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Web Scraping all ResearchGate Publications in Python

Web Scraping all ResearchGate Publications in Python

Dmitriy Zub ☀️'s photo
Dmitriy Zub ☀️
·

4 min read

Table of contents

What will be scraped

image

Prerequisites

Basic knowledge scraping with CSS selectors

If you haven't scraped with CSS selectors, there's a dedicated blog post of mine about how to use CSS selectors when web-scraping that covers what it is, pros and cons, and why they're matter from a web-scraping perspective and show the most common approaches of using CSS selectors when web scraping.

Separate virtual environment

If you didn't work with a virtual environment before, have a look at the dedicated Python virtual environments tutorial using Virtualenv and Poetry blog post of mine to get familiar.

Reduce the chance of being blocked

There's a chance that a request might be blocked. Have a look at how to reduce the chance of being blocked while web-scraping, there are eleven methods to bypass blocks from most websites.

Install libraries:

pip install parsel playwright

Full Code

from parsel import Selector
from playwright.sync_api import sync_playwright
import json


def scrape_researchgate_publications(query: str):
    with sync_playwright() as p:

        browser = p.chromium.launch(headless=True, slow_mo=50)
        page = browser.new_page(user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.64 Safari/537.36")

        publications = []
        page_num = 1

        while True:
            page.goto(f"https://www.researchgate.net/search/publication?q={query}&page={page_num}")
            selector = Selector(text=page.content())

            for publication in selector.css(".nova-legacy-c-card__body--spacing-inherit"):
                title = publication.css(".nova-legacy-v-publication-item__title .nova-legacy-e-link--theme-bare::text").get().title()
                title_link = f'https://www.researchgate.net{publication.css(".nova-legacy-v-publication-item__title .nova-legacy-e-link--theme-bare::attr(href)").get()}'
                publication_type = publication.css(".nova-legacy-v-publication-item__badge::text").get()
                publication_date = publication.css(".nova-legacy-v-publication-item__meta-data-item:nth-child(1) span::text").get()
                publication_doi = publication.css(".nova-legacy-v-publication-item__meta-data-item:nth-child(2) span").xpath("normalize-space()").get()
                publication_isbn = publication.css(".nova-legacy-v-publication-item__meta-data-item:nth-child(3) span").xpath("normalize-space()").get()
                authors = publication.css(".nova-legacy-v-person-inline-item__fullname::text").getall()
                source_link = f'https://www.researchgate.net{publication.css(".nova-legacy-v-publication-item__preview-source .nova-legacy-e-link--theme-bare::attr(href)").get()}'

                publications.append({
                    "title": title,
                    "link": title_link,
                    "source_link": source_link,
                    "publication_type": publication_type,
                    "publication_date": publication_date,
                    "publication_doi": publication_doi,
                    "publication_isbn": publication_isbn,
                    "authors": authors
                })

            print(f"page number: {page_num}")

            # checks if next page arrow key is greyed out `attr(rel)` (inactive) and breaks out of the loop
            if selector.css(".nova-legacy-c-button-group__item:nth-child(9) a::attr(rel)").get():
                break
            else:
                page_num += 1


        print(json.dumps(publications, indent=2, ensure_ascii=False))

        browser.close()


scrape_researchgate_publications(query="coffee")

Code explanation

Import libraries:

from parsel import Selector
from playwright.sync_api import sync_playwright
import json
CodeExplanation
parselto parse HTML/XML documents. Supports XPath.
playwrightto render the page with a browser instance.
jsonto convert Python dictionary to JSON string.

Define a function and open a playwright with a context manager::

def scrape_researchgate_publications(query: str):
    with sync_playwright() as p:
        # ...
CodeExplanation
query: strto tell Python that query should be an str.

Lunch a browser instance, open new_page with passed user-agent:

browser = p.chromium.launch(headless=True, slow_mo=50)
page = browser.new_page(user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.64 Safari/537.36")
CodeExplanation
p.chromium.launch()to launch Chromium browser instance.
headlessto explicitly tell playwright to run in headless mode even though it's a defaut value.
slow_moto tell playwright to slow down execution.
browser.new_page()to open new page. user_agent is used to act a real user makes a request from the browser. If not used, it will default to playwright value which is None. Check what's your user-agent.

Add a temporary list, set up a while loop, and open a new URL:

authors = []

while True:
    page.goto(f"https://www.researchgate.net/search/publication?q={query}&page={page_num}")
    selector = Selector(text=page.content())
    # ...
CodeExplanation
goto()to make a request to specific URL with passed query and page parameters.
Selector()to pass returned HTML data with page.content() and process it.

