# Crossref Scraper: DOI Metadata and Citations | $1/1K (`one_dollar_store/crossref-doi-metadata`) Actor

$1 per 1,000 results, platform usage included. Scholarly works from the official Crossref API: DOI, title, authors, journal, publisher, citation count, funders, licence and retraction notices. Search by text, DOI, author or ISSN. No key.

- **URL**: https://apify.com/one_dollar_store/crossref-doi-metadata.md
- **Developed by:** [One Dollar Store](https://apify.com/one_dollar_store) (community)
- **Categories:** Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Crossref Scraper: DOI metadata, citation counts and retraction notices

**$1 per 1,000 works. Platform usage included. No rental, no minimum.**

Pull scholarly works from the official Crossref database. Search by title words, journal, author, ISSN, type of work and publication dates, or look up exact DOIs. Each work comes back as one flat row with DOI, title, authors, journal, publisher, publication date, how many works cite it, funders, licence links and any retraction or correction notice. You can sort by newest, oldest or most cited. No API key and no account with Crossref.

[Open it in Apify Console](https://console.apify.com/actors/Wpsnqec42S2Y9yWCg/input)

### What it costs

| Works | This actor |
|---|---|
| 1,000 | $1.00 |
| 10,000 | $10.00 |
| 100,000 | $100.00 |

Plus $0.001 per run. One flat price, platform usage included, so there is no separate compute or proxy bill. Only work rows are charged. Checking the citation counts of 300 papers every week costs about $0.30 a run, or about $1.20 a month.

### Why use it

- **Official data, no key.** Rows come straight from the Crossref REST API, which needs no sign up. Crossref says almost none of the metadata is subject to copyright.
- **Search that keeps only real matches.** Crossref ranks works that contain just one of your words high. By default the actor keeps only works where every word of your search text appears in the field you chose, and reads more pages to find them.
- **Most cited first.** Sort a topic by citation count and the well known works come first, with the count on every row.
- **Retractions on the row.** `isRetracted` is true when Crossref lists a retraction or withdrawal notice, and `updatedBy` lists the notices, so a literature list can be checked before it is used.
- **Safe for scheduled jobs.** A DOI Crossref does not hold, or a search nobody matches, ends with zero rows and a short message, not a crash.

### Quick start

1. Open the actor, keep the example values, press **Start**.
2. Or paste this input, which returns the 50 most cited works with machine learning in the title:

```json
{ "searchTexts": ["machine learning"], "sortBy": "mostCited", "maxItems": 50 }
```

3. Download the results as JSON, CSV or Excel, or read them through the API.

### Use it from code

```bash
curl -X POST "https://api.apify.com/v2/acts/one_dollar_store~crossref-doi-metadata/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"dois": ["10.1038/nature14539", "10.1126/science.1127647"], "includeReferences": true, "maxItems": 10}'
```

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("one_dollar_store/crossref-doi-metadata").call(
    run_input={"searchTexts": ["climate change"], "publishedFrom": "2020", "sortBy": "mostCited", "maxItems": 100}
)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["publishedDate"], item["citedByCount"], item["title"])
```

### Use it from an AI assistant

Claude, ChatGPT, Cursor and other assistants can call this actor through Apify's MCP server. Add it with this server address:

