# Scientific Literature Search > 学術文献検索・取得スキル。PubMed E-utilities、Semantic Scholar、 OpenAlex、EuropePMC、CrossRef の 5 大学術データベース API を統合した 文献検索パイプライン。MeSH 構造化検索、引用ネットワーク分析、 著者/機関メトリクス、全文取得、PICO ベース検索戦略対応。 29 の ToolUniverse SMCP ツールと連携。 - Skill: `nahisaho/scientific-literature-search-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add nahisaho/scientific-literature-search-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/nahisaho/scientific-literature-search-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: nahisaho (https://skillmd.com/u/nahisaho) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/nahisaho/scientific-literature-search-2 --- # Scientific Literature Search PubMed / Semantic Scholar / OpenAlex / EuropePMC / CrossRef の 5 大学術 DB を統合した文献検索・メタデータ取得パイプラインを提供する。 ## When to Use - PubMed で MeSH 用語を用いた構造化検索が必要なとき - Semantic Scholar でセマンティック類似論文を発見するとき - OpenAlex で著者・機関・ジャーナルメトリクスを分析するとき - 特定論文の引用/被引用ネットワークを構築するとき - 系統的レビューのためのマルチ DB 横断検索が必要なとき --- ## Quick Start ## 1. PubMed E-utilities 検索 ```python import requests import xml.etree.ElementTree as ET import pandas as pd import time def pubmed_search(query, max_results=100, sort="relevance", date_from=None, date_to=None, rettype="xml"): """ PubMed E-utilities による構造化検索。 Parameters: query: str — PubMed クエリ (MeSH 構文対応) max_results: int — 最大取得件数 sort: "relevance" or "date" date_from/to: "YYYY/MM/DD" フォーマット """ base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" # Step 1: esearch — PMID 取得 params = { "db": "pubmed", "term": query, "retmax": max_results, "sort": sort, "retmode": "json", } if date_from: params["mindate"] = date_from params["datetype"] = "pdat" if date_to: params["maxdate"] = date_to resp = requests.get(f"{base}/esearch.fcgi", params=params) data = resp.json() pmids = data["esearchresult"]["idlist"] total = int(data["esearchresult"]["count"]) print(f"PubMed search: {total} total results, fetching {len(pmids)}") if not pmids: return pd.DataFrame() # Step 2: efetch — 詳細取得 time.sleep(0.4) fetch_params = { "db": "pubmed", "id": ",".join(pmids), "rettype": "xml", "retmode": "xml", } resp = requests.get(f"{base}/efetch.fcgi", params=fetch_params) root = ET.fromstring(resp.text) articles = [] for article in root.findall(".//PubmedArticle"): medline = article.find(".//MedlineCitation") pmid = medline.findtext("PMID", "") title = medline.findtext(".//ArticleTitle", "") abstract = medline.findtext(".//AbstractText", "") journal = medline.findtext(".//Journal/Title", "") year = medline.findtext(".//PubDate/Year", "") authors = [] for author in medline.findall(".//Author"): last = author.findtext("LastName", "") fore = author.findtext("ForeName", "") if last: authors.append(f"{last} {fore}") mesh_terms = [m.findtext("DescriptorName", "") for m in medline.findall(".//MeshHeading")] articles.append({ "pmid": pmid, "title": title, "abstract": abstract[:500], "journal": journal, "year": year, "authors": "; ".join(authors[:5]), "mesh_terms": "; ".join(mesh_terms[:10]), }) return pd.DataFrame(articles) ``` ## 2. Semantic Scholar API 検索 ```python def semantic_scholar_search(query, max_results=50, year_from=None, fields=None): """ Semantic Scholar Academic Graph API 検索。 Parameters: query: str — 検索クエリ fields: list — 取得フィールド """ url = "https://api.semanticscholar.org/graph/v1/paper/search" if fields is None: fields = ["title", "abstract", "year", "citationCount", "influentialCitationCount", "authors", "url", "openAccessPdf"] params = { "query": query, "limit": min(max_results, 100), "fields": ",".join(fields), } if year_from: params["year"] = f"{year_from}-" resp = requests.get(url, params=params) data = resp.json() papers = data.get("data", []) results = [] for p in papers: results.append({ "paper_id": p.get("paperId", ""), "title": p.get("title", ""), "abstract": (p.get("abstract") or "")[:300], "year": p.get("year"), "citations": p.get("citationCount", 0), "influential_citations": p.get("influentialCitationCount", 0), "authors": "; ".join([a.get("name", "") for a in (p.get("authors") or [])[:5]]), "url": p.get("url", ""), "open_access": bool(p.get("openAccessPdf")), }) df = pd.DataFrame(results) print(f"Semantic Scholar: {data.get('total', 0)} total, " f"{len(df)} fetched") return df ``` ## 3. OpenAlex 著者・機関メトリクス ```python def openalex_search(query, entity_type="works", max_results=50, filters=None): """ OpenAlex API 検索 (250M+ works, 90M+ authors)。 