Author

Jason A. Hilton

Stanford UniversityORCID 0000-0002-1196-4871Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:database / resource· 3 Assay:scRNA-seq· 3 Approach:benchmark study· 2 Research Focus:benchmarking· 2 Approach:statistical model· 2 Approach:experimental assay· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

Anup MahurkariD· 1 shared Artem SokoloviD· 1 shared Bart DeplanckeiD· 1 shared Brian D. AevermanniD· 1 shared Brian Mott· 1 shared Brian R. HerbiD· 1 shared Brian Raymor· 1 shared Christina ErnstiD· 1 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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1 shared paperBioinformatics

Anup MahurkarORCID

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Author profile →

3 papers

Recent·Most cited

A collaborative submission model for building high-quality data resources at scale through partnership: lessons from single-cell genomics

Jason A. Hilton, Jim Chaffer, Jenny Chien, Idan Gabdank, Brian Mott, Erica M. Rutherford, Corinn Small, Jennifer L. Zamanian, Brian D. Aevermann, J. Michael Cherry, Teri E. Klein

The paper describes a collaborative submission model for building high-quality single-cell genomics data resources, emphasizing partnership between contributors and curators to improve metadata quality and resource scalability.

bioRxiv · Aug 4, 2026 · PreprintGenetics

scFAIR Consortium: a decentralized hub for single-cell RNA-Seq data standardization and unification

Vincent Gardeux, Sara Carsanaro, Wen J. Chen, Fabrice P. A. David, Damien Goutte-Gattat, Jason A. Hilton, Tiago Lubiana, Nikhil Patel, Brian Raymor, Ida Zucchi, Bart Deplancke, Christina Ernst, David Osumi-Sutherland, Marc Robinson‐Rechavi, Paul W. Sternberg, Frederic Bastian

The scFAIR Consortium develops a unified metadata schema and portal to improve FAIRness of scRNA-seq data through standardized annotations and semantic search.

bioRxiv · Jun 8, 2026 · PreprintBioinformatics

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

Yichen Wang, Irzam Sarfraz, Wei Kheng Teh, Artem Sokolov, Brian R. Herb, Heather H. Creasy, Isaac Virshup, Ruben Dries, Kylee Degatano, Anup Mahurkar, Daniel Schnell, Pedro Madrigal, Jason A. Hilton, Nils Gehlenborg, Timothy L. Tickle, Joshua David Campbell

The paper introduces MAMS, a metadata standard for single-cell data matrices and analysis workflows, to improve reproducibility and interoperability across datasets.

bioRxiv · Mar 6, 2023 · PreprintBioinformatics