Electrospinning-Data.org is a FAIR data platform that organizes electrospinning experiments into a structured, failure-inclusive corpus to enable predictive modeling and inverse design of nanofiber morphologies.
Negative results are disappearing from most disciplines and countries.Scientometrics, 90(3): 891–904, March 2012
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
LLMs trained on success-only literature degrade as research tools, data sources, and reviewers unless failure data is systematically published.
Bibliometric mapping shows the TCM-radiotherapy adjuvant field has matured through cyclical evolution and thematic specialization while exhibiting homogeneous positive reporting bias.
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.
citing papers explorer
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Electrospinning-Data.org: A FAIR, Structured Knowledge Resource for Nanofiber Fabrication
Electrospinning-Data.org is a FAIR data platform that organizes electrospinning experiments into a structured, failure-inclusive corpus to enable predictive modeling and inverse design of nanofiber morphologies.
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LLMs Have Made Failure Worth Publishing
LLMs trained on success-only literature degrade as research tools, data sources, and reviewers unless failure data is systematically published.
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Mapping the maturation of TCM as an adjuvant to radiotherapy
Bibliometric mapping shows the TCM-radiotherapy adjuvant field has matured through cyclical evolution and thematic specialization while exhibiting homogeneous positive reporting bias.
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Learning Nonlinear Dynamics: Improving the Estimation Efficiency and Reliability of Gaussian Process State-Space Models
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.