Scrape ResearchGate All Authors, Researchers in Python

What will be scraped

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.

📌Note: if you'll be using this code snippet without proxies, the requests will be blocked in a short period of time. Scraping with proxies large amount of data is a way to go.

Install libraries:

pip install parsel playwright

Full Code

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


def scrape_researchgate_profile(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")

        authors = []
        page_num = 1

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

            for author in selector.css(".nova-legacy-c-card__body--spacing-inherit"):
                name = author.css(".nova-legacy-v-person-item__title a::text").get()
                thumbnail = author.css(".nova-legacy-v-person-item__image img::attr(src)").get()
                profile_page = f'https://www.researchgate.net/{author.css("a.nova-legacy-c-button::attr(href)").get()}'
                institution = author.css(".nova-legacy-v-person-item__stack-item:nth-child(3) span::text").get()
                department = author.css(".nova-legacy-v-person-item__stack-item:nth-child(4) span").xpath("normalize-space()").get()
                skills = author.css(".nova-legacy-v-person-item__stack-item:nth-child(5) span").xpath("normalize-space()").getall()
                last_publication = author.css(".nova-legacy-v-person-item__info-section-list-item .nova-legacy-e-link--theme-bare::text").get()
                last_publication_link = f'https://www.researchgate.net{author.css(".nova-legacy-v-person-item__info-section-list-item .nova-legacy-e-link--theme-bare::attr(href)").get()}'

                authors.append({
                    "name": name,
                    "profile_page": profile_page,
                    "institution": institution,
                    "department": department,
                    "thumbnail": thumbnail,
                    "last_publication": {
                        "title": last_publication,
                        "link": last_publication_link
                    },
                    "skills": skills,
                })

            print(f"Extracting Page: {page_num}")

            # checks if 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:
                # paginate to the next page
                page_num += 1


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

        browser.close()


scrape_researchgate_profile(query="coffee")

Code explanation

Import libraries:

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

Code	Explanation
`parsel`	to parse HTML/XML documents. Supports XPath.
`playwright`	to render the page with a browser instance.
`json`	to convert Python dictionary to JSON string.

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

def scrape_researchgate_profile(query: str):
    with sync_playwright() as p:
        # ...

Code	Explanation
`query: str`	to 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")

Code	Explanation
`p.chromium.launch()`	to launch Chromium browser instance.
`headless`	to explicitly tell `playwright` to run in headless mode even though it's a defaut value.
`slow_mo`	to 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/researcher?q={query}&page={page_num}")
    selector = Selector(text=page.content())
    # ...

Code	Explanation
`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 author in selector.css(".nova-legacy-c-card__body--spacing-inherit"):
    name = author.css(".nova-legacy-v-person-item__title a::text").get()
    thumbnail = author.css(".nova-legacy-v-person-item__image img::attr(src)").get()
    profile_page = f'https://www.researchgate.net/{author.css("a.nova-legacy-c-button::attr(href)").get()}'
    institution = author.css(".nova-legacy-v-person-item__stack-item:nth-child(3) span::text").get()
    department = author.css(".nova-legacy-v-person-item__stack-item:nth-child(4) span").xpath("normalize-space()").get()
    skills = author.css(".nova-legacy-v-person-item__stack-item:nth-child(5) span").xpath("normalize-space()").getall()
    last_publication = author.css(".nova-legacy-v-person-item__info-section-list-item .nova-legacy-e-link--theme-bare::text").get()
    last_publication_link = f'https://www.researchgate.net{author.css(".nova-legacy-v-person-item__info-section-list-item .nova-legacy-e-link--theme-bare::attr(href)").get()}'

    authors.append({
        "name": name,
        "profile_page": profile_page,
        "institution": institution,
        "department": department,
        "thumbnail": thumbnail,
        "last_publication": {
            "title": last_publication,
            "link": last_publication_link
        },
        "skills": skills,
    })

Code	Explanation
`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(authors, indent=2, ensure_ascii=False))

browser.close()

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

Part of the JSON output:

[
  {
    "name": "Marina Ramón-Gonçalves", # first profile
    "profile_page": "https://www.researchgate.net/profile/Marina-Ramon-Goncalves?_sg=VbWMth8Ia1hDG-6tFnNUWm4c8t6xlBHy2Ac-2PdZeBK6CS3nym5PM5OeoSzha90f2B6hpuoyBMwm24U",
    "institution": "Centro Nacional de Investigaciones Metalúrgicas (CENIM)",
    "department": "Reciclado de materiales",
    "thumbnail": "https://i1.rgstatic.net/ii/profile.image/845010970898442-1578477723875_Q64/Marina-Ramon-Goncalves.jpg",
    "last_publication": {
      "title": "Extraction of polyphenols and synthesis of new activated carbon from spent coffe...",
      "link": "https://www.researchgate.netpublication/337577823_Extraction_of_polyphenols_and_synthesis_of_new_activated_carbon_from_spent_coffee_grounds?_sg=2y4OuZz32W46AWcUGmwYbW05QFj3zkS1QR_MVxvKwqJG-abFPLF6cIuaJAO_Mn5juJZWkfEgdBwnA5Q"
    },
    "skills": [
      "Polyphenols",
      "Coffee",
      "Extraction",
      "Antioxidant Activity",
      "Chromatography"
    ]
  }, ... other profiles
  {
    "name": "Kingsten Okka", # last profile
    "profile_page": "https://www.researchgate.net/profile/Kingsten-Okka?_sg=l1w_rzLrAUCRFtoo3Nh2-ZDAaG2t0NX5IHiSV5TF2eOsDdlP8oSuHnGglAm5tU6OFME9wgfyAd-Rnhs",
    "institution": "University of Southern Queensland ",
    "department": "School of Agricultural, Computational and Environmental Sciences",
    "thumbnail": "https://i1.rgstatic.net/ii/profile.image/584138105032704-1516280785714_Q64/Kingsten-Okka.jpg",
    "last_publication": {
      "title": null,
      "link": "https://www.researchgate.netNone"
    },
    "skills": [
      "Agricultural Entomology",
      "Coffee"
    ]
  }
]

Scrape ResearchGate All Authors, Researchers in Python

Table of contents

What will be scraped

Prerequisites

Full Code

Code explanation

Links