We extract World University Rankings, Impact Rankings, teaching metrics, and research citations from timeshighereducation.com. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for World Rankings objects from timeshighereducation.com. All fields typed and schema-versioned.
"rank": "1", "name": "University of Oxford", "country": "United Kingdom", "overall_score": 98.5, "teaching_score": 96.6, "research_score": 100.0, "student_population": 20965
| # | rank | name | country | overall_score | teaching_score | research_score |
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Complete list of extractable fields for Impact Rankings objects from timeshighereducation.com. All fields typed and schema-versioned.
"rank": "1", "name": "Western Sydney University", "sdg_4_score": 89.4, "sdg_5_score": 92.1, "overall_score": 99.4, "year": 2023
| # | rank | name | sdg_1_score | sdg_2_score | sdg_3_score | sdg_4_score |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Subject Rankings objects from timeshighereducation.com. All fields typed and schema-versioned.
"rank": "1", "name": "Stanford University", "subject": "Computer Science", "overall_score": 97.8, "research_score": 98.2, "teaching_score": 96.4
| # | rank | name | subject | overall_score | citations_score | industry_income |
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Complete list of extractable fields for University Profiles objects from timeshighereducation.com. All fields typed and schema-versioned.
"name": "Massachusetts Institute of Technology", "country": "United States", "region": "North America", "website_url": "https://web.mit.edu", "founded_year": 1861, "total_students": 11934
| # | name | country | region | description | key_statistics | campus_image_urls |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Historical Data objects from timeshighereducation.com. All fields typed and schema-versioned.
"name": "Harvard University", "year": 2018, "rank": "6", "overall_score": 93.2, "teaching_score": 89.0, "research_score": 97.4
| # | name | year | rank | overall_score | teaching_score | research_score |
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Our scraper maps the entire Times Higher Education dataset, from top-level World University Rankings to granular subject scores and institutional profiles.
Capture the flagship ranking table, including overall scores, teaching metrics, research environment, and citation impact.
Extract university performance against the United Nations Sustainable Development Goals, including individual SDG scores.
Scrape specific tables for Computer Science, Engineering, Business, Medicine, and other academic disciplines.
Pull historical ranking data across multiple years to track institutional performance trajectories over time.
Extract detailed university profile pages, including campus descriptions, key statistics, and contact URLs.
Capture student-to-staff ratios, international student percentages, and female-to-male student ratios.
Extract knowledge transfer metrics and industry income scores used in the ranking methodology.
Scrape specific tables for Asia, Latin America, Emerging Economies, and Young University rankings.
Schedule pipelines to run immediately when new annual rankings are published by Times Higher Education.
Brief in. Clean data out.
Specify the rankings, years, and metrics you need. We design the extraction schema together.
We configure Scrapy and Playwright crawlers to handle paginated tables and dynamic metric rendering.
Schema validation, null-rate checks, and score outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Times Higher Education uses complex paginated data tables and dynamic rendering. Here is how we maintain stable extraction.
Times Higher Education ranking tables are heavily JavaScript-rendered and load data dynamically. We run full Playwright browser sessions to ensure all columns and metrics hydrate correctly before extraction.
Ranking tables span hundreds of pages, often with dynamic URLs. Our crawlers systematically paginate through all tiers, capturing universities outside the top 100 where data structure often changes.
Excessive requests to ranking APIs trigger rate limits and blocks. We use residential ISP proxies with randomised request timing to distribute load and maintain continuous extraction.
Ranking methodologies change over time, resulting in altered column headers. Our pipeline normalises historical and current metrics into a consistent schema for easy downstream querying.
Not all universities report data for every metric, leading to null values or asterisks in the tables. We parse these cleanly to ensure numeric fields remain strictly typed in your database.
University leadership teams track their performance metrics against peer institutions to identify areas for strategic investment.
EdTech platforms integrate ranking data to help prospective students filter and compare universities globally.
Researchers analyse long-term trends in higher education funding, research output, and internationalisation.
Education ministries use ranking metrics to assess national higher education performance and allocate research grants.
Private equity firms evaluating EdTech or student housing investments use university popularity and growth metrics as proxy indicators.
Consultancies build proprietary models using historical ranking data to advise universities on improving their global standing.
"Times Higher Education holds the most cited university ranking dataset globally, but extracting historical metric changes requires a dedicated pipeline."
Most teams underestimate the investment required to extract ranking data consistently. Times Higher Education paginates heavily and renders metrics dynamically. DataFlirt absorbs that complexity so your engineers can focus on institutional analysis, not web scraping infrastructure.
Everything supported by our timeshighereducation.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering and dynamic table hydration.
We maintain pools of residential ISP proxies to avoid rate limits and IP bans during high-volume ranking extractions.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and automated delivery.
Data delivered to where your team already works — no new tooling required.
About timeshighereducation.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available ranking data is generally permissible. DataFlirt targets only public, non-authenticated university rankings and profiles. We do not bypass paywalls or extract proprietary THE DataPoints subscription data.
Yes. We can extract historical ranking tables from previous years that are publicly accessible on the platform, allowing you to build time-series datasets of university performance.
Universities ranked below certain thresholds are often grouped into ranges (e.g., '601-800'). We extract these exactly as published and can optionally parse them into lower and upper bound integer columns for easier querying.
Yes. Beyond the overall score, we capture teaching, research, citations, industry income, and international outlook scores for the World University Rankings, as well as specific SDG scores for the Impact Rankings.
Times Higher Education typically updates its major rankings annually. We can schedule pipelines to run automatically upon new releases or run one-off historical extractions.
Absolutely. We provide a sample run of the top 200 universities from the World University Rankings so you can validate the schema and data quality before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical rankings dump or continuous monitoring of new ranking releases, we scope, build, and operate the pipeline. Tell us what you need.
