Evidence map›Paper›PMID 42444005›Full record

ArticleJournal of biomedical semantics2026

ACCELERATE-BASSO: early experiences and emerging best practices for ontology development in behavioral and social science research.

Yue Yu, Lu Kang, Susan Michie, William D Duncan, John Beverley, Fang Li, Daniel W McNeil, Nan Huo, HaiFang Li, Matthew Horridge and 3 more

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

13 authors.

Yue Yu *Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.ORCID http://orcid.org/0000-0002-3900-1217
Lu Kang *Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.ORCID http://orcid.org/0009-0004-4198-8315
Susan MichieUCL Centre for Behaviour Change, University College London, London, UK.ORCID http://orcid.org/0000-0003-0063-6378
William D DuncanDepartment of Community Dentistry and Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, USA.ORCID http://orcid.org/0000-0001-9625-1899
John BeverleyDepartment of Philosophy, University at Buffalo, Buffalo, NY, USA.ORCID http://orcid.org/0000-0002-1118-1738
Fang LiDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.ORCID http://orcid.org/0000-0001-8865-7717
Daniel W McNeilDepartment of Community Dentistry and Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, USA.ORCID http://orcid.org/0000-0002-0766-8455
Nan HuoDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.ORCID http://orcid.org/0000-0002-4980-2421
HaiFang LiDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.ORCID http://orcid.org/0009-0003-1098-4023
Matthew HorridgeDivision of Computational Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-8921-6593
Mark A MusenDivision of Computational Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-3325-793X
Jiang BianDepartment of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN, USA.ORCID http://orcid.org/0000-0002-2238-5429
Cui TaoDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA. Tao.Cui@mayo.edu.ORCID http://orcid.org/0000-0002-4267-1924

Funding

ACCELERATE-BASSO: Coordinating Center for Accelerating Behavioral and Social Science through OntologyU24AG088019 · NIA · UNIVERSITY OF FLORIDA · PI Jiang Bian, Mark A Musen · 2024 to 2026
$2.3M
Standardizing and Harmonizing Behavioral and Social Science Research Factors in Alzheimer's Disease through Ontology-Based ApproachesU01AG088076 · NIA · MAYO CLINIC JACKSONVILLE · PI Jiang Bian, Cui Tao · 2024 to 2026
$2.3M
Promoting Health Aging through Semantic Enrichment of Solitude Research (PHASES)U01AG088074 · NIA · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI John Beverley, William Duncan · 2024 to 2026
$2.3M
Developing an Ontology for Dental Care-Related Fear and Anxiety:  Toward an Understanding of Problems in Dental Care UtilizationU01DE033978 · NIDCR · UNIVERSITY OF FLORIDA · PI William Duncan, Daniel W McNeil · 2024 to 2026
$1.9M
Advancing Prevention Research In Cancer through Ontology Tools (The APRICOT project) Project Narrative U01CA291884 · NCI · UNIVERSITY COLLEGE LONDON · PI Susan Michie · 2024 to 2026
$1.4M
NCI NIH HHS U01 CA291884NCI NIH HHS U01CA291884NIA NIH HHS U01 AG088074NIA NIH HHS U01AG088074NIA NIH HHS U01 AG088076NIA NIH HHS U01AG088076NIA NIH HHS U24 AG088019NIA NIH HHS U24AG088019NIDCR NIH HHS U01 DE033978NIDCR NIH HHS U01DE033978
6 · The paper itself

Abstract

backgroundBehavioral and social science research (BSSR) is essential for understanding human behavior and informing interventions, policy, and health outcomes; however, such research faces persistent challenges related to fragmented knowledge, imprecise and inconsistent terminology, and limited interoperability across studies. Ontologies provide a promising solution by enabling standardized, machine-readable representations of concepts and relationships to support data integration, knowledge synthesis, and reproducibility.

resultsWe present an initial overview of the ACCELERATE-BASSO Research Network, an NIH-supported consortium established to advance BSSR through ontology-driven approaches. Specifically, we describe the role of the Dissemination and Coordination Center (DCC) and the Best Practices Working Group (BPWG) in supporting a network of projects focusing on accelerating BSSR through ontology development and use. Based on the consortium's early experiences, we summarize emerging ontology-driven practices, current interoperability efforts, and common methodological considerations identified across the participating projects. Rather than proposing a formal consensus framework, this work provides an initial consortium perspective on current ontology development activities and shared lessons learned. We also discuss the potential role of ontologies in supporting AI-related applications in BSSR.

conclusionsThis paper provides an initial consortium perspective on ontology-driven approaches for advancing BSSR. By synthesizing early experiences, emerging practices, and shared challenges across multiple projects, it offers insights to inform future ontology development, interoperability, and community consensus while laying the foundation for more systematic evaluation and broader adoption within the BSSR community.

Indexed as

Biological OntologiesResearchSocial SciencesHumansBehavioral and social scienceBest practicesKnowledge representationOntologiesOntologyOntology engineering

Identifiers

PMID42444005
PMCPMC13685320

What OpenQuestion holds

Textmetadata
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Registered trials

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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.