Research and public data

crossref.org

Updated Apr 28, 2026

CrossRef - Scraping & Data Extraction

Notes an AI agent reads before it works on crossref.org: what to open, what to call, what breaks. Written by browser-harness contributors after real runs, and kept on GitHub under the MIT license.

Run it in Browser Use Cloud Source on GitHub

Give it to your agent

Any coding agent

Start the task with this line. The Markdown version is the same notes without the page around them.

Read https://browser-use.com/skills/crossref.md, then <your task>

Browser Harness

Save the files into your workspace and turn domain skills on. The code below calls its helpers, such as http_get and js. What Browser Harness is.

d="${BH_AGENT_WORKSPACE:-$HOME/.config/browser-harness/agent-workspace}/domain-skills/crossref"
mkdir -p "$d" && cd "$d"
curl -sLO https://raw.githubusercontent.com/browser-use/browser-harness/main/agent-workspace/domain-skills/crossref/scraping.md
export BH_DOMAIN_SKILLS=1

https://api.crossref.org - scholarly DOI and citation metadata. Never use the browser for CrossRef. Completely free, no auth required. All workflows use http_get.

Do this first

Always add mailto=your@email.com to every request - it moves you into the polite pool, which doubles the rate limit and concurrency allowance. The difference is measurable and the cost is zero.

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"  # set once, append to every URL
 
# Single DOI lookup — fastest way to get metadata for a known paper
data = json.loads(http_get(f"https://api.crossref.org/works/10.1038/s41586-021-03819-2?{MAILTO}"))
msg = data['message']
# msg keys: DOI, title, author, published, type, container-title, volume, issue,
#           page, is-referenced-by-count, references-count, abstract (optional), ...

Common workflows

DOI lookup - single paper

from helpers import http_get
import json, re
 
MAILTO = "mailto=your@email.com"
 
def fetch_work(doi):
    data = json.loads(http_get(f"https://api.crossref.org/works/{doi}?{MAILTO}"))
    return data['message']
 
def parse_date(d):
    """[[2021, 7, 15]] -> '2021-7-15'. Handles partial dates like [[2021]]."""
    if not d: return None
    parts = d.get('date-parts', [[]])[0]
    return '-'.join(str(p) for p in parts if p is not None)
 
def clean_abstract(raw):
    """Strip JATS XML tags. Abstract field contains tags like <jats:p>, <jats:italic>."""
    return re.sub(r'<[^>]+>', ' ', raw).strip() if raw else None
 
w = fetch_work("10.1038/s41586-021-03819-2")  # AlphaFold2
 
print("DOI:", w['DOI'])                                    # 10.1038/s41586-021-03819-2
print("Title:", w['title'][0])                             # Highly accurate protein structure...
print("Type:", w['type'])                                  # journal-article
print("Publisher:", w['publisher'])                        # Springer Science and Business Media LLC
print("Journal:", w.get('container-title', [''])[0])      # Nature
print("Volume:", w.get('volume'))                          # 596
print("Issue:", w.get('issue'))                            # 7873
print("Page:", w.get('page'))                              # 583-589
print("published:", parse_date(w.get('published')))        # 2021-7-15  (online date)
print("published-online:", parse_date(w.get('published-online')))  # 2021-7-15
print("published-print:", parse_date(w.get('published-print')))    # 2021-8-26
print("Citations:", w.get('is-referenced-by-count'))       # 40260
print("References:", w.get('references-count'))            # 84
print("Abstract:", clean_abstract(w.get('abstract', ''))[:100] if w.get('abstract') else None)
# Confirmed output (2026-04-18):
# DOI: 10.1038/s41586-021-03819-2
# Title: Highly accurate protein structure prediction with AlphaFold
# Type: journal-article
# Journal: Nature
# Volume: 596 | Issue: 7873 | Page: 583-589
# published: 2021-7-15 | published-print: 2021-8-26
# Citations: 40260

