We extract syllabi, lecture transcripts, reading assignments, and media links from Open Yale Courses. 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 Course Metadata objects from oyc.yale.edu. All fields typed and schema-versioned.
"course_id": "PLSC-114", "title": "Introduction to Political Philosophy", "department": "Political Science", "instructor": "Steven B. Smith", "course_number": "PLSC 114", "sessions_count": 24, "scraped_at": "2026-05-12T09:14:00Z"
| # | course_id | title | department | instructor | course_number | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lectures objects from oyc.yale.edu. All fields typed and schema-versioned.
"lecture_id": "PLSC-114-01", "course_id": "PLSC-114", "session_number": 1, "title": "Introduction: What is Political Philosophy?", "video_url": "https://www.youtube.com/watch?v=...", "audio_url": "https://oyc.yale.edu/audio/...", "scraped_at": "2026-05-12T09:14:00Z"
| # | lecture_id | course_id | session_number | title | overview | transcript_html |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Transcripts objects from oyc.yale.edu. All fields typed and schema-versioned.
"lecture_id": "PLSC-114-01", "course_id": "PLSC-114", "speaker": "Professor Steven B. Smith", "timestamp": "00:01:23", "text_segment": "We begin today with the oldest and most fundamental question...", "segment_index": 4, "scraped_at": "2026-05-12T09:14:00Z"
| # | lecture_id | course_id | speaker | timestamp | text_segment | segment_index |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reading Assignments objects from oyc.yale.edu. All fields typed and schema-versioned.
"course_id": "PLSC-114", "lecture_id": "PLSC-114-02", "assignment_title": "Apology", "author": "Plato", "publication": "Four Texts on Socrates", "required": true, "scraped_at": "2026-05-12T09:14:00Z"
| # | course_id | lecture_id | assignment_title | author | publication | pages |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Materials & PDFs objects from oyc.yale.edu. All fields typed and schema-versioned.
"course_id": "PLSC-114", "material_type": "Exam", "title": "Midterm Examination", "file_url": "https://oyc.yale.edu/sites/default/files/midterm.pdf", "format": "PDF", "download_status": "verified", "scraped_at": "2026-05-12T09:14:00Z"
| # | course_id | material_type | title | file_url | format | size_bytes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our pipeline handles the legacy DOM structure of oyc.yale.edu, extracting text, PDFs, and media links into queryable relational formats.
Extract department listings, course numbers, titles, and descriptions across the entire Open Yale Courses repository.
Full text extraction from HTML transcript pages, capturing speaker attributions and segmenting by available timestamps.
Capture YouTube, audio, and low-bandwidth video URLs for every lecture session.
Convert unstructured syllabus pages into structured reading lists, exam dates, and assignment schedules.
Extract instructor names, titles, and biographical information linked to each course.
Identify and map URLs for exams, problem sets, and solution documents embedded within course pages.
Link lectures, transcripts, and reading assignments back to their parent course IDs for easy database ingestion.
Navigate older HTML table structures and inconsistent markup common in legacy academic web platforms.
Clean transcript HTML formatting, strip extraneous tags, and normalise character encodings for NLP workloads.
Brief in. Clean data out.
Select specific departments, individual courses, or request the entire oyc.yale.edu catalogue.
We configure Scrapy crawlers adapted to Open Yale Courses' specific HTML structure and asset paths.
Schema validation, transcript completeness checks, and media URL verification before delivery.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Open Yale Courses relies on older web architectures. We structure this unstructured data reliably.
Academic websites built in the 2000s often use deeply nested tables and inline styles. Our parsers normalise this legacy markup into clean, predictable JSON objects.
We break long, poorly formatted transcript pages into logical text segments, preserving speaker attributions and paragraph structures for easier NLP processing.
PDFs and audio files often use relative paths that break when scraped. We resolve all media links to absolute URLs, verifying endpoint availability during the crawl.
We configure our crawlers with conservative concurrency limits and polite request delays to avoid overwhelming university infrastructure or triggering IP blocks.
Different professors format their syllabi differently. We use heuristic matching to map varied reading lists and schedules into a single, unified schema.
AI labs use high-quality, academically rigorous transcript corpora to fine-tune language models on complex reasoning and subject matter expertise.
Course aggregators ingest metadata and syllabus details to build unified search engines for open courseware.
Researchers use matched audio URLs and text transcripts to train and evaluate text-to-speech or automated captioning models.
Pedagogical researchers analyse syllabus construction, reading list diversity, and lecture structures across different departments.
Enterprise teams build retrieval-augmented generation systems that query verified academic materials rather than the open web.
Digital libraries and academic institutions structure reading lists and material links to augment their own catalogues.
"Open Yale Courses contains thousands of hours of Ivy League instruction, but extracting it from legacy HTML requires dedicated parsing logic."
Academic repositories often suffer from inconsistent formatting and outdated web standards. DataFlirt normalises this unstructured text, linking transcripts, reading lists, and video assets into a clean relational schema ready for ML training or database ingestion.
Everything supported by our oyc.yale.edu scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We use BeautifulSoup and Scrapy to reliably navigate and extract data from older DOM structures that modern headless browsers struggle to interpret efficiently.
Custom middleware verifies the HTTP status of linked PDFs, audio files, and video endpoints, ensuring your database only contains valid URLs.
Pipelines run on AWS Lambda for burst scaling, managed by Apache Airflow to handle scheduling, retries, and delivery to your data warehouse.
Data delivered to where your team already works — no new tooling required.
About oyc.yale.edu scraping, legality, and pipeline operations.
Ask us directly →DataFlirt only extracts publicly available information from oyc.yale.edu. Open Yale Courses provides these materials freely to the public, often under Creative Commons licenses. Clients must review the specific terms of use and licensing agreements on the Yale website to ensure their intended use of the data complies with Yale's policies.
We parse the HTML transcript pages, stripping out legacy web formatting while preserving structural elements like paragraphs and speaker changes. The output is clean, UTF-8 encoded text suitable for NLP tasks.
We extract and verify the absolute URLs pointing to the media files (e.g., YouTube links, MP3 paths). We do not download or host the multi-gigabyte media files ourselves, but deliver the links so your systems can fetch them if needed.
Open Yale Courses is a relatively static repository. We typically configure pipelines to run monthly to detect newly added courses, corrected transcripts, or updated syllabus documents.
Our standard pipeline extracts the metadata and URLs for PDFs (exams, problem sets). Full OCR or text extraction from inside the PDFs requires a custom processing step, which we can implement upon request.
Yes. We can scope the crawl to target specific departments (e.g., Political Science, Physics) or even specific course IDs, rather than traversing the entire catalogue.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a single department's syllabi or the entire transcript corpus for LLM training - we scope, build, and operate the pipeline.