```
https://mcp.apify.com?tools=one_dollar_store/crossref-doi-metadata
```

Then ask, for example: "Find the ten most cited papers since 2020 with graph neural network in the title, and tell me if any is retracted."

### Switching from another Crossref actor

Most Crossref actors take a search query, a year range and a result limit. Here they are `searchTexts`, `publishedFrom` and `publishedUntil`, and `maxItems`. Add `dois` for exact lookups, `author` and `issns` to narrow, and `sortBy` to order the results. The core output fields are `doi`, `title`, `authors`, `journal`, `publishedDate`, `citedByCount` and `doiUrl`.

### Input

| Field | Type | Default | What it does |
|---|---|---|---|
| `searchTexts` | list of text | none | Words to search for, one search per line. |
| `searchIn` | `title`, `bibliographic`, `journal`, `everything` | `title` | Where the words are looked for. `bibliographic` is title, authors and year together. `everything` is the loosest. |
| `requireAllWords` | true or false | true | Keep only works where every word of the search text (or of the author name) appears in the chosen field. Turn it off to take Crossref's own ranking as it is. |
| `dois` | list of text | none | Exact DOIs. A doi.org address in front of the DOI works too. A malformed entry is skipped with a warning. |
| `author` | text | none | Author name. Every word must appear in one author's name when `requireAllWords` is on. |
| `issns` | list of text | none | Only works from these journals or series. Print and online ISSNs both work. |
| `workType` | `any`, `journal-article`, `proceedings-article`, `book-chapter`, `book`, `monograph`, `posted-content`, `dissertation`, `dataset`, `report`, `peer-review`, `reference-entry`, `standard` | `any` | Only this kind of work. |
| `publishedFrom`, `publishedUntil` | text | none | A year (`2020`), a month (`2020-06`) or a day (`2020-06-15`). A year or month as an upper bound includes the whole year or month. |
| `sortBy` | `relevance`, `newest`, `oldest`, `mostCited` | `relevance` | Order of the results. The other three sort all matches before the first rows are returned. |
| `includeReferences` | true or false | false | Adds `referenceDois`, the DOIs of the works each work cites. Rows get longer. |
| `maxItems` | number | 100 | Total works for the run, 10 to 10,000. |

You need at least one of `searchTexts`, `dois`, `author` or `issns`. Filters apply to searches. A DOI lookup returns exactly the DOIs you gave.

Example, the most cited recent preprints about protein folding:

```json
{
  "searchTexts": ["protein folding"],
  "workType": "posted-content",
  "publishedFrom": "2022",
  "sortBy": "mostCited",
  "maxItems": 100
}
```

### Output fields

| Field | Meaning |
|---|---|
| `doi`, `doiUrl`, `landingPageUrl` | The DOI in lower case, its doi.org link, and the address the publisher registered for it. |
| `title`, `subtitle`, `type` | Title, subtitle and kind of work. |
| `publishedDate`, `publishedYear` | Publication date as the publisher gave it (a year, a month or a full date), and the year. |
| `journal`, `journalShort`, `volume`, `issue`, `page`, `articleNumber` | Where the work appears. |
| `publisher`, `issn`, `isbn` | Publisher and identifiers. |
| `authors`, `authorCount`, `authorOrcids`, `affiliations` | Author names in order, how many, ORCID links where given, and distinct affiliations. |
| `citedByCount`, `referenceCount` | Works in Crossref that cite this one, and references the work lists. |
| `referenceDois` | DOIs of cited works. Only with `includeReferences`. |
| `subjects`, `licenses`, `funders` | Subject terms, licence links and funder names. |
| `isRetracted`, `updatedBy`, `updatesWork` | True when a retraction or withdrawal notice is listed. Notices that update the work as `type:doi`, for example `retraction:10.1016/s0140-6736(10)60175-4`. On a notice, the work it updates. |
| `addedToCrossref`, `query` | When Crossref first created the record, and which search found the row. |

Fields a work does not have are left out. Abstracts are not returned: Crossref says some abstracts are protected by copyright.

### Sample results

Real rows from test runs on 20 October 2026, with some fields left out and long author lists cut to three names.

