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  "Package": "bibnets",
  "Title": "Importing, Constructing, and Exporting Bibliometric Networks",
  "Version": "0.4.4",
  "Authors@R": "c(\nperson(\"Mohammed\", \"Saqr\", , \"saqr@saqr.me\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-5881-3109\")),\nperson(\"Sonsoles\", \"López-Pernas\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-9621-1392\")))",
  "Description": "Imports, constructs, and exports bibliometric networks\nfrom scholarly metadata. Reads 'Scopus', 'Web of Science',\n'BibTeX', 'RIS', 'OpenAlex', 'Lens.org', 'Dimensions', and\n'Crossref' exports. Goes beyond standard co-networks with\nattention-weighted networks (lead, last, proximity, circular\nposition weights), position-aware counting (harmonic,\narithmetic, geometric, golden-ratio), similarity and\ndissimilarity normalisations, temporal networks with fixed,\nsliding, and cumulative windows, disparity-filter backbone\nextraction, historiograph construction, and local citation\nscoring. Methods described in López-Pernas, Saqr & Apiola\n(2023) <doi:10.1007/978-3-031-25336-2_5>.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/mohsaqr/bibnets",
  "BugReports": "https://github.com/mohsaqr/bibnets/issues",
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  "Author": "Mohammed Saqr [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0001-5881-3109>), Sonsoles López-Pernas\n[aut] (ORCID: <https://orcid.org/0000-0002-9621-1392>)",
  "Maintainer": "Mohammed Saqr <saqr@saqr.me>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-05-20 09:10:14 UTC",
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    "conetwork",
    "country_network",
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    "local_citations",
    "normalize",
    "prune",
    "read_biblio",
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    "read_crossref",
    "read_dimensions",
    "read_lens",
    "read_openalex",
    "read_openalex_csv",
    "read_ris",
    "read_scopus",
    "read_wos",
    "reference_network",
    "source_network",
    "split_field",
    "temporal_network",
    "to_cograph",
    "to_gephi",
    "to_graphml",
    "to_igraph",
    "to_matrix",
    "to_tbl_graph"
  ],
  "_datasets": [
    {
      "name": "biblio_data",
      "title": "Example bibliometric dataset",
      "object": "biblio_data",
      "class": [
        "data.frame"
      ],
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        "title",
        "year",
        "journal",
        "doi",
        "cited_by_count",
        "authors",
        "references",
        "keywords"
      ],
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      "table": true,
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      "title": "OpenAlex Gold Open Access Learning Analytics dataset",
      "object": "open_alex_gold_open_access_learning_analytics",
      "class": [
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        "title",
        "year",
        "journal",
        "doi",
        "cited_by_count",
        "type",
        "authors",
        "keywords",
        "affiliations",
        "countries"
      ],
      "rows": 1508,
      "table": true,
      "tojson": true
    },
    {
      "name": "scopus_quantum_cloud",
      "title": "Scopus dataset — Green Cloud Computing and Quantization (2020–2025)",
      "object": "scopus_quantum_cloud",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "title",
        "year",
        "journal",
        "doi",
        "cited_by_count",
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        "authors",
        "references",
        "keywords",
        "affiliations"
      ],
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      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "author_network",
      "title": "Build an author network",
      "topics": [
        "author_network"
      ]
    },
    {
      "page": "backbone",
      "title": "Extract network backbone using the disparity filter",
      "topics": [
        "backbone"
      ]
    },
    {
      "page": "biblio_data",
      "title": "Example bibliometric dataset",
      "topics": [
        "biblio_data"
      ]
    },
    {
      "page": "conetwork",
      "title": "Build a co-occurrence network from any field",
      "topics": [
        "conetwork"
      ]
    },
    {
      "page": "country_network",
      "title": "Build a country network",
      "topics": [
        "country_network"
      ]
    },
    {
      "page": "document_network",
      "title": "Build a document network",
      "topics": [
        "document_network"
      ]
    },
    {
      "page": "filter_top",
      "title": "Filter edges to top-n nodes",
      "topics": [
        "filter_top"
      ]
    },
    {
      "page": "historiograph",
      "title": "Build a historiograph (chronological citation network)",
      "topics": [
        "historiograph"
      ]
    },
    {
      "page": "institution_network",
      "title": "Build an institution network",
      "topics": [
        "institution_network"
      ]
    },
    {
      "page": "keyword_network",
      "title": "Build a keyword co-occurrence network",
      "topics": [
        "keyword_network"
      ]
    },
    {
      "page": "local_citations",
      "title": "Compute local citation scores",
      "topics": [
        "local_citations"
      ]
    },
    {
      "page": "normalize",
      "title": "Normalize a co-occurrence matrix",
      "topics": [
        "normalize"
