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From data sharing to data publishing [version 2; peer review: 2 approved, 1 approved with reservations].

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Life science researchers are slow to adopt data sharing. The study advocates for data publishing, especially for neuroscience data, proposing practical steps for implementation.

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Area of Science:

  • Neuroscience
  • Life Sciences
  • Scientific Data Management

Background:

  • Data sharing is crucial for scientific advancement but faces slow adoption in life sciences.
  • Current data repositories are not sufficiently utilized by the research community.
  • Sociological and cultural factors impede widespread data sharing practices.

Purpose of the Study:

  • To analyze the reasons behind the slow adoption of data sharing in life sciences.
  • To propose data publishing as a more effective alternative to data sharing.
  • To focus on the specific needs and opportunities within neuroscience data.

Main Methods:

  • Analysis of sociological and cultural contexts within scientific research.
  • Comparative assessment of data sharing versus data publishing models.
  • Development of practical strategies for implementing data publishing.

Main Results:

  • Identified sociological and cultural barriers hindering data sharing.
  • Argued for data publishing as a superior model for scientific data dissemination.
  • Outlined actionable steps for transitioning to a data publishing framework.

Conclusions:

  • Data publishing offers a more robust solution for scientific data accessibility than traditional data sharing.
  • Neuroscience data presents a prime area for implementing data publishing initiatives.
  • Practical steps are proposed to facilitate the adoption of data publishing across research communities.