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ABSTRACT: Background
Improving rigor and transparency measures should lead to improvements in reproducibility across the scientific literature; however, the assessment of measures of transparency tends to be very difficult if performed manually.Objective
This study addresses the enhancement of the Rigor and Transparency Index (RTI, version 2.0), which attempts to automatically assess the rigor and transparency of journals, institutions, and countries using manuscripts scored on criteria found in reproducibility guidelines (eg, Materials Design, Analysis, and Reporting checklist criteria).Methods
The RTI tracks 27 entity types using natural language processing techniques such as Bidirectional Long Short-term Memory Conditional Random Field-based models and regular expressions;
SUBMITTER: Menke J
PROVIDER: S-EPMC9274430 | biostudies-literature | 2022 Jun
REPOSITORIES: biostudies-literature