DOI lookup - extract authors with ORCID

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"
data = json.loads(http_get(f"https://api.crossref.org/works/10.1038/s41586-021-03819-2?{MAILTO}"))
authors = data['message'].get('author', [])
 
for a in authors[:3]:
    name = f"{a.get('given', '')} {a.get('family', '')}".strip()
    # ORCID is a full URL, not a bare ID — strip the prefix
    orcid_url = a.get('ORCID')  # e.g. 'https://orcid.org/0000-0001-6169-6580'
    orcid_id = orcid_url.replace('https://orcid.org/', '') if orcid_url else None
    authenticated = a.get('authenticated-orcid', False)  # False = self-reported, True = verified
    affiliations = [aff.get('name', '') for aff in a.get('affiliation', [])]
    print(f"{name} | ORCID: {orcid_id} | auth={authenticated} | seq={a['sequence']}")
# Confirmed output:
# John Jumper | ORCID: 0000-0001-6169-6580 | auth=False | seq=first
# Richard Evans | ORCID: None | auth=False | seq=additional
# Alexander Pritzel | ORCID: None | auth=False | seq=additional

Batch DOI lookup (parallel - 5 calls in ~0.3s)

from helpers import http_get
from concurrent.futures import ThreadPoolExecutor
import json
 
MAILTO = "mailto=your@email.com"
 
def fetch_work(doi):
    try:
        data = json.loads(http_get(f"https://api.crossref.org/works/{doi}?{MAILTO}"))
        msg = data['message']
        return {
            'doi': doi,
            'title': msg.get('title', [''])[0],
            'year': (msg.get('published', {}).get('date-parts') or [[None]])[0][0],
            'citations': msg.get('is-referenced-by-count'),
            'type': msg.get('type'),
        }
    except Exception as e:
        return {'doi': doi, 'error': str(e)}
 
dois = [
    "10.1038/nature12345",
    "10.1038/s41586-021-03819-2",
    "10.1056/NEJMoa2034577",
    "10.1126/science.1260419",
    "10.1038/s41586-024-07487-w",
]
 
# max_workers=5 safe; polite pool: 10 req/s, concurrency=3 (see Rate limits)
with ThreadPoolExecutor(max_workers=5) as ex:
    results = list(ex.map(fetch_work, dois))
 
for r in results:
    print(r['year'], f"cites={r['citations']}", r['title'][:50])
# Confirmed output (2026-04-18, ~0.296s total):
# 2013 cites=465 LRG1 promotes angiogenesis by modulating endotheli
# 2021 cites=40260 Highly accurate protein structure prediction with
# 2020 cites=13752 Safety and Efficacy of the BNT162b2 mRNA Covid-19
# 2015 cites=13553 Tissue-based map of the human proteome
# 2024 cites=12037 Accurate structure prediction of biomolecular inte

Search works by keyword

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"
 
# Broad keyword search
data = json.loads(http_get(
    f"https://api.crossref.org/works?query=machine+learning&rows=5&{MAILTO}"
))
msg = data['message']
print("Total results:", msg['total-results'])   # 2,805,391
for item in msg['items']:
    title = item.get('title', ['(no title)'])[0][:60]
    doi   = item.get('DOI', '')
    year  = (item.get('published', {}).get('date-parts') or [[None]])[0][0]
    type_ = item.get('type', '')
    print(f"  [{type_}] {year} {title}")
    print(f"    DOI: {doi}")

Search by author + title (targeted)

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"
 
data = json.loads(http_get(
    f"https://api.crossref.org/works?query.author=Lecun&query.title=deep+learning&rows=5&{MAILTO}"
))
msg = data['message']
print("Total results:", msg['total-results'])   # 62
for item in msg['items'][:3]:
    title   = item.get('title', [''])[0][:60]
    authors = ', '.join(a.get('family', '') for a in item.get('author', [])[:2])
    year    = (item.get('published', {}).get('date-parts') or [[None]])[0][0]
    print(f"  {year} {title}")
    print(f"    Authors: {authors}  DOI: {item.get('DOI')}")
# Confirmed output:
# 2015 Deep learning & convolutional networks
#   Authors: LeCun  DOI: 10.1109/hotchips.2015.7477328

Filter by date, type, and sort by citations

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"
 
data = json.loads(http_get(
    f"https://api.crossref.org/works"
    f"?filter=from-pub-date:2024-01-01,type:journal-article"
    f"&rows=5&sort=is-referenced-by-count&order=desc&{MAILTO}"
))
msg = data['message']
print("Total 2024+ journal articles:", msg['total-results'])   # 14,565,456
for item in msg['items'][:3]:
    title  = item.get('title', [''])[0][:60]
    cites  = item.get('is-referenced-by-count', 0)
    year   = (item.get('published', {}).get('date-parts') or [[None]])[0][0]
    print(f"  {year} cites={cites} {title}")
# Confirmed output:
# 2024 cites=17371 Global cancer statistics 2022: GLOBOCAN estimates...
# 2024 cites=12037 Accurate structure prediction of biomolecular int...

