Research Data Extraction API — Extract Data for Academic Research

Research data extraction from the web powers thousands of academic studies every year — from social science surveys of online content to computer science datasets built from web pages. SimpleCrawl gives researchers a structured, reproducible way to extract data from websites without building custom scrapers for each source.

Why Researchers Need a Scraping API

Academic research that involves web data faces practical challenges:

1. Reproducibility. Other researchers need to replicate your data collection. A well-documented API call is more reproducible than a custom scraping script with brittle CSS selectors.
2. Scale. Manual data collection limits sample sizes. An API lets you collect data from thousands of pages.
3. Consistency. Different pages require different parsing logic. A general-purpose extraction API handles this.
4. Ethics and compliance. A managed API respects rate limits and robots.txt by default.

SimpleCrawl addresses all four: reproducible API calls, batch processing for scale, consistent extraction across sites, and built-in rate limiting.

Common Research Use Cases

Content Analysis

Extract and analyze text content from websites for qualitative or quantitative analysis:

import simplecrawl
import pandas as pd

client = simplecrawl.Client(api_key="YOUR_KEY")

news_urls = [
    "https://news-site-a.com/article/1",
    "https://news-site-b.com/story/climate-change",
    # ... hundreds of URLs
]

articles = []
for url in news_urls:
    result = client.scrape(url, output="json", schema={
        "title": "string",
        "author": "string",
        "published_date": "string",
        "body_text": "string",
        "word_count": "number",
        "topics_mentioned": ["string"],
    })
    articles.append({"url": url, **result.data})

df = pd.DataFrame(articles)
df.to_csv("research_dataset.csv", index=False)

Public Data Collection

Government websites, NGO reports, and public databases often lack downloadable datasets. Extract structured data directly:

result = client.scrape(
    "https://data.gov.example/environmental/air-quality",
    output="json",
    schema={
        "monitoring_stations": [{
            "station_name": "string",
            "location": "string",
            "aqi_value": "number",
            "pollutant": "string",
            "last_updated": "string",
        }]
    }
)

Literature Review

Collect metadata from academic paper listings:

result = client.scrape(
    "https://scholar.example.com/search?q=web+scraping+ethics",
    output="json",
    schema={
        "papers": [{
            "title": "string",
            "authors": ["string"],
            "year": "number",
            "citation_count": "number",
            "abstract_snippet": "string",
            "source_url": "string",
        }]
    }
)

Social Science Research

Analyze public forum discussions, product reviews, or community content:

result = client.scrape(
    "https://forum.example.com/topic/remote-work-discussion",
    output="json",
    schema={
        "thread_title": "string",
        "posts": [{
            "author": "string",
            "date": "string",
            "content": "string",
            "upvotes": "number",
        }]
    }
)

Building a Research Data Pipeline

Step 1: Define Your Collection Protocol

Document your methodology in a way that other researchers can reproduce:

COLLECTION_PROTOCOL = {
    "api": "SimpleCrawl v1",
    "api_key": "REDACTED",
    "collection_date": "2026-03-01",
    "sources": [
        {
            "name": "News Source A",
            "base_url": "https://news-a.com",
            "sample_method": "Most recent 100 articles from politics section",
            "schema": {
                "title": "string",
                "body_text": "string",
                "published_date": "string",
                "author": "string",
            }
        }
    ],
    "rate_limit": "1 request per 2 seconds",
    "error_handling": "Retry 3 times, then log as missing",
}

Step 2: Collect Data with Error Handling

import time
import json
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("research_collector")

def collect_with_retry(url, schema, max_retries=3, delay=2):
    for attempt in range(max_retries):
        try:
            result = client.scrape(url, output="json", schema=schema)
            return {"url": url, "data": result.data, "status": "success"}
        except simplecrawl.RateLimitError:
            logger.warning(f"Rate limited on {url}, waiting {delay * 2}s")
            time.sleep(delay * 2)
        except simplecrawl.NotFoundError:
            logger.warning(f"Page not found: {url}")
            return {"url": url, "data": None, "status": "not_found"}
        except Exception as e:
            logger.warning(f"Attempt {attempt + 1} failed for {url}: {e}")
            time.sleep(delay)

    return {"url": url, "data": None, "status": "failed"}

dataset = []
for url in research_urls:
    result = collect_with_retry(url, article_schema)
    dataset.append(result)
    time.sleep(2)
    logger.info(f"Collected {len(dataset)}/{len(research_urls)}")

with open("raw_dataset.json", "w") as f:
    json.dump(dataset, f, indent=2)

