DBLP Scraper — Publications, Authors & Venues

The fastest way to extract structured computer science research data from DBLP, the largest open bibliography of CS publications — no API key, no login, no rate-limit headaches.

What does DBLP Scraper do?

DBLP Scraper queries the official DBLP search API (dblp.org/search/publ/api, /author/api, /venue/api) in bulk using keyword or phrase queries, then paginates the results automatically in pages of up to 1,000 records. It operates in three distinct modes: publications (papers, conference proceedings, journal articles), authors (researcher profiles with affiliations and awards), and venues (conferences and journals). All endpoints return JSON natively — no HTML parsing, no browser required. A single run on a broad query such as "deep learning" can return tens of thousands of paper records covering titles, author lists, venue names, publication years, DOIs, electronic edition links, DBLP keys, page ranges, and volume numbers. The actor uses Apify's datacenter proxy pool for resilience and retries automatically on transient network errors.

Who is it for?

• Academic researchers who need bulk publication data for meta-analysis, citation network studies, or bibliometrics.
• Data scientists and ML engineers building datasets of CS papers for training or benchmarking.
• Tech intelligence analysts tracking research trends, prolific authors, or dominant venues in specific subfields.
• University librarians and knowledge managers assembling curated reading lists or institution-level publication inventories.
• Competitive intelligence teams monitoring how often companies or labs appear in academic literature.

Use cases

• Build a dataset of all neural-network papers published in the last decade for a systematic literature review.
• Extract the full publication list of a specific researcher (e.g., "Yann LeCun") to map their career output.
• Identify the top venues (conferences/journals) in a niche like "federated learning" or "quantum computing".
• Feed paper titles and DOIs into an LLM pipeline that summarizes or clusters research trends.
• Track how many papers mention a specific technology keyword year-over-year.

Why use DBLP Scraper?

• Completely keyless — uses the official public DBLP API, no account or token required.
• 13 output fields per publication: title, authors, venue, year, type, DOI, ee URL, DBLP key, pages, volume, URL, open-access flag, mode.
• Bulk pagination — retrieves up to 10,000+ records per run by paginating DBLP's 1,000-per-page API automatically.
• Three search modes — publications, authors, venues — all in one actor.
• Export to CSV, JSON, or Excel — all Apify storage formats supported out of the box.
• Pay-per-result pricing — you only pay for what you actually extract.

What data can you extract?

Each record from a publications search returns:

Field	Type	Description
title	string	Full paper title
authors	string	Comma-separated list of author names
venue	string	Conference name or journal title
year	string	Publication year (e.g., "2023")
type	string	Record type: "Journal Articles", "Conference and Workshop Papers", etc.
doi	string	Digital Object Identifier (e.g., "10.1016/j.neunet.2023.10.057")
ee	string	Electronic edition URL (full-text link, often open access)
key	string	DBLP internal key (e.g., "journals/nn/Smith24")
pages	string	Page range (e.g., "1-15")
volume	string	Journal volume number
url	string	DBLP record URL
access	string	Open access status ("open" or empty)
mode	string	The mode used for this run

Example output record (publications mode):

{
  "title": "An exact mapping from ReLU networks to spiking neural networks",
  "authors": "Ana Stanojevic, Stanislaw Wozniak, Guillaume Bellec, Giovanni Cherubini, Angeliki Pantazi, Wulfram Gerstner",
  "venue": "Neural Networks",
  "year": "2024",
  "type": "Journal Articles",
  "doi": "10.1016/J.NEUNET.2023.10.057",
  "ee": "https://doi.org/10.1016/j.neunet.2023.10.057",
  "key": "journals/nn/StanojevicWBCPG24",
  "pages": "622",
  "volume": "169",
  "url": "https://dblp.org/rec/journals/nn/StanojevicWBCPG24",
  "access": "open",
  "mode": "publications"
}

For authors mode, title/authors hold the researcher name, venue holds affiliations, and volume holds awards (e.g., "Turing Award").

