands, limited access to materials, lack of reviewer recognition, unmeasured review quality, and lengthy review times.

Purpose of the Study:

  • To identify key problems within the current scientific peer-review process.
  • To propose a novel, open, and interactive review system to address these issues.
  • To enhance the pace, validity, and credibility of scientific research.

Main Methods:

  • Distributing reviews to multiple specialists for focused evaluation.
  • Providing reviewers with comprehensive materials and methods for thorough assessment.
  • Implementing reviewer acknowledgment and quality quantification systems.
  • Facilitating dynamic, post-publication review and discussion.

Main Results:

  • The proposed system aims to resolve issues of reviewer burden and expertise.
  • Enhanced transparency and scrutiny through material access are expected.
  • Reviewer contributions and review quality can be quantitatively assessed.
  • The system allows for continuous evolution of scientific discourse.

Conclusions:

  • Adapting open-source and social networking technologies can revolutionize peer review.
  • An open, interactive model can quantify article significance, review quality, and reviewer reputation.
  • This approach promises to improve the overall scientific publishing ecosystem.