Filter with `has-abstract:true`

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"
 
# Only return works that have an abstract (useful since ~30-70% do not)
data = json.loads(http_get(
    f"https://api.crossref.org/works"
    f"?filter=from-pub-date:2023-01-01,until-pub-date:2023-12-31"
    f",type:journal-article,has-abstract:true"
    f"&rows=3&sort=is-referenced-by-count&order=desc&{MAILTO}"
))
msg = data['message']
print("2023 journal articles with abstract:", msg['total-results'])   # 3,041,841
for item in msg['items']:
    print(item.get('title', [''])[0][:60], '| cites:', item.get('is-referenced-by-count'))
# Confirmed output:
# Cancer statistics, 2023 | cites: 12919
# Evolutionary-scale prediction of atomic-level protein struct | cites: 4352

Cursor pagination (large result sets)

Standard offset pagination (start=) caps at a few thousand results. Use cursor for full sweeps.

from helpers import http_get
from urllib.parse import quote
import json
 
MAILTO = "mailto=your@email.com"
 
# First page: cursor=*
data = json.loads(http_get(
    f"https://api.crossref.org/works?query=covid&rows=100&cursor=*&{MAILTO}"
))
msg = data['message']
print("Total results:", msg['total-results'])   # 897,660
items = msg['items']
next_cursor = msg['next-cursor']   # base64 string like "DnF1ZXJ5VGhlbkZldGNoJA..."
 
# Next pages: pass URL-encoded cursor
while next_cursor and items:
    data = json.loads(http_get(
        f"https://api.crossref.org/works?query=covid&rows=100"
        f"&cursor={quote(next_cursor)}&{MAILTO}"
    ))
    msg = data['message']
    items = msg.get('items', [])
    next_cursor = msg.get('next-cursor')
    # process items...
    break  # remove for full sweep

Fetch specific fields only (`select=`)

Reduces response size significantly for bulk operations:

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"
 
data = json.loads(http_get(
    f"https://api.crossref.org/works?query=cancer&rows=5"
    f"&select=DOI,title,author&{MAILTO}"
))
# Warning: if a field is absent for a record, it simply won't appear in that item
for item in data['message']['items']:
    print(list(item.keys()))   # only ['DOI', 'title'] or ['DOI', 'title', 'author']
    # Note: select= does NOT guarantee the field appears — absent fields are just omitted

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"
 
data = json.loads(http_get(
    f"https://api.crossref.org/works?query=machine+learning&rows=0"
    f"&facet=type-name:*&{MAILTO}"
))
msg = data['message']
type_facet = msg['facets']['type-name']
for k, v in sorted(type_facet['values'].items(), key=lambda x: -x[1]):
    print(f"  {k}: {v:,}")
# Confirmed output (all CrossRef, 2026-04-18):
# Journal Article: 1,628,997 (for query=machine+learning scope)
# Conference Paper: 501,433
# Chapter: 455,907
# Posted Content: 87,937
# ...

Journal info by ISSN

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"
 
# Nature (ISSN 0028-0836)
data = json.loads(http_get(f"https://api.crossref.org/journals/0028-0836?{MAILTO}"))
msg = data['message']
print("Title:", msg['title'])                          # Nature
print("Publisher:", msg['publisher'])                  # Springer Science and Business Media LLC
print("ISSN:", msg['ISSN'])                            # ['0028-0836', '1476-4687']
print("Total DOIs:", msg['counts']['total-dois'])       # 445,417
print("Subjects:", msg.get('subjects', []))             # [] (not always populated)
 
# Search journals by name
data2 = json.loads(http_get(f"https://api.crossref.org/journals?query=nature&rows=3&{MAILTO}"))
for j in data2['message']['items']:
    print(f"{j.get('title')} | ISSN: {j.get('ISSN')} | DOIs: {j.get('counts', {}).get('total-dois')}")
# Confirmed output:
# NatureJobs | ISSN: [] | DOIs: 0
# Naturen | ISSN: ['0028-0887', '1504-3118'] | DOIs: 1055