      ]
    },
    {
      "page": "open_alex_gold_open_access_learning_analytics",
      "title": "OpenAlex Gold Open Access Learning Analytics dataset",
      "topics": [
        "open_alex_gold_open_access_learning_analytics"
      ]
    },
    {
      "page": "print.bibnets_network",
      "title": "Print a bibnets network edge list",
      "topics": [
        "print.bibnets_network"
      ]
    },
    {
      "page": "prune",
      "title": "Prune a weighted edge list",
      "topics": [
        "prune"
      ]
    },
    {
      "page": "read_biblio",
      "title": "Read bibliometric data",
      "topics": [
        "read_biblio"
      ]
    },
    {
      "page": "read_bibtex",
      "title": "Read a BibTeX file",
      "topics": [
        "read_bibtex"
      ]
    },
    {
      "page": "read_crossref",
      "title": "Convert Crossref API data to bibnets format",
      "topics": [
        "read_crossref"
      ]
    },
    {
      "page": "read_dimensions",
      "title": "Read Dimensions CSV export",
      "topics": [
        "read_dimensions"
      ]
    },
    {
      "page": "read_lens",
      "title": "Read Lens.org CSV export",
      "topics": [
        "read_lens"
      ]
    },
    {
      "page": "read_openalex",
      "title": "Convert OpenAlex data to bibnets format",
      "topics": [
        "read_openalex"
      ]
    },
    {
      "page": "read_openalex_csv",
      "title": "Read a flat OpenAlex CSV export",
      "topics": [
        "read_openalex_csv"
      ]
    },
    {
      "page": "read_ris",
      "title": "Read an RIS file",
      "topics": [
        "read_ris"
      ]
    },
    {
      "page": "read_scopus",
      "title": "Read Scopus CSV export",
      "topics": [
        "read_scopus"
      ]
    },
    {
      "page": "read_wos",
      "title": "Read Web of Science plaintext or tab-delimited export",
      "topics": [
        "read_wos"
      ]
    },
    {
      "page": "reference_network",
      "title": "Build a reference network",
      "topics": [
        "reference_network"
      ]
    },
    {
      "page": "scopus_quantum_cloud",
      "title": "Scopus dataset — Green Cloud Computing and Quantization (2020–2025)",
      "topics": [
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      ]
    },
    {
      "page": "source_network",
      "title": "Build a source (journal) network",
      "topics": [
        "source_network"
      ]
    },
    {
      "page": "split_field",
      "title": "Parse semicolon-delimited strings into list-column",
      "topics": [
        "split_field"
      ]
    },
    {
      "page": "summary.bibnets_network",
      "title": "Summarise a bibnets network",
      "topics": [
        "summary.bibnets_network"
      ]
    },
    {
      "page": "temporal_network",
      "title": "Build time-windowed networks",
      "topics": [
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    },
    {
      "page": "to_cograph",
      "title": "Prepare network for cograph::splot()",
      "topics": [
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    },
    {
      "page": "to_gephi",
      "title": "Export to Gephi node and edge tables",
      "topics": [
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    },
    {
      "page": "to_graphml",
      "title": "Export to GraphML",
      "topics": [
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      "topics": [
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    {
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      "title": "Convert edge data frame to tbl_graph",
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      "title": "Getting Started with bibnets",
      "engine": "knitr::rmarkdown",
      "headings": [
        "What bibnets Builds",
        "Data Used in This Vignette",
        "Reading Your Own Data",
        "Author Collaboration",
        "Counting Methods",
        "Attention-Style Position Weights",
        "Reference Co-citation",
        "Document Coupling and Citation",
        "Keyword Co-occurrence",
        "Countries, Institutions, and Sources",
        "Generic Co-networks",
        "Normalization",
        "Reducing Large Networks",
        "Temporal Networks",
        "Local Citations and Historiographs",
        "Exporting Results",
        "Interpreting a bibnets_network"
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      "created": "2026-05-20 09:10:14",
      "modified": "2026-05-20 09:10:14",
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      "filename": "reading-data.html",
      "title": "Reading bibliometric data into bibnets",
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        "1. Introduction and the standard schema",
        "2. read_biblio()",
        "3. Worked example — OpenAlex flat CSV",
        "4. Scopus",
        "5. Web of Science",
        "6. OpenAlex — two paths",
        "Path A: in-memory tibble from openalexR",
        "Path B: flat CSV",
        "7. Dimensions",
        "8. Lens.org",
        "9. BibTeX & RIS",
        "10. Crossref via rcrossref",
        "11. Generic CSV — read_biblio(format = \"generic\", ...)",
        "12. Building data manually",
        "13. The split_field() helper",
        "14. Combining data from multiple sources",
        "15. Inspecting and sanity-checking",
        "16. Troubleshooting",
        "Further reading"
      ],
      "created": "2026-05-20 09:10:14",
      "modified": "2026-05-20 09:10:14",
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