Qresp, a tool for curating, discovering and exploring reproducible scientific papers

Area of Science:

• Computer Science
• Information Science
• Scientific Publishing

Background:

• Scientific data reproducibility is a growing concern.
• Lack of accessible data and procedures hinders verification.
• Current methods for data sharing are often fragmented.

Purpose of the Study:

• To propose a strategy for enhancing scientific data reproducibility.
• To present a simple tool, Qresp, for curating and accessing scientific data.
• To improve the searchability and discoverability of scientific information.

Main Methods:

• Developing a distributed system for data and procedure availability.
• Creating a graphical user interface (GUI) named Qresp.
• Implementing metadata generation for enhanced searchability.
• Enabling automatic data access from scientific publications.

Main Results:

• Qresp facilitates the curation of scientific papers by generating metadata.
• The tool allows users to explore curated papers.
• Users can automatically access data presented in scientific publications through Qresp.
• The proposed strategy supports distributed data availability.

Conclusions:

• Qresp offers a practical solution for improving scientific data reproducibility.
• The tool enhances the accessibility and discoverability of research data.
• This approach contributes to more transparent and verifiable scientific research.