Funder search

from helpers import http_get
import json
 
MAILTO = "mailto=your@email.com"
 
data = json.loads(http_get(
    f"https://api.crossref.org/funders?query=national+science+foundation&rows=3&{MAILTO}"
))
msg = data['message']
print("Total funders:", msg['total-results'])   # 108
for f in msg['items']:
    print(f"  ID: {f['id']} | {f['name']}")
    print(f"    Alt names: {f.get('alt-names', [])[:2]}")
    print(f"    URI: {f.get('uri')}")
# Confirmed output:
# ID: 501100001711 | Schweizerischer Nationalfonds zur Förderung...
# ID: 100000143 | Division of Computing and Communication Foundations

DOI content negotiation (alternative, no CrossRef API needed)

The doi.org resolver can return formatted metadata directly via Accept header:

import urllib.request, json
 
def doi_to_csl(doi):
    """Fetch CSL-JSON via DOI content negotiation. Same data as CrossRef API."""
    req = urllib.request.Request(
        f"https://doi.org/{doi}",
        headers={"Accept": "application/vnd.citationstyles.csl+json",
                 "User-Agent": "Mozilla/5.0"}
    )
    with urllib.request.urlopen(req, timeout=20) as r:
        return json.loads(r.read().decode())
 
def doi_to_bibtex(doi):
    """Fetch BibTeX via DOI content negotiation."""
    req = urllib.request.Request(
        f"https://doi.org/{doi}",
        headers={"Accept": "application/x-bibtex", "User-Agent": "Mozilla/5.0"}
    )
    with urllib.request.urlopen(req, timeout=20) as r:
        return r.read().decode()
 
csl = doi_to_csl("10.1038/nature12345")
print("Title:", csl['title'])   # LRG1 promotes angiogenesis...
print("Type:", csl['type'])     # journal-article
 
bib = doi_to_bibtex("10.1038/nature12345")
print(bib[:200])
# @article{Wang_2013, title={LRG1 promotes angiogenesis...

Field reference

Work object - complete field list

All fields are potentially absent unless marked required. Fields marked (R) are always present.

Field	Type	Notes
`DOI` (R)	string	e.g. `"10.1038/s41586-021-03819-2"`
`URL` (R)	string	`"https://doi.org/10.1038/s41586-021-03819-2"`
`title` (R)	list[str]	Always a list; access `title[0]`
`type` (R)	string	e.g. `"journal-article"` - see type table below
`publisher`	string
`container-title`	list[str]	Journal name; access `[0]`
`short-container-title`	list[str]	Abbreviated journal name
`ISSN`	list[str]	May contain print and online ISSN
`volume`	string	Note: string not int (`"596"`)
`issue`	string
`page`	string	e.g. `"583-589"`
`author`	list[object]	See author fields below
`published`	date-object	Best single date - use this
`published-online`	date-object	Online-first date
`published-print`	date-object	Print edition date
`issued`	date-object	Usually same as `published`
`is-referenced-by-count`	int	Inbound citations to this work
`references-count`	int	Outbound references from this work
`reference`	list[object]	Full reference list (when deposited)
`abstract`	string	JATS XML markup; ~30-70% of works; strip tags before use
`subject`	list[str]	Subject classification (often empty)
`language`	string	e.g. `"en"`
`license`	list[object]	Each: `{URL, start, delay-in-days, content-version}`
`funder`	list[object]	Each: `{name, DOI, award}`
`link`	list[object]	Full-text links
`relation`	object	Related DOIs (e.g. preprint → article)
`assertion`	list[object]	Publisher-specific metadata
`alternative-id`	list[str]	Publisher's internal IDs
`member`	string	CrossRef member ID
`prefix`	string	DOI prefix
`score`	float	Relevance score (search results only)
`source`	string	e.g. `"Crossref"`
`indexed`	date-object	When CrossRef indexed this record
`deposited`	date-object	When publisher last deposited metadata
`created`	date-object	When CrossRef record was first created

