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PubMed Scraper: how to get New Citations by Query as JSON/CSV (API, Python, no code)

Pull daily-new PubMed citations matching any query. This guide shows the output fields, a working input, and Python, JavaScript and cURL code for the Actor v0iddo/pubmed-daily-citations-tracker on Apify.

Open PubMed Scraper on Apify

What data you get

One row per result. Every row can be downloaded as JSON, CSV, Excel or HTML, or read through the API.

FieldLabelTypeExample
pmidPmidtext42926261
titleTitletextGeneration of Functional Gene Knockout Melanoma Cell Lines by CRISP...
authorsAuthorslist
journalJournaltextMethods Mol Biol
yearYeartext2027
publicationDatePublication Datetext2027
entrezDateEntrez Datetext2027/01/01 00:00
doiDoitext10.1007/978-1-0716-5619-8_2
issnIssntext1064-3745
volumeVolumetext3089
issueIssuetext
pagesPagestext25-46
languagetexteng
publicationTypelist["Journal Article"]
querytextCRISPR[ti]
urltexthttps://pubmed.ncbi.nlm.nih.gov/42926261/
abstracttextRecent advances in the treatment of metastatic melanoma have emerge...
meshTermslist["CRISPR-Cas Systems", "Animals", "Gene Knockout Techniques"]

Input example

This is the input that is pre-filled on the Actor page. Change the values for your own run.

{
  "queries": [
    "CRISPR[ti]"
  ],
  "maxResultsPerQuery": 20
}
Input fieldTypeWhat it does
queries (required)arrayPubMed E-utilities query strings.
sinceDaysintegerFilter by entrez_date / publication date >= now-sinceDays.
maxResultsPerQueryintegerHard cap per query (after date filter).
includeAbstractbooleanFetch the abstract via efetch.
includeMeshTermsbooleanFetch MeSH descriptors via efetch.
emailstringNCBI prefers identifying who is calling.

Output sample

Real rows from a run of the Actor. Fields that name people are left out of the sample.

[
  {
    "pmid": "42926261",
    "title": "Generation of Functional Gene Knockout Melanoma Cell Lines by CRISPR-Cas9 Gene Editing.",
    "journal": "Methods Mol Biol",
    "year": "2027",
    "publicationDate": "2027",
    "entrezDate": "2027/01/01 00:00",
    "doi": "10.1007/978-1-0716-5619-8_2",
    "issn": "1064-3745",
    "volume": "3089",
    "pages": "25-46",
    "language": "eng",
    "publicationType": [
      "Journal Article"
    ],
    "query": "CRISPR[ti]",
    "url": "https://pubmed.ncbi.nlm.nih.gov/42926261/",
    "abstract": "Recent advances in the treatment of metastatic melanoma have emerged only from advances in our understanding of melanoma development and...",
    "meshTerms": [
      "CRISPR-Cas Systems",
      "Animals",
      "Gene Knockout Techniques"
    ]
  },
  {
    "pmid": "42926121",
    "journal": "Methods",
    "year": "2026",
    "publicationDate": "2026 Oct 24",
    "entrezDate": "2026/10/24 00:00",
    "doi": "10.1016/j.ymeth.2026.10.007",
    "issn": "1046-2023",
    "language": "eng",
    "publicationType": [
      "Journal Article"
    ],
    "query": "CRISPR[ti]",
    "url": "https://pubmed.ncbi.nlm.nih.gov/42926121/",
    "abstract": "BACKGROUND: Efficient, reproducible gene knockout in cardiomyocyte-related cell models is critical for cardiovascular research but remain..."
  }
]

Code: Python, JavaScript and cURL

Get your API token in Apify Console under Settings, then Integrations. The Actor id is v0iddo/pubmed-daily-citations-tracker.

Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {'queries': ['CRISPR[ti]'], 'maxResultsPerQuery': 20}

run = client.actor("v0iddo/pubmed-daily-citations-tracker").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript (apify-client)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const input = {
  "queries": [
    "CRISPR[ti]"
  ],
  "maxResultsPerQuery": 20
};

const run = await client.actor('v0iddo/pubmed-daily-citations-tracker').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

cURL

curl -X POST "https://api.apify.com/v2/acts/v0iddo~pubmed-daily-citations-tracker/run-sync-get-dataset-items" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"queries": ["CRISPR[ti]"], "maxResultsPerQuery": 20}'

Price

$1.00 per 1,000 results. You pay per result row. There is no separate charge per run. Higher Apify plans pay less per result, down to $0.70 per 1,000. See the pricing tab for your plan. A new Apify account gets $5 of platform credit every month.

FAQ

What data does the PubMed Scraper return?

Each result is one row in an Apify dataset. Fields include pmid, title, authors, journal, year, publicationDate, entrezDate, doi, and more. The table above shows every field with an example value.

How do I use the PubMed Scraper from Python?

Install the apify-client package, create a client with your Apify API token, call the Actor v0iddo/pubmed-daily-citations-tracker with an input like the one above, and read the rows from the run's dataset. The Python snippet above does exactly that.

Can I use it without writing code?

Yes. Open the Actor page on Apify, fill in the input form (an example is already filled in), press Start, and download the results as JSON, CSV, Excel or HTML. You can also schedule runs there.

How much does it cost?

$1.00 per 1,000 results. You pay per result row. There is no separate charge per run. Higher Apify plans pay less per result, down to $0.70 per 1,000. A new Apify account gets $5 of platform credit every month.

How do I limit the number of results?

Set a maximum cost for a run in the run options on Apify, and the run stops when it is reached.

Can I send the results to Google Sheets, Make, Zapier or n8n?

Yes. Apify datasets connect to Google Sheets, Slack, Make, Zapier and n8n, and a webhook can call your own service when a run finishes. You can also export the dataset as JSON, CSV or Excel.

Open PubMed Scraper on Apify

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