Article in GigaScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
36 authors.
Sierra A T MoxonEnvironmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0002-8719-7760
Harold SolbrigSchool of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA.ORCID 0000-0002-5928-3071
Nomi L HarrisEnvironmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0001-6315-3707
Patrick KalitaEnvironmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0002-6150-307X
Mark A MillerEnvironmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Sujay PatilEnvironmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0001-6142-1106
Kevin SchaperDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0003-3311-7320
Chris BizonRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA.ORCID 0000-0002-9491-7674
J Harry CaufieldEnvironmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0001-5705-7831
Damion M DooleyFaculty of Health Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.ORCID 0000-0002-8844-9165
William D DuncanCommunity Dentistry and Behavioral Science, University of Florida College of Dentistry, Gainesville, FL 32610, USA.ORCID 0000-0001-9625-1899
A J IrelandEnvironmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0003-1982-9065
Julius O B JacobsenWilliam Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK.ORCID 0000-0002-3265-1591
Madan KrishnamurthyDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0002-9767-3636
Carlo KrollDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0009-0008-4562-7399
David LinkeReaction Engineering & Catalyst Development, Leibniz Institute for Catalysis (LIKAT), Rostock 18059, Germany.ORCID 0000-0002-5898-1820
Ryan LyScientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0001-9238-0642
Deepak R UnniPersonalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, Basel 4051, Switzerland.ORCID 0000-0002-3583-7340
Gaurav VaidyaRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA.ORCID 0000-0003-0587-0454
Wouter-Michiel A M VierdagGenome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Baden-Württemberg 69117, Germany.ORCID 0000-0003-1666-5421
LinkML Community Contributors
Oliver RuebelScientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0001-9902-1984
Christopher G ChuteSchools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD 21287, USA.ORCID 0000-0001-5437-2545
Matthew H BrushDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0002-1048-5019
Melissa A HaendelDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0001-9114-8737
Christopher J MungallEnvironmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0002-6601-2165
Funding
A Community Resource for Single Cell Data in the BrainU24MH130919 · NIMH · ALLEN INSTITUTE · PI Michael Hawrylycz, Carol Lynn Thompson · 2022 to 2026
$9.9M
Evaluation and optimization of NWB neurophysiology software and data in the cloudU24NS120057 · NINDS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI DICHTER, BENJAMIN K, RUEBEL, OLIVER · 2021 to 2025
$5.9M
NCI NIH HHS 19×077Q HHSN261201500003INHGRI NIH HHSNIH HHS 1OT2TR005712-01NIH HHS 1U01DE033978-01NIH HHS 1U24MH130919-01NIH HHS U24NS120057NIMH NIH HHS 4DP2MH129986NIMH NIH HHS U24 MH130919U.S. Department of Energy DE-AC02-05CH11231Wellcome Trust 313291/Z/24/Z
6 · The paper itself
Abstract
backgroundScientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult.
findingsLinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics.
conclusionsLinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
LinkML: an open data modeling framework. · full record | OpenQuestion