```json
[
  {"doi": "10.1038/nature14539", "title": "Deep learning", "type": "journal-article", "publishedDate": "2015-05-27", "journal": "Nature", "publisher": "Springer Science and Business Media LLC", "authors": ["Yann LeCun", "Yoshua Bengio", "Geoffrey Hinton"], "citedByCount": 78519, "referenceCount": 103},
  {"doi": "10.1016/s0140-6736(97)11096-0", "title": "RETRACTED: Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children", "type": "journal-article", "publishedDate": "1998-02", "journal": "The Lancet", "publisher": "Elsevier BV", "authors": ["AJ Wakefield", "SH Murch", "A Anthony"], "citedByCount": 2043, "referenceCount": 26, "isRetracted": true, "updatedBy": ["correction:10.1016/s0140-6736(04)15715-2", "retraction:10.1016/s0140-6736(10)60175-4"]},
  {"doi": "10.1016/j.neucom.2005.12.126", "title": "Extreme learning machine: Theory and applications", "type": "journal-article", "publishedDate": "2006-12", "journal": "Neurocomputing", "publisher": "Elsevier BV", "authors": ["Guang-Bin Huang", "Qin-Yu Zhu", "Chee-Kheong Siew"], "citedByCount": 11895, "referenceCount": 26}
]
```

### Limits

- **Crossref asks for one request at a time.** Its public access level allows 5 requests a second and one at a time, and the actor sends requests one by one and slows down to whatever limits Crossref reports. A request returns up to 500 works, so the limit is not what slows a run.
- **Delivery speed is about 8 rows a second.** In one test 1,000 works came back in 124 seconds, of which about 12 seconds was reading from Crossref. A run has a 600 second time limit by default, so it delivers about 4,500 works. Raise **Timeout** under Run options for more, or split a search by publication date.
- **Matching every word means reading more.** Crossref returns works that contain only some of your words. With `requireAllWords` on, the actor reads pages and keeps the full matches. For `machine learning` in the title, in Crossref's own order, 20 works were kept out of 1,000 read. Sorted by most cited, 1,000 kept took 3,500 read. A search stops after reading 20,000 works, with a warning, so a narrow search such as a rare phrase can return fewer works than exist. Add a date range or a type to narrow it.
- **A search can match millions of works.** A run delivers at most `maxItems`, up to 10,000.
- **Only Crossref DOIs.** DOIs registered with other agencies, such as many datasets, are not in Crossref and come back as missing.
- **Counts are Crossref's.** `citedByCount` counts citing works that have deposited references in Crossref. It is lower than Google Scholar or Scopus and changes over time.
- **Dates can be partial.** `publishedDate` is a year, a year and month, or a full date, as the publisher gave it. For works published both online and in print it is the earlier date.
- **Cited DOIs need deposited references.** `referenceDois` lists only references that carry a DOI, and some publishers deposit none.
- **No abstracts.** Crossref says some abstracts are protected by copyright, so the actor does not return them.
- **Not a verdict on a paper.** A retraction notice is what the publisher told Crossref. Check the publisher's page before you rely on it.

### FAQ

**How much does it cost to scrape 10,000 Crossref works?**
$10, plus $0.001 for each run.

**Do I pay for compute or proxy on top?**
No. Platform usage is included in the $1 per 1,000 price. The official API is read directly, so no proxy is used.

**How do I get the most cited papers on a topic?**
Put the topic in `searchTexts` and set `sortBy` to `mostCited`. Add `publishedFrom` to limit it to recent work.

**How do I look up a list of DOIs?**
Put them in `dois`, one per line. They are read 40 at a time. Entries with a doi.org link in front work too.

**How do I find retracted papers?**
Look at `isRetracted` and `updatedBy` on each row. To check a reading list, look up its DOIs and filter the results on `isRetracted`.

**Where does the data come from?**
From the Crossref REST API (api.crossref.org). No key and no login are needed. Crossref states that almost none of the metadata is subject to copyright and that you may use it for any purpose.

**Is it legal?**
The actor reads public data only, with no login. How you use it is up to you: follow Crossref's terms and the law where you live.

**Is this an official Crossref product?**
No. It is an independent tool and is not affiliated with or endorsed by Crossref.

**Do I need an Apify account?**
Yes. Actors run from an Apify account, and a free account is enough to start: it includes $5 of platform usage every month. You can [sign up here](https://www.apify.com?fpr=ymnoit). This is a referral link and it costs you nothing.

**Is there a limit on the free plan?**
Yes. A run started from a free plan account delivers the first 10 results, then ends with a message and a link to the plans. Any paid plan returns everything you ask for, up to your own limit. The price per result is the same on every plan.

