Crossref Guide

These tools answer three questions about a journal's or publisher's Crossref metadata: what's in it, what it connects to, and how it has changed over time — measured live from Crossref, in your browser.

Nothing to install, no account, no API key. Nothing you look at is uploaded or stored: each tool reads the public Crossref REST API directly and does the work in the page (requests join Crossref's polite pool). If you can open a web page, you can use them. This guide walks you through a first session end to end.

These are prototypes. The Crossref tools are newer than the DataCite suite and are still being refined. They read Crossref's live data; the scores they produce are honest but the catalog is deliberately smaller than the DataCite one (see below). Use the Feedback link in the top bar to report issues or suggest improvements.
You only need one thing to start: a journal name or ISSN (e.g. 1932-6203), a Crossref member (publisher) entered as member:ID (e.g. member:340), or an organization's ROR (e.g. ror:04qw24q55). Paste a single DOI to look at one record. Both tools take the same input, and they hand your selection to each other as you go.

Your first session, step by step

Work through these in order the first time. It takes about twenty minutes and ends with a concrete list of records to improve.

1
Start with Crossref Completeness — what's in your metadata?

Type a journal name or ISSN, a member:ID, or a ROR and press Explore. The tool samples the works and scores them against the FAIR use cases that Crossref metadata can express.

What you'll see: a FAIR Total and three use-case chips — Text (can people find it?), Identifiers (can people connect to it?), and Connections (who and what is connected?). Click any chip to see the concepts behind it, each with a completeness bar.

Low numbers here are normal. They tell you which parts of the records might be candidates for improvement. See the tool's About tab for the exact Crossref field behind every concept.

Try it on an example publisher (PLOS) →
2
Drill into a weak concept — what's actually there?

Click any concept name (e.g. Funder). You'll get the real values from the sampled works.

What you'll see: each distinct value with its Occurrences (how many times it appears) and DOIs (how many works it appears in) — plus how many works carry the concept at all. This is where "we do record funders" meets "…in 14 of 4,000 works."

This is one way to spot common values and inconsistencies: several spellings of one funder, or a concept that scores well but holds thin values.

3
Move to Crossref Connectivity — does your metadata connect?

Completeness asks whether a name is present. Connectivity asks whether that name carries the identifier that links it to the rest of the world — an ORCID for a person, a ROR for an affiliation, a Funder Registry ID or ROR for a funder.

Use the Crossref Completeness ⇄ Crossref Connectivity links in the top bar and both tools measure the same selection — target, content type, era, and query travel with the link (a fresh sample of the same population is drawn).

What you'll see: one bar per question — identifiers and affiliations for authors, the same for editors & translators, and identifiers for funders. Green = identified in every occurrence, yellow = some, red = never.
Crossref has no publisher-identifier or typed-rights fields, so the publisher and rights bars from the DataCite tool have no analog here. Affiliation RORs are young in Crossref (publisher-asserted, still a small share of works), so a low ROR bar is often the ecosystem, not just this member.
4
Click a bar — get a list of records to fix

Every bar opens a list of the distinct people, organizations, or funders behind it.

What you'll see: for each one — Occurrences, Identified, and To fix. All three are links: they open the exact DOIs, so "we should add ORCIDs" becomes a specific list of works.

Start with the quick wins (shown first, tagged in yellow). A quick win is an entity that already has an identifier on some works but not all — you don't have to look anything up, just copy what you already have.

5
Find the identifiers you're missing — ROR and ORCID Retrievers

In the drill-downs, click Find RORs or Find ORCIDs. Every name that still needs an identifier is handed to the matching retriever automatically.

What you'll see: candidate matches for each name — for people, each ORCID iD with their most recent employment and journal to help tell two "J. Smith"s apart; for organizations, ROR matches for each affiliation or funder string. Results are color-coded, best in green. Export as CSV/TSV and take them back to your deposits.

Organizations and people are routed correctly on their own: an organizational author goes to ROR, a person goes to ORCID. In Crossref that split is structural — an author is a person when it carries given/family and an organization when it carries a name instead.

6
Check for duplicates — the same entity with several spellings

Scroll to Potential duplicates in Connectivity. It clusters names that look like the same person or organization using fuzzy matching.

What you'll see: a canonical name with its spellings, each with Occurrences and Identified (both link to the works). A ⚠ shared-id conflict means two spellings carry the same identifier — sometimes the same name, sometimes one needs fixing (two people under one ORCID, or one person under several).

This section uses fuzzy matching so it is advisory: it suggests, you decide.

7
See how the metadata has changed — Crossref Metadata Evolution

Below the connectivity bars, the Crossref Metadata Evolution section reads Crossref Participation Report for the member — Crossref's coverage numbers, computed over all of the member's records (not the sample above).