For venues mode, title/venue hold the venue name, key/volume hold the acronym, and url links to the DBLP venue page.

How to use

Option A — Search publications by keyword

1. Open the actor and set Mode to publications.
2. Enter your Search Query (e.g., "transformer attention mechanism").
3. Set Max Results (e.g., 1000 for a broad search).
4. Click Start.

{
  "mode": "publications",
  "query": "transformer attention mechanism",
  "maxResults": 1000,
  "proxyConfiguration": { "useApifyProxy": true }
}

Option B — Search authors or venues

1. Set Mode to authors or venues.
2. Enter your query (e.g., "Geoffrey Hinton" for authors, "ICML" for venues).
3. Set Max Results.
4. Click Start.

{
  "mode": "authors",
  "query": "Yoshua Bengio",
  "maxResults": 50,
  "proxyConfiguration": { "useApifyProxy": true }
}

Input parameters

Parameter	Type	Default	Description
mode	string	"publications"	Search mode: publications, authors, or venues
query	string	(required)	Keyword or phrase to search on DBLP
maxResults	integer	500	Maximum number of results to retrieve (1–10,000)
proxyConfiguration	object	Apify proxy	Proxy settings; datacenter proxies work fine

Full input example:

{
  "mode": "publications",
  "query": "federated learning privacy",
  "maxResults": 2000,
  "proxyConfiguration": { "useApifyProxy": true }
}

Output example

{
  "title": "Communication-Efficient Learning of Deep Networks from Decentralized Data",
  "authors": "H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas",
  "venue": "AISTATS",
  "year": "2017",
  "type": "Conference and Workshop Papers",
  "doi": "10.48550/arXiv.1602.05629",
  "ee": "http://proceedings.mlr.press/v54/mcmahan17a.html",
  "key": "conf/aistats/McMahanMRHA17",
  "pages": "1273-1282",
  "volume": "54",
  "url": "https://dblp.org/rec/conf/aistats/McMahanMRHA17",
  "access": "open",
  "mode": "publications"
}

Tips for best results

• Use specific multi-word queries like "graph neural network node classification" rather than just "graph" to get relevant results faster.
• DBLP wildcards are automatic — the API adds * to your terms internally, so "neural network" matches "neural networks", "neural networking", etc.
• Start with maxResults 500 to validate your query, then scale to 5,000+ for comprehensive sweeps.
• Use author mode for people searches — searching "Yann LeCun" in publications mode finds all papers; switching to authors mode returns the researcher profile with affiliations.
• Check the access field — records marked "open" have free full-text available at the ee URL.
• Use the key field for deduplication — DBLP keys are unique identifiers per record and stable across time.
• Venues mode is great for discovery — search a topic abbreviation like "NeurIPS" or "CVPR" to get the official venue metadata.
• Combine with Apify schedules — run weekly on a query like "large language models" to track the latest papers automatically.
• Export to Excel from the dataset view to get a spreadsheet you can filter by year or venue immediately.
• For institution-level analysis, run multiple author-mode queries for names associated with a university and aggregate by affiliation.

Integrations

• Google Sheets — use Apify's Google Sheets integration to push results directly to a spreadsheet for collaborative review.
• Slack — configure a webhook to receive a Slack notification with a run summary when the actor finishes.
• Zapier / Make — trigger downstream workflows (e.g., add papers to a Notion database) via Apify webhooks.
• Schedule — use Apify Scheduler to run the actor daily or weekly on a saved query and accumulate a growing dataset.
• API / SDK — call the actor programmatically and pipe results into your own data pipeline.