Parameters: query: str — 検索クエリ entity_type: "works", "authors", "institutions", "concepts" filters: dict — OpenAlex フィルタ (e.g., {"from_publication_date": "2020-01-01"}) """ base = "https://api.openalex.org" url = f"{base}/{entity_type}" params = { "search": query, "per-page": min(max_results, 200), "mailto": "research@example.com", } if filters: filter_parts = [f"{k}:{v}" for k, v in filters.items()] params["filter"] = ",".join(filter_parts) resp = requests.get(url, params=params) data = resp.json() results = data.get("results", []) total = data.get("meta", {}).get("count", 0) print(f"OpenAlex {entity_type}: {total} total, {len(results)} fetched") if entity_type == "works": return pd.DataFrame([{ "id": r.get("id", ""), "title": r.get("title", ""), "year": r.get("publication_year"), "citations": r.get("cited_by_count", 0), "doi": r.get("doi", ""), "type": r.get("type", ""), "open_access": r.get("open_access", {}).get("is_oa", False), } for r in results]) elif entity_type == "authors": return pd.DataFrame([{ "id": r.get("id", ""), "name": r.get("display_name", ""), "works_count": r.get("works_count", 0), "citations": r.get("cited_by_count", 0), "h_index": r.get("summary_stats", {}).get("h_index", 0), "institution": (r.get("last_known_institutions") or [{}])[0].get( "display_name", "") if r.get("last_known_institutions") else "", } for r in results]) return results ``` ## 4. EuropePMC 全文検索 ```python def europepmc_search(query, max_results=50, source="MED", open_access_only=False): """ EuropePMC REST API 検索 (39M+ records)。 Parameters: query: str — 検索クエリ source: "MED" (PubMed), "PMC" (PubMed Central), "AGR", "CBA" open_access_only: True でオープンアクセスのみ """ url = "https://www.ebi.ac.uk/europepmc/webservices/rest/search" q = query if open_access_only: q += " AND OPEN_ACCESS:y" params = { "query": q, "resultType": "core", "pageSize": min(max_results, 100), "format": "json", "src": source, } resp = requests.get(url, params=params) data = resp.json() results = data.get("resultList", {}).get("result", []) total = data.get("hitCount", 0) articles = [] for r in results: articles.append({ "pmid": r.get("pmid", ""), "pmcid": r.get("pmcid", ""), "title": r.get("title", ""), "journal": r.get("journalTitle", ""), "year": r.get("pubYear", ""), "citations": r.get("citedByCount", 0), "has_fulltext": r.get("hasTextMinedTerms", "N") == "Y", "is_open_access": r.get("isOpenAccess", "N") == "Y", }) df = pd.DataFrame(articles) print(f"EuropePMC: {total} total, {len(df)} fetched") return df ``` ## 5. CrossRef メタデータ取得 ```python def crossref_search(query, max_results=50, filter_type=None, from_date=None, sort="relevance"): """ CrossRef REST API 検索 (DOI メタデータ)。 Parameters: query: str — 検索クエリ filter_type: str — DOI タイプフィルタ ("journal-article", "book-chapter") from_date: str — "YYYY-MM-DD" sort: "relevance", "published", "is-referenced-by-count" """ url = "https://api.crossref.org/works" params = { "query": query, "rows": min(max_results, 100), "sort": sort, "mailto": "research@example.com", } 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) data = resp.json() items = data.get("message", {}).get("items", []) total = data.get("message", {}).get("total-results", 0) results = [] for item in items: title = item.get("title", [""])[0] if item.get("title") else "" results.append({ "doi": item.get("DOI", ""), "title": title, "journal": item.get("container-title", [""])[0] if item.get("container-title") else "", "year": item.get("published", {}).get("date-parts", [[None]])[0][0], "citations": item.get("is-referenced-by-count", 0), "type": item.get("type", ""), "publisher": item.get("publisher", ""), }) df = pd.DataFrame(results) print(f"CrossRef: {total} total, {len(df)} fetched") return df ``` ## 6. 