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scientific-preprint-archive プレプリント・オープンアクセスアーカイブ検索スキル。bioRxiv/medRxiv
プレプリント検索、arXiv 論文取得、PMC フルテキスト、DOAJ OA ジャーナル、
Unpaywall OA リンク、CORE/HAL/Zenodo/OpenAIRE/OSF/Fatcat/DBLP
統合文献アクセスパイプライン。
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name scientific-preprint-archive description プレプリント・オープンアクセスアーカイブ検索スキル。bioRxiv/medRxiv
プレプリント検索、arXiv 論文取得、PMC フルテキスト、DOAJ OA ジャーナル、
Unpaywall OA リンク、CORE/HAL/Zenodo/OpenAIRE/OSF/Fatcat/DBLP
統合文献アクセスパイプライン。
Scientific Preprint Archive
bioRxiv / medRxiv / arXiv / PMC / DOAJ / Unpaywall / CORE / HAL /
Zenodo / OpenAIRE / OSF Preprints / Fatcat / DBLP を統合した
プレプリント・オープンアクセス文献検索パイプラインを提供する。
When to Use
最新のプレプリントを bioRxiv / medRxiv から検索するとき
arXiv の機械学習・計算科学論文を取得するとき
PMC フルテキスト XML を取得してテキストマイニングするとき
OA 版のリンクを Unpaywall で見つけるとき
CORE / Zenodo / OpenAIRE など複数アーカイブを横断検索するとき
系統的レビューの文献収集で網羅的プレプリント検索が必要なとき
DBLP から計算機科学文献メタデータを取得するとき
Quick Start
1. bioRxiv / medRxiv プレプリント検索
import requests
import pandas as pd
from datetime import datetime, timedelta
BIORXIV_API = "https://api.biorxiv.org"
def search_biorxiv (query, server="biorxiv" , days=30 , cursor=0 ):
"""
bioRxiv/medRxiv プレプリント検索。
Parameters:
query: str — 検索クエリ
server: str — "biorxiv" or "medrxiv"
days: int — 過去何日分を検索するか
cursor: int — ページネーション offset
ToolUniverse:
bioRxiv_search_preprints(query=query, server=server)
bioRxiv_get_preprint_details(doi=doi)
medRxiv_search_preprints(query=query)
"""
end_date = datetime.now().strftime("%Y-%m-%d" )
start_date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d"
f"{BIORXIV_API} /details/{server} /{start_date} /{end_date} /{cursor} "
for
in
"collection"
"title"
""
"abstract"
""
if
in
or
in
"doi"
"doi"
""
"title"
"title"
""
"authors"
"authors"
""
"date"
"date"
""
"category"
"category"
""
"server"
"abstract"
"abstract"
""
300
print
f"{server} search '{query} ': {len (df)} preprints (last {days} days)"
return
2. arXiv 論文検索 import urllib.parse
import xml.etree.ElementTree as ET
ARXIV_API = "http://export.arxiv.org/api/query"
def search_arxiv (query, category=None , max_results=50 , sort_by="submittedDate" ):
"""
arXiv 論文検索。
Parameters:
query: str — 検索クエリ
category: str — arXiv カテゴリ (e.g., "q-bio.GN", "cs.LG", "stat.ML")
max_results: int — 最大取得数
sort_by: str — "submittedDate", "lastUpdatedDate", "relevance"
ToolUniverse:
arXiv_search_papers(query=query, category=category)
arXiv_get_paper(arxiv_id=arxiv_id)
"""
search_query = f"all:{query} "
if category:
search_query += f"+AND+cat:{category} "
params = {
"search_query" : search_query,
"start" : 0 ,
"max_results" : max_results,
"sortBy" : sort_by,
"sortOrder" : "descending" ,
}
resp = requests.get(ARXIV_API, params=params)
resp.raise_for_status()
ns = {"atom" : "http://www.w3.org/2005/Atom" , "arxiv" : "http://arxiv.org/schemas/atom" }
root = ET.fromstring(resp.text)
results = []
for entry in root.findall("atom:entry" , ns):
categories = [c.get("term" ) for c in entry.findall("atom:category" , ns)]
results.append({
"arxiv_id" : entry.find("atom:id" , ns).text.split("/abs/" )[-1 ],
"title" : entry.find("atom:title" , ns).text.strip().replace("\n" , " " ),
"authors" : ", " .join(
a.find("atom:name" , ns).text
for a in entry.findall("atom:author" , ns)
),
"published" : entry.find("atom:published" , ns).text[:10 ],
"categories" : categories,
"abstract" : entry.find("atom:summary" , ns).text.strip()[:300 ],
"pdf_url" : next (
(l.get("href" ) for l in entry.findall("atom:link" , ns)
if l.get("title" ) == "pdf" ), ""
),
})
df = pd.DataFrame(results)
cat_str = f" [{category} ]" if category else ""
print (f"arXiv search '{query} '{cat_str} : {len (df)} papers" )
return df
3. PMC フルテキストアクセス PMC_API = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
def get_pmc_fulltext (pmcid, email="user@example.com" ):
"""
PMC フルテキスト XML 取得。
Parameters:
