Advancing data honesty in experimental biology DOI Creative Commons
Shahar Dubiner, Matan Arbel

Journal of Experimental Biology, Journal Year: 2024, Volume and Issue: 227(9)

Published: April 30, 2024

ABSTRACT The ease with which scientific data, particularly certain types of raw data in experimental biology, can be fabricated without trace begs urgent attention. This is thought to a widespread problem across the academic world, where published results are major currency, incentivizing publication (usually positive) at cost lax rigor and even fraudulent data. Although solutions improve sharing methodological transparency increasingly being implemented, inability detect dishonesty within remains an inherent flaw way we judge research. We therefore propose that one solution would development non-modifiable format could alongside results; enable authentication from earliest stages collection. A further extension this tool allow changes initial original version tracked, so every reviewer reader follow logical footsteps author unintentional errors or intentional manipulations Were such developed, not advocate its use as prerequisite for journal submission; rather, envisage authors given option provide authentication. Only who did manipulate fabricate their risking discovery, mere choice do already increases credibility (much like ‘honest signaling’ animals). strongly believe enhance honesty encourage more reliable science.

Language: Английский

A Tripartite Evolutionary Game Analysis of Enterprise Data Sharing Under Government Regulations DOI Creative Commons
Ying Dong,

Zhongyuan Sun,

Luyi Qiu

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(3), P. 151 - 151

Published: Feb. 24, 2025

The tripartite evolutionary game model focuses on the strategic choices and laws of three parties in dynamic interaction. By constructing a involving government, Enterprise A, B, this paper analyzes enterprise data sharing from perspective government regulation uses simulation method to assign simulate parameters model. Furthermore, trends behavioral strategies are analyzed under changes factors such as government’s costs, penalties, rewards, compensation fees for enterprises obtain shared data. findings indicate that when benefits obtained by relatively high, incurred other party’s sufficient compensate losses caused sharing, will tend choose “data-sharing”. At time, combined strategy “no-regulation, data-sharing, data-sharing” reaches an equilibrium point. In combination strategy, initial willingness not affect final result. rewards result enterprises. However, direction parties. When low, more inclined toward “no-data-sharing”.

Language: Английский

Citations

0
Advancing data honesty in experimental biology DOI Creative Commons
Shahar Dubiner, Matan Arbel

Journal of Experimental Biology, Journal Year: 2024, Volume and Issue: 227(9)

Published: April 30, 2024

ABSTRACT The ease with which scientific data, particularly certain types of raw data in experimental biology, can be fabricated without trace begs urgent attention. This is thought to a widespread problem across the academic world, where published results are major currency, incentivizing publication (usually positive) at cost lax rigor and even fraudulent data. Although solutions improve sharing methodological transparency increasingly being implemented, inability detect dishonesty within remains an inherent flaw way we judge research. We therefore propose that one solution would development non-modifiable format could alongside results; enable authentication from earliest stages collection. A further extension this tool allow changes initial original version tracked, so every reviewer reader follow logical footsteps author unintentional errors or intentional manipulations Were such developed, not advocate its use as prerequisite for journal submission; rather, envisage authors given option provide authentication. Only who did manipulate fabricate their risking discovery, mere choice do already increases credibility (much like ‘honest signaling’ animals). strongly believe enhance honesty encourage more reliable science.

Language: Английский

Citations

0