Author object fields

Field	Notes
`given`	Given/first name
`family`	Family/last name
`sequence`	`"first"` or `"additional"`
`affiliation`	list of `{name, place}` - usually `[]`
`ORCID`	Full URL `"https://orcid.org/0000-0001-..."` - strip prefix to get bare ID
`authenticated-orcid`	`true` = verified via ORCID OAuth; `false` = self-reported
`name`	Used instead of given/family for organizations

Date object structure

# All date fields share this structure:
date_obj = {
    "date-parts": [[2021, 7, 15]],  # [[year, month, day]] — month/day may be absent
    "date-time": "2021-07-15T00:00:00Z",  # not always present
    "timestamp": 1626307200000               # not always present
}
 
# Safe extraction (handles [[2021]] or [[2021, 7]] partial dates):
def parse_date(d):
    if not d: return None
    parts = (d.get('date-parts') or [[]])[0]
    return '-'.join(str(p) for p in parts if p is not None)

Use these exact strings in filter=type:.... The facet type-name values are display names only.

filter `type:` value	Facet display name	Count (all CrossRef)
`journal-article`	Journal Article	121,030,194
`book-chapter`	Chapter	24,359,059
`proceedings-article`	Conference Paper	9,744,754
`dataset`	Dataset	3,424,142
`posted-content`	Posted Content (preprints)	3,203,320
`dissertation`	Dissertation	1,044,461
`peer-review`	Peer Review	1,028,287
`report`	Report	906,301
`book`	Book	870,949
`monograph`	Monograph	788,401

Query parameters reference

Parameter	Notes
`query`	Full-text keyword search across title, abstract, author
`query.author`	Author name search only
`query.title`	Title search only
`query.bibliographic`	Combined title + author + journal search
`rows`	Results per page (default 20, max 1000)
`offset`	Offset for pagination (max ~10,000 effective)
`cursor`	Use `cursor=*` for first page, then URL-encode `next-cursor` value
`sort`	`relevance`, `is-referenced-by-count`, `published`, `indexed`
`order`	`asc` or `desc`
`filter`	Comma-separated `key:value` pairs (see filters below)
`select`	Comma-separated field names to return
`facet`	`type-name:*` for type counts; `publisher-name:10` for top publishers
`mailto`	Your email - enables polite pool (higher limits)

Filter keys reference

Filter key	Example	Notes
`doi`	`doi:10.1038/nature12345`	Exact DOI match
`type`	`type:journal-article`	See type table above for valid values
`from-pub-date`	`from-pub-date:2024-01-01`	ISO date or `YYYY`
`until-pub-date`	`until-pub-date:2024-12-31`
`from-index-date`	`from-index-date:2024-01-01`	When CrossRef indexed it
`has-abstract`	`has-abstract:true`	Only works with deposited abstract
`has-orcid`	`has-orcid:true`	At least one author has ORCID
`has-full-text`	`has-full-text:true`	Has full-text link
`has-references`	`has-references:true`	Has deposited reference list
`is-update`	`is-update:true`	Corrections, retractions
`issn`	`issn:0028-0836`	Filter by journal ISSN
`publisher-name`	`publisher-name:elsevier`	Partial match
`funder`	`funder:100000001`	Funder DOI or CrossRef funder ID

Rate limits

CrossRef has two pools based on whether mailto= is present:

Pool	Triggered by	Rate limit	Concurrency
polite	`mailto=` param present	10 req/s	3 concurrent
public	no `mailto=`	5 req/s	1 concurrent

Headers returned: x-rate-limit-limit, x-rate-limit-interval, x-concurrency-limit, x-api-pool.

In practice with polite pool: 10 rapid sequential calls complete in ~2.7s (avg 0.27s/req) with no throttling. 5 parallel calls complete in ~0.3s. Stay at max_workers=5 to respect the concurrency limit.

No per-day or per-hour cap. If you exceed limits, responses slow or return HTTP 429. No ban. Add time.sleep(0.1) between calls for sustained bulk crawls.