pmcid: str — PMC ID (e.g., "PMC1234567")
email: str — NCBI API 用メールアドレス
ToolUniverse:
PMC_get_fulltext(pmcid=pmcid)
"""
params = {
"db" : "pmc" ,
"id" : pmcid.replace("PMC" , "" ),
"rettype" : "xml" ,
"email" : email,
}
resp = requests.get(f"{PMC_API} /efetch.fcgi" , params=params)
resp.raise_for_status()
root = ET.fromstring(resp.text)
article = root.find(".//article" )
sections = {}
for sec in article.findall(".//sec" ) if article is not None else []:
title = sec.find("title" )
if title is not None and title.text:
paragraphs = [p.text for p in sec.findall("p" ) if p.text]
sections[title.text] = " " .join(paragraphs)
print (f"PMC {pmcid} : {len (sections)} sections retrieved" )
return sections
4. Unpaywall OA リンク検索 UNPAYWALL_API = "https://api.unpaywall.org/v2"
def find_oa_version (doi, email="user@example.com" ):
"""
Unpaywall で論文の OA 版リンクを検索。
Parameters:
doi: str — DOI
email: str — API 利用者メール
ToolUniverse:
Unpaywall_get_oa_status(doi=doi)
"""
resp = requests.get(f"{UNPAYWALL_API} /{doi} " , params={"email" : email})
resp.raise_for_status()
data = resp.json()
oa_locations = data.get("oa_locations" , [])
result = {
"doi" : doi,
"is_oa" : data.get("is_oa" , False ),
"oa_status" : data.get("oa_status" , "" ),
"best_oa_url" : data.get("best_oa_location" , {}).get("url_for_pdf" , "" ),
"journal" : data.get("journal_name" , "" ),
"publisher" : data.get("publisher" , "" ),
"n_oa_locations" : len (oa_locations),
"locations" : [
{
"url" : loc.get("url_for_pdf" ) or loc.get("url" ),
"host_type" : loc.get("host_type" ),
"version" : loc.get("version" ),
}
for loc in oa_locations
],
}
print (f"Unpaywall {doi} : OA={result['is_oa' ]} , status={result['oa_status' ]} " )
return result
5. CORE 統合検索 CORE_API = "https://api.core.ac.uk/v3"
def search_core (query, api_key, limit=25 ):
"""
CORE リポジトリ横断検索 (1.4 億+ 論文)。
Parameters:
query: str — 検索クエリ
api_key: str — CORE API キー
limit: int — 最大取得数
ToolUniverse:
CORE_search_works(query=query)
CORE_get_work(core_id=core_id)
"""
headers = {"Authorization" : f"Bearer {api_key} " }
params = {"q" : query, "limit" : limit}
resp = requests.get(f"{CORE_API} /search/works" , headers=headers, params=params)
resp.raise_for_status()
data = resp.json()
results = []
for work in data.get("results" , []):
results.append({
"core_id" : work.get("id" , "" ),
"title" : work.get("title" , "" ),
"authors" : ", " .join(
a.get("name" , "" ) for a in work.get("authors" , [])
),
"year" : work.get("yearPublished" , "" ),
"doi" : work.get("doi" , "" ),
"download_url" : work.get("downloadUrl" , "" ),
"abstract" : (work.get("abstract" ) or "" )[:300 ],
})
df = pd.DataFrame(results)
print (f"CORE search '{query} ': {len (df)} works" )
return df
6. Zenodo レコード検索 ZENODO_API = "https://zenodo.org/api"
def search_zenodo (query, resource_type=None , size=25 ):
"""
Zenodo レコード検索 (データセット・ソフトウェア・論文)。
Parameters:
query: str — 検索クエリ
resource_type: str — "publication", "dataset", "software", "poster"
size: int — 最大取得数
ToolUniverse:
Zenodo_search_records(query=query, type=resource_type)
"""
params = {"q" : query, "size" : size}
if resource_type:
params["type" ] = resource_type
resp = requests.get(f"{ZENODO_API} /records" , params=params)
resp.raise_for_status()
data = resp.json()
results = []
for hit in data.get("hits" , {}).get("hits" , []):
meta = hit.get("metadata" , {})
results.append({
"zenodo_id" : hit.get("id" , "" ),
"doi" : meta.get("doi" , "" ),
"title" : meta.get("title" , "" ),
"creators" : ", " .join(
c.get("name" , "" ) for c in meta.get("creators" , [])
),
"resource_type" : meta.get("resource_type" , {}).get("type" , "" ),
"publication_date" : meta.get("publication_date" , "" ),
"access_right" : meta.get("access_right" , "" ),
})
df = pd.DataFrame(results)
print (f"Zenodo search '{query} ': {len (df)} records" )
return df
7. DOAJ OA ジャーナル検索 DOAJ_API = "https://doaj.org/api"
