# CrossRef — Scraping & Data Extraction

`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.

```python
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

```python
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

```python
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)

```python
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

```python
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)

```python
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

```python
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`

```python
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.

```python
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:

```python
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
```

### Count by type using facets

```python
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

```python
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

```python
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:

```python
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

```python
# 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)
```

### Type identifiers (filter param values vs facet display names)

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 `&amp;` — `"Deep learning &amp; 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.