Research papers, authors, and institutions are complex entities with bibliometric data scattered across multiple databases. Entity Enricher lets you define custom schemas for citation counts, h-index values, institutional affiliations, methodology details, and funding sources -- all enriched and cross-validated by multiple AI models.
Bibliometric data is distributed across PubMed, Scopus, Web of Science, Google Scholar, and institutional repositories. Citation counts vary between indexes, author affiliations change over time, and metadata quality is inconsistent. Manual aggregation is tedious and error-prone, especially for systematic reviews or research landscape analysis.
Entity Enricher's approach is uniquely suited to this challenge. Multiple LLMs each bring different training data and knowledge, producing richer coverage. Multi-model fusion then reconciles differences -- if two models report different citation counts, the median value is selected automatically, or an arbitration model can reason about which source is most reliable.
Define exactly the bibliometric and methodological fields your analysis needs. Use AI schema generation to create this schema from a sample publication record.
{
"name": "ResearchEntity",
"properties": {
"title": { "type": "string", "is_key": true },
"authors": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"affiliation": { "type": "string" },
"orcid": { "type": "string" },
"h_index": { "type": "number" }
}
}
},
"doi": { "type": "string" },
"publication_year": { "type": "number" },
"journal": { "type": "string" },
"impact_factor": { "type": "number" },
"citation_count": { "type": "number" },
"abstract_summary": { "type": "string" },
"methodology": {
"type": "object",
"properties": {
"study_type": { "type": "string" },
"sample_size": { "type": "number" },
"statistical_methods": { "type": "array" },
"peer_reviewed": { "type": "boolean" }
}
},
"keywords": { "type": "array", "items": { "type": "string" } },
"funding_sources": {
"type": "array",
"items": {
"type": "object",
"properties": {
"funder": { "type": "string" },
"grant_id": { "type": "string" }
}
}
}
}
}Separating bibliometric data from methodology and institutional information allows each expertise domain to receive a focused prompt, resulting in more accurate enrichment.
| Field | Expertise | Description |
|---|---|---|
| title | General | Full publication title and alternate titles |
| authors | Bibliometric | Author names, affiliations, ORCID IDs, and h-index values |
| citation_count | Bibliometric | Total citation count from major indexes |
| impact_factor | Bibliometric | Journal impact factor at time of publication |
| methodology | Methodology | Study design, sample size, and statistical methods used |
| keywords | General | MeSH terms, author keywords, and classification codes |
| funding_sources | Institutional | Funding agencies, grant numbers, and amounts |
| abstract_summary | General | Concise summary of the research findings |
Define fields for bibliometric data, methodology, and institutional information. Paste a sample publication record and let AI generate the schema.
Provide paper titles, DOIs, or author names. Use batch mode to process reading lists or systematic review candidate sets.
Multiple LLMs independently research each publication, leveraging different training data for broader coverage of citation databases and institutional records.
Download structured bibliometric data as JSON for programmatic analysis or Excel for manual review and annotation.
Enrich candidate papers with methodology details, sample sizes, and quality indicators for screening and eligibility assessment.
Map publication trends, collaboration networks, and funding patterns across a research domain or therapeutic area.
Enrich researcher profiles with h-index, institutional history, grant funding, and collaboration networks for hiring or partnership decisions.
Identify funding sources, grant amounts, and funded topics in your research area to inform grant strategy.
Define your bibliometric schema, cross-validate with multiple AI models, and get structured research intelligence for systematic reviews and landscape analysis.
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