12,637 papers · updated 18 Sept 2026livingmeta.ai
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Research theme

Reproducibility with AI

The Reproducibility with AI theme comprises 110 papers in this corpus published between 2007 and 2026. Work here is dominated by Empirical Study, Design Science, Case Study. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Empirical Study19 (17%)
  • Design Science15 (14%)
  • Case Study13 (12%)
  • Literature Review11 (10%)
  • Content Analysis9 (8%)
  • Conceptual9 (8%)

Research domains

  • Research Integrity97 (88%)
  • Research Productivity7 (6%)
  • Data Analysis3 (3%)
  • Peer Review1 (1%)
  • Scholarly Infrastructure1 (1%)

Frequent sub-topics

multiverse analysis for robustness of EEG biomarkers in machine learning · 1Data Availability Statements in mega journals; data sharing practices and transparency · 1AI and radiomics reproducibility crisis in clinical research · 1transparency, open science, privacy, and algorithmic bias in digital research · 1open science adoption in communication research · 1quantum software defect dataset reproducibility and maintenance · 1stochastic dynamics of AI assistance on code quality; methodological caution in causal inference · 1validation of machine learning prediction models with clustered repeated-measures data, clinical prediction model standards, digital twins · 1

Open research gaps

  • The authors identify that "it is unclear precisely how replication relates to design science research—that is, what outcomes replication produces and how researchers should apply it within design scie
  • The abstract identifies a need for "an integrated framework that connects multi-source disease phenotyping, survival-ready cohort construction, and downstream analysis on the RAP," which the presented
  • The paper identifies the need for modifications to existing preregistration frameworks to accommodate qualitative research practices and calls for greater engagement with preregistration in the qualit
  • Need to conduct empirical evaluations looking at both outcome reporting bias and study publication bias in the same RCT cohort to investigate relative importance
  • Need to examine effects of funding sources (pharmaceutical vs. non-pharmaceutical) on both types of bias

Representative papers

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