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| Auteurs principaux: | , , |
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| Format: | Preprint |
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2026
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| Accès en ligne: | https://arxiv.org/abs/2603.27841 |
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| _version_ | 1866910210656829440 |
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| author | Mahdian, Mehrab Ender, Ferenc Pardy, Tamas |
| author_facet | Mahdian, Mehrab Ender, Ferenc Pardy, Tamas |
| contents | Electrospinning is a versatile nanofabrication technique whose outcomes emerge from a complex, high-dimensional interplay between solution properties, processing parameters, and environmental conditions. Optimizing this parameter space for targeted fiber morphology is inherently challenging, often driving extensive trial-and-error experimentation and generating vast experimental data across laboratories worldwide. Yet this knowledge remains fragmented and underutilized due to inconsistent reporting and a pervasive bias toward successful outcomes, limiting reproducibility and hindering data-driven research. Here we introduce Electrospinning-Data.org, a FAIR-aligned data aggregation infrastructure that organizes dispersed electrospinning experiments into structured, reusable, and failure-aware scientific records. The platform is built around a unified process-structure-property data model linking experimental inputs, environmental conditions, and nanofiber morphology, annotated through a controlled vocabulary, within a consistent, machine-readable schema. A two-stage moderation pipeline combining automated validation with expert review supports data quality and long-term interoperability. The resulting structured, failure-inclusive corpus provides a framework for data-driven research, including predictive modelling, inverse design of target morphologies, and systematic mapping of instability regimes that would otherwise require extensive trial-and-error experimentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27841 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Electrospinning-Data.org: A FAIR, Structured Knowledge Resource for Nanofiber Fabrication Mahdian, Mehrab Ender, Ferenc Pardy, Tamas Databases Materials Science Electrospinning is a versatile nanofabrication technique whose outcomes emerge from a complex, high-dimensional interplay between solution properties, processing parameters, and environmental conditions. Optimizing this parameter space for targeted fiber morphology is inherently challenging, often driving extensive trial-and-error experimentation and generating vast experimental data across laboratories worldwide. Yet this knowledge remains fragmented and underutilized due to inconsistent reporting and a pervasive bias toward successful outcomes, limiting reproducibility and hindering data-driven research. Here we introduce Electrospinning-Data.org, a FAIR-aligned data aggregation infrastructure that organizes dispersed electrospinning experiments into structured, reusable, and failure-aware scientific records. The platform is built around a unified process-structure-property data model linking experimental inputs, environmental conditions, and nanofiber morphology, annotated through a controlled vocabulary, within a consistent, machine-readable schema. A two-stage moderation pipeline combining automated validation with expert review supports data quality and long-term interoperability. The resulting structured, failure-inclusive corpus provides a framework for data-driven research, including predictive modelling, inverse design of target morphologies, and systematic mapping of instability regimes that would otherwise require extensive trial-and-error experimentation. |
| title | Electrospinning-Data.org: A FAIR, Structured Knowledge Resource for Nanofiber Fabrication |
| topic | Databases Materials Science |
| url | https://arxiv.org/abs/2603.27841 |