Gotchas

mailto= doubles your rate limit and concurrency. Public pool: 5 req/s, concurrency=1. Polite pool: 10 req/s, concurrency=3. Always add ?mailto=your@email.com to every request - confirmed by reading x-api-pool response header.
title, container-title, ISSN are always lists, not strings. Access with title[0], container-title[0] etc. Do not rely on there being only one entry - container-title can have multiple values.
Abstract contains JATS XML markup. The abstract field is not plain text - it contains tags like <jats:p>, <jats:italic>, <jats:sup>. Strip with re.sub(r'<[^>]+>', ' ', abstract). About 30-70% of works have an abstract at all; journal articles 2023 with has-abstract:true filter: 3,041,841 / ~5.5M total = ~55%.
ORCID is a full URL, not just the ID. a['ORCID'] = "https://orcid.org/0000-0001-6169-6580". Strip with .replace('https://orcid.org/', '') to get the bare ID. authenticated-orcid: false means self-asserted (not verified via OAuth).
published vs published-print vs published-online. Online-first is common in journals - a paper may be online months before its print issue. published is CrossRef's best single date and equals published-online when both exist. For preprints (posted-content type), look for posted instead of published-print - it may only have posted and published. Partial dates like [[2023]] (year only) are valid - always use parse_date() to handle missing month/day.
404 raises HTTPError, not a JSON error response. An invalid DOI (e.g. 10.9999/doesnotexist) raises urllib.error.HTTPError: HTTP Error 404: Not Found. Wrap fetch_work() in try/except for any untrusted DOI list.
volume and issue are strings, not integers. CrossRef stores them as strings - "596", not 596. Don't compare with == to an int.
Filter type values are hyphenated lowercase, not the facet display names. filter=type:journal-article works. filter=type:journal article, filter=type:Journal Article, and filter=type:conference-paper all return HTTP 400. Conference papers are proceedings-article.
select= does not guarantee field presence. When you select=DOI,title,author, a record that has no author still omits the author key - it doesn't return author: []. Always use .get().
Cursor pagination required for >10,000 results. Offset pagination (offset=) is limited to around 10,000 results. For bulk sweeps, use cursor=* for the first page, then URL-encode the returned next-cursor value with urllib.parse.quote(). The cursor expires if unused for too long.
rows max is 1000 per call. Requesting more silently returns 1000. For cursor-based sweeps of large result sets (millions of records), rows=1000 with cursor is the most efficient approach.
HTML entities in titles. Titles may contain HTML entities like & - "Deep learning & convolutional networks". Decode with html.unescape() if needed.
funder search works-count field is None. The funder search result object has a works-count key that is always None in the search response. To get actual work counts for a funder, fetch the funder directly: GET /funders/{id}.
subject is often an empty list. The subject field in works is populated inconsistently - many journal articles have subject: [] even for well-indexed journals like Nature.
Affiliation is usually empty. author[i]['affiliation'] is [] for the majority of records, even for papers published in 2024. CrossRef has been working on affiliation deposit, but coverage is inconsistent.

scraping.md · updated Apr 28, 2026 · 2,937 words

Sites change. These notes describe what worked when they were last updated, not a guarantee they still do. If something broke, the fix is a pull request to browser-harness.

CrossRef - Scraping & Data Extraction

Give it to your agent

Any coding agent

Browser Harness

Do this first

Common workflows

DOI lookup - single paper

DOI lookup - extract authors with ORCID

Batch DOI lookup (parallel - 5 calls in ~0.3s)

Search works by keyword

Search by author + title (targeted)

Filter by date, type, and sort by citations

Filter with `has-abstract:true`

Fetch specific fields only (`select=`)

Count by type using facets

Journal info by ISSN

Funder search

DOI content negotiation (alternative, no CrossRef API needed)

Field reference

Work object - complete field list

Author object fields

Date object structure

Type identifiers (filter param values vs facet display names)

Query parameters reference

Filter keys reference

Rate limits

Gotchas

Give it to your agent

Any coding agent

Browser Harness

Do this first

Common workflows

DOI lookup - single paper

DOI lookup - extract authors with ORCID

Batch DOI lookup (parallel - 5 calls in ~0.3s)

Search works by keyword

Search by author + title (targeted)

Filter by date, type, and sort by citations

Filter with has-abstract:true

Cursor pagination (large result sets)

Fetch specific fields only (select=)

Count by type using facets

Journal info by ISSN

Funder search

DOI content negotiation (alternative, no CrossRef API needed)

Field reference

Work object - complete field list

Author object fields

Date object structure

Type identifiers (filter param values vs facet display names)

Query parameters reference

Filter keys reference

Rate limits

Gotchas

More research and public data skills

Filter with `has-abstract:true`

Fetch specific fields only (`select=`)