Replication Crisis Data
Documented replication failures across psychology, biology, and economics — meta-research data.
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What Is Replication Crisis Data?
Replication crisis data documents systematic failures in scientific reproducibility across psychology, biology, economics, and machine learning research. This meta-research captures the prevalence of studies that cannot be replicated, the methodological barriers preventing reproducibility, and longitudinal trends in research quality. The dataset reflects a critical juncture in scientific practice where findings previously considered robust have proven unreliable when independent teams attempt to reproduce them. Replication crisis data is essential for understanding research integrity, identifying disciplinary vulnerabilities, and tracking improvements in scientific methodology over time.
Market Data
240,355 empirical articles
Psychology articles examined (2004-2024)
Source: Advances in Methods and Practices in Psychological Science
Markedly stronger p-values reported across all subdisciplines
Trend in p-values across psychology
Source: Advances in Methods and Practices in Psychological Science
64% cite data quality as dominant challenge
Data quality as top barrier
Source: Integrate.io
77%
Organizations rating quality average or worse
Source: Integrate.io
Who Uses This Data
What AI models do with it.do with it.
Research Institutions & Universities
Meta-analyses of replication trends to improve methodology, identify high-risk research practices, and strengthen research governance frameworks across disciplines.
Grant-Making Bodies & Funding Agencies
Evaluate research portfolios for reproducibility risk, allocate resources toward methodologically sound research, and establish funding criteria that reduce replication failures.
Publishers & Journal Editors
Implement peer review standards, develop policies on statistical reporting, and monitor publication trends to identify systemic issues in research conduct.
Science Policy & Regulatory Bodies
Inform policy decisions on research integrity, establish national research standards, and guide investment in scientific infrastructure addressing reproducibility challenges.
Pricing depends on the proposed terms
We do not have a verified, comparable price for your dataset. Consider the permitted uses, license term, exclusivity, provenance, coverage and quality. A seller asking price is a proposal, not an appraisal or guaranteed sale. Compare prices only when the source, date, currency, unit and license scope are known.
What Buyers Expect
What makes it valuable.valuable.
Methodological Transparency
Complete documentation of study protocols, statistical methods, p-value distributions, and effect size reporting across disciplines; data must enable detection of p-hacking, selective reporting, and questionable research practices.
Longitudinal Coverage
Multi-year or decade-spanning datasets tracking replication attempts, success rates, and trend analysis across fields; temporal data essential for measuring improvements in research practices.
Cross-Disciplinary Scope
Comprehensive coverage spanning psychology, biology, economics, machine learning, and other fields where replication crises have been documented; discipline-specific replication rates and barriers.
Reproducibility Metrics
Standardized measures of effect size, statistical significance thresholds, replication success rates, and barriers to reproducibility (e.g., data quality, skills gaps, ungoverned AI use) enabling comparative analysis.
Actionable Insights
Identification of high-risk research practices, disciplinary vulnerabilities, and evidence-based recommendations for improving research integrity and methodology.
Potential applications and organizations
Who's buying.buying.
Monitor replication trends in their disciplines, benchmark research quality against peer institutions, and design curriculum improvements in research methodology.
De-risk clinical trial design, identify reproducibility risks in preclinical research, and strengthen internal validation protocols to reduce costly failed trials.
Allocate grants toward methodologically rigorous research, establish reproducibility standards for funded projects, and track improvements in research integrity over time.
Implement evidence-based peer review policies, monitor p-value distribution in accepted manuscripts, and develop transparency initiatives to reduce publication bias.
FAQ
Common questions.questions.
What disciplines are covered in replication crisis data?
Replication crisis data spans psychology, biology, economics, machine learning, and other research fields. The available meta-research emphasizes psychology, where analysis of 240,355 empirical articles from 2004 to 2024 tracks p-value distributions and replication likelihood across all psychological subdisciplines.
How does this data help prevent future replication failures?
By documenting patterns in unreproducible research—such as p-hacking, selective reporting, data quality issues, and methodological shortcuts—replication crisis data enables researchers, funders, and publishers to identify high-risk practices before they enter the literature. Longitudinal analysis shows whether disciplines are improving over time.
What are the main barriers to reproducibility captured in this data?
Key barriers documented include data quality issues (cited by 64% of organizations as a top challenge), skills gaps in statistical methodology, insufficient transparency in methodology, inadequate peer review, and ungoverned use of generative AI in research workflows. Machine learning reproducibility faces additional challenges around software dependencies and computational environment variation.
Who should purchase replication crisis data and why?
Researchers and meta-analysts study replication patterns to understand disciplinary health. Universities use it to improve research training. Funding agencies leverage it to allocate grants toward rigorous research. Publishers apply it to strengthen editorial standards. Pharmaceutical companies use replication metrics to de-risk clinical trial design. Policy makers reference it to inform national research integrity initiatives.
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