DOI Resolution Guide

SkillDev tools

DOI content negotiation and metadata retrieval techniques

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the DOI Resolution Guide skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/43-wentorai-research-plugins/skills/literature/metadata/doi-resolution-guide/SKILL.md and read by ahel’s review.

Master DOI content negotiation to programmatically retrieve structured metadata, citation data, and formatted references from any Digital Object Identifier.

What Is DOI Content Negotiation?

Every DOI (e.g., 10.1038/s41586-021-03819-2) resolves to a landing page by default. However, the DOI system supports HTTP content negotiation: by sending different Accept headers, you can retrieve structured metadata in various formats instead of an HTML page.

The DOI resolver endpoint is https://doi.org/{doi} or equivalently https://dx.doi.org/{doi}.

Supported Metadata Formats

Accept HeaderFormatUse Case
application/vnd.citationstyles.csl+jsonCSL-JSONProgrammatic metadata extraction
text/x-bibliography; style=apaFormatted citationReady-to-paste APA reference
text/x-bibliography; style=bibtexBibTeXLaTeX bibliography import
application/x-bibtexBibTeX (alt)LaTeX bibliography import
application/rdf+xmlRDF/XMLLinked data applications
text/turtleTurtle RDFLinked data applications
application/vnd.crossref.unixref+xmlCrossRef UnixrefFull CrossRef metadata

Retrieving Metadata via Content Negotiation

Get CSL-JSON (Most Useful for Programmatic Access)

curl -LH "Accept: application/vnd.citationstyles.csl+json" \
  https://doi.org/10.1038/s41586-021-03819-2
import requests

doi = "10.1038/s41586-021-03819-2"
headers = {"Accept": "application/vnd.citationstyles.csl+json"}
response = requests.get(f"https://doi.org/{doi}", headers=headers, allow_redirects=True)

metadata = response.json()
print(f"Title: {metadata['title']}")
print(f"Authors: {', '.join(a.get('family', '') for a in metadata.get('author', []))}")
print(f"Journal: {metadata.get('container-title', 'N/A')}")
print(f"Year: {metadata.get('published', {}).get('date-parts', [[None]])[0][0]}")
print(f"Type: {metadata.get('type')}")

Get a Formatted Citation

# APA format
curl -LH "Accept: text/x-bibliography; style=apa" \
  https://doi.org/10.1038/s41586-021-03819-2

# Chicago format
curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \
  https://doi.org/10.1038/s41586-021-03819-2

# Harvard format
curl -LH "Accept: text/x-bibliography; style=harvard-cite-them-right" \
  https://doi.org/10.1038/s41586-021-03819-2

Get BibTeX for LaTeX

curl -LH "Accept: application/x-bibtex" \
  https://doi.org/10.1038/s41586-021-03819-2

Output:

@article{Jumper_2021,
  title={Highly accurate protein structure prediction with AlphaFold},
  volume={596},
  DOI={10.1038/s41586-021-03819-2},
  journal={Nature},
  author={Jumper, John and Evans, Richard and ...},
  year={2021},
  pages={583--589}
}

Using the CrossRef API

The CrossRef API provides richer metadata and supports batch queries without content negotiation.

Single Paper Lookup

import requests

doi = "10.1038/s41586-021-03819-2"
response = requests.get(
    f"https://api.crossref.org/works/{doi}",
    headers={"User-Agent": "ResearchClaw/1.0 (mailto:you@university.edu)"}
)

work = response.json()["message"]
print(f"Title: {work['title'][0]}")
print(f"Publisher: {work['publisher']}")
print(f"Citation count: {work.get('is-referenced-by-count', 0)}")
print(f"Reference count: {work.get('references-count', 0)}")
print(f"License: {work.get('license', [{}])[0].get('URL', 'N/A')}")

Batch DOI Resolution

dois = [
    "10.1038/s41586-021-03819-2",
    "10.1126/science.abj8754",
    "10.1016/j.cell.2021.06.025"
]

results = []
for doi in dois:
    resp = requests.get(
        f"https://api.crossref.org/works/{doi}",
        headers={"User-Agent": "ResearchClaw/1.0 (mailto:you@university.edu)"}
    )
    if resp.status_code == 200:
        results.append(resp.json()["message"])
    else:
        print(f"Failed to resolve: {doi}")

DOI Validation and Normalization

import re

def normalize_doi(raw_input):
    """Extract and normalize a DOI from various input formats."""
    # Match DOI pattern: 10.XXXX/...
    match = re.search(r'(10\.\d{4,9}/[^\s]+)', raw_input)
    if match:
        doi = match.group(1)
        # Remove trailing punctuation
        doi = doi.rstrip('.,;:)')
        return doi.lower()
    return None

# Examples
normalize_doi("https://doi.org/10.1038/s41586-021-03819-2")  # 10.1038/s41586-021-03819-2
normalize_doi("DOI: 10.1038/s41586-021-03819-2.")            # 10.1038/s41586-021-03819-2
normalize_doi("See paper at doi.org/10.1038/s41586-021-03819-2 for details")  # works too

Practical Tips

  • Polite pool: CrossRef provides faster responses to requests with a User-Agent header that includes a mailto: contact. This is their "polite pool" with higher rate limits.
  • OpenAlex alternative: OpenAlex (https://api.openalex.org/works/doi:10.xxx/yyy) provides similar metadata for free, with richer entity linking.
  • Handle redirects: Always use allow_redirects=True (or -L in curl) as DOIs redirect through the resolver.
  • Caching: DOI metadata rarely changes. Cache resolved metadata locally to avoid redundant API calls.
  • Rate limits: CrossRef allows 50 requests/second in the polite pool. For bulk operations, use their data dumps instead.

See Also

  • doi-content-negotiation -- Detailed API reference for retrieving metadata in multiple formats via HTTP content negotiation.

Signals

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