Iterate over author results on each page, extract the data and append to a temporary list:

for publication in selector.css(".nova-legacy-c-card__body--spacing-inherit"):
    title = publication.css(".nova-legacy-v-publication-item__title .nova-legacy-e-link--theme-bare::text").get().title()
    title_link = f'https://www.researchgate.net{publication.css(".nova-legacy-v-publication-item__title .nova-legacy-e-link--theme-bare::attr(href)").get()}'
    publication_type = publication.css(".nova-legacy-v-publication-item__badge::text").get()
    publication_date = publication.css(".nova-legacy-v-publication-item__meta-data-item:nth-child(1) span::text").get()
    publication_doi = publication.css(".nova-legacy-v-publication-item__meta-data-item:nth-child(2) span").xpath("normalize-space()").get()
    publication_isbn = publication.css(".nova-legacy-v-publication-item__meta-data-item:nth-child(3) span").xpath("normalize-space()").get()
    authors = publication.css(".nova-legacy-v-person-inline-item__fullname::text").getall()
    source_link = f'https://www.researchgate.net{publication.css(".nova-legacy-v-publication-item__preview-source .nova-legacy-e-link--theme-bare::attr(href)").get()}'

    publications.append({
        "title": title,
        "link": title_link,
        "source_link": source_link,
        "publication_type": publication_type,
        "publication_date": publication_date,
        "publication_doi": publication_doi,
        "publication_isbn": publication_isbn,
        "authors": authors
    })
CodeExplanation
css()to parse data from the passed CSS selector(s). Every CSS query traslates to XPath using csselect package under the hood.
::text/::attr(attribute)to extract textual or attribute data from the node.
get()/getall()to get actual data from a matched node, or to get a list of matched data from nodes.
xpath("normalize-space()")to parse blank text node as well. By default, blank text node is be skipped by XPath.

Check if the next page is present and paginate:

# checks if the next page arrow key is greyed out `attr(rel)` (inactive) -> breaks out of the loop
if selector.css(".nova-legacy-c-button-group__item:nth-child(9) a::attr(rel)").get():
    break
else:
    page_num += 1

Print extracted data, and close browser instance:

print(json.dumps(publications, indent=2, ensure_ascii=False))

browser.close()

# call the function
scrape_researchgate_publications(query="coffee")

Part of the JSON output:

[
   {
      "title":"The Social Life Of Coffee Turkey’S Local Coffees",
      "link":"https://www.researchgate.netpublication/360540595_The_Social_Life_of_Coffee_Turkey%27s_Local_Coffees?_sg=kzuAi6HlFbSbnLEwtGr3BA_eiFtDIe1VEA4uvJlkBHOcbSjh5XlSQe6GpYvrbi12M0Z2MQ6grwnq9fI",
      "source_link":"https://www.researchgate.netpublication/360540595_The_Social_Life_of_Coffee_Turkey%27s_Local_Coffees?_sg=kzuAi6HlFbSbnLEwtGr3BA_eiFtDIe1VEA4uvJlkBHOcbSjh5XlSQe6GpYvrbi12M0Z2MQ6grwnq9fI",
      "publication_type":"Conference Paper",
      "publication_date":"Apr 2022",
      "publication_doi":null,
      "publication_isbn":null,
      "authors":[
         "Gülşen Berat Torusdağ",
         "Merve Uçkan Çakır",
         "Cinucen Okat"
      ]
   },
   {
      "title":"Coffee With The Algorithm",
      "link":"https://www.researchgate.netpublication/359599064_Coffee_with_the_Algorithm?_sg=3KHP4SXHm_BSCowhgsa4a2B0xmiOUMyuHX2nfqVwRilnvd1grx55EWuJqO0VzbtuG-16TpsDTUywp0o",
      "source_link":"https://www.researchgate.netNone",
      "publication_type":"Chapter",
      "publication_date":"Mar 2022",
      "publication_doi":"DOI: 10.4324/9781003170884-10",
      "publication_isbn":"ISBN: 9781003170884",
      "authors":[
         "Jakob Svensson"
      ]
   }, ... other publications
   {
      "title":"Coffee In Chhattisgarh", # last publication
      "link":"https://www.researchgate.netpublication/353118247_COFFEE_IN_CHHATTISGARH?_sg=CsJ66DoWjFfkMNdujuE-R9aVTZA4kVb_9lGiy1IrYXls1Nur4XFMdh2s5E9zkF5Skb5ZZzh663USfBA",
      "source_link":"https://www.researchgate.netNone",
      "publication_type":"Technical Report",
      "publication_date":"Jul 2021",
      "publication_doi":null,
      "publication_isbn":null,
      "authors":[
         "Krishan Pal Singh",
         "Beena Nair Singh",
         "Dushyant Singh Thakur",
         "Anurag Kerketta",
         "Shailendra Kumar Sahu"
      ]
   }
]

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