Update to Beeblio

{
  "description": "Build a reproducible bibliometric or science-mapping study for a Beeblio project using ChatGPT's sandbox. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps of a research field.",
  "included_files": [],
  "name": "beeblio-bibliometrics",
  "skill_md_contents": "---\nname: beeblio-bibliometrics\ndescription: Build a reproducible bibliometric or science-mapping study for a Beeblio project using ChatGPT's sandbox. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps of a research field.\n---\n\n# Bibliometric study in Beeblio\n\nBibliometric maps depend on a defined corpus. Start a new run under `3-Analysis/bibliometric/<run-name>/` and record a study design: question, field-specific synonyms, databases, queries, retrieval date, years, document types, language rules, screening criteria, and planned metrics. Do not reuse a fixed topic vocabulary across fields. Preserve the raw metadata and collection script or query log so another researcher can reconstruct the corpus.\n\n## Collect and screen\n\nUse `search_literature` for focused discovery and `get_paper` for individual metadata. For a larger corpus, collect from a documented scholarly API or dataset in ChatGPT's sandbox only when that environment has the needed access; obey the provider's terms and rate limits. Beeblio's in-app `fetch_openalex_works` is not an MCP tool. If comprehensive collection is unavailable, analyze the accessible sample and label it as such.\n\nDeduplicate records by stable work identifiers. Screen titles and abstracts against the design, record inclusion and exclusion counts, and keep the selected corpus as a coding sheet. Avoid selecting solely the most cited works: older papers have had more time to accumulate citations. If limiting corpus size, use a defensible year-aware or otherwise documented strategy and report the resulting bias. Do not interpret missing affiliation, references, or keywords as zeros.\n\n## Compute and interpret\n\nUse saved scripts in ChatGPT's sandbox to calculate publication and citation trends, sources, authors, countries, keywords, and themes when the metadata supports them. For co-citation, bibliographic coupling, or collaboration networks, state the edge definition, counting method, thresholds, and missing-reference coverage. Distinguish diagnostic plots from findings worth discussing. Check plotted counts against the coding sheet and label each figure with its sample size and filters.\n\nUse `beeblio-data-visualization` for chart design. Write a synthesis that explains patterns, competing interpretations, retrieval bias, database coverage, and research gaps. Avoid claiming that citation counts measure study quality or that a keyword cluster proves a theoretical relationship.\n\nSave the study design, source manifest, coding sheet, scripts, chart data, figures, and a Markdown report back to Beeblio. Use `write_text_file` for small text artifacts and `upload_artifact` for larger or binary outputs, with new paths under `3-Analysis/` and `4-Reports/`; place derived datasets under `2-Data/derived/` through `upload_artifact`. Verify required files with `stat_file` before reporting completion. If an artifact cannot be transferred, name that limitation explicitly.\n"
}