API usage

cURL:

curl -X POST \
  "https://api.apify.com/v2/acts/logiover~dblp-scraper/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"mode":"publications","query":"neural networks","maxResults":500}'

Node.js:

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const run = await client.actor('logiover/dblp-scraper').call({
  mode: 'publications',
  query: 'neural networks',
  maxResults: 500,
});
const dataset = await client.dataset(run.defaultDatasetId).listItems();
console.log(dataset.items);

Python:

from apify_client import ApifyClient
client = ApifyClient("YOUR_TOKEN")
run = client.actor("logiover/dblp-scraper").call(run_input={
    "mode": "publications",
    "query": "neural networks",
    "maxResults": 500,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["title"], item["year"])

Use with AI agents (MCP)

DBLP Scraper is available via the Apify MCP server, so any Claude or GPT-based agent can call it directly. Example prompt: "Use the DBLP scraper to find the 200 most recent papers on 'graph attention networks' and summarize the top 10 by citation impact." The agent will invoke the actor, retrieve results, and synthesize findings without any manual steps.

FAQ

Does this actor require an API key or login?

No. DBLP provides its search API completely publicly. You do not need a DBLP account, API key, or any credentials. The actor works out of the box.

How many results can I retrieve in a single run?

DBLP reports up to 112,000+ results for broad queries like "neural networks". The actor paginates automatically. Set maxResults up to 10,000 and the actor will page through DBLP's 1,000-per-request API until your limit is reached or results are exhausted.

What publication types does DBLP cover?

DBLP indexes journal articles, conference and workshop papers, books, book chapters, editorials, theses, and informal publications (e.g., arXiv preprints). The type field in each record tells you exactly what you're looking at.

Why am I getting zero results?

Check your query spelling. DBLP adds wildcards automatically so short common terms usually return results. If you search a very specific phrase with rare terms, try broadening it. Also confirm mode matches what you're looking for — searching an author name in publications mode finds their papers, while authors mode finds their profile.

Some fields are empty — is that normal?

Yes. Not every paper has a DOI, page numbers, or volume (especially arXiv preprints and informal papers). The ee field (electronic edition URL) is the most reliable link to the actual paper.

Can I export results to Excel?

Yes. After a run completes, open the dataset in the Apify UI and click "Export" to download CSV, JSON, XLSX, or XML. You can also use the Apify API to retrieve results programmatically in any format.

How fast does the actor run?

For 500 results it typically completes in under 30 seconds. For 5,000 results expect 2–5 minutes. DBLP's API is fast and stable; most of the time is network latency and Apify's proxy routing.

Is DBLP data always up to date?

DBLP updates continuously as publishers submit new records. In practice, papers appear in DBLP within days to weeks of publication. For the very latest preprints, also query the arXiv API directly.

How often should I re-run for fresh data?

For active research areas, weekly runs will capture new publications. For stable historical queries (e.g., "Hinton papers before 2010"), a single run is sufficient as the data rarely changes.

Can I scrape a specific author's full publication list?

Use mode: "publications" with the author's name as the query (e.g., "Yann LeCun"). DBLP's search matches author names precisely. For disambiguation, add a co-author or institution keyword.

What is the DBLP key field?

The DBLP key is a stable, unique identifier for each record, structured as type/venue/AuthorYear (e.g., journals/nn/Smith24). Use it to deduplicate results across multiple runs or to construct direct DBLP record URLs.

Is there a related actor for Google Scholar or Semantic Scholar?

Yes — check out the other actors in the Logiover store for academic data sources. DBLP focuses on computer science; for broader academic search you may want to combine it with specialized scrapers for other databases.

Is it legal?

DBLP is a public, open-access bibliography service operated by Schloss Dagstuhl. Its search API is documented and publicly accessible without authentication. Scraping publicly available metadata for research, analysis, and non-commercial use is generally considered lawful under standard fair-use and database-use principles. This actor does not bypass any access controls, does not scrape full paper texts (only metadata), and respects DBLP's server resources via polite request pacing. Always review DBLP's terms of service and your jurisdiction's data regulations before using the output commercially.

Related scrapers

• Logiover GitHub Activity Scraper — track open source contributions alongside academic output.
• Logiover CVE Advisory Scraper — monitor security vulnerabilities for tech stacks identified in research papers.
• Logiover CT Monitor — certificate transparency monitoring for domains mentioned in CS research.