# Scientific Crossref Metadata > CrossRef メタデータスキル。CrossRef REST API による DOI 解決・論文メタデータ・引用数・ジャーナル情報・ 助成金情報検索。ToolUniverse 連携: crossref。 - Skill: `nahisaho/scientific-crossref-metadata-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add nahisaho/scientific-crossref-metadata-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/nahisaho/scientific-crossref-metadata-2/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: nahisaho (https://skillmd.com/u/nahisaho) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/nahisaho/scientific-crossref-metadata-2 --- # Scientific CrossRef Metadata CrossRef REST API を活用した学術文献 DOI 解決・メタデータ検索・ 引用分析・ジャーナル情報・助成金レジストリ検索パイプラインを 提供する。 ## When to Use - DOI から論文メタデータを取得するとき - 学術文献をタイトル・著者で検索するとき - ジャーナルの ISSN やインパクト情報を調べるとき - 論文の引用数・被引用数を確認するとき - 研究助成金の情報を検索するとき - 参考文献リストのメタデータを一括取得するとき - 特定出版社やジャーナルの出版傾向を分析するとき --- ## Quick Start ## 1. DOI 解決・論文メタデータ ```python import requests import pandas as pd CR_BASE = "https://api.crossref.org" CR_HEADERS = { "User-Agent": "SATORI/0.18.0 (mailto:your@email.com)", } def crossref_resolve_doi(doi): """ CrossRef — DOI からメタデータ取得。 Parameters: doi: str — DOI (例: "10.1038/s41586-020-2649-2") """ url = f"{CR_BASE}/works/{doi}" resp = requests.get(url, headers=CR_HEADERS, timeout=30) resp.raise_for_status() item = resp.json().get("message", {}) authors = [] for a in item.get("author", []): name = f"{a.get('given', '')} {a.get('family', '')}" authors.append(name.strip()) result = { "doi": item.get("DOI", ""), "title": " ".join(item.get("title", [])), "authors": "; ".join(authors[:10]), "journal": " ".join( item.get("container-title", [])), "publisher": item.get("publisher", ""), "type": item.get("type", ""), "published_date": _cr_date( item.get("published-print") or item.get("published-online")), "citation_count": item.get( "is-referenced-by-count", 0), "reference_count": item.get("reference-count", 0), "issn": ", ".join(item.get("ISSN", [])), "url": item.get("URL", ""), "abstract": (item.get("abstract") or "")[:500], "funder": "; ".join( f.get("name", "") for f in item.get("funder", [])), "license": _cr_license(item), } print(f"CrossRef DOI: {doi}") print(f" {result['title'][:80]}") print(f" Citations: {result['citation_count']}") return result def _cr_date(date_obj): if not date_obj: return "" parts = date_obj.get("date-parts", [[]])[0] return "-".join(str(p) for p in parts) def _cr_license(item): licenses = item.get("license", []) if licenses: return licenses[0].get("content-version", "") return "" ``` ## 2. 論文検索 ```python def crossref_search_works(query, limit=50, sort="relevance", filter_type=None, from_date=None): """ CrossRef — 論文検索。 Parameters: query: str — 検索クエリ limit: int — 最大結果数 sort: str — ソート ("relevance", "published", "is-referenced-by-count") filter_type: str — 文献タイプフィルタ (例: "journal-article") from_date: str — 開始日 (例: "2020-01-01") """ url = f"{CR_BASE}/works" params = { "query": query, "rows": min(limit, 1000), "sort": sort, } filters = [] if filter_type: filters.append(f"type:{filter_type}") if from_date: filters.append(f"from-pub-date:{from_date}") if filters: params["filter"] = ",".join(filters) resp = requests.get(url, params=params, headers=CR_HEADERS, timeout=30) resp.raise_for_status() data = resp.json().get("message", {}) results = [] for item in data.get("items", []): authors = [] for a in item.get("author", []): name = f"{a.get('given', '')} {a.get('family', '')}" authors.append(name.strip()) results.append({ "doi": item.get("DOI", ""), "title": " ".join(item.get("title", [])), "authors": "; ".join(authors[:5]), "journal": " ".join( item.get("container-title", [])), "year": _cr_date( item.get("published-print") or item.get("published-online")), "citations": item.get( "is-referenced-by-count", 0), "type": item.get("type", ""), }) df = pd.DataFrame(results) total = data.get("total-results", 0) print(f"CrossRef search: {len(df)}/{total} works " f"(query='{query}')") return df ``` ## 3. ジャーナル情報・助成金検索 ```python def crossref_journal_info(issn): """ CrossRef — ジャーナル情報取得。 