We extract course metadata, syllabi, reading lists, lecture notes, and video links from MIT OCW. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
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 ocw.mit.edu. All fields typed and schema-versioned.
"course_id": "18.06", "title": "Linear Algebra", "department": "Mathematics", "level": "Undergraduate", "semester": "Spring", "year": "2010", "instructors": "['Prof. Gilbert Strang']", "topics": "['Mathematics', 'Linear Algebra', 'Differential Equations']"
| # | course_id | title | department | level | instructors | semester |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus & Structure objects from ocw.mit.edu. All fields typed and schema-versioned.
"course_id": "18.06", "prerequisites": "['18.02']", "corequisites": "[]", "format": "Lecture, Recitation", "hours_per_week": 12, "grading_policy": "Homework 25%, Exams 75%", "textbooks": "['Introduction to Linear Algebra, 4th Edition']"
| # | course_id | prerequisites | corequisites | grading_policy | calendar_events | course_outcomes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lecture Resources objects from ocw.mit.edu. All fields typed and schema-versioned.
"resource_id": "res_18_06_lec1", "course_id": "18.06", "session_number": 1, "topic": "The Geometry of Linear Equations", "resource_type": "Lecture Notes", "file_url": "https://ocw.mit.edu/courses/18-06-linear-algebra/notes.pdf", "file_format": "application/pdf", "download_link": "https://ocw.mit.edu/ans7870/18/18.06/notes.pdf"
| # | resource_id | course_id | session_number | topic | resource_type | file_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Video Metadata objects from ocw.mit.edu. All fields typed and schema-versioned.
"video_id": "ZK3O402wf1c", "course_id": "18.06", "session_number": 1, "title": "Lecture 1: The Geometry of Linear Equations", "youtube_url": "https://www.youtube.com/watch?v=ZK3O402wf1c", "duration": "39:49", "speakers": "['Prof. Gilbert Strang']", "transcript_url": "https://ocw.mit.edu/courses/18-06/transcript.pdf"
| # | video_id | course_id | session_number | title | youtube_url | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reading Lists objects from ocw.mit.edu. All fields typed and schema-versioned.
"reading_id": "rd_18_06_01", "course_id": "18.06", "week_number": 1, "author": "Gilbert Strang", "title": "Introduction to Linear Algebra", "publisher": "Wellesley-Cambridge Press", "isbn": "9780980232714", "required_status": true
| # | reading_id | course_id | week_number | author | title | publisher |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
MIT OCW contains over two decades of course materials. We handle the legacy templates, nested resource directories, and complex metadata mapping so you receive clean, normalised datasets.
Extract department taxonomies, course numbers, titles, instructors, and cross-listed variants across the entire ocw.mit.edu domain.
Traverse nested directories to map lecture notes, problem sets, and exams directly to their corresponding syllabus sessions.
Extract YouTube embed URLs, durations, speaker names, and transcript links from lecture pages.
Structure unstructured syllabus pages into prerequisites, corequisites, grading policies, and weekly topic schedules.
Normalise instructor names and map them to their respective departments and historical course offerings.
Resolve relative paths and redirect chains to provide absolute download URLs for all course materials.
Identify and merge courses that exist under multiple department codes to prevent duplicate records in your warehouse.
Distinguish between different iterations of the same course (e.g., Spring 2010 vs Fall 2020) and maintain version history.
Ensure all required fields like course_id and department meet strict type and null-rate constraints before delivery.
Run one-off bulk exports or configure continuous pipelines at monthly cadences to capture new course publications.
Brief in. Clean data out.
Provide specific departments, course levels, or resource types. We design the extraction schema together.
We configure Scrapy crawlers, handle legacy HTML templates, and map nested resource directories for ocw.mit.edu.
Schema validation, null-rate checks, link resolution testing, and sample course extraction before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from MIT OCW requires navigating twenty years of web development history. Here is how we maintain pipeline stability.
MIT OCW hosts courses dating back to 2001. Our parsers automatically detect the underlying HTML template version, routing extraction logic through legacy static parsers or modern React component extractors to ensure consistent output.
Course materials are often buried in deeply nested directory structures. We build recursive crawlers that traverse these trees, resolving relative paths and associating every PDF or zip file back to its parent course and session.
Lecture videos are embedded via YouTube. We extract the iframe src attributes, resolve the canonical YouTube IDs, and capture associated metadata like transcripts and timestamps directly from the DOM.
Academic syllabi are highly unstructured. We use regex and NLP heuristics to extract structured prerequisites, grading weights, and reading list ISBNs from free-text paragraphs.
We maintain a hash index of last-seen values for course indices. Subsequent runs only parse newly added courses or updated materials, reducing compute cost and downstream processing load.
Machine learning teams ingest course materials, lecture notes, and transcripts to fine-tune large language models for educational assistance.
Universities and EdTech platforms analyse syllabus structures and reading lists to benchmark and design their own course offerings.
Learning platforms aggregate MIT OCW metadata to build unified search engines for open educational resources.
Researchers map the evolution of specific disciplines over two decades by tracking changes in course topics and required reading lists.
HR tech companies extract course outcomes and topics to build competency frameworks and skill ontologies.
Vector database providers index lecture transcripts and PDF contents to build semantic search applications for academic knowledge.
"MIT OpenCourseWare holds the highest density of structured academic knowledge on the public web, but extracting it requires parsing decades of varying HTML templates and nested asset directories."
Most engineering teams underestimate the complexity of academic scraping. MIT OCW contains courses spanning twenty years, meaning your pipeline must handle legacy static HTML alongside modern React components, resolve thousands of relative PDF links, and map YouTube metadata to course syllabi. DataFlirt manages this pipeline entirely.
Everything supported by our ocw.mit.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.
Scrapy orchestrates the broad crawl across department indices, while Playwright handles modern React-based course pages requiring JavaScript execution.
Custom middleware resolves redirect chains and validates content-types to ensure asset links point directly to accessible PDF or ZIP files.
Pipelines run on Kubernetes. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About ocw.mit.edu scraping, legality, and pipeline operations.
Ask us directly →Yes. MIT OpenCourseWare is designed to be a public resource, and its content is generally licensed under Creative Commons (CC BY-NC-SA). Scraping publicly available metadata and links does not violate these terms, provided the extraction respects server load limits and the data is used in accordance with the CC license.
We maintain multiple parser templates. During the crawl, our system inspects the DOM structure and routes the HTML to the appropriate legacy or modern parser, ensuring consistent JSON output regardless of the source page format.
By default, we extract and validate the absolute URLs pointing to the PDF and ZIP assets. If your use case requires it, we can configure the pipeline to download the actual files and sync them directly to your S3 bucket.
We extract the URLs for transcripts provided directly on the OCW platform. For videos without OCW-hosted transcripts, we can integrate secondary pipelines to extract closed captions directly from the YouTube embeds.
MIT OCW typically publishes new courses on a semester-based schedule. We recommend configuring pipelines to run monthly to capture new additions and syllabus updates without unnecessary compute overhead.
We offer one-off bulk extractions for the entire current OCW catalogue, or managed ongoing pipelines with monthly diff deliveries. Contact us to scope the exact departments or data fields required.
Yes. We provide sample JSON or CSV exports covering a subset of departments (e.g., Mathematics or Computer Science) so you can validate the schema and field completeness before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full historical catalogue dump or continuous monitoring for new course publications, we scope, build, and operate the pipeline. Tell us what you need.