def search_doaj_articles (query, page=1 , page_size=25 ):
"""
DOAJ OA ジャーナル記事検索。
ToolUniverse:
DOAJ_search_articles(query=query)
"""
params = {"q" : query, "page" : page, "pageSize" : page_size}
resp = requests.get(f"{DOAJ_API} /search/articles/{query} " )
resp.raise_for_status()
data = resp.json()
results = []
for item in data.get("results" , []):
bib = item.get("bibjson" , {})
results.append({
"doi" : bib.get("identifier" , [{}])[0 ].get("id" , "" ),
"title" : bib.get("title" , "" ),
"journal" : bib.get("journal" , {}).get("title" , "" ),
"year" : bib.get("year" , "" ),
"authors" : ", " .join(
a.get("name" , "" ) for a in bib.get("author" , [])
),
"keywords" : bib.get("keywords" , []),
})
df = pd.DataFrame(results)
print (f"DOAJ search '{query} ': {len (df)} OA articles" )
return df
8. OpenAIRE 研究成果物検索 OPENAIRE_API = "https://api.openaire.eu/search"
def search_openaire (query, result_type="publication" , size=25 ):
"""
OpenAIRE 研究成果物検索 (EU 助成研究中心)。
ToolUniverse:
OpenAIRE_search_publications(query=query)
"""
params = {
"keywords" : query,
"size" : size,
"format" : "json" ,
}
resp = requests.get(f"{OPENAIRE_API} /{result_type} s" , params=params)
resp.raise_for_status()
data = resp.json()
results_list = (
data.get("response" , {}).get("results" , {}).get("result" , [])
)
results = []
for item in results_list:
meta = item.get("metadata" , {}).get("oaf:entity" , {}).get("oaf:result" , {})
results.append({
"title" : meta.get("title" , {}).get("$" , "" ),
"date" : meta.get("dateofacceptance" , {}).get("$" , "" ),
"publisher" : meta.get("publisher" , {}).get("$" , "" ),
})
df = pd.DataFrame(results)
print (f"OpenAIRE search '{query} ': {len (df)} results" )
return df
9. 統合マルチアーカイブ検索パイプライン def multi_archive_search (query, archives=None , **kwargs ):
"""
複数プレプリント/OA アーカイブ横断検索。
Parameters:
query: str — 検索クエリ
archives: list — ["biorxiv", "medrxiv", "arxiv", "core", "zenodo", "doaj", "openaire"]
"""
if archives is None :
archives = ["biorxiv" , "arxiv" , "core" ]
all_results = {}
search_funcs = {
"biorxiv" : lambda q: search_biorxiv(q, server="biorxiv" ),
"medrxiv" : lambda q: search_biorxiv(q, server="medrxiv" ),
"arxiv" : lambda q: search_arxiv(q),
"core" : lambda q: search_core(q, api_key=kwargs.get("core_api_key" , "" )),
"zenodo" : lambda q: search_zenodo(q),
"doaj" : lambda q: search_doaj_articles(q),
"openaire" : lambda q: search_openaire(q),
}
for archive in archives:
if archive in search_funcs:
try :
df = search_funcs[archive](query)
all_results[archive] = df
print (f" ✓ {archive} : {len (df)} results" )
except Exception as e:
print (f" ✗ {archive} : {e} " )
all_results[archive] = pd.DataFrame()
total = sum (len (df) for df in all_results.values())
print (f"\nTotal: {total} results across {len (archives)} archives" )
return all_results
利用可能ツール 以下のツールが ToolUniverse SMCP 経由で利用可能:
ToolUniverse カテゴリ 主なツール biorxivbioRxiv_search_preprints, bioRxiv_get_preprint_detailsmedrxivmedRxiv_search_preprintsarxivarXiv_search_papers, arXiv_get_paperpmcPMC_get_fulltextdoajDOAJ_search_articlesunpaywallUnpaywall_get_oa_statushalHAL_searchcoreCORE_search_works, CORE_get_workzenodoZenodo_search_recordsopenaireOpenAIRE_search_publicationsosf_preprintsOSF_search_preprintsfatcatFatcat_search_releasesdblpDBLP_search_publications
パイプライン出力 出力ファイル 説明 連携先スキル results/preprint_search.csv横断検索結果 → literature-search, systematic-review results/oa_availability.jsonOA ステータス・リンク → deep-research results/fulltext_corpus/フルテキストコーパス → text-mining-nlp, biomedical-pubtator results/arxiv_papers.csvarXiv 論文メタデータ → deep-learning, graph-neural-networks
パイプライン統合 literature-search ──→ preprint-archive ──→ systematic-review
(PubMed/OpenAlex) (bioRxiv/arXiv/CORE) (PRISMA 2020)
│
├──→ text-mining-nlp (NER/KG)
├──→ biomedical-pubtator (PubTator NER)
└──→ deep-research (エビデンス統合)