What you'll see: a radar plot comparing backfile records against current ones (last three calendar years) across fourteen checks — where the current line reaches past the backfile line, recent metadata is richer than older records. Pick a content type, open the All types strip for every type at a glance, and Copy plot / Copy strip to drop the figure into a slide.
Crossref's public report at crossref.org/members/prep shows ten of these checks; the radar plots all fourteen from the API, adding Affil RORs, Funder RORs, Update policies (Crossmark), and Descriptions.

This answers the question a single completeness score can't: have these metadata been improving?

For a finer view, open Crossref History: it samples your works for every deposit year, scores each year with the same FAIR use cases, and plots the trend lines — with a radar grid and a year-by-year movie. Any year's exact sample can be opened in Completeness or Connectivity with one click.

8
Take it with you

Both tools export. Reports as JSON, HTML, or PDF; the numbers as CSV; charts and the evolution radar as PNG (save or copy straight to the clipboard). Every view is also a plain URL — bookmark it or paste it to a colleague and they'll see exactly what you saw.

For bulk work across many members, the repository also ships companion Python scripts (crossrefParticipation.py, comparePrepConnectivity.py, prepBoxes.py) that harvest Participation Reports and compare them to sampled connectivity.

Which tool answers which question?

Your questionTool
Are the elements I care about present?Crossref Completeness
What values exist in a given element?Completeness → click a concept
Do my authors, affiliations, and funders have ORCIDs and RORs?Crossref Connectivity
Which works should I fix first?Connectivity → quick wins / To fix
What ORCID/ROR should this name have?ORCID / ROR Retriever
Is the same person/organization here under several spellings?Connectivity → Potential duplicates
Are our metadata improving?Connectivity → Crossref Metadata Evolution
How has completeness changed, year by year?Crossref History

Reading the numbers honestly

Crossref scores are not comparable to DataCite scores. The two ecosystems carry different metadata, so a Crossref FAIR Total and a DataCite FAIR Total are measured against different metadata. DataCite has many datasets which have different metadata than Crossref journal articles. Compare a Crossref member to another Crossref member, not to a DataCite repository.
Completeness uses a reduced catalog. Crossref Completeness scores only the concepts included in Crossref metadata — three FAIR use cases (Text, Identifiers, Connections) over about two dozen concepts. Concepts with no faithful Crossref field are left out rather than scored as structural zeros. Four are honest approximations (Date Submitted from accepted/posted dates, Date Available from a license start date, Rights from a license URL, Resource Format from a link's MIME type); the About tab lists every mapping.
Completeness is presence, not quality. A 100% score means the element is there in every work — not that it's correct, rich, or useful. A title of "Untitled" still counts. Use completeness to find gaps, then look at the values.
Connectivity percentages are occurrence-based. The bar's % is identified occurrences ÷ all occurrences. The colored segments count distinct entities — one author in 40 works is one entity. One view tells you how much of the metadata is connected, the other how many people or organizations you'd have to look up.
Metadata Evolution is Crossref's own numbers. The Participation Report radar is computed by Crossref over all of a member's records — not the sample the bars above use — so it and the sampled bars can differ. Current means published in the current calendar year or the two previous ones; everything older is backfile. The radar is only shown for a member:ID target (Crossref publishes these reports per member).

Focusing on subsets for a specific project

Four controls, on both Completeness and Connectivity:

You can also paste a single DOI into the box to look at exactly one work — handy for checking a fix, or for showing colleagues what a well-formed record looks like.

Words you'll see

TermWhat it means here
Member IDA Crossref member (usually a publisher), entered as member:340. One member can deposit many journals.
ISSNA journal's identifier, like 1932-6203. Use it to scope a single journal instead of a whole publisher.
Content typeThe kind of work — journal-article, posted-content (preprint), book-chapter, dataset, grant, and so on.
EraCurrent = published in the current or two previous calendar years; backfile = published earlier. Crossref's Participation Report definition.
Participation ReportCrossref's own coverage numbers for a member (the figures behind crossref.org/members/prep), computed over all a member's records.
OccurrenceOne appearance of a value. A funder named in 30 works is 30 occurrences.
EntityOne distinct person, organization, or funder — however many works it appears in.
Quick winAn entity that already has an identifier on some works but not others. Copy it across — no lookup needed.
ORCID / RORPersistent identifiers for people (ORCID) and organizations (ROR). They're what turn a name into a connection.
Funder Registry IDCrossref's identifier for a funding organization (a Funder Registry DOI); funders increasingly also carry a ROR.

Where to go next

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