Parameters: issn: str — ISSN (例: "0028-0836") """ url = f"{CR_BASE}/journals/{issn}" resp = requests.get(url, headers=CR_HEADERS, timeout=30) resp.raise_for_status() data = resp.json().get("message", {}) counts = data.get("counts", {}) result = { "issn": issn, "title": data.get("title", ""), "publisher": data.get("publisher", ""), "subjects": "; ".join( s.get("name", "") for s in data.get("subjects", [])), "total_dois": counts.get("total-dois", 0), "current_dois": counts.get("current-dois", 0), "backfile_dois": counts.get("backfile-dois", 0), } print(f"CrossRef journal: {result['title']} " f"({result['total_dois']} DOIs)") return result def crossref_search_funders(query, limit=20): """ CrossRef — 助成金機関検索。 Parameters: query: str — 機関名 (例: "NIH", "JSPS") limit: int — 最大結果数 """ url = f"{CR_BASE}/funders" params = {"query": query, "rows": limit} resp = requests.get(url, params=params, headers=CR_HEADERS, timeout=30) resp.raise_for_status() data = resp.json().get("message", {}) results = [] for item in data.get("items", []): results.append({ "funder_id": item.get("id", ""), "name": item.get("name", ""), "location": item.get("location", ""), "alt_names": "; ".join( item.get("alt-names", [])[:3]), "work_count": item.get("work-count", 0), }) df = pd.DataFrame(results) print(f"CrossRef funders: {len(df)} (query='{query}')") return df ``` ## 4. CrossRef 統合パイプライン ```python def crossref_pipeline(query, dois=None, output_dir="results"): """ CrossRef 統合パイプライン。 Parameters: query: str — 検索クエリ dois: list[str] — DOI リスト (直接解決) output_dir: str — 出力ディレクトリ """ from pathlib import Path output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) # 1) 論文検索 works = crossref_search_works(query) works.to_csv(output_dir / "works.csv", index=False) # 2) DOI 一括解決 if dois: resolved = [] for doi in dois: try: meta = crossref_resolve_doi(doi) resolved.append(meta) except Exception as e: print(f" Warning: {doi} — {e}") continue resolved_df = pd.DataFrame(resolved) resolved_df.to_csv(output_dir / "doi_resolved.csv", index=False) # 3) 引用分析 if not works.empty: stats = { "total_works": len(works), "total_citations": works["citations"].sum(), "mean_citations": works["citations"].mean(), "median_citations": works["citations"].median(), "max_citations": works["citations"].max(), } pd.DataFrame([stats]).to_csv( output_dir / "citation_stats.csv", index=False) print(f"CrossRef pipeline: {output_dir}") return {"works": works} ``` --- ## ToolUniverse 連携 | TU Key | ツール名 | 連携内容 | |--------|---------|---------| | `crossref` | CrossRef | DOI 解決・メタデータ・引用・ジャーナル情報 | ## パイプライン統合 ``` literature-search → crossref-metadata → citation-checker (PubMed/NCBI) (CrossRef REST API) (引用品質検証) │ │ ↓ semantic-scholar ──────┘ deep-research (S2 Academic Graph) │ (知識統合) ↓ bibliometrics (書誌計量分析) ``` ## パイプライン出力 | ファイル | 説明 | 次スキル | |---------|------|---------| | `results/works.csv` | 論文検索結果 | → semantic-scholar | | `results/doi_resolved.csv` | DOI メタデータ | → citation-checker | | `results/citation_stats.csv` | 引用統計 | → bibliometrics |