{
  "abstract": "Neuroscientific research and applications in real world environments are often constrained by limited access, equipment limitations, and high costs. Consequently, robustly selecting constructs to focus effort on is a high-impact task. This study introduces a two-stage large language model (LLM) pipeline that generates research roadmaps from existing real-world data to inform research priorities in the field, given the state of the art. Stage 1 uses retrieval-augmented generation on operational logs to identify candidate psychological, social, and neurocognitive constructs and map their co-occurrence. Stage 2 evaluates how well these constructs are represented in the literature across four dimensions that commonly inform prioritization: ecological validity, neural correlates, biosensor protocols, and brain computer interface readiness, producing an evidence gap matrix. Pipeline evaluation combined human review, deterministic computations, and human-LLM alignment analysis. Applying the pipeline to Mars Desert Research Station logs, Stage 1 identified 19 constructs. Stage 2 highlighted critical gaps in neuroimaging and measurement protocols, particularly for social domains, thereby identifying potential research priorities and risks. This methodology offers a generalizable starting point for synthesizing operational logs into actionable research roadmaps, but the evaluation showed it still required human scrutiny.",
  "abstract_shown": true,
  "affiliations": [
    "Computer Science Department, University of Colorado Boulder, Boulder, CO, United States."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Cara A Spencer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/34q4ytvtxmsjj.bib",
  "cited_by": 0,
  "doi": "10.3389/fnhum.2026.1884892",
  "fulltext_url": "https://europepmc.org/articles/PMC13713852",
  "id": "34q4ytvtxmsjj",
  "issns": [
    "1662-5161"
  ],
  "landing_url": "https://europepmc.org/article/MED/42953176",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "1702|Topic Modeling"
    ]
  ],
  "openalex": "W7221778929",
  "pdf_url": "https://europepmc.org/articles/PMC13713852?pdf=render",
  "pmcid": "PMC13713852",
  "pmid": "42953176",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/34q4ytvtxmsjj.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:41Z",
      "native_id": "MED:42953176",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42953176"
    },
    {
      "first_seen": "2026-11-01T12:21:27Z",
      "native_id": "PMC13713852",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fnhum.2026.1884892"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "journal-openalex:2805|Neural and Behavioral Psychology Studies",
      "subfield": "neuroscience"
    }
  ],
  "title": "A two-stage RAG-LLM pipeline for roadmapping neuroscience research in operational environments.",
  "title_text": "A two-stage RAG-LLM pipeline for roadmapping neuroscience research in operational environments.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:27Z",
  "url": "https://research.draconitic.com/paper/34q4ytvtxmsjj",
  "venue": "Frontiers in human neuroscience",
  "versions": []
}