{
  "abstract": "Background Behavioral 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. Results We 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. Conclusions This 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Artificial Intelligence and Informatics, Mayo Clinic",
    "UCL Centre for Behaviour Change, University College London",
    "Department of Community Dentistry and Behavioral Science, College of Dentistry, University of Florida",
    "Department of Philosophy, University at Buffalo",
    "Division of Computational Medicine, Stanford University School of Medicine",
    "Department of Biostatistics and Health Data Science, Indiana University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yue Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Lu Kang"
    },
    {
      "affs": [
        1
      ],
      "name": "Susan Michie"
    },
    {
      "affs": [
        2
      ],
      "name": "William D. Duncan"
    },
    {
      "affs": [
        3
      ],
      "name": "John Beverley"
    },
    {
      "affs": [
        0
      ],
      "name": "Fang Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Daniel W. McNeil"
    },
    {
      "affs": [
        0
      ],
      "name": "Nan Huo"
    },
    {
      "affs": [
        0
      ],
      "name": "HaiFang Li"
    },
    {
      "affs": [
        4
      ],
      "name": "Matthew Horridge"
    },
    {
      "affs": [
        4
      ],
      "name": "Mark A. Musen"
    },
    {
      "affs": [
        5
      ],
      "name": "Jiang Bian"
    },
    {
      "affs": [
        0
      ],
      "name": "Cui Tao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1mmb6dfgsr6mb.bib",
  "cited_by": 0,
  "doi": "10.1186/s13326-026-00363-8",
  "fulltext_url": "https://doi.org/10.1186/s13326-026-00363-8",
  "id": "1mmb6dfgsr6mb",
  "issns": [
    "2041-1480"
  ],
  "landing_url": "https://doi.org/10.1186/s13326-026-00363-8",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R858-859.7"
    ],
    [
      "openalex",
      "1312|Biomedical Text Mining and Ontologies"
    ]
  ],
  "openalex": "W7168142257",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/1mmb6dfgsr6mb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:46:30Z",
      "native_id": "0b55edce167d4b1eacd93f36a2f4adfc",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s13326-026-00363-8"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Biomedical Text Mining and Ontologies",
      "subfield": "molecular-biology"
    }
  ],
  "title": "ACCELERATE-BASSO: early experiences and emerging best practices for ontology development in behavioral and social science research",
  "title_text": "ACCELERATE-BASSO: early experiences and emerging best practices for ontology development in behavioral and social science research",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:20:51Z",
  "url": "https://research.draconitic.com/paper/1mmb6dfgsr6mb",
  "venue": "Journal of Biomedical Semantics",
  "versions": []
}