Step 3: Validate and Clean

def validate_record(record):
    if record["status"] != "success":
        return False
    data = record["data"]
    if not data.get("title") or not data.get("body_text"):
        return False
    if data.get("word_count", 0) < 100:
        return False
    return True

valid_records = [r for r in dataset if validate_record(r)]
failed_records = [r for r in dataset if not validate_record(r)]

logger.info(f"Valid: {len(valid_records)}, Failed: {len(failed_records)}")
logger.info(f"Collection success rate: {len(valid_records)/len(dataset)*100:.1f}%")

Step 4: Export for Analysis

import pandas as pd

rows = []
for record in valid_records:
    rows.append({
        "url": record["url"],
        **record["data"]
    })

df = pd.DataFrame(rows)

df.to_csv("cleaned_dataset.csv", index=False)
df.to_parquet("cleaned_dataset.parquet")

df.describe().to_csv("dataset_statistics.csv")

Ethical Web Scraping for Research

IRB Considerations

Institutional Review Boards (IRBs) may require review of web scraping projects, especially when collecting data that could identify individuals. Key considerations:

• Public vs. private data. Publicly accessible web pages are generally fair game. Data behind login walls requires more scrutiny.
• Anonymization. Strip identifying information if not essential to the research question.
• Storage. Store research data securely, especially if it contains any personal information.
• Consent. For social media research, consider whether users had a reasonable expectation of privacy.

Rate Limiting and Politeness

SimpleCrawl's managed infrastructure handles rate limiting by default, but researchers should also:

• Space requests 1–2 seconds apart for non-urgent collection
• Use batch mode during off-peak hours for large collections
• Respect robots.txt (use our Robots.txt Checker)
• Do not scrape the same page more frequently than necessary

Data Licensing

Web content is copyrighted. Scraping for research generally falls under fair use (US) or text and data mining exceptions (EU), but:

• Do not republish full text from scraped sources
• Cite original sources in your research
• Check if the source has a specific data use policy
• For commercial applications of research data, consult legal counsel

Cost for Research Projects

Most academic research projects fit within the Starter plan. The one-time cost of $29 for up to 5,000 pages is comparable to other research data sources and far cheaper than manual data collection.

Integration with Research Tools

Jupyter Notebooks

import simplecrawl
import pandas as pd

client = simplecrawl.Client(api_key="YOUR_KEY")

urls = pd.read_csv("study_urls.csv")["url"].tolist()

results = []
for url in urls:
    r = client.scrape(url, output="json", schema=schema)
    results.append(r.data)

df = pd.DataFrame(results)
df.head()

R (via API)

library(httr)
library(jsonlite)

scrape_url <- function(url, schema) {
  resp <- POST(
    "https://api.simplecrawl.com/scrape",
    add_headers(Authorization = "Bearer YOUR_KEY"),
    body = list(url = url, output = "json", schema = schema),
    encode = "json"
  )
  content(resp, "parsed")
}

result <- scrape_url(
  "https://example.com/data-page",
  list(title = "string", value = "number")
)

Export Formats

SimpleCrawl's JSON output converts easily to standard research formats:

• CSV/TSV — via pandas or any data processing library
• Parquet — for large datasets with type preservation
• JSON Lines — for streaming processing
• SQLite — for queryable local databases

FAQ

Is web scraping for academic research legal?

In most jurisdictions, scraping publicly available web content for non-commercial academic research is legal. The US has fair use protections, and the EU's Copyright Directive includes specific text and data mining exceptions for research. However, scraping personal data requires additional care under GDPR/CCPA. Always consult your institution's legal office.

Do I need IRB approval for web scraping?

It depends on what you are scraping. Public datasets, news articles, and government data generally do not require IRB review. Research involving identifiable human subjects (social media posts, forum discussions) may require review. Check with your institution's IRB office early in your project planning.

How do I cite web-scraped data in my paper?

Document your methodology thoroughly: the API used, collection dates, URL patterns, extraction schemas, and success rates. Provide the collection code as supplementary material. Cite SimpleCrawl as a data collection tool and cite the original web sources as data sources.

Can I share my scraped dataset?

You can share extracted metadata and structured data (titles, dates, statistics). Sharing full article text may violate copyright. Common practice is to share the list of URLs, extraction code, and extracted metadata — allowing other researchers to reproduce the collection.

How reproducible is API-based scraping?

More reproducible than custom scripts. Your collection code is a series of API calls with defined schemas. Other researchers can re-run the same code. The main variability is that web pages change over time — document your collection date and consider archiving pages via the Wayback Machine.

What about the Wayback Machine?

The Internet Archive's Wayback Machine is complementary to live scraping. Use it to access historical versions of pages, verify that content has not changed since collection, and provide permanent references for your data sources.

Get Started

Start collecting research data with SimpleCrawl. Join the waitlist for 500 free credits — enough for a pilot study or literature survey. For AI-powered analysis of collected data, combine with our RAG pipeline guide.