### More from The One Dollar Store

Every actor here costs $1 per 1,000 results, platform usage included. See the whole store: https://apify.com/one_dollar_store

**Health and science**

- [NPI Provider Search](https://apify.com/one_dollar_store/npi-providers-search): US healthcare providers from the NPI registry.
- [PubMed Articles Scraper](https://apify.com/one_dollar_store/pubmed-articles-scraper): Biomedical articles with abstracts and authors.
- [ClinicalTrials.gov Scraper](https://apify.com/one_dollar_store/clinical-trials-search): Clinical studies by condition, drug or sponsor.
- [Product Recalls Scraper](https://apify.com/one_dollar_store/consumer-product-recalls-cpsc): US consumer product recalls from the CPSC.
- [EPA ECHO Scraper](https://apify.com/one_dollar_store/epa-echo-facility-compliance): US facility inspections and violations.
- [NIH RePORTER Scraper](https://apify.com/one_dollar_store/nih-reporter-grants): US NIH research grants and awards.
- [USGS Earthquakes Scraper](https://apify.com/one_dollar_store/usgs-earthquakes-scraper): Earthquake events with magnitude and place.
- [FDA Recalls Scraper](https://apify.com/one_dollar_store/fda-recalls-scraper): US FDA enforcement reports and recalls.
- [CMS Hospital Quality](https://apify.com/one_dollar_store/cms-hospital-quality-data): US hospital ratings and quality measures.
- [FDA 510(k) Scraper](https://apify.com/one_dollar_store/fda-510k-device-clearances): Cleared medical devices with applicant and date.

### Changelog

- October 2026: first release.

# Actor input Schema

## `searchTexts` (type: `array`):

Words to look for in titles, authors, journals and other metadata, one search per line, for example machine learning. Leave empty if you give DOIs, an author or ISSNs.

## `searchIn` (type: `string`):

Where the search texts are looked for. Title searches the work title. Title, authors and year uses Crossref's bibliographic search, which suits citation look ups. Everything searches all metadata and is the loosest.

## `requireAllWords` (type: `boolean`):

Crossref also returns works that contain only some of your words, and ranks them high. Leave this on to keep only works where every word of the search text (or author name) appears in the field you chose. The actor reads more pages to find them.

## `dois` (type: `array`):

Look up exact DOIs, for example 10.1038/nature14539. A link such as https://doi.org/10.1038/nature14539 also works. DOIs that Crossref does not hold are left out.

## `author` (type: `string`):

Only works with a matching author, for example Geoffrey Hinton. Combines with the search texts and filters below.

## `issns` (type: `array`):

Only works from these journals or series, for example 0028-0836. Either the print or the online ISSN works.

## `workType` (type: `string`):

Only this kind of work.

## `publishedFrom` (type: `string`):

Earliest publication date. Use a year (2020), a month (2020-06) or a day (2020-06-15).

## `publishedUntil` (type: `string`):

Latest publication date, in the same formats. A year includes that whole year.

## `sortBy` (type: `string`):

Best match is Crossref's relevance order. The other choices sort all matches before the first rows are returned.

## `includeReferences` (type: `boolean`):

Adds the DOIs of the works each work cites, where the publisher deposited its reference list. Rows get longer.

## `maxItems` (type: `integer`):

The run stops after this many works in total. Minimum 10.

## Actor input object example

```json
{
  "searchTexts": [
    "machine learning"
  ],
  "searchIn": "title",
  "requireAllWords": true,
  "workType": "any",
  "sortBy": "mostCited",
  "includeReferences": false,
  "maxItems": 50
}
```

# Actor output Schema

## `results` (type: `string`):

No description

## `summary` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "searchTexts": [
        "machine learning"
    ],
    "sortBy": "mostCited",
    "maxItems": 50
};

// Run the Actor and wait for it to finish
const run = await client.actor("one_dollar_store/crossref-doi-metadata").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "searchTexts": ["machine learning"],
    "sortBy": "mostCited",
    "maxItems": 50,
}

# Run the Actor and wait for it to finish
run = client.actor("one_dollar_store/crossref-doi-metadata").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "searchTexts": [
    "machine learning"
  ],
  "sortBy": "mostCited",
  "maxItems": 50
}' |
apify call one_dollar_store/crossref-doi-metadata --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,one_dollar_store/crossref-doi-metadata"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Wpsnqec42S2Y9yWCg/builds/Fv4Sh2d5z7gdBhL9y/openapi.json