引用ネットワーク構築 ```python def build_citation_network(seed_pmids, depth=1, max_per_level=20): """ PubMed 論文の引用/被引用ネットワーク構築。 Parameters: seed_pmids: list — 起点となる PMID リスト depth: int — 探索深度 (1=直接引用のみ) max_per_level: int — 各レベルの最大ノード数 """ import networkx as nx G = nx.DiGraph() visited = set() current_level = set(seed_pmids) for level in range(depth + 1): next_level = set() for pmid in list(current_level)[:max_per_level]: if pmid in visited: continue visited.add(pmid) G.add_node(pmid, level=level, is_seed=(level == 0)) # elink で引用関係取得 time.sleep(0.4) url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi" params = { "dbfrom": "pubmed", "db": "pubmed", "id": pmid, "linkname": "pubmed_pubmed_citedin", "retmode": "json", } try: resp = requests.get(url, params=params) data = resp.json() links = data.get("linksets", [{}])[0].get("linksetdbs", []) if links: cited_by = [l["id"] for l in links[0].get("links", [])[:max_per_level]] for citing in cited_by: G.add_edge(citing, pmid) next_level.add(citing) except Exception: continue current_level = next_level - visited print(f"Citation network: {G.number_of_nodes()} nodes, " f"{G.number_of_edges()} edges, depth={depth}") return G ``` ## References ### Output Files | ファイル | 形式 | |---|---| | `results/pubmed_search.csv` | CSV | | `results/semantic_scholar_results.csv` | CSV | | `results/openalex_results.csv` | CSV | | `results/europepmc_results.csv` | CSV | | `results/crossref_results.csv` | CSV | | `results/citation_network.graphml` | GraphML | | `figures/citation_network.png` | PNG | ### 利用可能ツール > [ToolUniverse](https://github.com/mims-harvard/ToolUniverse) SMCP 経由で利用可能な外部ツール。 | カテゴリ | 主要ツール | 用途 | |---|---|---| | PubMed | `PubMed_search_articles` | PubMed 論文検索 | | PubMed | `PubMed_get_article` | 論文詳細取得 | | PubMed | `PubMed_get_cited_by` | 被引用論文取得 | | PubMed | `PubMed_get_related` | 関連論文検索 | | PubMed | `PubMed_get_links` | 関連リンク取得 | | PubMed | `PubMed_Guidelines_Search` | ガイドライン検索 | | EuropePMC | `EuropePMC_search_articles` | EuropePMC 検索 | | EuropePMC | `EuropePMC_get_article` | 記事詳細取得 | | EuropePMC | `EuropePMC_get_references` | 参考文献取得 | | EuropePMC | `EuropePMC_get_citations` | 引用取得 | | EuropePMC | `EuropePMC_get_fulltext` | 全文取得 | | EuropePMC | `EuropePMC_get_fulltext_snippets` | 全文スニペット | | EuropePMC | `EuropePMC_Guidelines_Search` | ガイドライン検索 | | Semantic Scholar | `SemanticScholar_search_papers` | セマンティック論文検索 | | Semantic Scholar | `SemanticScholar_get_pdf_snippets` | PDF スニペット | | OpenAlex | `openalex_literature_search` | 文献検索 | | OpenAlex | `openalex_get_work` | 論文詳細 | | OpenAlex | `openalex_get_work_by_doi` | DOI → 論文 | | OpenAlex | `openalex_search_works` | 論文検索 (REST) | | OpenAlex | `openalex_get_author` | 著者詳細 | | OpenAlex | `openalex_search_authors` | 著者検索 | | OpenAlex | `openalex_get_institution` | 機関詳細 | | OpenAlex | `openalex_search_institutions` | 機関検索 | | CrossRef | `Crossref_search_works` | DOI メタデータ検索 | | CrossRef | `Crossref_get_work` | 論文メタデータ取得 | | CrossRef | `Crossref_get_funder` | 助成機関情報 | | CrossRef | `Crossref_get_journal` | ジャーナル情報 | | CrossRef | `Crossref_list_funders` | 助成機関一覧 | | CrossRef | `Crossref_list_types` | 出版タイプ一覧 | ### 参照スキル | スキル | 関連 | |---|---| | `scientific-deep-research` | 文献検索 → 深層リサーチ | | `scientific-citation-checker` | 引用検証連携 | | `scientific-systematic-review` | 系統的検索戦略 | | `scientific-meta-analysis` | メタアナリシス文献収集 | | `scientific-academic-writing` | 関連文献の自動発見 | | `scientific-text-mining-nlp` | 抽出テキストの NLP 解析 | ### 依存パッケージ `requests`, `pandas`, `networkx`, `xml.etree.ElementTree` (stdlib)